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How to Write Quantitative Research Questions: Types With Examples

For research to be effective, it becomes crucial to properly formulate the quantitative research questions in a correct way. Otherwise, you will not get the answers you were looking for.

Has it ever happened that you conducted a quantitative research study and found out the results you were expecting are quite different from the actual results?

This could happen due to many factors like the unpredictable nature of respondents, errors in calculation, research bias, etc. However, your quantitative research usually does not provide reliable results when questions are not written correctly.

We get it! Structuring the quantitative research questions can be a difficult task.

Hence, in this blog, we will share a few bits of advice on how to write good quantitative research questions. We will also look at different types of quantitative research questions along with their examples.

Let’s start:

How to Write Quantitative Research Questions?

When you want to obtain actionable insight into the trends and patterns of the research topic to make sense of it, quantitative research questions are your best bet.

Being objective in nature, these questions provide you with detailed information about the research topic and help in collecting quantifiable data that can be easily analyzed. This data can be generalized to the entire population and help make data-driven and sound decisions.

Respondents find it easier to answer quantitative survey questions than qualitative questions. At the same time, researchers can also analyze them quickly using various statistical models.

However, when it comes to writing the quantitative research questions, one can get a little overwhelmed as the entire study depends on the types of questions used.

There is no “one good way” to prepare these questions. However, to design well-structured quantitative research questions, you can follow the 4-steps approach given below:

1. Select the Type of Quantitative Question

The first step is to determine which type of quantitative question you want to add to your study. There are three types of quantitative questions:

• Descriptive
• Comparative 
• Relationship-based

This will help you choose the correct words and phrases while constructing the question. At the same time, it will also assist readers in understanding the question correctly.

2. Identify the Type of Variable

The second step involves identifying the type of variable you are trying to measure, manipulate, or control. Basically, there are two types of variables:

• Independent variable (a variable that is being manipulated)
• Dependent variable (outcome variable)

If you plan to use descriptive research questions, you have to deal with a number of dependent variables. However, where you plan to create comparative or relationship research questions, you will deal with both dependent and independent variables.

3. Select the Suitable Structure

The next step is determining the structure of the research question. It involves:

• Identifying the components of the question. It involves the type of dependent or independent variable and a group of interest (the group from which the researcher tries to conclude the population).
• The number of different components used. Like, as to how many variables and groups are being examined.
• Order in which these are presented. For example, the independent variable before the dependent variable or vice versa.

4. Draft the Complete Research Question

The last step involves identifying the problem or issue that you are trying to address in the form of complete quantitative survey questions . Also, make sure to build an exhaustive list of response options to make sure your respondents select the correct response. If you miss adding important answer options, then the ones chosen by respondents may not be entirely true.

Types of Quantitative Research Questions With Examples

Quantitative research questions are generally used to answer the “who” and “what” of the research topic. For quantitative research to be effective, it is crucial that the respondents are able to answer your questions concisely and precisely. With that in mind, let’s look in greater detail at the three types of formats you can use when preparing quantitative market research questions.

1. Descriptive

Descriptive research questions are used to collect participants’ opinions about the variable that you want to quantify. It is the most effortless way to measure the particular variable (single or multiple variables) you are interested in on a large scale. Usually, descriptive research questions begin with “ how much,” “how often,” “what percentage,” “what proportion,” etc.

Examples of descriptive research questions include:

2. Comparative

Comparative research questions help you identify the difference between two or more groups based on one or more variables. In general, a comparative research question is used to quantify one variable; however, you can use two or more variables depending on your market research objectives.

Comparative research questions examples include:

3. Relationship-based

Relationship research questions are used to identify trends, causal relationships, or associations between two or more variables. It is not vital to distinguish between causal relationships, trends, or associations while using these types of questions. These questions begin with “What is the relationship” between independent and dependent variables, amongst or between two or more groups.

Relationship-based quantitative questions examples include:

Ready to Write Your Quantitative Research Questions?

So, there you have it. It was all about quantitative research question types and their examples. By now, you must have figured out a way to write quantitative research questions for your survey to collect actionable customer feedback.

Now, the only thing you need is a good survey maker tool , like ProProfs Survey Maker , that will glide your process of designing and conducting your surveys . You also get access to various survey question types, both qualitative and quantitative, that you can add to any kind of survey along with professionally-designed survey templates .

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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What Are Quantitative Survey Questions? Types and Examples

Table of contents: 

• Types of quantitative survey questions - with examples 
• Quantitative question formats
• How to write quantitative survey questions 
• Examples of quantitative survey questions 

Leveraging quantilope for your quantitative survey 

In a quantitative research study brands will gather numeric data for most of their questions through formats like numerical scale questions or ranking questions. However, brands can also include some non-quantitative questions throughout their quantitative study - like open-ended questions, where respondents will type in their own feedback to a question prompt. Even so, open-ended answers can be numerically coded to sift through feedback easily (e.g. anyone who writes in 'Pepsi' in a soda study would be assigned the number '1', to look at Pepsi feedback as a whole).  One of the biggest benefits of using a quantitative research approach is that insights around a research topic can undergo statistical analysis; the same can’t be said for qualitative data like focus group feedback or interviews. Another major difference between quantitative and qualitative research methods is that quantitative surveys require respondents to choose from a limited number of choices in a close-ended question - generating clear, actionable takeaways. However, these distinct quantitative takeaways often pair well with freeform qualitative responses - making quant and qual a great team to use together.  The rest of this article focuses on quantitative research, taking a closer look at quantitative survey question types and question formats/layouts. 

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Types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions - with examples 

Quantitative questions come in many forms, each with different benefits depending on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139784">your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives. Below we’ll explore some of these dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139785">survey question dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139785" data-dropdown-placement-param="top" data-term-id="281139785"> types, which are commonly used together in a single survey to keep things interesting for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . The style of questioning used during dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139739">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139750">data dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139750" data-dropdown-placement-param="top" data-term-id="281139750"> collection is important, as a good mix of the right types of questions will deliver rich data, limit dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent fatigue, and optimize the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139757">response rate . dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">Questionnaires should be enjoyable - and varying the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755">types of dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139755" data-dropdown-placement-param="top" data-term-id="281139755">quantitative research dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755"> questions used throughout your survey will help achieve that. 

Descriptive survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139763">Descriptive research questions (also known as usage and attitude, or, U&A questions) seek a general indication or prediction about how a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139773">group of people behaves or will behave, how that group is characterized, or how a group thinks.

For example, a business might want to know what portion of adult men shave, and how often they do so. To find this out, they will survey men (the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience ) and ask descriptive questions about their frequency of shaving (e.g. daily, a few times a week, once per week, and so on.) Each of these frequencies get assigned a numerical ‘code’ so that it’s simple to chart and analyze the data later on; daily might be assigned ‘5’, a few times a week might be assigned ‘4’, and so on. That way, brands can create charts using the ‘top two’ and ‘bottom two’ values in a descriptive question to view these metrics side by side.

Another business might want to know how important local transit issues are to residents, so dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions will allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to indicate the degrees of opinion attached to various transit issues. Perhaps the transit business running this survey would use a sliding numeric scale to see how important a particular issue is.

Comparative survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139782">Comparative research questions are concerned with comparing individuals or groups of people based on one or more variables. These questions might be posed when a business wants to find out which segment of its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience might be more profitable, or which types of products might appeal to different sets of consumers.

For example, a business might want to know how the popularity of its chocolate bars is spread out across its entire customer base (i.e. do women prefer a certain flavor? Are children drawn to candy bars by certain packaging attributes? etc.). Questions in this case will be designed to profile and ‘compare’ segments of the market.

Other businesses might be looking to compare coffee consumption among older and younger consumers (i.e. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic segments), the difference in smartphone usage between younger men and women, or how women from different regions differ in their approach to skincare.

Relationship-based survey questions

As the name suggests, relationship-based survey questions are concerned with the relationship between two or more variables within one or more dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic groups. This might be a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link between one thing and the other - for example, the consumption of caffeine and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents ’ reported energy levels throughout the day. In this case, a coffee or energy drink brand might be interested in how energy levels differ between those who drink their caffeinated line of beverages and those who drink decaf/non-caffeinated beverages.

Alternatively, it might be a case of two or more factors co-existing, without there necessarily being a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link - for example, a particular type of air freshener being more popular amongst a certain dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic (maybe one that is controlled wirelessly via Bluetooth is more popular among younger homeowners than one that’s plugged into the wall with no controls). Knowing that millennials favor air fresheners which have options for swapping out scents and setting up schedules would be valuable information for new product development.

Advanced method survey questions

Aside from descriptive, comparative, and relationship-based survey questions, brands can opt to include advanced methodologies in their quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire for richer depth. Though advanced methods are more complex in terms of the insights output, quantilope’s Consumer Intelligence Platform automates the setup and analysis of these methods so that researchers of any background or skillset can leverage them with ease.

With quantilope’s pre-programmed suite of 12 advanced methodologies , including MaxDiff , TURF , Implicit , and more, users can drag and drop any of these into a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire and customize for their own dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives.

For example, consider a beverage company that’s looking to expand its flavor profiles. This brand would benefit from a MaxDiff which forces dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to make tradeoff decisions between a set of flavors. A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent might say that coconut is their most-preferred flavor, and lime their least (when in a consideration set with strawberry), yet later on in the MaxDiff that same dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent may say Strawberry is their most-preferred flavor (over black cherry and kiwi). While this is just one example of an advanced method, instantly you can see how much richer and more actionable these quantitative metrics become compared to a standard usage and attitude question .

Advanced methods can be used alongside descriptive, comparison, or relationship questions to add a new layer of context wherever a business sees fit. Back to table of contents 

Quantitative question formats  

So we’ve covered the kinds of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative research questions you might want to answer using dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research , but how do these translate into the actual format of questions that you might include on your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire ?

Thinking ahead to your reporting process during your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire setup is actually quite important, as the available chart types differ among the types of questions asked; some question data is compatible with bar chart displays, others pie charts, others in trended line graphs, etc. Also consider how well the questions you’re asking will translate onto different devices that your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents might be using to complete the survey (mobile, PC, or tablet).

Single Select questions

Single select questions are the simplest form of quantitative questioning, as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents are asked to choose just one answer from a list of items, which tend to be ‘either/or’, ‘yes/no’, or ‘true/false’ questions. These questions are useful when you need to get a clear answer without any qualifying nuances.

Multi-select questions

Multi-select questions (aka, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139767">multiple choice ) offer more flexibility for responses, allowing for a number of responses on a single question. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents can be asked to ‘check all that apply’ or a cap can be applied (e.g. ‘select up to 3 choices’).

For example:

Aside from asking text-based questions like the above examples, a brand could also use a single or multi-select question to ask respondents to select the image they prefer more (like different iterations of a logo design, packaging options, branding colors, etc.). 

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139766">scale dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139766" data-dropdown-placement-param="top" data-term-id="281139766"> questions

A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert scale   is widely used as a convenient and easy-to-interpret rating method. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents find it easy to indicate their degree of feelings by selecting the response they most identify with.

Slider scales

Slider scales are another good interactive way of formatting questions. They allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to customize their level of feeling about a question, with a bit more variance and nuance allowed than a numeric scale:

One particularly common use of a slider scale in a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139770">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139770" data-dropdown-placement-param="top" data-term-id="281139770"> study is known as a NPS (Net Promoter Score) - a way to measure dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139775">customer experience and loyalty . A 0-10 scale is used to ask customers how likely they are to recommend a brand’s product or services to others. The NPS score is calculated by subtracting the percentage of ‘detractors’ (those who respond with a 0-6) from the percentage of promoters (those who respond with a 9-10). dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents who select 7-8 are known as ‘passives’.

For example: 

Drag and drop questions

Drag-and-drop question formats are a more ‘gamified’ approach to survey capture as they ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to do more than simply check boxes or slide a scale. Drag-and-drop question formats are great for ranking exercises - asking dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to place answer options in a certain order by dragging with their mouse. For example, you could ask survey takers to put pizza toppings in order of preference by dragging options from a list of possible answers to a box displaying their personal preferences:

Matrix questions

Matrix   questions are a great way to consolidate a number of questions that ask for the same type of response (e.g. single select yes/no, true/false, or multi-select lists). They are mutually beneficial - making a survey look less daunting for the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent , and easier for a brand to set up than asking multiple separate questions.

Items in a matrix question are presented one by one, as respondents cycle through the pages selecting one answer for each coffee flavor shown. 

While the above example shows a single-matrix question - meaning a respondent can only select one answer per element (in this case, coffee flavors), a matrix setup can also be used for multiple-choice questions - allowing respondents to choose multiple answers per element shown, or for rating questions - allowing respondents to assign a rating (e.g. 1-5) for a list of elements at once.  Back to table of contents 

How to write dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

We’ve reviewed the types of questions you might ask in a quantitative survey, and how you might format those questions, but now for the actual crafting of the content.

When considering which questions to include in your survey, you’ll first want to establish what your research goals are and how these relate to your business goals. For example, thinking about the three types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions explained above - descriptive, comparative, and relationship-based - which type (or which combination) will best meet your research needs? The questions you ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents may be phrased in similar ways no matter what kind of layout you leverage, but you should have a good idea of how you’ll want to analyze the results as that will make it much easier to correctly set up your survey.

Quantitative questions tend to start with words like ‘how much,’ ‘how often,’ ‘to what degree,’ ‘what do you think of,’ ‘which of the following’ - anything that establishes what consumers do or think and that can be assigned a numerical code or value. Be sure to also include ‘other’ or ‘none of the above’ options in your quant questions, accommodating those who don’t feel the pre-set answers reflect their true opinion. As mentioned earlier, you can always include a small number of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139748">open-ended questions in your quant survey to account for any ideas or expanded feedback that the pre-coded questions don’t (or can’t) cover. Back to table of contents 

Examples of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">Quantitative survey questions impose limits on the answers that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents can choose from, and this is a good thing when it comes to measuring consumer opinions on a large scale and comparing across dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . A large volume of freeform, open-ended answers is interesting when looking for themes from qualitative studies, but impractical to wade through when dealing with a large dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139756">sample size , and impossible to subject to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139774">statistical analysis .

For example, a quantitative survey might aim to establish consumers' smartphone habits. This could include their frequency of buying a new smartphone, the considerations that drive purchase, which features they use their phone for, and how much they like their smartphone.

Some examples of quantitative survey questions relating to these habits would be:

Q. How often do you buy a new smartphone?

[single select question]

More than once per year

Every 1-2 years

Every 3-5 years

Every 6+ years

Q. Thinking about when you buy a smartphone, please rank the following factors in order of importance:

[drag and drop ranking question]

screen size

storage capacity

Q. How often do you use the following features on your smartphone?

[matrix question]

Q. How do you feel about your current smartphone?

[sliding scale]

I love it <-------> I hate it

Answers from these above questions, and others within the survey, would be analyzed to paint a picture of smartphone usage and attitude trends across a population and its sub-groups. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">Qualitative research might then be carried out to explore those findings further - for example, people’s detailed attitudes towards their smartphones, how they feel about the amount of time they spend on it, and how features could be improved. Back to table of contents 

quantilope’s Consumer Intelligence Platform specializes in automated, advanced survey insights so that researchers of any skill level can benefit from quick, high-quality consumer insights. With 12 advanced methods to choose from and a wide variety of quantitative question formats, quantilope is your one-stop-shop for all things dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research (including its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139776">in-depth dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">qualitative research solution - inColor ).

When it comes to building your survey, you decide how you want to go about it. You can start with a blank slate and drop questions into your survey from a pre-programmed list, or you can get a head start with a survey dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139765">template for a particular business use case (like concept testing ) and customize from there. Once your survey is ready to launch, simply specify your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience , connect any panel (quantilope is panel agnostic), and watch as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139783">answer questions in your survey in real-time by monitoring the fieldwork section of your project. AI-driven dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139764">data analysis takes the raw data and converts it into actionable findings so you never have to worry about manual calculations or statistical testing.

Whether you want to run your quantitative study entirely on your own or with the help of a classically trained research team member, the choice is yours on quantilope’s platform. For more information on how quantilope can help with your next dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139768">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139768" data-dropdown-placement-param="top" data-term-id="281139768"> project , get in touch below!

Get in touch to learn more about quantitative research with quantilope!

Related posts, van westendorp price sensitivity meter questions, quantilope & organic valley: understanding consumer values behind behaviors, quantilope & wire webinar: solving the research dilemma with ai, a full year of better brand health tracking in the soda category.

Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

• Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
• Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
• Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
• Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

Research Writing and Analysis

• NVivo Group and Study Sessions
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• Step 1: Seek Out Evidence
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Quantitative Research Questions

• Qualitative Research Questions
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Research Questions Tutorial

What is a Quantitative Research Question?

A research question is the driving question(s) behind your research. It should be about an issue that you are genuinely curious and/or passionate about. A good research question is:

Clear :  The purpose of the study should be clear to the reader, without additional explanation.

Focused :  The question is specific. Narrow enough in scope that it can be thoroughly explored within the page limits of the research paper. It brings the common thread that weaves throughout the paper.

Concise :  Clarity should be obtained in the fewest possible words. This is not the place to add unnecessary descriptors and fluff (i.e. “very”).

Complex :  A true research question is not a yes/no question. It brings together a collection of ideas obtained from extensive research, without losing focus or clarity.

Arguable :  It doesn’t provide a definitive answer. Rather, it presents a potential position that future studies could debate.

The format of a research question will depend on a number of factors, including the area of discipline, the proposed research design, and the anticipated analysis.

Unclear:   Does loneliness cause the jitters? Clear:   What is the relationship between feelings of loneliness, as measured by the Lonely Inventory, and uncontrollable shaking?

