how will you improve your writing skills in quantitative research

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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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Open Access

Peer-reviewed

Research Article

Improving quantitative writing one sentence at a time

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Biology Department, Santa Clara University, Santa Clara, California, United States of America

Roles Formal analysis, Writing – original draft

Roles Data curation, Funding acquisition, Validation, Writing – review & editing

• Tracy Ruscetti, 
• Katherine Krueger, 
• Christelle Sabatier

• Published: September 12, 2018
• https://doi.org/10.1371/journal.pone.0203109
• Reader Comments

Scientific writing, particularly quantitative writing, is difficult to master. To help undergraduate students write more clearly about data, we sought to deconstruct writing into discrete, specific elements. We focused on statements typically used to describe data found in the results sections of research articles (quantitative comparative statements, QC). In this paper, we define the essential components of a QC statement and the rules that govern those components. Clearly defined rules allowed us to quantify writing quality of QC statements (4C scoring). Using 4C scoring, we measured student writing gains in a post-test at the end of the term compared to a pre-test (37% improvement). In addition to overall score, 4C scoring provided insight into common writing mistakes by measuring presence/absence of each essential component. Student writing quality in lab reports improved when they practiced writing isolated QC statements. Although we observed a significant increase in writing quality in lab reports describing a simple experiment, we noted a decrease in writing quality when the complexity of the experimental system increased. Our data suggest a negative correlation of writing quality with complexity. We discuss how our data aligns with existing cognitive theories of writing and how science instructors might improve the scientific writing of their students.

Citation: Ruscetti T, Krueger K, Sabatier C (2018) Improving quantitative writing one sentence at a time. PLoS ONE 13(9): e0203109. https://doi.org/10.1371/journal.pone.0203109

Editor: Mitchell Rabinowitz, Fordham University, UNITED STATES

Received: August 26, 2017; Accepted: August 15, 2018; Published: September 12, 2018

Copyright: © 2018 Ruscetti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received financial support from Santa Clara University through the Faculty Development Office (T.R.) and the Office of Assessment (T.R. and C.S.).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Written communication of data is at the core of scholarly discourse among scientists and is an important learning goal for science students in undergraduate education [ 1 ]. For scientists, the currency of scientific dialogue is the research article, which presents essential information required to convince an audience that data are compelling, findings are relevant, and interpretations are valid [ 2 , 3 ]. Writing lab reports that contain the elements of a research article is a widely used method to help students develop critical thinking and quantitative reasoning skills. In our introductory, lab-intensive Cell and Molecular Biology course, we focus on helping students develop the “results” section of their lab report. Students integrate tables, graphs, and text to