Unfocused:   What’s the best way to learn? Focused:   In what ways do different teaching styles affect recall and retention in middle schoolers?

Verbose :  Can reading different books of varying genres influence a person’s performance on a test that measures familiarity and knowledge of different words?

Concise:   How does exposure to words through reading novels influence a person’s language development?

Definitive:   What is my favorite color? Arguable:   What is the most popular color amongst teens in America?

Developing a Quantitative Research Question

Developing a research question.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Quantitative Methods

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• Cite this living reference work entry

• Juwel Rana 2 , 3 , 4 ,
• Patricia Luna Gutierrez 5 &
• John C. Oldroyd 6  

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Quantitative analysis ; Quantitative research methods ; Study design

Quantitative method is the collection and analysis of numerical data to answer scientific research questions. Quantitative method is used to summarize, average, find patterns, make predictions, and test causal associations as well as generalizing results to wider populations. It allows us to quantify effect sizes, determine the strength of associations, rank priorities, and weigh the strength of evidence of effectiveness.

Introduction

This entry aims to introduce the most common ways to use numbers and statistics to describe variables, establish relationships among variables, and build numerical understanding of a topic. In general, the quantitative research process uses a deductive approach (Neuman 2014 ; Leavy 2017 ), extrapolating from a particular case to the general situation (Babones 2016 ).

In practical ways, quantitative methods are an approach to studying a research topic. In research, the...

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Babones S (2016) Interpretive quantitative methods for the social sciences. Sociology. https://doi.org/10.1177/0038038515583637

Balnaves M, Caputi P (2001) Introduction to quantitative research methods: an investigative approach. Sage, London

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Brenner PS (2020) Understanding survey methodology: sociological theory and applications. Springer, Boston

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Creswell JW (2014) Research design: qualitative, quantitative, and mixed methods approaches. Sage, London

Leavy P (2017) Research design. The Gilford Press, New York

Mertens W, Pugliese A, Recker J (2018) Quantitative data analysis, research methods: information, systems, and contexts: second edition. https://doi.org/10.1016/B978-0-08-102220-7.00018-2

Neuman LW (2014) Social research methods: qualitative and quantitative approaches. Pearson Education Limited, Edinburgh

Treiman DJ (2009) Quantitative data analysis: doing social research to test ideas. Jossey-Bass, San Francisco

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Department of Public Health, School of Health and Life Sciences, North South University, Dhaka, Bangladesh

Department of Biostatistics and Epidemiology, School of Health and Health Sciences, University of Massachusetts Amherst, MA, USA

Department of Research and Innovation, South Asia Institute for Social Transformation (SAIST), Dhaka, Bangladesh

Independent Researcher, Masatepe, Nicaragua

Patricia Luna Gutierrez

School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia

John C. Oldroyd

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Rana, J., Gutierrez, P.L., Oldroyd, J.C. (2021). Quantitative Methods. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_460-1

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How to structure quantitative research questions

There is no "one best way" to structure a quantitative research question. However, to create a well-structured quantitative research question, we recommend an approach that is based on four steps : (1) Choosing the type of quantitative research question you are trying to create (i.e., descriptive, comparative or relationship-based); (2) Identifying the different types of variables you are trying to measure, manipulate and/or control, as well as any groups you may be interested in; (3) Selecting the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved; and (4) Writing out the problem or issues you are trying to address in the form of a complete research question. In this article, we discuss each of these four steps , as well as providing examples for the three types of quantitative research question you may want to create: descriptive , comparative and relationship-based research questions .

• STEP ONE: Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create
• STEP TWO: Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in
• STEP THREE: Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved
• STEP FOUR: Write out the problem or issues you are trying to address in the form of a complete research question

STEP ONE Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create

The type of quantitative research question that you use in your dissertation (i.e., descriptive , comparative and/or relationship-based ) needs to be reflected in the way that you write out the research question; that is, the word choice and phrasing that you use when constructing a research question tells the reader whether it is a descriptive, comparative or relationship-based research question. Therefore, in order to know how to structure your quantitative research question, you need to start by selecting the type of quantitative research question you are trying to create: descriptive, comparative and/or relationship-based.

STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in

Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure , manipulate and/or control . If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable , should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal , dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables). It also explains the difference between independent and dependent variables , which you need to understand to create quantitative research questions.

To provide a brief explanation; a variable is not only something that you measure , but also something that you can manipulate and control for. In most undergraduate and master's level dissertations, you are only likely to measure and manipulate variables. You are unlikely to carry out research that requires you to control for variables, although some supervisors will expect this additional level of complexity. If you plan to only create descriptive research questions , you may simply have a number of dependent variables that you need to measure. However, where you plan to create comparative and/or relationship-based research questions , you will deal with both dependent and independent variables . An independent variable (sometimes called an experimental or predictor variable ) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable ). For example, if we were interested in investigating the relationship between gender and attitudes towards music piracy amongst adolescents , the independent variable would be gender and the dependent variable attitudes towards music piracy . This example also highlights the need to identify the group(s) you are interested in. In this example, the group of interest are adolescents .

Once you identifying the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in, it is possible to start thinking about the way that the three types of quantitative research question can be structured . This is discussed next.

STEP THREE Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved

The structure of the three types of quantitative research question differs, reflecting the goals of the question, the types of variables, and the number of variables and groups involved. By structure , we mean the components of a research question (i.e., the types of variables, groups of interest), the number of these different components (i.e., how many variables and groups are being investigated), and the order that these should be presented (e.g., independent variables before dependent variables). The appropriate structure for each of these quantitative research questions is set out below:

Structure of descriptive research questions

• Structure of comparative research questions
• Structure of relationship-based research questions

There are six steps required to construct a descriptive research question: (1) choose your starting phrase; (2) identify and name the dependent variable; (3) identify the group(s) you are interested in; (4) decide whether dependent variable or group(s) should be included first, last or in two parts; (5) include any words that provide greater context to your question; and (6) write out the descriptive research question. Each of these steps is discussed in turn:

Choose your starting phrase

Identify and name the dependent variable

Identify the group(s) you are interested in

Decide whether the dependent variable or group(s) should be included first, last or in two parts

Include any words that provide greater context to your question

Write out the descriptive research question

FIRST Choose your starting phrase

You can start descriptive research questions with any of the following phrases:

How many? How often? How frequently? How much? What percentage? What proportion? To what extent? What is? What are?

Some of these starting phrases are highlighted in blue text in the examples below:

How many calories do American men and women consume per day?

How often do British university students use Facebook each week?

What are the most important factors that influence the career choices of Australian university students?

What proportion of British male and female university students use the top 5 social networks?

What percentage of American men and women exceed their daily calorific allowance?

SECOND Identify and name the dependent variable

All descriptive research questions have a dependent variable. You need to identify what this is. However, how the dependent variable is written out in a research question and what you call it are often two different things. In the examples below, we have illustrated the name of the dependent variable and highlighted how it would be written out in the blue text .

The first two examples highlight that while the name of the dependent variable is the same, namely daily calorific intake , the way that this dependent variable is written out differs in each case.

THIRD Identify the group(s) you are interested in

All descriptive research questions have at least one group , but can have multiple groups . You need to identify this group(s). In the examples below, we have identified the group(s) in the green text .

What are the most important factors that influence the career choices of Australian university students ?

The examples illustrate the difference between the use of a single group (e.g., British university students ) and multiple groups (e.g., American men and women ).

FOURTH Decide whether the dependent variable or group(s) should be included first, last or in two parts

Sometimes it makes more sense for the dependent variable to appear before the group(s) you are interested in, but sometimes it is the opposite way around. The following examples illustrate this, with the group(s) in green text and the dependent variable in blue text :

Group 1st; dependent variable 2nd:

How often do British university students use Facebook each week ?

Dependent variable 1st; group 2nd:

Sometimes, the dependent variable needs to be broken into two parts around the group(s) you are interested in so that the research question flows. Again, the group(s) are in green text and the dependent variable is in blue text :

How many calories do American men and women consume per day ?

Of course, you could choose to restructure the question above so that you do not have to split the dependent variable into two parts. For example:

How many calories are consumed per day by American men and women ?

When deciding whether the dependent variable or group(s) should be included first or last, and whether the dependent variable should be broken into two parts, the main thing you need to think about is flow : Does the question flow? Is it easy to read?

FIFTH Include any words that provide greater context to your question

Sometimes the name of the dependent variable provides all the explanation we need to know what we are trying to measure. Take the following examples:

In the first example, the dependent variable is daily calorific intake (i.e., calories consumed per day). Clearly, this descriptive research question is asking us to measure the number of calories American men and women consume per day. In the second example, the dependent variable is Facebook usage per week. Again, the name of this dependent variable makes it easy for us to understand that we are trying to measure the often (i.e., how frequently; e.g., 16 times per week) British university students use Facebook.

However, sometimes a descriptive research question is not simply interested in measuring the dependent variable in its entirety, but a particular component of the dependent variable. Take the following examples in red text :

In the first example, the research question is not simply interested in the daily calorific intake of American men and women, but what percentage of these American men and women exceeded their daily calorific allowance. So the dependent variable is still daily calorific intake, but the research question aims to understand a particular component of that dependent variable (i.e., the percentage of American men and women exceeding the recommend daily calorific allowance). In the second example, the research question is not only interested in what the factors influencing career choices are, but which of these factors are the most important.

Therefore, when you think about constructing your descriptive research question, make sure you have included any words that provide greater context to your question.

SIXTH Write out the descriptive research question

Once you have these details ? (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the group(s) you are interested in, and (4) any potential joining words ? you can write out the descriptive research question in full. The example descriptive research questions discussed above are written out in full below:

In the section that follows, the structure of comparative research questions is discussed.

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• Quantitative research questions: Types, tips & examples

Defne Çobanoğlu

Deciding on your next survey’s goal gives you a starting point as to what kind of questions you will use on your survey. And if you want to do concrete market research, give a data summary to your supervisors, or make informed decisions based on the data you collect, you should use quantitative survey questions.

In this article, we have gathered more than 100 survey question examples about gender, marketing, stress, psychology, academic performance, social media, and mental health to get you started. You can add these questions to your next research survey, or you can use them to get inspiration to write many more. Let us get started!

• What is a quantitative research question?

The quantitative research question is a type of question where the person asking the question wants to obtain a numeric answer that will provide them with a tangible answer. It involves collecting objective, measurable data about a particular subject or topic, often through surveys, experiments, or other structured methods.

The definition of a quantitative research question

The data collected is typically numerical in nature, such as ratings, counts, measurements, or percentages . So, an answer to this type of question can be confidentially used when creating a quantitative analysis.

Quantitative vs. qualitative research questions

The main difference between quantitative and qualitative questions is what you want to achieve from the question and methods of data collection. Qualitative research focuses on exploring and understanding complex phenomena, experiences, and perspectives . And qualitative research questions aim to gather detailed descriptions and subjective experiences to gain insights.

On the other hand, quantitative research aims to answer questions that involve measuring and quantifying variables, examining relationships, and making statistical deductions. It mainly relies on structured data collection methods, such as surveys, experiments, observations, and existing datasets, in order to collect numerical data .

• How to write a quantitative research question

If you want to obtain concrete data on a research topic, you should use quantitative research questions. They give you numerical answers such as ratings, measurements, counts, or percentages. That makes it easier to conclude a quantitative analysis. Therefore, use questions that will give you answers like; “three times a week”, “about 11”, “20% of the students”, etc. Here are some question starters to have in mind to give you quantitative research questions ideas:

• How frequently?
• What percentage?
• To what extent?
• What proportion?
• On a scale of…

Here are some simple examples:

• How often do you go to the gym in a week?
• How much do you spend on groceries?
• How many phone calls do you make a day?
• Types of quantitative questions

When you try to get numerical answers, the only option is not the multiple-choice one. You can use different types of quantitative research questions to make the form more interesting, visually appealing, and detailed if you use a smart survey creator, such as forms.app, you can make use of its multiple smart form fields to build your form. Let us see what are some good options to use on your next survey.

Star rating:

It is a good way to ask people their opinions, and the survey takers can rate criteria based on different categories. Each star represents an equivalent numeric value, and they typically range from 1 to 5. Even if they are clicking on stars, you get numeric data in the end.

A star rating question example

Opinion scale:

It is basically the same thing with the stars but instead, the survey takers rate criteria as numbers from 1-5 or 1-10. It is better to keep in mind the best way for this is using a 1-5 scale, with 5 being the best and 1 being the worst rating.

An opinion scale question example

Picture selection:

Having people choose their opinions in a picture selection form is a good way to go. It is a good option to use when you are creating a survey for market research and such.

A picture selection question example

Multiple-choice:

When you ask people a question such as; “what are the reasons that negatively affect your mental health?” it is better to let them choose multiple reasons rather than a single one. You would not want to limit the target audience by making them choose only one thing on the list.

A multiple-choice question example

Selection matrix:

In this type of question, you can make multiple sentences, categories, and statements, and survey takers can answer them accordingly. They allow you to get the answers as one question rather than setting up multiple questions.

A selection matrix example

• 100+ Quantitative research questions to ask in your research surveys

In your next survey, you can use any of the questions below, or you can create your own. If you use smart questions focused on a subject or aspect, it will make it easier for you to make an informed analysis at the end. Now, let us start with the first one:

Quantitative research questions about gender

A question example about quantitative research about gender

Quantitative research questions about gender aim to gather numerical data to quantify and analyze gender-related patterns, differences, and associations. They focus on exploring gender-related issues and investigating gender influences on several aspects of life.

1 - What is the difference in average earnings between male and female employees in a specific industry?

2 - How does gender affect academic achievement in STEM subjects among high school students?

3 - What is the percentage of women in leadership positions in Fortune 500 companies?

4 - What is the impact of gender on access to and utilization of health services?

5 - What is the percentage of female students speaking in a classroom as opposed to male students?

6 - How does gender influence consumer preferences and purchasing behavior in the fashion industry?

7 - What are the gender differences in response to specific marketing strategies for a particular product?

8 - What is the correlation between gender and mental health outcomes in a specific population?

9 - How does gender influence the perception of work-life balance among working professionals?

10 - How often do you feel discriminated against in a work environment because of your gender?

11 - What is the effect of gender on smoking at the ages 14-18?

Quantitative research questions about stress

A question example about quantitative research about stress

Research questions about stress aim to investigate different aspects of stress, its causes, and its consequences. Researchers can measure stress levels and examine the relationships between stress and other variables. Also, they can analyze patterns and trends associated with stress after collecting appropriate data.

12 - On a scale of 1 to 10, how often do you feel stressed?

13 - What is the prevalence of stress among college students?

14 - How does stress impact academic achievement among high school students?

15 - How does mindfulness meditation training impact stress levels in university students?

16 - What are the primary sources of work-related stress among employees?

17 - What is the relationship between stress levels and job performance among healthcare professionals?

18 - Who are the people in your life that cause you the most stress?

19 - In the last month, how often have you felt that you were unable to control important things in your life?

20 - How does workplace stress influence employee turnover rates in a specific organization?

21 - What is the correlation between stress levels and physical health in young people?

22 - What are the demographic factors (such as age, gender, or income) associated with higher levels of stress?

23 - What is the impact of stress on sleep quality and duration among adults?

24 - What are the stress levels experienced by parents of children with special needs compared to parents of typically developing children?

25 - What is the effectiveness of stress management interventions in reducing stress levels among individuals with chronic illnesses?

26 - What is the impact of daily meditation helping stress levels?

27 - What are the factors contributing to job-related stress among healthcare professionals in a specific specialty?

Quantitative research questions in Psychology

A question example about quantitative research in psychology

Quantitative research questions in psychology cover a range of psychological topics, including mental health, personality, behavior, and social dynamics. The aim of these questions is to collect quantitative data to examine relationships, assess the effectiveness of interventions, and identify factors associated with psychological events.

28 - What is the relationship between self-esteem and academic performance in high school students?

29 - How does exposure to violent media affect aggressive behavior in children?

30 - What is the prevalence of depression among college students?

31 - How is parental attachment style associated with the development of anxiety disorders in children?

32 - How many times a month should one use professional therapy?

33 - What are the factors influencing job satisfaction among employees in a specific industry?

34 - What are the predictors of job performance among healthcare professionals?

35 - Generally, at what age do children start getting psychological help?

36 - What is the effect of cognitive-behavioral therapy on reducing symptoms of post-traumatic stress disorder?

37 - How does the classroom environment affect academic motivation and achievement in elementary school students?

38 - What is the effectiveness of a cognitive training program in improving memory function in older adults?

39 - How do exercise frequency and intensity impact symptoms of anxiety and depression in individuals with diagnosed mental health conditions?

40 - What is the correlation between sleep duration and academic performance in college students?

41 - How does parental divorce during childhood impact the development of attachment styles in adulthood?

42 - What is the relationship between self-esteem and job satisfaction among working professionals?

43 - What are the predictors of eating disorder symptoms in adolescent females?

44 - At what age the teenage girls prone to depression?

45 - What is the correlation between young adults and suicide rates?

46 - What is the effect of a specific cognitive training program on improving cognitive functioning in elders?

47 - How does the presence of social support networks impact resilience levels in individuals who have experienced traumatic events?

48 - What are the effects of a specific therapeutic intervention on reducing symptoms of anxiety in individuals with a generalized anxiety disorder?

49 - What is the correlation between social media use and symptoms of depression in young adults?

50 - How does mindfulness meditation training influence stress levels in individuals with high-stress occupations?

51 - How does exposure to violent video games affect aggressive behavior in adolescents?

Quantitative research questions about mental health

A question example about quantitative research about mental health

Quantitative research questions about mental health focus on various aspects of mental health, including the prevalence of disorders, risk factors, treatment interventions, and the impact of lifestyle factors. 

52 - How does the frequency of social media use relate to levels of depressive symptoms in adolescents?

53 - What is the correlation between sleep quality and mental health outcomes in adults with diagnosed mental health conditions?

54 - What is the percentage of people diagnosed with anxiety disorder that has a college education?

55 - What kind of activities helps with your mental health?

56 - How many times a week do you spare time for your mental well-being?

57 - What is the effect of a specific psychotherapy intervention on reducing symptoms of depression?

58 - What are the factors determining treatment adherence in patients with schizophrenia?

59 - How do exercise frequency and intensity relate to anxiety levels?

60 - What is the relationship between social support and endurance in individuals with a history of trauma?

61 - How does stigma surrounding mental illness influence help-seeking behavior among college students?

62 - What is the prevalence of anxiety disorders among college students?

Quantitative research questions about social media

A question example about quantitative research about social media

Quantitative research questions about social media try to explore various aspects of social media, including its impact on psychological well-being, behavior, relationships, and society. They aim to collect quantitative data to analyze relations, examine effects, and measure the influence of social media.

63 - How many times a day do you check your social media accounts?

64 - How much time do you spend on social media every day?

65 - How many social media accounts do you own?

66 - What is the correlation between social media engagement and academic performance in high school students?

67 - What are the most used social media accounts among teenagers?

68 - What is the psychological effect of social media accounts on young people?

69 - What is the relationship between social media use and self-esteem among adolescents?

70 - How does the frequency of social media use relate to levels of loneliness in young adults?

71 - How does exposure to idealized body images on social media impact body dissatisfaction in women?

72 - What are the predictors of problematic social media use among college students?

73 - How does social media use influence political attitudes and behaviors among young adults?

74 - What is the effect of social media advertising on consumer purchasing behavior and brand loyalty?

75 - What is the association between cyberbullying on social media and mental health outcomes among teenagers?

76 - How does social media use affect sleep quality and duration in adults?

77 - How does social media use impact interpersonal relationships and social support among individuals in long-distance relationships?

Quantitative research questions about academic performance

A question example about quantitative research about academic performance

Quantitative research questions about academic performance focus on academic performance, the predictors, and the elements affecting it negatively and positively. They aim to collect quantitative data to figure out the relation between academic performance and the environment of the students and make informed decisions.

78 - What is the correlation between student attendance rates and academic achievement in a specific grade level?

79 - How does parental involvement in education relate to students' academic performance?

80 - What is the impact of classroom size on student academic outcomes?

81 - What are the predictors of academic success among undergraduate students in a specific major?

82 - How many times were you absent during the last semester?

83 - What is the correlation between student engagement in extracurricular activities and their academic performance?

84 - What is the effect of peer tutoring programs on student grades and test scores?

85 - How do student motivation and self-efficacy influence academic achievement in a specific academic setting?

86 - What is the relationship between study habits and academic performance among high school students?

87 - How does the implementation of a specific teaching methodology or instructional approach impact student achievement in a particular subject?

Quantitative research questions about marketing

A question example about quantitative research about marketing

Quantitative research questions about marketing explore various aspects of marketing, including advertising effectiveness, consumer behavior, branding, pricing, and customer satisfaction. They involve collecting quantitative data to analyze relationships and assess the impact of marketing strategies. 

88 - What is the correlation between advertising expenditure and sales revenue for a specific product?

89 - As a consumer, how often do you make purchasing decisions based on marketing exposure?

90 - What are the top 5 brands that stand out to you because of ads of their quality?

91 - How does brand loyalty relate to customer satisfaction and repeat purchase behavior?

92 - What is the impact of pricing strategies on consumer purchase intentions and price sensitivity?

93 - When making a purchase, how important is the packaging of the product to you?

94 - What is the effectiveness of different marketing channels (e.g., social media, television, email marketing) in reaching and engaging the target audience?

95 - How does product packaging design influence consumer perception and purchase decisions?

96 - What are the key factors influencing customer loyalty in the retail industry?

97 - What is the relationship between online customer reviews and purchase decisions in e-commerce?

98 - How do brand reputation and perception affect consumer trust and willingness to recommend a product or service?

99 - What are the channels you visit to ensure the quality of the product you will purchase?

100 - How does the personalization of marketing messages impact customer engagement and response rates?

101 - What is the effect of promotional offers (e.g., discounts, coupons) on consumer purchase behavior?

102 - What is the effect of ad placement on popular social media accounts on teenagers?

• Tips for creating quantitative research questions

When you want to create your survey, you should be professional and collect the data systematically. That will help you have clear results. In order to achieve this: 

• Use clear and unambiguous language
• Avoid leading or biased questions 
• Use different question types 
• Keep the length of your survey at an appropriate level

After you create your survey in a systematic manner and use a competitive analysis framework to record your findings, you can achieve the concrete results you want. Also, always remember to obtain the necessary ethical approvals and informed consent required for your research study.

• How to create a quantitative research survey

When you are creating your next survey, you can go old-fashion and write everything down on a piece of paper and try to get people to fill them out. However, there is a much easier option thanks to online survey tools. And a great survey maker you can use is forms.app. It has over 1000 ready-to-use templates, and each of them is as useful. Now, let us go through the steps to creating a quantitative survey using forms.app:

1 - Go to forms.app and log in to your account (or create one for free).

2 - Go to the dropdown menu and click on the templates option .

3 - Choose one of the survey templates and click on the “use template” button and customize it as much as you want by adding question fields and changing the visuals as much as you want.

4 - Or, you can decide on starting from scratch and build everything from the start in a matter of minutes.

5 - Save your changes, and by clicking on the “eye” icon on the upper left side of the page, see the final result.

6 - Copy the unique link and share it with your audience. If you want, you can also embed the survey on the page of your choosing.

• Key points to take away

Creating a simple survey to collect numerical values to make informed and supported plans is very easy. It can be done with a simple and effective form creator, such as forms.app. It has many functional form fields and is also completely adjustable.

You can easily create your own research survey with the questions we have gathered for you. It should be mentioned that you should keep in mind to have a structured plan to go with. Because only then can you analyze your results effectively and repeat the research if it is needed.

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

• Form Features
• Data Collection

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98 Quantitative Research Questions & Examples

As researchers, we know how powerful quantitative research data can be in helping answer strategic questions. Here, I’ve detailed 23 use cases and curated 98 quantitative market research questions with examples – making this a post you should add to your bookmark list , so you can quickly refer back.

I’ve formatted this post to show you 10-15 questions for each use case. At the end of each section, I also share a quicker way to get similar insights using modern market research tools like Similarweb.

What is a quantitative research question?

Quantitative market research questions tell you the what, how, when, and where of a subject. From trendspotting to identifying patterns or establishing averages– using quantitative data is a clear and effective way to start solving business problems.

Types of quantitative research questions

Quantitative market research questions are divided into two main types: descriptive and causal.

• Descriptive research questions seek to quantify a phenomenon by focusing on a certain population or phenomenon to measure certain aspects of it, such as frequency, average, or relationship.
• Causal research questions explore the cause-and-effect relationship between two or more variables.

The ultimate list of questions for quantitative market research

Get clear explanations of the different applications and approaches to quantitative research–with the added bonus of seeing what questions to ask and how they can impact your business.

Examples of quantitative research questions for competitive analysis

A powerful example of quantitative research in play is when it’s used to inform a competitive analysis . A process that’s used to analyze and understand how industry leaders and companies of interest are performing.

Pro Tip: Collect data systematically, and use a competitive analysis framework to record your findings. You can refer back to it when you repeat the process later in the year.

• What is the market share of our major competitors?
• What is the average purchase price of our competitors’ products?
• How often do our competitors release new products?
• What is the total number of customer reviews for our competitors’ products?
• What is the average rating of our competitors’ products?
• What is the average customer satisfaction score for our competitors?
• What is the average return rate of our competitors’ products?
• What is the average shipping time for our competitors’ products?
• What is the average price discount offered by our competitors?
• What is the average lifespan of our competitors’ products?

With this data, you can determine your position in the market and benchmark your performance against rival companies. It can then be used to improve offerings, service standards, pricing, positioning, and operational effectiveness. Notice that all questions can be answered with a numerical response , a key component of all successful examples of quantitative market research questions.

Quantitative research question example: market analysis

‍♀️ Question: What is the market share of our major competitors?

Insight sought: Industry market share of leaders and key competitors.

Challenges with traditional quantitative research methods: Outdated data is a major consideration; data freshness remains critical, yet is often tricky to obtain using traditional research methods. Markets shift fast, so being able to obtain and track market share in real time is a challenge many face.

A new approach: Similarweb enables you to track this key business KPI in real-time using digital data directly from the platform. On any day, you can see what your market share is, along with any players in your market. Plus, you get to see rising stars showing significant growth, who may pose a threat through market disruption or new tactics.

⏰ Time to insight: 30 seconds

✅ How it’s done: Using Similarweb’s Web Industry Analysis, two digital metrics give you the intel needed to decipher the market share in any industry. I’m using the Banking, Credit, and Lending market throughout these examples. I’ve selected the US market, analyzing the performance of the previous 3 months.

• Share of visits 

Here, I can see the top players in my market based on the number of unique visitors to their sites. On top of the raw data that shows me the volume of visitors as a figure, I can quickly see the two players ( Capital One and Chase ) that have grown and by what percentage. On the side, you can see rising players in the industry. Now, while my initial question was to establish the market share of my major competitors, I can see there are a few disruptive players in my market who I’d want to track too; Synchrony.com being one of particular interest, given their substantial growth and traffic numbers.

• Share of search 

Viewing the overall market size based on total search volumes, you can explore industry leaders in more detail. The top websites are the top five players, ranking by traffic share . You can also view the month-over-month change in visits, which shows you who is performing best at any given time . It’s the same five names, with Paypal and Chase leading the pack. However, I see Wells Fargo is better at attracting repeat visitors, while Capital One and Bank of America perform better at drawing in unique visitors.

In answer to my question, what is the market share of my major competitors, I can quickly use Similarweb’s quantitative data to get my answer.

This traffic share visual can be downloaded from the platform. It plots the ten industry leader’s market share and allocates the remaining share to the rest of the market.

I can also download a market quadrant analysis, which takes two key data points, traffic share and unique visitors, and plots the industry leaders. All supporting raw data can be downloaded in .xls format or connected to other business intelligence platforms via the API.

Quantitative research questions for consumer behavior studies

These studies measure and analyze consumer behavior, preferences, and habits . Any type of audience analysis helps companies better understand customer intent, and adjust offerings, messaging, campaigns, SEO, and ultimately offer more relevant products and services within a market.

• What is the average amount consumers spend on a certain product each month?
• What percentage of consumers are likely to purchase a product based on its price?
• How do the demographics of the target audience affect their purchasing behavior?
• What type of incentive is most likely to increase the likelihood of purchase?
• How does the store’s location impact product sales and turnover?
• What are the key drivers of product loyalty among consumers?
• What are the most commonly cited reasons for not buying a product?
• How does the availability of product information impact purchasing decisions?
• What is the average time consumers spend researching a product before buying it?
• How often do consumers use social media when making a purchase decision?

While applying a qualitative approach to such studies is also possible, it’s a great example of quantitative market research in action. For larger corporations, studies that involve a large, relevant sample size of a target market deliver vital consumer insights at scale .

Read More: 83 Qualitative Research Questions & Examples

Quantitative research question and answer: content strategy and analysis

‍♀️ Question: What type of content performed best in the market this past month?

Insight sought: Establish high-performing campaigns and promotions in a market.

Challenges with traditional quantitative research methods: Whether you consider putting together a panel yourself, or paying a company to do it for you, quantitative research at scale is costly and time-consuming. What’s more, you have to ensure that sampling is done right and represents your target audience.

A new approach: Data analysis is the foundation of our entire business. For over 10 years, Similarweb has developed a unique , multi-dimensional approach to understanding the digital world. To see the specific campaigns that resonate most with a target audience, use Similarweb’s Popular Pages feature. Key metrics show which campaigns achieve the best results for any site (including rival firms), campaign take-up, and periodic changes in performance and interest.

✅ How it’s done: I’ve chosen Capital One and Wells Fargo to review. Using the Popular Pages campaign filter, I can view all pages identified by a URL parameter UTM. For clarity, I’ve highlighted specific campaigns showing high-growth and increasing popularity. I can view any site’s trending, new, or best-performing pages using a different filter.

In this example, I have highlighted three campaigns showing healthy growth, covering teen checking accounts, performance savings accounts, and add-cash-in-store. Next, I will perform the same check for another key competitor in my market.

Here, I can see financial health tools campaigns with over 300% month-over-month growth and smarter credit and FICO campaigns showing strong performance. This tells me that campaigns focussing on education and tools are growing in popularity within this market. 

Examples of quantitative research questions for brand tracking

These studies are designed to measure customers’ awareness, perceptions, behaviors, and attitudes toward a brand over time. Different applications include measuring brand awareness , brand equity, customer satisfaction, and purchase or usage intent.

These types of research surveys ask questions about brand knowledge, brand attributes, brand perceptions, and brand loyalty . The data collected can then be used to understand the current state of a brand’s performance, identify improvements, and track the success of marketing initiatives.

• To what extent is Brand Z associated with innovation?
• How do consumers rate the quality of Brand Z’s products and services?
• How has the awareness of Brand Z changed over the past 6 months?
• How does Brand Z compare to its competitors in terms of customer satisfaction?
• To what extent do consumers trust Brand Z?
• How likely are consumers to recommend Brand Z?
• What factors influence consumers’ purchase decisions when considering Brand Z?
• What is the average customer satisfaction score for equity?
• How does equity’s customer service compare to its competitors?
• How do customer perceptions of equity’s brand values compare to its competitors?

Quantitative research question example and answer: brand tracking

‍♀️ Question: How has the awareness of Brand Z changed over the past 6 months?

Insight sought: How has brand awareness changed for my business and competitors over time.

⏰ Time to insight: 2 minutes

✅ How it’s done: Using Similarweb’s search overview, I can quickly identify which brands in my chosen market have the highest brand awareness over any time period or location. I can view these stats as a custom market or examine brands individually.

Here, I’ve chosen a custom view that shows me five companies side-by-side. In the top right-hand corner, under branded traffic, you get a quick snapshot of the share of website visits that were generated by branded keywords. A branded keyword is when a consumer types the brand name + a search term.

Below that, you will see the search traffic and engagement section. Here, I’ve filtered the results to show me branded traffic as a percentage of total traffic. Similarweb shows me how branded search volumes grow or decline monthly. Helping me answer the question of how brand awareness has changed over time.

Quantitative research questions for consumer ad testing

Another example of using quantitative research to impact change and improve results is ad testing. It measures the effectiveness of different advertising campaigns. It’s often known as A/B testing , where different visuals, content, calls-to-action, and design elements are experimented with to see which works best. It can show the impact of different ads on engagement and conversions.

A range of quantitative market research questions can be asked and analyzed to determine the optimal approach.

• How does changing the ad’s headline affect the number of people who click on the ad?
• How does varying the ad’s design affect its click-through rate?
• How does altering the ad’s call-to-action affect the number of conversions?
• How does adjusting the ad’s color scheme influence the number of people who view the ad?
• How does manipulating the ad’s text length affect the average amount of time a user spends on the landing page?
• How does changing the ad’s placement on the page affect the amount of money spent on the ad?
• How does varying the ad’s targeting parameters affect the number of impressions?
• How does altering the ad’s call-to-action language impact the click-through rate?

Quantitative question examples for social media monitoring

Quantitative market research can be applied to measure and analyze the impact of social media on a brand’s awareness, engagement, and reputation . By tracking key metrics such as the number of followers, impressions, and shares, brands can:

• Assess the success of their social media campaigns
• Understand what content resonates with customers
• Spot potential areas for improvement
• How often are people talking about our brand on social media channels?
• How many times has our brand been mentioned in the past month?
• What are the most popular topics related to our brand on social media?
• What is the sentiment associated with our brand across social media channels?
• How do our competitors compare in terms of social media presence?
• What is the average response time for customer inquiries on social media?
• What percentage of followers are actively engaging with our brand?
• What are the most popular hashtags associated with our brand?
• What types of content generate the most engagement on social media?
• How does our brand compare to our competitors in terms of reach and engagement on social media?

Example of quantitative research question and answer: social media monitoring

‍♀️ Question: How does our brand compare to our competitors in terms of reach and engagement on social media?

Insight sought: The social channels that most effectively drive traffic and engagement in my market

✅ How it’s done: Similarweb Digital Research Intelligence shows you a marketing channels overview at both an industry and market level. With it, you can view the most effective social media channels in any industry and drill down to compare social performance across a custom group of competitors or an individual company.

Here, I’ve taken the five closest rivals in my market and clicked to expand social media channel data. Wells Fargo and Bank of America have generated the highest traffic volume from social media, with over 6.6 million referrals this year. Next, I can see the exact percentage of traffic generated by each channel and its relative share of traffic for each competitor. This shows me the most effective channels are YouTube, Facebook, LinkedIn, and Reddit – in that order.

In 30-seconds, I’ve discovered the following:

• YouTube is the most popular social network in my market.
• Facebook and LinkedIn are the second and third most popular channels.
• Wells Fargo is my primary target for a more in-depth review, with the highest performance on the top two channels.
• Bank of America is outperforming all key players significantly on LinkedIn.
• American Express has found a high referral opportunity on Reddit that others have been unable to match.

Power-up Your Market Research with Similarweb Today

Examples of quantitative research questions for online polls.

This is one of the oldest known uses of quantitative market research. It dates back to the 19th century when they were first used in America to try and predict the outcome of the presidential elections.

Polls are just short versions of surveys but provide a point-in-time perspective across a large group of people. You can add a poll to your website as a widget, to an email, or if you’ve got a budget to spend, you might use a company like YouGov to add questions to one of their online polls and distribute it to an audience en-masse.

• What is your annual income?
• In what age group do you fall?
• On average, how much do you spend on our products per month?
• How likely are you to recommend our products to others?
• How satisfied are you with our customer service?
• How likely are you to purchase our products in the future?
• On a scale of 1 to 10, how important is price when it comes to buying our products?
• How likely are you to use our products in the next six months?
• What other brands of products do you purchase?
• How would you rate our products compared to our competitors?

Quantitative research questions for eye tracking studies

These research studies measure how people look and respond to different websites or ad elements. It’s traditionally an example of quantitative research used by enterprise firms but is becoming more common in the SMB space due to easier access to such technologies.

• How much time do participants spend looking at each visual element of the product or ad?
• How does the order of presentation affect the impact of time spent looking at each visual element?
• How does the size of the visual elements affect the amount of time spent looking at them?
• What is the average time participants spend looking at the product or ad as a whole?
• What is the average number of fixations participants make when looking at the product or ad?
• Are there any visual elements that participants consistently ignore?
• How does the product’s design or advertising affect the average number of fixations?
• How do different types of participants (age, gender, etc.) interact with the product or ad differently?
• Is there a correlation between the amount of time spent looking at the product or ad and the participants’ purchase decision?
• How does the user’s experience with similar products or ads affect the amount of time spent looking at the current product or ad?

Quantitative question examples for customer segmentation

Segmentation is becoming more important as organizations large and small seek to offer more personalized experiences. Effective segmentation helps businesses understand their customer’s needs–which can result in more targeted marketing, increased conversions, higher levels of loyalty, and better brand awareness.

If you’re just starting to segment your market, and want to know the best quantitative research questions to ask to help you do this, here are 20 to choose from.

Examples of quantitative research questions to segment customers

• What is your age range?
• What is your annual household income?
• What is your preferred online shopping method?
• What is your occupation?
• What types of products do you typically purchase?
• Are you a frequent shopper?
• How often do you purchase products online?
• What is your typical budget for online purchases?
• What is your primary motivation for purchasing products online?
• What factors influence your decision to purchase a product online?
• What device do you use most often when shopping online?
• What type of product categories are you most interested in?
• Do you prefer to shop online for convenience or for a better price?
• What type of discounts or promotions do you look for when making online purchases?
• How do you prefer to receive notifications about product promotions or discounts?
• What type of payment methods do you prefer when shopping online?
• What methods do you use to compare different products and prices when shopping online?
• What type of customer service do you expect when shopping online?
• What type of product reviews do you consider when making online purchases?
• How do you prefer to interact with a brand when shopping online?

Examples of quantitative research questions for analyzing customer segments

• What is the average age of customers in each segment?
• How do spending habits vary across customer segments?
• What is the average length of time customers spend in each segment?
• How does loyalty vary across customer segments?
• What is the average purchase size in each segment?
• What is the average frequency of purchases in each segment?
• What is the average customer lifetime value in each segment?
• How does customer satisfaction vary across customer segments?
• What is the average response rate to campaigns in each segment?
• How does customer engagement vary across customer segments?

These questions are ideal to ask once you’ve already defined your segments. We’ve written a useful post that covers the ins and outs of what market segmentation is and how to do it.

Additional applications of quantitative research questions

I’ve covered ten use cases for quantitative questions in detail. Still, there are other instances where you can put quantitative research to good use.

Product usage studies: Measure how customers use a product or service.

Preference testing: Testing of customer preferences for different products or services.

Sales analysis: Analysis of sales data to identify trends and patterns.

Distribution analysis: Analyzing distribution channels to determine the most efficient and effective way to reach customers.

Focus groups: Groups of consumers brought together to discuss and provide feedback on a particular product, service, or marketing campaign.

Consumer interviews: Conducted with customers to understand their behavior and preferences better.

Mystery shopping: Mystery shoppers are sent to stores to measure customer service levels and product availability.

Conjoint analysis: Analysis of how consumers value different attributes of a product or service.

Regression analysis: Statistical analysis used to identify relationships between different variables.

A/B testing: Testing two or more different versions of a product or service to determine which one performs better.

Brand equity studies: Measure, compare and analyze brand recognition, loyalty, and consumer perception.

Exit surveys: Collect numerical data to analyze employee experience and reasons for leaving, providing insight into how to improve the work environment and retain employees.

Price sensitivity testing: Measuring responses to different pricing models to find the optimal pricing model, and identify areas if and where discounts or incentives might be beneficial.

Quantitative market research survey examples

A recent GreenBook study shows that 89% of people in the market research industry use online surveys frequently–and for good reason. They’re quick and easy to set up, the cost is minimal, and they’re highly scalable too.

Questions are always formatted to provide close-ended answers that can be quantified. If you wish to collect free-text responses, this ventures into the realm of qualitative research . Here are a few examples.

Brand Loyalty Surveys: Companies use online surveys to measure customers’ loyalty to their brand. They include questions about how long an individual has been a customer, their overall satisfaction with the service or product, and the likelihood of them recommending the brand to others.

Customer Satisfaction Surveys: These surveys may include questions about the customer’s experience, their overall satisfaction, and the likelihood they will recommend a product or service to others.

Pricing Studies: This type of research reveals how customers value their products or services. These surveys may include questions about the customer’s willingness to pay for the product, the customer’s perception of the price and value, and their comparison of the price to other similar items.

Product/Service Usage Studies: These surveys measure how customers use their products or services. They can include questions about how often customers use a product, their preferred features, and overall satisfaction.

Here’s an example of a typical survey we’ve used when testing out potential features with groups of clients. After they’ve had the chance to use the feature for a period, we send a short survey, then use the feedback to determine the viability of the feature for future release.

Employee Experience Surveys: Another great example of quantitative data in action, and one we use at Similarweb to measure employee satisfaction. Many online platforms are available to help you conduct them; here, we use Culture AMP . The ability to manipulate the data, spot patterns or trends, then identify the core successes and development areas are astounding.

How to answer quantitative research questions with Similarweb

For the vast majority of applications I’ve covered in this post, there’s a more modern, quicker, and more efficient way to obtain similar insights online. Gone are the days when companies need to use expensive outdated data or pay hefty sums of money to market research firms to conduct broad studies to get the answers they need.

By this point, I hope you’ve seen how quick and easy it is to use Similarweb to do market research the modern way. But I’ve only scratched the surface of its capabilities.

Take two to watch this introductory video and see what else you can uncover.

Added bonus: Similarweb API

If you need to crunch large volumes of data and already use tools like Tableau or PowerBI, you can seamlessly connect Similarweb via the API and pipe in the data. So for faster analysis of big data, you can leverage Similarweb data to use alongside the visualization tools you already know and love.

Similarweb’s suite of market intelligence solutions offers unbiased, accurate, honest insights you can trust. With a world of data at your fingertips, use Similarweb Research Intelligence to uncover facts that help inform your research and strengthen your position.

Take a look at:

• Our Market Research suite
• Our Benchmarking tools
• Our Audience Insights tool
• Our Company Research module
• Our Consumer Journey Tracker
• Our Competitive Analysis Tool

Wrapping up

Today’s markets change at lightning speed. To keep up and succeed, companies need access to insights and intel they can depend on to be timely and on-point. While quantitative market research questions can and should always be asked, it’s important to leverage technology to increase your speed to insight, and thus improve reaction times and response to market shifts.

What is quantitative market research?

Quantitative market research is a form of research that uses numerical data to gain insights into the behavior and preferences of customers. It is used to measure and track the performance of products, services, and campaigns.

How does quantitative market research help businesses?

Quantitative market research can help businesses identify customer trends, measure customer satisfaction, and develop effective marketing strategies. It can also provide valuable insights into customer behavior, preferences, and attitudes.

What types of questions should be included in a quantitative market research survey?

Questions in a quantitative market research survey should be focused, clear, and specific. Questions should be structured to collect quantitative data, such as numbers, percentages, or frequency of responses.

What methods can be used to collect quantitative market research data?

Common methods used to collect quantitative market research data include surveys, interviews, focus groups, polls, and online questionnaires.

What are the advantages and disadvantages of using quantitative market research?

The advantages of using quantitative market research include the ability to collect data quickly, the ability to analyze data in a structured way, and the ability to identify trends. Disadvantages include the potential for bias, the cost of collecting data, and the difficulty in interpreting results.

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Statistical Research Questions: Five Examples for Quantitative Analysis

Table of contents, introduction.

How are statistical research questions for quantitative analysis written? This article provides five examples of statistical research questions that will allow statistical analysis to take place.

In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze. This knowledge is elemental in framing research questions that shall guide you in identifying the appropriate statistical test to use in your research.

Thus, before writing your statistical research questions and reading the examples in this article, read first the article that enumerates the  four types of measurement scales . Knowing the four types of measurement scales will enable you to appreciate the formulation or structuring of research questions.

Once you feel confident that you can correctly identify the nature of your data, the following examples of statistical research questions will strengthen your understanding. Asking these questions can help you unravel unexpected outcomes or discoveries particularly while doing exploratory data analysis .

Five Examples of Statistical Research Questions

In writing the statistical research questions, I provide a topic that shows the variables of the study, the study description, and a link to the original scientific article to give you a glimpse of the real-world examples.

Topic 1: Physical Fitness and Academic Achievement

A study was conducted to determine the relationship between physical fitness and academic achievement. The subjects of the study include school children in urban schools.

Statistical Research Question No. 1

Is there a significant relationship between physical fitness and academic achievement?

Notice that this study correlated two variables, namely 1) physical fitness, and 2) academic achievement.

To allow statistical analysis to take place, there is a need to define what is physical fitness, as well as academic achievement. The researchers measured physical fitness in terms of  the number of physical fitness tests  that the students passed during their physical education class. It’s simply counting the ‘number of PE tests passed.’

On the other hand, the researchers measured academic achievement in terms of a passing score in Mathematics and English. The variable is the  number of passing scores  in both Mathematics and English.

Both variables are ratio variables. 

Given the statistical research question, the appropriate statistical test can be applied to determine the relationship. A Pearson correlation coefficient test will test the significance and degree of the relationship. But the more sophisticated higher level statistical test can be applied if there is a need to correlate with other variables.

In the particular study mentioned, the researchers used  multivariate logistic regression analyses  to assess the probability of passing the tests, controlling for students’ weight status, ethnicity, gender, grade, and socioeconomic status. For the novice researcher, this requires further study of multivariate (or many variables) statistical tests. You may study it on your own.

Most of what I discuss in the statistics articles I wrote came from self-study. It’s easier to understand concepts now as there are a lot of resource materials available online. Videos and ebooks from places like Youtube, Veoh, The Internet Archives, among others, provide free educational materials. Online education will be the norm of the future. I describe this situation in my post about  Education 4.0 .

The following video sheds light on the frequently used statistical tests and their selection. It is an excellent resource for beginners. Just maintain an open mind to get rid of your dislike for numbers; that is, if you are one of those who have a hard time understanding mathematical concepts. My ebook on  statistical tests and their selection  provides many examples.

Source: Chomitz et al. (2009)

Topic 2: Climate Conditions and Consumption of Bottled Water

This study attempted to correlate climate conditions with the decision of people in Ecuador to consume bottled water, including the volume consumed. Specifically, the researchers investigated if the increase in average ambient temperature affects the consumption of bottled water.

Statistical Research Question No. 2

Is there a significant relationship between average temperature and amount of bottled water consumed?

In this instance, the variables measured include the  average temperature in the areas studied  and the  volume of water consumed . Temperature is an  interval variable,  while volume is a  ratio variable .

In this example, the variables include the  average temperature  and  volume of bottled water . The first variable (average temperature) is an interval variable, and the latter (volume of water) is a ratio variable.

Now, it’s easy to identify the statistical test to analyze the relationship between the two variables. You may refer to my previous post titled  Parametric Statistics: Four Widely Used Parametric Tests and When to Use Them . Using the figure supplied in that article, the appropriate test to use is, again, Pearson’s Correlation Coefficient.

Source: Zapata (2021)

Topic 3: Nursing Home Staff Size and Number of COVID-19 Cases

An investigation sought to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 3

Is there a significant relationship between the number of unique employees working in skilled nursing homes and the following:

• number of weekly confirmed COVID-19 cases among residents and staff, and
• number of weekly COVID-19 deaths among residents.

Note that this study on COVID-19 looked into three variables, namely 1) number of unique employees working in skilled nursing homes, 2) number of weekly confirmed cases among residents and staff, and 3) number of weekly COVID-19 deaths among residents.

We call the variable  number of unique employees  the  independent variable , and the other two variables ( number of weekly confirmed cases among residents and staff  and  number of weekly COVID-19 deaths among residents ) as the  dependent variables .

This correlation study determined if the number of staff members in nursing homes influences the number of COVID-19 cases and deaths. It aims to understand if staffing has got to do with the transmission of the deadly coronavirus. Thus, the study’s outcome could inform policy on staffing in nursing homes during the pandemic.

A simple Pearson test may be used to correlate one variable with another variable. But the study used multiple variables. Hence, they produced  regression models  that show how multiple variables affect the outcome. Some of the variables in the study may be redundant, meaning, those variables may represent the same attribute of a population.  Stepwise multiple regression models  take care of those redundancies. Using this statistical test requires further study and experience.

Source: McGarry et al. (2021)

Topic 4: Surrounding Greenness, Stress, and Memory

Scientific evidence has shown that surrounding greenness has multiple health-related benefits. Health benefits include better cognitive functioning or better intellectual activity such as thinking, reasoning, or remembering things. These findings, however, are not well understood. A study, therefore, analyzed the relationship between surrounding greenness and memory performance, with stress as a mediating variable.

Statistical Research Question No. 4

Is there a significant relationship between exposure to and use of natural environments, stress, and memory performance?

As this article is behind a paywall and we cannot see the full article, we can content ourselves with the knowledge that three major variables were explored in this study. These are 1) exposure to and use of natural environments, 2) stress, and 3) memory performance.

Referring to the abstract of this study,  exposure to and use of natural environments  as a variable of the study may be measured in terms of the days spent by the respondent in green surroundings. That will be a ratio variable as we can count it and has an absolute zero point. Stress levels can be measured using standardized instruments like the  Perceived Stress Scale . The third variable, i.e., memory performance in terms of short-term, working memory, and overall memory may be measured using a variety of  memory assessment tools as described by Murray (2016) .

As you become more familiar and well-versed in identifying the variables you would like to investigate in your study, reading studies like this requires reading the method or methodology section. This section will tell you how the researchers measured the variables of their study. Knowing how those variables are quantified can help you design your research and formulate the appropriate statistical research questions.

Source: Lega et al. (2021)

Topic 5: Income and Happiness

This recent finding is an interesting read and is available online. Just click on the link I provide as the source below. The study sought to determine if income plays a role in people’s happiness across three age groups: young (18-30 years), middle (31-64 years), and old (65 or older). The literature review suggests that income has a positive effect on an individual’s sense of happiness. That’s because more money increases opportunities to fulfill dreams and buy more goods and services.

Reading the abstract, we can readily identify one of the variables used in the study, i.e., money. It’s easy to count that. But for happiness, that is a largely subjective matter. Happiness varies between individuals. So how did the researcher measured happiness? As previously mentioned, we need to see the methodology portion to find out why.

If you click on the link to the full text of the paper on pages 10 and 11, you will read that the researcher measured happiness using a 10-point scale. The scale was categorized into three namely, 1) unhappy, 2) happy, and 3) very happy.

An investigation was conducted to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 5

Is there a significant relationship between income and happiness?

Source: Måseide (2021)

Now the statistical test used by the researcher is, honestly, beyond me. I may be able to understand it how to use it but doing so requires further study. Although I have initially did some readings on logit models, ordered logit model and generalized ordered logit model are way beyond my self-study in statistics.

Anyhow, those variables found with asterisk (***, **, and **) on page 24 tell us that there are significant relationships between income and happiness. You just have to look at the probability values and refer to the bottom of the table for the level of significance of those relationships.

I do hope that upon reaching this part of the article, you are now well familiar on how to write statistical research questions. Practice makes perfect.

References:

Chomitz, V. R., Slining, M. M., McGowan, R. J., Mitchell, S. E., Dawson, G. F., & Hacker, K. A. (2009). Is there a relationship between physical fitness and academic achievement? Positive results from public school children in the northeastern United States.  Journal of School Health ,  79 (1), 30-37.

Lega, C., Gidlow, C., Jones, M., Ellis, N., & Hurst, G. (2021). The relationship between surrounding greenness, stress and memory.  Urban Forestry & Urban Greening ,  59 , 126974.

Måseide, H. (2021). Income and Happiness: Does the relationship vary with age?

McGarry, B. E., Gandhi, A. D., Grabowski, D. C., & Barnett, M. L. (2021). Larger Nursing Home Staff Size Linked To Higher Number Of COVID-19 Cases In 2020: Study examines the relationship between staff size and COVID-19 cases in nursing homes and skilled nursing facilities. Health Affairs, 40(8), 1261-1269.

Zapata, O. (2021). The relationship between climate conditions and consumption of bottled water: A potential link between climate change and plastic pollution. Ecological Economics, 187, 107090.

© P. A. Regoniel 12 October 2021 | Updated 08 January 2024

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Writing a research article: how to paraphrase, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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75 Comments

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Quantitative Survey Questions: Definition, Types and Examples

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Quantitative Survey Questions: Definition

Types of quantitative survey questions with examples, how to design quantitative survey questions.

Quantitative survey questions are defined as objective questions used to gain detailed insights from respondents about a survey research topic. The answers received for these quantitative survey questions are analyzed and a research report is generated on the basis of this

 data . These questions form the core of a survey and are used to gather numerical data to determine statistical results.

The primary stage before conducting an online survey will be to decide the objective of the survey. Every research should have an answer to this integral question: “What are the expected results of your survey?”. Once the answer to this question is figured out, the secondary stage will be deciding the type of required data: quantitative or qualitative data .

LEARN ABOUT: Survey Mistakes And How to Avoid

Deciding the data type indicates the type of information required from the research process. While qualitative data provides detailed information about the subject, quantitative data will provide effective and precise information.

Quantitative survey questions are thus, channels for collecting quantitative data . Feedback received to quantitative survey questions is related to, measured by or measuring a “quantity” or a statistic and not the “quality” of the parameter.   

Learn more: Survey Questions

Quantitative survey questions should be such that they offer respondents a medium to answer accurately. On the basis of this factor, quantitative survey questions are divided into three types:

1. Descriptive Survey Questions: Descriptive survey questions are used to gain information about a variable or multiple variables to associate a quantity to the variable.

It is the simplest type of quantitative survey questions and helps researchers in quantifying the variables by surveying a large sample of their target market.

LEARN ABOUT: Survey Sample Sizes

Most widely implemented descriptive analysis questions start with “What is this..”, “How much..”, “What is the percentage of..” and such similar questions. A popular example of a descriptive survey is an exit poll as it contains a question: “What is the percentage of candidate X winning this election?” or in a demographic segmentation survey: “How many people between the age of 18-25 exercise daily?”

Learn more: Demographic Survey Questions

Other examples of descriptive survey questions are:

• Variable: Cuisine
• Target Group: Mexicans
• Variable: Facets that transform career decisions
• Target Group: Indian students
• Variable: Number of citizens looking for better opportunities
• Target Group: Chinese citizens

In every example mentioned above, researchers should focus on quantifying the variable. The only factor that changes is the parameter of measurement. Every example mentions a different quantitative sample question which needs to be measured by different parameters.

LEARN ABOUT: Testimonial Questions

The answers for descriptive survey questions are definitional for the research topic and they quantify the topics of analysis. Usually, a descriptive research will require a long list of descriptive questions but experimental research or relationship-based research will be effective with a couple of descriptive survey questions.

Learn more: Quantitative Market Research & Descriptive Research vs Correlational Research

2. Comparative Survey Questions: Comparative survey questions are used to establish a comparison between two or more groups on the basis of one or more dependable variables. These quantitative survey questions begin with “What is the difference in” [dependable variable] between [two or more groups]?. This question will be enough to realize that the main objective of comparative questions is to form a comparative relationship between the groups under consideration.

LEARN ABOUT:   Structured Question & Structured Questionnaire

Comparative survey question examples:

• Dependable Variable: Cuisine preferences
• Comparison Groups: Mexican adults and children
• Dependable Variable: Factors that transform career decisions
• Comparison Groups: Indian and Australian students
• Dependable Variable: Political notions
• Comparison Groups: Asian and American citizens

The various groups mentioned in the above-mentioned options indicate independent variables (Mexican people or country of students). These independent variables could be based on gender questions , ethnicity or education. It is the dependable variable that determines the complexity of comparative survey questions.

LEARN ABOUT: Average Order Value

3. Relationship Survey Questions: Relationship survey questions are used to understand the association, trends and causal comparative research  relationship between two or more variables. When discussing research topics, the term relationship/causal survey questions should be carefully used since it is a widely used type of research design , i.e., experimental research – where the cause and effect between two or more variables. These questions start with “What is the relationship” [between or amongst] followed by a string of independent [gender or ethnicity] and dependent variables [career, political beliefs etc.]?

• Dependent Variable: Food preferences
• Independent Variable: Age
• Relationship groups: Mexico
• Dependent Variable: University admission
• Independent Variable: Family income
• Relationship groups: American students
• Dependent Variable: Lifestyle
• Independent Variable: Socio-economic class, ethnicity, education
• Relationship groups: China

Learn more: What is Research?

There are four critical steps to follow while designing quantitative survey questions:

1. Select the type of quantitative survey question: The objective of the research is reflected in the chosen type of quantitative survey question. For the respondents to have a clear understanding of the survey, researchers should select the desired type of quantitative survey question.  

2. Recognize the filtered dependent and independent variables along with the target group/s: Irrespective of the type of selected quantitative survey question (descriptive, comparative or relationship based), researchers should decide on the dependent and independent variables and also the target audiences .

LEARN ABOUT: Product Survey Questions

There are four levels of measurement variables – one of which can be chosen for creating quantitative survey questions. Nominal variables indicate the names of variables, Ordinal variables indicate names and order of variables, Interval variables indicate name, order and an established interval between ordered variables and Ratio variables indicate the name, order, an established interval and also an absolute zero value.

A variable can not only be calculated but also can be manipulated and controlled. For descriptive survey questions, there can be multiple variables for which questions can be formed. In the other two types of quantitative survey questions (comparative and relationship-based), dependent and independent variables are to be decided. Independent variables are those which are manipulated in order to observe the change in the dependent variables.

Learn more: Quantitative Observation

3. Choose the right structure according to the decided type of quantitative survey question: As discussed in the previous section, appropriate structures have to be chosen to create quantitative survey questions. The intention of creating these survey questions should align with the structure of the question.

LEARN ABOUT: Level of Analysis

This structure indicates – 1) Variables 2) Groups and 3) Order in which the variables and groups should appear in the question.

4. Note the roadblocks you are trying to solve in order to create a thorough survey question: Analyze the ease of reading these questions once the right structure is in place. Will the respondents be able to easily understand the questions? – Ensure this factor before finalizing the quantitative survey questions.

Learn more:

• Nominal Scale
• Ordinal Scale
• Interval Scale
• Ratio Scale
• Nominal Data

You can use QuestionPro for survey questions like income survey questions , Quantitative survey questions, Ethnicity survey questions, and life survey questions.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The ultimate guide to quantitative data analysis

Numbers help us make sense of the world. We collect quantitative data on our speed and distance as we drive, the number of hours we spend on our cell phones, and how much we save at the grocery store.

Our businesses run on numbers, too. We spend hours poring over key performance indicators (KPIs) like lead-to-client conversions, net profit margins, and bounce and churn rates.

But all of this quantitative data can feel overwhelming and confusing. Lists and spreadsheets of numbers don’t tell you much on their own—you have to conduct quantitative data analysis to understand them and make informed decisions.

Last updated

Reading time.

This guide explains what quantitative data analysis is and why it’s important, and gives you a four-step process to conduct a quantitative data analysis, so you know exactly what’s happening in your business and what your users need .

Collect quantitative customer data with Hotjar

Use Hotjar’s tools to gather the customer insights you need to make quantitative data analysis a breeze.

What is quantitative data analysis? 

Quantitative data analysis is the process of analyzing and interpreting numerical data. It helps you make sense of information by identifying patterns, trends, and relationships between variables through mathematical calculations and statistical tests. 

With quantitative data analysis, you turn spreadsheets of individual data points into meaningful insights to drive informed decisions. Columns of numbers from an experiment or survey transform into useful insights—like which marketing campaign asset your average customer prefers or which website factors are most closely connected to your bounce rate. 

Without analytics, data is just noise. Analyzing data helps you make decisions which are informed and free from bias.

What quantitative data analysis is not

But as powerful as quantitative data analysis is, it’s not without its limitations. It only gives you the what, not the why . For example, it can tell you how many website visitors or conversions you have on an average day, but it can’t tell you why users visited your site or made a purchase.

For the why behind user behavior, you need qualitative data analysis , a process for making sense of qualitative research like open-ended survey responses, interview clips, or behavioral observations. By analyzing non-numerical data, you gain useful contextual insights to shape your strategy, product, and messaging. 

Quantitative data analysis vs. qualitative data analysis 

Let’s take an even deeper dive into the differences between quantitative data analysis and qualitative data analysis to explore what they do and when you need them.

The bottom line: quantitative data analysis and qualitative data analysis are complementary processes. They work hand-in-hand to tell you what’s happening in your business and why.  

💡 Pro tip: easily toggle between quantitative and qualitative data analysis with Hotjar Funnels . 

The Funnels tool helps you visualize quantitative metrics like drop-off and conversion rates in your sales or conversion funnel to understand when and where users leave your website. You can break down your data even further to compare conversion performance by user segment.

Spot a potential issue? A single click takes you to relevant session recordings , where you see user behaviors like mouse movements, scrolls, and clicks. With this qualitative data to provide context, you'll better understand what you need to optimize to streamline the user experience (UX) and increase conversions .

Hotjar Funnels lets you quickly explore the story behind the quantitative data

4 benefits of quantitative data analysis

There’s a reason product, web design, and marketing teams take time to analyze metrics: the process pays off big time. 

Four major benefits of quantitative data analysis include:

1. Make confident decisions 

With quantitative data analysis, you know you’ve got data-driven insights to back up your decisions . For example, if you launch a concept testing survey to gauge user reactions to a new logo design, and 92% of users rate it ‘very good’—you'll feel certain when you give the designer the green light. 

Since you’re relying less on intuition and more on facts, you reduce the risks of making the wrong decision. (You’ll also find it way easier to get buy-in from team members and stakeholders for your next proposed project. 🙌)

2. Reduce costs

By crunching the numbers, you can spot opportunities to reduce spend . For example, if an ad campaign has lower-than-average click-through rates , you might decide to cut your losses and invest your budget elsewhere. 

Or, by analyzing ecommerce metrics , like website traffic by source, you may find you’re getting very little return on investment from a certain social media channel—and scale back spending in that area.

3. Personalize the user experience

Quantitative data analysis helps you map the customer journey , so you get a better sense of customers’ demographics, what page elements they interact with on your site, and where they drop off or convert . 

These insights let you better personalize your website, product, or communication, so you can segment ads, emails, and website content for specific user personas or target groups.

4. Improve user satisfaction and delight

Quantitative data analysis lets you see where your website or product is doing well—and where it falls short for your users . For example, you might see stellar results from KPIs like time on page, but conversion rates for that page are low. 

These quantitative insights encourage you to dive deeper into qualitative data to see why that’s happening—looking for moments of confusion or frustration on session recordings, for example—so you can make adjustments and optimize your conversions by improving customer satisfaction and delight.

💡Pro tip: use Net Promoter Score® (NPS) surveys to capture quantifiable customer satisfaction data that’s easy for you to analyze and interpret. 

With an NPS tool like Hotjar, you can create an on-page survey to ask users how likely they are to recommend you to others on a scale from 0 to 10. (And for added context, you can ask follow-up questions about why customers selected the rating they did—rich qualitative data is always a bonus!)

Hotjar graphs your quantitative NPS data to show changes over time

4 steps to effective quantitative data analysis 

Quantitative data analysis sounds way more intimidating than it actually is. Here’s how to make sense of your company’s numbers in just four steps:

1. Collect data

Before you can actually start the analysis process, you need data to analyze. This involves conducting quantitative research and collecting numerical data from various sources, including: 

Interviews or focus groups 

Website analytics

Observations, from tools like heatmaps or session recordings

Questionnaires, like surveys or on-page feedback widgets

Just ensure the questions you ask in your surveys are close-ended questions—providing respondents with select choices to choose from instead of open-ended questions that allow for free responses.

Hotjar’s pricing plans survey template provides close-ended questions

 2. Clean data

Once you’ve collected your data, it’s time to clean it up. Look through your results to find errors, duplicates, and omissions. Keep an eye out for outliers, too. Outliers are data points that differ significantly from the rest of the set—and they can skew your results if you don’t remove them.

By taking the time to clean your data set, you ensure your data is accurate, consistent, and relevant before it’s time to analyze. 

3. Analyze and interpret data

At this point, your data’s all cleaned up and ready for the main event. This step involves crunching the numbers to find patterns and trends via mathematical and statistical methods. 

Two main branches of quantitative data analysis exist: 

Descriptive analysis : methods to summarize or describe attributes of your data set. For example, you may calculate key stats like distribution and frequency, or mean, median, and mode.

Inferential analysis : methods that let you draw conclusions from statistics—like analyzing the relationship between variables or making predictions. These methods include t-tests, cross-tabulation, and factor analysis. (For more detailed explanations and how-tos, head to our guide on quantitative data analysis methods.)

Then, interpret your data to determine the best course of action. What does the data suggest you do ? For example, if your analysis shows a strong correlation between email open rate and time sent, you may explore optimal send times for each user segment.

4. Visualize and share data

Once you’ve analyzed and interpreted your data, create easy-to-read, engaging data visualizations—like charts, graphs, and tables—to present your results to team members and stakeholders. Data visualizations highlight similarities and differences between data sets and show the relationships between variables.

Software can do this part for you. For example, the Hotjar Dashboard shows all of your key metrics in one place—and automatically creates bar graphs to show how your top pages’ performance compares. And with just one click, you can navigate to the Trends tool to analyze product metrics for different segments on a single chart. 

Hotjar Trends lets you compare metrics across segments

Discover rich user insights with quantitative data analysis

Conducting quantitative data analysis takes a little bit of time and know-how, but it’s much more manageable than you might think. 

By choosing the right methods and following clear steps, you gain insights into product performance and customer experience —and you’ll be well on your way to making better decisions and creating more customer satisfaction and loyalty.

FAQs about quantitative data analysis

What is quantitative data analysis.

Quantitative data analysis is the process of making sense of numerical data through mathematical calculations and statistical tests. It helps you identify patterns, relationships, and trends to make better decisions.

How is quantitative data analysis different from qualitative data analysis?

Quantitative and qualitative data analysis are both essential processes for making sense of quantitative and qualitative research .

Quantitative data analysis helps you summarize and interpret numerical results from close-ended questions to understand what is happening. Qualitative data analysis helps you summarize and interpret non-numerical results, like opinions or behavior, to understand why the numbers look like they do.

 If you want to make strong data-driven decisions, you need both.

What are some benefits of quantitative data analysis?

Quantitative data analysis turns numbers into rich insights. Some benefits of this process include: 

Making more confident decisions

Identifying ways to cut costs

Personalizing the user experience

Improving customer satisfaction

What methods can I use to analyze quantitative data?

Quantitative data analysis has two branches: descriptive statistics and inferential statistics. 

Descriptive statistics provide a snapshot of the data’s features by calculating measures like mean, median, and mode. 

Inferential statistics , as the name implies, involves making inferences about what the data means. Dozens of methods exist for this branch of quantitative data analysis, but three commonly used techniques are: 

Cross tabulation

Factor analysis

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• Published: 15 May 2024

More than news! Mapping the deliberative potential of a political online ecosystem with digital trace data

• Lisa Oswald   ORCID: orcid.org/0000-0002-8418-282X 1  

Humanities and Social Sciences Communications volume  11 , Article number:  629 ( 2024 ) Cite this article

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Does the internet facilitate everyday public deliberation? Previous research on this question has largely focused on specific aspects, such as online news media diets or political discussions on social media. However, increasingly complex media environments are composed of different arenas with different respective potential for democracy. While previous work extensively dealt with the quality of political discussion online, it is a necessary but overlooked step, to consider the upstream features of digital infrastructure and usage. Using digital trace data from Germany, this study maps out which websites are relevant for online public discourse, introduces a measure of deliberative potential along six dimensions (information, communication, participation, connectivity, inclusivity and heterogeneity), and explores different types of websites alongside high level usage patterns. Besides a class of mainstream informational hubs, a class of quality information providers that includes most established public broadcasting sites was found. A third class of niche online forums hosts political discussions among more tightly-knit online communities, supporting previous findings of incidental exposure to political content online. While the mainstream information hubs in the sample attract a much larger volume of clicks, users spend relatively more time consuming political information on quality information sites as well as on niche online forums to engage with politics online. This project takes a more holistic perspective of the diverse ecosystem of online deliberation, while presenting a first quantitative exploration of a deliberative system.

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The role of the big geographic sort in online news circulation among U.S. Reddit users

Introduction.

The question as to what extent the internet enables (or hinders) public deliberation is a much disputed issue that has, so far, only partially been addressed and from rather specific angles. Researchers with a focus on social media platforms have considered active user communication by analyzing online discussion threads (e.g., Esau et al. 2021 ; Halpern and Gibbs 2013 ), whereas researchers with an interest in online news media diets, for example, have examined web browsing histories with a distinct news media frame (Guess 2021 ).

This study takes a step back and focuses on the concept of deliberative potential , examining the infrastructural affordances and audiences of different politically relevant websites in the German political online ecosystem. In order to allow claims about the well researched deliberative quality and the substance of discussions, a much more indepth analysis of the communicative acts would be necessary and is nothing this paper speak to (e.g., Steenbergen et al. 2003 ; Esau et al. 2021 ). The infrastructural elements of a website, such as the provision of political information, comment sections, petitions, etc. as well as the empirical mapping of political usage are features necessary to examine upstream. For example, a discrepancy between the deliberative potential given affordances and usage and the actual quality of discussions may eventually indicate a form of unused potential of online environments for public deliberation – directly implying the next question of why this is.

In other words, an exclusive focus on deliberative quality and relatedly, toxicity in online public discourse, overlooks important selection effects resulting in skewed participation that is central to public discourse in online environments (Kim et al. 2021 ). While the communicative acts available for analysis on social media or the comment sections of news outlets are predominantly produced by a highly active minority of users, the majority of those reading along while also forming political opinion remains invisible to the researcher and the public (Bright et al. 2019 ). An exclusive focus on digital traces of communication also risks overemphasizing policies to limit the impact of a skewed highly active minority while overlooking the unused potential of the silent majority for public discourse online. For a comprehensive understanding of the structural transformation of the public sphere in the digital age, one must go beyond the apparent usage behavior of few, but consider the largely invisible behavior of the majority of the public (Habermas 2021 ). Instead, the examination of web browsing histories across the broader public offers new perspectives to address these methodological challenges.

Furthermore, the academic discourse concerning the extent of homophily and selective exposure in so-called online ‘echo chambers’ considerably diverges between disciplines and methodological approaches (Pariser 2011 ; Sunstein 2002 ). Studies examining data within one specific platform find robust evidence for homophily in social networks (Cinelli et al., 2021 ; Cota et al. 2019 ; Guerrero-Solé and Lopez-Gonzalez 2019 ; Koiranen et al. 2019 ; Rivero 2019 ). However, studies considering data across multiple platforms and media types find evidence of a diversity of exposure (Fletcher and Nielsen 2018 ; Guess 2021 ; Lelkes 2020 ; Strauss et al. 2020 ; Yang et al. 2020 ). Facing this dispute, a more holistic and data-driven systematic consideration of online arenas for public discourse can help avoid the underestimation of exposure while, at the same time, avoiding the overestimation of siloed information. In line with theorists of deliberative democracy (e.g., Bächtiger and Parkinson 2019 ), this study demonstrates that the political usage of the internet or the engagement with political topics online goes far beyond the categories of news media consumption and social media discussion but should be measured more holistically, by mapping the diverse ecosystem of online deliberation.

This project links and expands upon existing streams of research on online communication and information, and focuses on the deliberative potential of websites as the structural basis for a constructive online public discourse. Bridging those streams of research are a necessary condition for a systematic and systemic assessment of the online public sphere (Bächtiger and Parkinson 2019 ). The following three research questions are addressed:

Which websites hold potential for online public discourse, including political information consumption and discussion online?

How is the political online ecosystem structured along infrastructural and usage characteristics?

How does the interplay between user demographics and different classes of websites look like?

Using digital trace data from Germany in combination with survey data and manual content coding to characterize a wide range of politically relevant websites, this study empirically tackles various questions of the online public sphere for the first time. With passive web tracking, the data collection is not geared towards one specific platform or website type (e.g. news media), but provides a more complete picture of online behavior, which is crucial for gaining a more holistic and realistic perspective of the online public sphere. The deliberative potential of websites is considered as a latent construct which is in line with an understanding of deliberation as the summative quality of a deliberative system in which different sites fulfill different democratic functions (Bächtiger and Parkinson 2019 ; Elstub et al. 2019 ; Esau et al. 2021 ). Furthermore, a latent classification of websites goes beyond a xylographic distinction between news sites and social media platforms. Using a broad initial scope together with a latent approach, one does not risk overlooking important, potentially overlapping arenas in which political communication is taking place and where people receive their information online.

Overall, this study shows that only a small proportion of online activity (1%) is concerned with politics. To the disappointment of social scientists, the majority of people spend most of their time accessing various forms of entertainment, shopping and work-related URLs. However, the vast majority of users in the sample (1190 out of 1282 individuals) engaged with some political content during the six-month observation period that included the 2017 national elections in Germany. Originally starting with a web tracking dataset of more than 56 million website visits, without setting a predefined exclusion frame for the type of website and using automated approaches together with manual cross validation, the analysis is narrowed down to a set of 69 central domains featuring content on a wide range of political topics relevant to the German public discourse.

Besides a cluster of highly-popular ‘mainstream’ sites that are visited by a broad range of users to read and discuss political information, a cluster of public broadcasting and journalistic news outlets was found, the ‘quality information providers’ that cover the highest density of political information. However, they are not characterized by a diverse user base. A third cluster of niche online forums was identified, often dedicated to specific topics or communities, that are mostly neglected in current analyses of the online public sphere. Manual cross checking confirmed that they host in-depth political discussions among sometimes tightly knit online communities such as fan forums. While the mainstream sites in the sample attract a much larger volume of clicks, users spend relatively more time consuming political information via public broadcasting and online news outlets as well as on niche online forums to discuss politics online.

In other words, while the German deliberative system seems to be a rather small fraction of the wider online environment, the consumption of political content is not as exclusive as the visible discussion patterns of few very active users on social media may imply. While mainstream platforms are most central to the topical flow of political information consumption within the system, the latent structure of deliberative potential highlights the persisting relevance of high quality public broadcasting as the backbone for democratic deliberation in Germany. Niche online forums provide particular potential for interpersonal trust building through prior exchanges about shared a-political interests as potential basis for the deliberation of conflicting political views among citizens.

Deliberation in online environments

Online communication has often been connected to an increase in affective political polarization, the spread of misinformation and the rise of radical counter publics (Bail 2021 ; Rathje et al. 2021 ; Bright 2018 ; Douglas et al. 2017 ; LorenzSpreen et al. 2021 ; Vosoughi et al. 2018 ).

However, in theory, constructive discussions among informed citizens should help to identify the best arguments for complex societal questions and therefore mitigate opinion polarization (Grönlund et al. 2015 ; Habermas 1984 ; Ugarriza and Caluwaerts 2014 ). While more and more deliberation researchers are concentrating their research efforts in the area of online discussions (Strandberg and Grönlund 2018 ), contradicting evidence is emerging on the quality of online discussions. While this evidence appears negative in many regards (Anderson et al. 2014 ; Coe et al. 2014 ; Sunstein 2002 ; Ziegele et al. 2020 ), there are positive exceptions, for example when it comes to discussions in the comments section of online newspapers (Manosevitch and Walker 2009 ; Rowe 2015 ).

There is a nuanced empirical discourse around the measurement of deliberative quality, including some widely-established coding schemes and the development of novel, more inclusive criteria of deliberative quality (Steenbergen et al. 2003 ; Graham, 2008 , 2012 ). Additional concepts discussed in the field are for example story-telling, humor, emotions, power, and the role of non-verbal communication for deliberative democracy (Esau et al. 2021 ; Gerber et al. 2018 ; Basu, 1999 ; Coleman and Moss, 2012 ; Krause, 2008 ; Follesdal, 2010 ; Mendonça et al. 2020 ). However, there is little empirical investigation of the infrastructural foundation of online deliberation—the potential for deliberation supplied online by websites providing political information and discussion spaces.

A promising but today mostly theoretical development in the field are systemic perspectives on deliberation. Deliberative systems theory, that appears particularly applicable in the digital domain, argues that different arenas fulfill different functions for democracy (Bächtiger and Parkinson, 2019 ; Ercan et al. 2017 ; Mansbridge et al. 2012 ). However, the empirical conceptualization of the deliberative potential of websites as the basis for a constructive public discourse remains largely unresolved. Even though online political deliberation might be a niche phenomenon rather than mainstream behavior, it is crucial to understand its structural foundation. Beauchamp ( 2020 ) describes the deliberativeness of discussions in online environments as a function of membership and structure. This project empirically maps these structures, an ecosystem of politically relevant websites, as the foundation of a deliberative system and the necessary condition for deliberation to occur in online environments.

Deliberative potential of websites

While the theoretical term ‘deliberative potential’ is not a novel concept in the field, the deliberative potential of websites has, so far, only been explored theoretically or with regard to specific domains (Conover et al. 2002 ; Mendonça and Ercan 2015 ). For example, Wiklund ( 2005 ) analyzed different municipal websites in Sweden with a focus on two dimensions, information services and communication services provided by the websites. In contrast, Richardson and Stanyer ( 2011 ) examined British online news outlets. They consider manifest characteristics of websites while still keeping a focus on communicative features such as online forums and the deliberative quality of communication.

In this study, the assessment of deliberative potential is rooted in the theory of deliberative democracy; the six dimensions of the concept are described in detail below. The notion that ‘different types of public deliberation online can be expected to display different characteristics and fulfill different functions in democratic opinion and will formation, as well as in decision making.’ (Esau et al. 2021 , p. 2) has especially influenced the definition of deliberative potential used throughout this project. While different types of deliberation, ranging from intimate personal discussion to anonymous public communication fulfil different functions, they are also likely to occur in entirely different arenas that come with distinct infrastructural setups and user constellations. In turn, these arenas are not just the playing field for political discussion but shape discussions with their respective potential (Beauchamp 2020 ).

The dimensions of deliberative potential are structured along three core functional dimensions that are determined by the infrastructure of the website as the basis for deliberation (see Table 1 ). Three additional higher-level dimensions are defined by the respective usage patterns or demand-side characteristics. For example, a forum that enables reciprocity in communication is regarded as necessary basis for online deliberation. However, in line with theories of deliberative democracy (Bächtiger and Parkinson 2019 ; Habermas et al. 1974 ) only the consideration of heterogeneous arguments within an inclusive debate that is accessible for a diverse citizenship can make a discussion truly deliberative. This conception is not particular to the online sphere. Already in face-to-face citizen assemblies, the deliberative quality as well as the outcomes of deliberation depended on who is deliberating, regardless of the venue’s infrastructure (Warren 2021 ). While from a normative point of view, the combination of certain characteristics is favored, the systemic notion of deliberation does not require all arenas to fulfill all characteristics at the same time (Bächtiger and Parkinson 2019 ).

Therefore, in this project maps different structural preconditions for deliberative discourse, including both infrastructural aspects and patterns of how this infrastructure is used. In doing so, this project focuses on the description of the deliberative potential of online environments, rather than assessing the quality or issues of the discourse.

Information

The first dimension in the assessment of the deliberative potential of websites is the provision of relevant information. In 1789, Thomas Jefferson had already concluded that well informed citizens are the key to a healthy democracy (Jefferson 1789 ), a notion that still constitutes a core feature of deliberative democracy (Bächtiger and Parkinson 2019 ; Fishkin 2018 ). Information on parties, policies, institutions and procedures are the building blocks of political knowledge and are extensively researched concepts in the social sciences for good reason (Carpini and Keeter 1993 ; Prior 2005 ). Civic knowledge about institutions and processes can help citizens to better understand their interests as individuals and members of a group, it increases the consistency of views across issues and over time, and it increases trust, political participation and support for democratic values, such as tolerance for the needs of minorities (e.g. Galston 2001 ). Moving the perspective to the digital era, in the context of online deliberation, websites on which users find political information can serve as a resource for opinion and will formation (Esau et al. 2021 ). While information on political parties and issues potentially influence political opinions and inform vote choices, administrative information help citizens to understand democratic structures and procedures (Döring 2021 ).

Drawing upon both empirical findings on political and civic knowledge (Carpini and Keeter 1993 ; Munzert and Selb 2017 ) as well as previous research on the role of political information for deliberation (Wiklund 2005 ), three distinct criteria are included for the assessment to what extent a site provides relevant political information. This study assesses whether the site provides (1) information on political actors, institutions and political issues, (2) information on administrative procedures and local information, and (3) whether information provided by the site is journalistically curated or has, at least, undergone some other form of fact checking (such as e.g. on Wikipedia). Local information, for example on local initiatives and regulations is integrated into the category with administrative information, because they have the similarly enabling potential for civic engagement and political participation. The third information criterion serves as a basic manifest proxy for information quality.

Measuring the exposure to diverse news media is one important component to assess the informative potential of the online sphere for a functioning democracy. Previous projects focused on news access through social media sites which, however, risks neglecting less mainstream media outlets (Bakshy et al. 2015 ; Barberá et al. 2015 ; Eady et al. 2019 ). Other studies that collect data from the user perspective risk biased self-reports in surveys (Boxell et al. 2017 ; Lelkes, 2016 ). Facing these methodological challenges, web tracking data provide unique insights into real-life media diets. While Guess ( 2021 ) illuminates important aspects of online news media diets, for example, this study aims to capture the overall deliberative potential of the complex online public sphere using an even more inclusive scope.

Communication

It is important to note that this project does not consider the deliberative quality or the content of discussions taking place on a certain website, when looking at the communication dimension of deliberative potential. Instead, this study assesses whether the website provides users with the possibility to express and/or exchange political opinions with other users (Wiklund 2005 ). Such sites can serve as communicative spaces for interactional opinion and will formation (Esau et al. 2021 ). The dimension splits into two levels of communication. First, it is measured whether the website enables the expression of political opinions through the provision of comment sections, for example. In a second step, it is assessed whether the site fulfills the deliberative core criterion of potential reciprocity (Steenbergen et al. 2003 ). Communicative situations can only be characterized by reciprocity, if users have the option to reply to each other. Therefore, websites only fulfill the second criterion if a reference to previous comments is enabled, for example in online forums and on social media platforms, but also in comment sections of online news papers where ‘reply’ functions are enabled. The sole possibility to up-vote or down-vote comments, or to react to comments through ‘likes’ is not regarded as reciprocity. Following this approach, this study does not assess actual communication or specific elements such as listening to the arguments of others (Kriplean et al. 2012 ; Scudder 2020 ), but it assesses the structural foundation as preconditions for deliberative communication.

Participation

Websites that provide users with the possibility of online political participation can have a more or less direct impact on political decision-making or, at least, serve as a platform for the aggregation of interests (Esau et al. 2021 ).

It is a disputed issue, whether a link to decision-making is necessary to consider political communication as deliberation. While Thompson ( 2008 ) insists on the link to decision-making, the idea of deliberative polls (Fishkin et al. 2018 ), the Habermasian idea of diffuse communication in the public sphere as well as the deliberative systems approach adopt a broader definition of deliberation. By mapping the deliberative potential of the online ecosystem, this study includes opportunities for online political participation as desirable features of online political ecosystems without making a judgment about the definition of deliberation itself.

To assess the structural foundation of the link to decision-making, three distinct criteria are examined: (1) whether the website hosts petitions and/or opinion polls to collect, aggregate or organize public opinion (Richardson and Stanyer 2011 ), (2) whether the website enables citizens to get in contact with political actors (Wiklund, 2005 ), and (3) whether the website enables the political organization of citizens, for example by the formation of political interest groups or events such as discussion forums, demonstrations and other forms of political protest. Wiklund ( 2005 ) considered some of these criteria under the framework of the communicative services of a website. However, it might be worth distinguishing between forums for discussions among citizens and communicative acts that can have a more direct influence on political decision-making.

While this study considers the first three dimensions: information, communication and participation, as core dimensions of the deliberative potential of a platform, three additional criteria are assessed: connectivity, inclusiveness and heterogeneity that are defined through usage patterns and user characteristics.

Connectivity

The dimension of connectivity considers whether the website is connected to other politically relevant websites. These connections can, for example, enable further research by citizens on political issues or facilitate the implementation of intention to participate in the political process. Following the conceptualization of deliberative systems, an understanding of the links and flows between different sites is crucial for a systemic understanding of online public discourse (Dryzek 2012 ; Esau et al. 2021 ; Fleuß et al. 2018 ). For example, Fleuß et al. ( 2018 ) emphasized the transmissions between different loci as being an important aspect to measure deliberation in a systemic way. They proposed tracking the transmission of topics as they evolve within the system as well as tracking individuals who transmit ideas from one locus to another. While the analysis mainly operates within the arena of informal deliberation, the approach to operationalize connectivity, by tracking users’ subsequent visits to different websites featuring the same topics, gets very close to their theoretical idea of formalizing transmissions.

A body of literature outside the field of deliberation research that examines those links and flows between different online sites can be found in the field of inter-media agendasetting research. For example, media researchers have analyzed to what extent certain online publics are able to stimulate mass media publics, others have examined news diffusion processes from a temporal perspective or the Twitter networks of journalists as important nodes between digital and mass media (Messner and Distaso 2008 ; Wallsten 2007 ; Buhl et al. 2018 ; Neuberger et al. 2019 ).

In this course, digital trace data provides a unique opportunity to explore linkages between websites through the observation of real-life online behavior with network-analytical quantities. At the time of writing, this project is the first to formalize the connectivity of the different arenas of an online deliberative system empirically.

Inclusivity

The dimension of inclusivity appears to be an increasingly prominent aspect in the scientific discourse on deliberation. Mansbridge et al. ( 2012 ) describe three overall functions of a deliberative system: (1) an epistemic function to produce appropriately informed preferences and opinions, in this study, considered under the dimension of information, (2) an ethical function that creates respect between experts and citizens – these aspects could, for example, be a consequence of communication among citizens and contact between citizens and political actors, both captured in the dimensions of communication and participation –, and (3) a democratic function. Mansbridge et al. ( 2012 ) conceptualize the democratic function as promoting ‘an inclusive political process in terms of equality’ (p. 12), which implies the inclusion of multiple and plural voices.

This study explicitly considers the demographic variables of gender, age and educational Footnote 1 background in the assessment of inclusivity, to answer the question whether a website is used by a diverse set of individuals. This dimension, with a focus on demographic aspects, features of social groups, corresponds to Young’s ( 2002 ) concept of diverse perspectives for democratic representation. In the digital context, this dimension can further serve as indicator for low barriers of access. The unique combination of digital trace data with high-quality survey data allows a precise measurement of this dimension of deliberative potential.

Heterogeneity

One final important aspect, also implied in the conceptualization of the democratic functions of a deliberative system by Parkinson and Mansbridge ( 2012 ) is the inclusion of a variety of interests, concerns and claims. Furthermore, Young ( 2002 ) emphasizes the representation of diverse opinions, “any judgements or belief about how things are or ought to be” (p. 135) for a pluralistic democracy. This aspect is considered under the dimension of heterogeneity and assesses whether the website connects people holding diverse political opinions. This concept stands in contrast to the notion of ideological online ‘echo chambers’ in which users are argued to be mainly surrounded by similar others, holding opinion-reinforcing views (Pariser 2011 ; Sunstein 2002 ). In contrast to other researchers, who focused on the heterogeneity of information diets in online environments, this study considers the composition of users that visit a website (Bright et al. 2020 ; Dubois and Blank 2018 ; Guess 2021 ). More specifically, the approach taken in this study captures the heterogeneity of political orientations of users within a website through their explicit indication of political orientation on a left-to-right scale and their expressed party preferences in the context of the German federal election in 2017.

Both dimensions, inclusivity and heterogeneity are rooted in representation literature (Warren 2021 ). Random sampling would, under ideal experimental conditions with high compliance, ensure inclusivity and heterogeneity. Under natural conditions in online environments, the issues of inclusivity and heterogeneity as basis for discursive representation are more complex. This study considers the concepts of inclusivity and heterogeneity as theoretically distinct, as inclusivity builds on manifest demographic variables of the user whereas heterogeneity is a more latent construct of political attitudes and party preferences that possibly links more closely to political content featured online.

Methodological challenges

Around the beginning of the millennium, Steyaert ( 2000 ) had already emphasized the need for analytical tools that enable a systematic empirical analysis of digital democracy. However, most research in the field is still being conducted as explorative case studies, mostly with a focus on the content or the deliberative quality of communicative acts online (Felicetti et al. 2016 ; Jensen 2003 ; Jonsson 2015 ; Pedrini 2014 ). Also the rooting theorist of deliberative democracy and the concept of the public sphere, Jürgen Habermas, explicitly emphasized the methodological challenge of empirically examining online deliberation in a recent piece on the restructuring of the public sphere in the digital age (Habermas 2021 ). The conceptualization of deliberation as the emergent property of a system, involving the dynamics of contexts and platform design elements with different functions for democracy, comes with serious questions for empirical research (Esau et al. 2017 ; Boswell and Corbett 2017 ; Fleuß et al. 2018 ; Niemeyer et al. 2015 ).

Based on the current state of the empirical literature, this study identifies two key method-ological challenges in the analysis of online deliberation. First, given the ever-growing online landscape, it is crucial to know where on the web different branches of public discourse are taking place in order to make assumptions about their content and quality. The question as to which websites are used for political issues is not trivial as deliberation in online environments is getting increasingly pluralistic and incidental news exposure occurs regularly (Esau et al. 2017 ; Janssen and Kies 2005 ; Feezell 2018 ; Kim et al. 2013 ; Tewksbury et al. 2001 ; Yadamsuren and Erdelez 2010 ).

Second, most empirical research in the field of online deliberation, especially the assessment of the deliberative quality of communication, is researched on individual (active) behavior. However, most people on the web are passive consumers of content, also called ‘lurkers’ (Nonnecke and Preece 1999 ; Sun et al. 2014 ). This passive majority does not leave obvious digital traces in online forums and comment sections but they certainly do obtain political information from the web that shape their political opinions and actions. In the context of social media platforms, for example, passive users also experience social learning and constantly adapt their understandings of social norms by observing other people’s communication, while a highly active minority, also called ‘power users’, creates the majority of content online (Bright et al. 2019 ). This distinction between passive and active engagement in online public discourse has, with a slightly different angle, already been reflected in Habermas’ notion of a ‘two-track model’ of deliberation, emphasizing that most political deliberation happens in institutionalized form with the majority of citizens taking a pure spectator role (Habermas 1996 ).

In consequence, it remains largely unclear how this silent majority uses the web for political issues facing a heavy focus on communication data. It would be important to also examine passive exposure instead, to meaningfully define the boundaries of the public arena online. The question as to what extent websites enable public deliberation, under the further consideration of a systemic perspective, is what this study aims to answer with the assessment of the deliberative potential of websites.

Of course, deliberative potential does not directly imply deliberation. Online environments may provide accurate political information but also misinformation, they may enable deliberative discussion but also host toxic exchanges, they may provide platforms for civic engagement but also hostile participation (Freudenthaler and Wessler 2022 ; Quandt 2018 ). However, if the fundamental conditions of deliberative potential are not met in the infrastructure and usage of online environments, deliberation itself is impossible.

Methodology

The analysis is based on web tracking data that was collected within a six-month period in Germany, including the national elections in September 2017. The data is linked to rich survey data, including users’ demographics, political attitudes and other relevant political variables. This unique combination of two data sources allows the assessment of the deliberative potential of websites enriched by components that only become measurable in the interaction of user characteristics and usage behavior (connectivity and especially, inclusivity and heterogeneity).

The survey data was collected via the German YouGov Pulse panel with survey questions originally fielded to 1500 respondents in five waves. Using a quota-sampling procedure on the basis of the marginals from Best for Planning ( 2017 ), the sample mirrors the German online population with respect to gender, age and, to some degree, education. Respondents were asked to install a URL tracking software that uses passive metering technology to record detailed browser histories on an opt-in basis. Tracking could be paused for 15 min and respondents could end their participation at any time. This digital trace data includes more than 56 million website visits to almost 200,000 different domains by 1282 different individuals between July 2017 and December 2017. As this paper draws on data collected in a prior research project, details on the sampling procedure, the deployment of the passive metering software as well as privacy and ethical considerations can be found in part B of the supplementary information.

In a first step, the top 1000 domains were manually classified into categories (see Table C12 ). Those 1000 domains account for about 83% of website visits. This distribution is characteristic for web browsing data, in which central websites accumulate most activity while the majority of sites are only visited by very few users. The pre-labelled web tracking data was then merged with the survey data to allow for the description of the demographic profile of the sample Footnote 2 .

To develop a measurement for the deliberative potential of websites, the set of websites in scope had to be identified first. Considering the context of deliberative democracy, a focus on websites that, in the wider sense, play a role in the German online public discourse or feature political content appeared the most appropriate. Through this empirical approach, the notion of deliberation underlying the concept of deliberative potential is focused on political topics and set apart from everyday conversation or everyday deliberation that may only implicitly regard political issues (e.g., Maia 2017 ). The approach to be particularly inclusive in the first step sets this project apart from previous research, for example on online news media diets (Guess 2021 ), that also uses web tracking data but focuses exclusively on news websites. In order to gain a realistic picture of the online public sphere, it is important to consider all possible channels through which political information, communication and participation is enabled, especially because the exposure to political information makes up only a small proportion of users’ total online engagement.

Exploiting the fact that the data almost mirror the German online public demographically and include individuals’ browser histories for a period of about six months around the federal elections in Germany, websites accessed in a ‘political context’ were selected with a combination of automated keyword search, matching political keywords to the URL strings of tracked website visits, and manual cross checking by browsing the websites for instances of clearly political content (see Fig. 1 ). The relevance of these websites for the online public discourse in Germany in 2017 was then approximated using the number of website visits on the respective domain, aggregated across the sample, as a measure of engagement. The process of political website identification is described in detail in SI A .

Top: Manual construction of dictionary consisting of political topics in the Germany public discourse of 2017. Center: Mapping topic dictionary onto full URLs of website visits as automated selection step. Bottom: Manual cross-validation of automated selection and refinement of dictionary for systematic mismatches.

In a second step, the deliberative potential of those politically relevant websites was determined. To this end, the outlined six dimensions of deliberative potential were assessed: information, communication, participation, connectivity, inclusivity and heterogeneity (see Table 1 ). While the first three dimensions were evaluated with manual content coding only, the latter three were determined through a consideration of digital trace data in combination with linked survey data.

Content coding

In order to assess the first three core dimensions of the concept of deliberative potential, the full sample of relevant websites was assessed using quantitative content analysis on the websites’ infrastructure. The theoretical definitions of the dimensions were translated into operational definitions including concrete criteria that could be assessed using a binary rating system (0 representing ‘not present’, 1 representing ‘present’). The unit of analysis were website domains and the coding was conducted after manually visiting the website and assessing the overall structure of the page, posts, articles, and comment sections. A standardized code book (see Table 1 ), including all dimensions and sub-criteria was used to streamline the coding process.

Digital trace data

The availability of web tracking data in combination with survey data allows the enrichment of the manual assessment of deliberative potential with granular quantitative measures of online behavior. This micro-level behavioral data was used to assess criteria on a more macro level, the unit being websites rather than single users. The connectivity measure was constructed through network analytical measures of in-going and out-going traffic (Csardi et al., 2006 ). The measure of inclusivity was added based on demographic variables; heterogeneity based on the political preferences of users.

More specifically, in order to exploit the benefits of digital trace data to build the connectivity measure, a network of website visits was constructed with websites represented as nodes, and temporally subsequent website visits for one user, featuring the same topic, represented as edges. For example, if a user reads an online newspaper article featuring the name ‘Merkel’ in the URL and, following this, visits a social media discussion featuring ‘Merkel’, an edge was created between the nodes of the online newpaper and the social media platform. Only subsequent visits to different websites were counted, while self-loops were excluded from the network. This way, instead of considering the ‘dead’ hyperlink-infrastructure from the html text of the websites, a measure of actual in-going and out-going politically-relevant traffic was created for each website. These traffic flows can be quantified using the network analytical measures of in-degrees (in-going traffic) and out-degrees (out-going traffic) (see Fig. C4 in the SI ).

To assess the degree of inclusivity of a website, three distinct diversity indicators were calculated for each website for the variables age, gender and education. The widely-used entropy-based Shannon-Wiener diversity index was used as it is implemented in R (see SI A ; Dixon 2003 ; Grafton et al. 2012 ; Kiernan 2014 ; Oksanen 2013 ). A high inclusivity means that a website is accessed by individuals from different age groups, education levels or genders. The more different categories (for example age groups) and the more similar the engagement levels across those different groups, the higher the estimated inclusivity value of a particular website.

For the construction of heterogeneity criteria, a similar approach was used. The diversity assessment was applied to a variable measuring the political orientation of participants on a left-to-right scale and to their reported first votes in the 2017 federal election in Germany. According to the Shannon-Wiener diversity index, the heterogeneity of a website is comparably high if it is visited equally by individuals with different political orientation.

The dataset of individual websites, labeled with regard to the six criteria of deliberative potential, is one outcome of this study which is published along this manuscript. However, this dataset needs to be structured and summarised to be digestible and informative. The reduction of complexity by structuring data is the core purpose of clustering approaches, including latent class analysis, which is why it was used in this manuscript in a second step, after the rich classification of each website along six deliberative criteria.

Clustering websites with latent class analysis

After the assessment of all six dimensions of deliberative potential of websites, patterns of commonalities and differences were considered between websites to explore different ‘profiles’ of deliberative potential. In line with the latent understanding of deliberative potential, a latent class approach was used to identify groups of websites according to their deliberative potential. Besides this theoretical reason, an examination of the empirical relationships between different criteria, suggests the use of a latent composite measure as there are both, correlations within, but also between different dimensions of deliberative potential (see Fig. C5 ). More details on the latent class modeling approach can be found in SI A . Finally, after the identification of classes, an individual class membership prediction value was assigned to each website, allowing the categorization of websites into latent classes.

Politically relevant sites in Germany in 2017

Applying the two-stage process of website selection, consisting of the automated dictionary-based classification of websites as ‘politically relevant’ and the following manual cross validation, 69 central domains were identified in the sample that have played a role in the online public discourse in Germany in the second half of 2017. Those websites were visited by 1190 unique users, which included a large proportion of the original sample ( N  = 1282). It is important to note that this does not mean that, for example because highly frequented websites such as ‘Google’ and ‘Facebook’ are part of this set of 69 websites, those 1190 individuals simply used those platforms at least once in the six-month period. Instead, it means that they ‘googled’ some political keyword or visited political content on Facebook because the political filtering step took place before the compression of website visits into domains.

Starting from the original sample of more than 56 million tracked website visits, less than 1% (493,714 clicks) were politically relevant visits to those 69 domains. Table C12 summarizes the big picture of the overall online activity of the sample, illustrating that the engagement with political issues is not the dominant motive for many users to use the web. In contrast, the most frequented websites were social media platforms and search engines (mostly for apolitical content), email providers, online shopping, gaming, streaming, porn and online banking.

Furthermore, only slightly more than half of the politically relevant websites (52%) in the sample are explicitly labeled as news websites, and only 12% of the websites featuring political discussions are social media platforms.

Figure 2 summarizes the descriptives on the prevalence of deliberative potential criteria across the sample of politically relevant websites. While the majority of websites fulfills two out of three information criteria (most provide political information that underlie some form of journalistic curation or fact checking), only very few websites fulfill the criteria of participation. When it comes to the potential to host political discussions, about half of the platforms provide the possibility to express and discuss political opinions online while the other half neither enables expression nor reciprocity in communication. Only very few platforms enable the expression of political opinions in the form of comment sections without the possibility to reply to other comments. Considering the ‘demand side’ characteristics of demographic inclusivity and political opinion heterogeneity within websites’ user bases, both measured with the entropy-based Shannon-Wiener diversity index (more details see SI A ), there is a considerable overlap of density distributions. Websites attracting users of diverse age groups, genders and education levels appear to also attract users of diverse political orientations and party preferences. The distributions of both measures, though highly correlated with the overall engagement on a website, does not mirror the rather leftskewed metric of connectivity that reflects engagement links and flows between politically relevant platforms.

Left: How many of the 69 websites fulfill criteria? Center: How does the cumulative feature presence look like for the three infrastructural criteria? E.g. most websites fulfill 0 out of 4 participation criteria, 2 out of 3 information criteria and either 2/2 or 0/2 communication criteria. Right: How does the cumulative feature presence look like for the three usage-based criteria? Metrics scaled for better comparability.

To structure the political online environment along the complex set of deliberative potential criteria, a latent class analysis was conducted. Considering various model fit criteria and rounds of validation, a model with three latent classes was selected (see Fig. C6 , Table C2 and more description in the SI).

Latent class structure of the online ecosystem

Figure 3 summarizes the conditional probabilities of websites belonging to each of the three latent classes dependent on their fulfillment of each of the deliberative potential criteria. It also present exemplary sets of websites that were previously identified as politically relevant and sorted into the three estimated latent classes based on their respective predicted probabilities of class membership (See SI Table C1 for the full lists).

Based on response probability patterns and class membership, class 1 was named ‘mainstream hubs’, class 2 was named ‘quality information providers’ and class 3 was named ‘niche forums’. Full list of domains provided in SI Table C1 .

In summary, websites in class 1, from now on referred to as the ‘mainstream hubs’, show especially high class-conditional probabilities of fulfilling the dimensions of connectivity, inclusivity and heterogeneity, while websites assigned to class 2, the ‘quality information providers’ appear strong with regard to information criteria. Websites assigned to class 3, the ‘niche forums’ show rather low class-conditional probabilities for most criteria of deliberative potential, except for the communication dimension and political organization.

More specifically, the class of mainstream hubs (class 1) is composed of a diverse set of websites that fulfill the core criteria of information, communication and participation to some extent but which are especially characterized by a high degree of connectivity, demographic inclusivity and political opinion heterogeneity. Overall, those websites have the highest level of engagement measured by the number of website visits in the sample. Such sites are, for example, prominent high quality national newspapers like ‘Zeit’ and ‘Spiegel’, more tabloid outlets like ‘Bild’, social media platforms like ‘Facebook’ or ‘Twitter’, but also sites with particular functions, such as the online petitioning platform ‘Change’ or the voting advice application ‘Wahl-O-Mat’. What most of the websites in this class have in common is that they are highly-frequented websites that are nationally well known and relevant for political content across diverse German-speaking audiences.

The quality information providers (class 2) include almost exclusively established local, regional and national online news outlets and informative TV channels hosted by public service broadcasting with the exception of ‘RTL’ and ‘Sat1’, two private TV channels with broad online news sections. While ‘ARD’ is the leading national public service broadcasting channel in Germany, ‘MDR’, ‘WDR’, ‘SWR’ and ‘NDR’ are their regional channels. Websites like ‘Südkurier’ and ‘KStA’ (Kölner Stadtanzeiger) are examples of large regional and local news outlets, while ‘Berlin’ is the information platform hosted by the Berlin municipal government. All of those sites provide high quality, journalistically-curated information, often with specific local focus, but apparently, neither do they offer extensive possibilities for political discussion, nor do they attract attention from diverse audiences.

Finally, the class of niche forums (class 3) contains websites with rather low conditional probabilities of fulfilling explicit criteria of deliberative potential, except for the potential of political expression and reciprocity in communication, and potential for political organization. In this class, rather niche online forums for specific communities, as well as forums that are dedicated to specific topics like gaming, cooking or anime content were found. While many domains in this class do not appear politically relevant at first glance, it is important to note that a manual validation step was taken to establish whether political discussion were indeed taking place on those websites. Examples of websites in class 3 are an esoteric forum that vividly discussed the upcoming federal elections, computer forums in which discussions on the military intervention in Afghanistan were found, a forum for children’s second hand clothing (‘Mamikreisel’) and a forum dealing with issues of unemployment (‘Eloforum’) that hosted, partly in-depth, political discussions in niches of the forum.

In total, 34 websites were assigned to the mainstream hubs (class 1), 20 belong to the quality information providers (class 2) and 25 to the class of niche forums (class 3) Footnote 3 . The estimated mixing proportions corresponding to the share of observations belonging to each latent class are 49% for the mainstream hubs, 22% for the information providers and 29% for the niche forums.

The input criteria of deliberative potential form two natural groups: information, communication and participation are criteria that were coded manually and belong to the supply side of a website whereas connectivity, inclusivity and heterogeneity are coded computationally based on usage characteristics. This fundamental distinction is also reflected in the correlation-matrix between criteria. Therefore, the clustering process was repeated separately for the two groups of criteria (see SI C9 and C11 ). For the computationally-coded, demand-side criteria, a simple two factor solution was suggested with one class including all websites with high probabilities of fulfilling each criterion and one class with overall very low scores for connectivity, inclusivity and heterogeneity – in other words, high and low engagement websites. The model including only the manually-coded infrastructural criteria of information, communication and participation possibilities suggested a more interesting pattern that is in line with the findings from the main model including all criteria. A first class contains websites with an strong information profile, including all public broadcasting pages. A second class contains websites with an especially strong forum component or communication profile with pages that also enable participation to some extent. The last class is rather a residual class including websites with overall low probabilities of fulfilling any criteria. The overall pattern largely mirrors the findings from the main model, the difference being that the two meaningful classes of the infrastructural model also contain the highly popular mainstream hubs that are, in the main model, separated through distinct patterns in the engagement based metrics. The latent class structure of the main model using all criteria was robust to the inclusion of alternative input variables, such as users’ household income as feature of inclusivity and the size of the website, measured by the number of clicks as separate variable (see SI Fig. C12 and C13 ).

Engagement with different classes of sites

The measurement framework for the assessment of the deliberative potential of websites could, of course, be applied to various contexts for analytical and practical purposes. As one application, simple user-level engagement patterns, measured in the number of website visits as well as the duration of engagement is considered.

Given the underlying latent structure of deliberative potential dimensions, it does not surprise that the mainstream hubs are more frequently Footnote 4 accessed than quality information providers and niche forums (see Fig. C1a ). However, if engagement is measured as duration instead of clicks, the engagement distributions become more similar (see Fig. C1b ). This implies that people often access prominent websites like Google and Facebook in political contexts but that they tend to spend more time on public broadcasting platforms as well as small online forums to read news more carefully and, potentially, discuss political issues in depth within more tightly-knit communities compared to major social media platforms. More specifically, the duration per click ratio is only 35 s for mainstream hubs, on average, but 48 s for niche forums and almost a minute (59 s) for quality information providers. If the data were to be split, for example, just into news websites and social media platforms, this pattern would not have been observed (see Fig. C2a and C2b ).

Another application is to switch from the perspective of the ‘supply side’ characteristics to the ‘demand side’ characteristics, namely the demographics of users engaging with different classes of sites. Figure 4 and C3 summarize the engagement with different classes of sites for different genders, age groups and levels of formal education. Despite some minor, though intuitive tendencies (e.g., the engagement with quality information providers is stronger than the engagement with niche online forums in the subgroup with the highest level of formal education (Abitur) in Germany) there is no clear pattern of selection visible within subgroups according to those three rough demographic indicators. The exploration of more sophisticated variables such as political orientation, political efficacy or political knowledge as possible driving factors for the selection into engaging with political content online remains subject to a subsequent project.

Class 1: ‘mainstream hubs’, class 2: ‘quality information providers’ and class 3: ‘niche forums’. ‘Online’ includes engagement with any websites recorded by the browser plug-in, including any political and a-political website visits.

The deliberative nature of an online environment is, as Beauchamp ( 2020 ) puts it, a function of membership and structures. In order to examine this function empirically, as a first step, this project systematically mapped the deliberative potential of those structures for the online public sphere in Germany. While this study is descriptive in nature, it is important to understand how increasingly complex media environments are composed of different arenas with different potential functions for democracy. While most of the previous research focused on specific aspects, such as online news media diets or the content of discussions on social media platforms (e.g., Esau et al. 2021 ; Guess 2021 ), this study took a step back and examined the infrastructure and usage patterns as the basis for online deliberation.

This study is one attempt - of probably many imaginable strategies - to map characteristics of a deliberative system empirically, that aimed to build closely onto the literature, by selecting and operationalising six deliberative criteria, in one political context, the German political online ecosystem. The resulting latent class structure is the result of this analytical strategy taken but not the ‘ground truth’ structure of a deliberative system that should from now on be applied to other media systems or even to the German political online ecosystem captured at another point in time. It is an empirical snapshot with the purpose to complement theoretical advancements with empirical observations. While the criteria structure is theoretically informed and could be applied to other contexts, the latent class structure, together with its engagement structure will look different across time and political context, for example, more partisan media systems like the United States.

While political online engagement only makes up a small proportion (about 1% of website visits) of the overall online engagement in Germany, a large part of the sample (1190 out of 1282) did engage with some political topics at least at some point around the federal elections in 2017. It is worth noting that the website selection approach, including a strict manual cross validation of whether a website actually featured political content, focuses on the minimization of false positives rather than false negatives. This implies quite a strict definition of ‘politically relevant’ and tends to rather underestimate the prevalence of political engagement online. However, possibly to the disappointment of many social scientists, engagement with political content online is by no means the dominant form of engagement.

The results of the study clearly align with Guess ( 2021 ) who found a considerable overlap of news media diets within a US sample that goes against the common notion of selective exposure in online ‘echo chambers’. According to Guess ( 2021 ), this overlap originates from individuals’ common use of large mainstream hubs for political information. Correspondingly, in this German sample, the largest cluster of websites are highly-frequented sites that are commonly visited by a large proportion of users. These informational hubs can be understood to be a kind of general-interest intermediary that may indeed facilitate a common arena within the digital public sphere that offers shared experiences and the possibility of incidental encounters with diverse perspectives (Sunstein 2018 ).

The results of the latent class analysis further suggest that public service broadcasting still plays a major role in the German online public discourse even though these websites did not reach a particularly diverse audience within the sample. This finding aligns with previous work on deliberative democracy that, when mapping the television news ecosystem, identified an elite focused coverage within German public broadcasting (Wessler and Rinke 2014 ) which, however, speaks against the often implied view that public broadcasting is in itself lowering audience polarization through broad appeal. Furthermore, the reference to local or regional issues and information is a commonality of many websites assigned to the class of quality information providers. The local nature of political issues is often neglected when studying political online communication or when using digital trace data that do not have a geospatial component. However, on an interesting side note, Ellger et al. ( 2021 ) find that the decline of local newspapers can be related to an increase in political polarization, a relationship that could be given more attention in the study of online politics. While digital technology lets information flows transcend physical constraints, people still live in specific local contexts.

Furthermore, the analysis highlights a latent class of websites that is only mentioned in a small proportion of empirical studies on online deliberation. Wright ( 2012 ) coined the term ‘third spaces’ for non-political online spaces where political talk emerges based on case studies, similar to Graham ( 2012 ). This study demonstrates the importance of their early observations on a much larger basis. The class contains mostly niche forums dedicated to specific topics and communities which points to the phenomenon of incidental exposure to political issues online (Valeriani and Vaccari 2016 ; Yadamsuren and Erdelez 2010 ). Furthermore, these online communities might be comparably more tightly knit because of shared (apolitical) interests and fewer overall user numbers, which allows individuals to recognize each other (despite usually being pseudonymous, Moore et al. 2020 ). These forums, which, in comparison to large social media platforms, might be closer to offline social groups in which a basic form of trust can be established between members, can provide interesting possibilities for informal political discussions among citizens and might operate as important ‘weak ties’ between large online information and communication platforms within a deliberative online system (Esau et al. 2017 ; Granovetter 1973 ; S. W. Rosenberg 2014 ).

As visible among the mainstream hubs, website popularity is heavily ensconced in the three additional dimensions of deliberative potential (connectivity, inclusivity and heterogeneity). One obvious reason for this finding is that the degree of centrality of a node in a social network increases with the frequency of its interactions. Another measurement related explanation could be that the Shannon-Wiener diversity index puts more weight on richness than on evenness (Zeleny 2021 ), implying a rising index with more users. Therefore, caution must be taken against a substantive interpretation of the finding that the most heavily used platforms in the sample are, according to the measures, also the most ‘inclusive’ and ‘heterogeneous’. While they are indeed a common source of information and a common arena of political communication for citizens with different demographic profiles and heterogeneous political attitudes, it is still important to keep in mind that this does not prevent the formation of niche corners and sub-groups that might not speak to each other.

Another limitation to consider is that when classifying the content of the sites as political or not, the full URL-string was considered. While this often features the most important keywords of the page accessed, scraping the entire HTML text of the site might have been helpful in some cases Footnote 5 .

The manually selected set of keywords naturally comes with certain boundary conditions. It is systematically easier to rigorously identify specific political terms, such as the names of politicians and terms referring to party politics and administrative processes in comparison to political issues like education and social policy because terms like ‘family’ or ‘housing’ appear in many different political and apolitical contexts. Various efforts were taken to reduce this imbalance as much as possible (see SI D ).

Finally, the web tracking data is based on desktop use and does not include mobile devices. This certainly overlooks parts of users’ political online engagement and may even introduce non-random blind spots. Furthermore, due to the temporal asymmetry between the browser histories (collected in 2017) and the content analysis on the respective websites (conducted in 2021), one cannot rule out the possibility that some websites might have changed in terms of structure, content and function for online public discourse.

One may ask which websites show the highest deliberative potential but this study explicitly avoids a summative ranking as the core of a systemic understanding implies that different arenas can fulfill different functions for public discourse (Bächtiger and Parkinson 2019 ). This study suggests that the empirical reality maps this normative account. Given that deliberative theory is fundamentally normative, one may consider possible normative implications for online public discourse that follow from this empirical mapping of a deliberative system. Certain combinations of deliberative potential criteria, such as the provision of communicative spaces that are characterized as inclusive and heterogeneous or the provision of high-quality political information in spaces with high connectivity to other relevant sources, clearly appear as normatively desirable (Mansbridge et al. 2012 ). However, a distinction between websites that primarily provide information and other websites that specialize on discussions, seems hardly detrimental to public discourse. On the contrary, this distinction could reflect the ideal of a shared factual baseline that is built by quality information providers on which basis then conflicting discussions can safely occur in other arenas (Habermas 2021 ; Krause 2008 ).

Accordingly, this study shows that few websites fulfill all criteria and some combinations of deliberative criteria are more frequent than others: information providing infrastructure often comes with high usage, reflected in heterogeneity and inclusivity, while communication also occurs in niches. Furthermore, in previous accounts theoretically distinct classes of websites, such as major newspapers and social media platforms, empirically sort into the same class when focusing on affordances and usage. However, the outlined systemic understanding that one website does not have to serve all criteria and the empirical findings about skewed participation in public discourse may allow a hypothesis about the critical state of the online media system: perhaps one website should also not try to serve all criteria. For example, public broadcasting and established newspapers are the backbone of quality information providence in Germany. Their increasing presence on social media, on the one hand, perhaps reaches otherwise lost audiences but on the other hand, risks eroding their core function of quality information providence that serves as common factual baseline for deliberation (Habermas 2021 ) through constraints imposed by the structure of social media. Visible engagement in the comment sections showcases the opinions and rhetoric of a skewed minority while for the largely silent majority that becomes visible in this study, public broadcasting remains a core provider of quality political information. Moreover, entering the market of digital content creators and advertisers is a competition that public broadcasting in Germany would not even have to play, given public funding combined with independent agenda setting.

This project illustrates that the internet provides a plethora of sources for political information, arenas for political communication and some opportunities for online participation. This study clearly found potential for public deliberation in the German speaking web in 2017. Even though political content is only a small proportion of the overall content accessed online—the German deliberative system seems to be a rather small fraction of the wider online environment—almost everyone in the sample engaged with some political content around the federal election in 2017. This implies that the consumption of political content is not as exclusive as the visible discussion patterns of few very active users on social media may imply.

The infrastructure of a deliberative system goes far beyond news websites and social media platforms but includes a wide range of different types of popular and niche platforms with different primary functions. On some platforms, users get political information. However, it is not clear if those are accurate or misinformation. On other platforms, they can discuss political issues, deliberatively or not. While only very few websites in the sample offer possibilities for participation, the demand also seemed limited.

Mainstream hubs are most central in the network of topical links, whereas public broadcasting outlets and especially the niche forums are more at the periphery of the network. Considering the definition of links within the connectivity measure, this implies that users move beyond the quick bites of political information on mainstream platforms but read more on the topic elsewhere. Those platforms appear to act as general-interest intermediary that may indeed facilitate a common arena within the digital public sphere that, against the notion of online ‘echo chambers’, offers shared experiences and the possibility of encounters with diverse perspectives. This finding aligns with the current state of the literature, finding limited empirical support for the prevalence and impact of online ‘echo chambers’ (e.g. Flaxman et al. 2016 ; Guess et al. 2023 ; Guess et al., 2021; Dubois and Blank 2018 ). The class of information providers can be interpreted as evidence for the persisting centrality of high quality public broadcasting as the backbone for democratic deliberation in Germany. The question as to whether we stand at the beginning or the end of the public broadcasting era online could be determined using detailed information on the user base. This project demonstrated the presence of a-political spaces in which political discussion emerges on a large empirical basis. While niche online forums are especially characteristic for the earlier years of the internet, it will be interesting to see in which spaces more tightly knit online communities will form in the future as previous exchange around a-political shared interests may build mutual trust as important basis for the discussion of conflicting political views.

Even though the found latent class structure appears intuitive, this structure was war from obvious as previous theoretical accounts have rarely moved beyond an assumed a split between news media and social media, a cyclographic split that was fed forward into empirical studies. Furthermore, the results of this study do reveal several surprising aspects. First, negative findings on the deliberative quality online are contrasted by findings about the potential of the political online ecosystem when examining passive audiences in contrast to digital traces of active social media commenters. Second, the absence of central websites with low heterogeneity aligns with Guess et al. (2021) but provides more evidence against the otherwise common notion of online “echo chambers” (Sunstein 2002 ). Third, public broadcasting stood out as distinct class in a data driven, bottom-up approach, even with a sole focus on infrastructural elements and usage characteristics.

While this project empirically mapped the online media structures underlying online deliberation for the first time, the logical next step in the research agenda is the quantitative description of membership, the profiles of internet users engaging with political information and communication online. In particular because online political deliberation itself may not be a mainstream behavior, the mechanisms of selection into the online public discourse need to be determined.

Data availability

Extensive supplementary material, including all R scripts and publicly available data, supporting tables and figures, the dictionary used for website selection and a software statement can be found in the project’s repository on OSF under https://osf.io/atj5u/ .

An alternative model including participants’ household income as additional input criterion for inclusivity is reported in SI C13. In this German survey, ethnicity as another statistical marker of minority status was not asked.

The distribution of the self-reported political orientation of the sample approaches a normal distribution and also geographically, online activity patterns in the sample distribute about evenly across Germany.

The order of classes has no deeper meaning but is determined by configurations in the estimation process.

Cumulative engagement measures are baseline corrected, meaning that they show the share of website visits that users spend on e.g. quality information providers in relation to their total number of website visits in the measurement time frame.

However, an extremely robust scraper would have to be built in order to process hundreds of thousands of different domain structures (in the original full dataset). Future projects may try to build such a scraper, web-scrape all the sites and search for political topics in the full HTML text of websites instead of the URL-strings. The reference body (sites explicitly dealing with the 2017 German public discourse that I selected to generate keywords, see SI D) would then be similar enough to the target body (now being the full-text of websites instead of URL-text only) to use the semi-automated keyword extraction method proposed by King et al. ( 2017 ).

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Acknowledgements

I thank Simon Munzert, Pablo Barberá, Andrew Guess and JungHwan Yang for the provision of the data and the inspiring and constructive discussions during the 2022 MEOF workshop in Princeton. I thank Simon Munzert for his continuous guidance and supervision on the project, as well as André Bächtiger and all anonymous reviewers for their helpful comments to the manuscript. The collection of data used in this study was generously funded by a grant from the Volkswagen Foundation Computational Social Science Initiative, reference 92 143. I acknowledge funding from the German Academic Scholarship Foundation in the form of a PhD stipend.

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Oswald, L. More than news! Mapping the deliberative potential of a political online ecosystem with digital trace data. Humanit Soc Sci Commun 11 , 629 (2024). https://doi.org/10.1057/s41599-024-03115-0

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A QUANTITATIVE ASSESSMENT OF VISUAL FUNCTION FOR YOUNG AND MEDICALLY COMPLEX CHILDREN WITH CEREBRAL VISUAL IMPAIRMENT: DEVELOPMENT AND INTER-RATER RELIABILITY

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Background Cerebral Visual Impairment (CVI) is the most common cause of low vision in children. Standardized, quantifiable measures of visual function are needed.

Objective This study developed and evaluated a new method for quantifying visual function in young and medically complex children with CVI using remote videoconferencing.

Methods Children diagnosed with CVI who had been unable to complete clinic-based recognition acuity tests were recruited from a low-vision rehabilitation clinic(n=22)Video-based Visual Function Assessment (VFA) was implemented using videoconference technology. Three low-vision rehabilitation clinicians independently scored recordings of each child’s VFA. Interclass correlations for inter-rater reliability was analyzed using intraclass correlations (ICC). Correlations were estimated between the video-based VFA scores and both clinically obtained acuity measures and children’s cognitive age equivalence.

Results Inter-rater reliability was analyzed using intraclass correlations (ICC). Correlations were estimated between the VFA scores, clinically obtained acuity measures, and cognitive age equivalence. ICCs showed good agreement (ICC and 95% CI 0.835 (0.701-0.916)) on VFA scores across raters and agreement was comparable to that from previous, similar studies. VFA scores strongly correlated (r= -0.706, p=0.002) with clinically obtained acuity measures. VFA scores and the cognitive age equivalence were moderately correlated (r= 0.518, p=0.005), with notable variation in VFA scores for participants below a ten month cognitive age-equivalence. The variability in VFA scores among children with lowest cognitive age-equivalence may have been an artifact of the study’s scoring method, or may represent existent variability in visual function for children with the lowest cognitive age-equivalence.

Conclusions Our new VFA is a reliable, quantitative measure of visual function for young and medically complex children with CVI. Future study of the VFA intrarater reliability and validity is warranted.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by the EyeSight Foundation of Alabama, Alie B. Gorrie Low Vision Research Fund and Research to Prevent Blindness. Additional support came from the National Institutes of Health [UL1 TR003096 to R.O.] and Grant T32 HS013852.

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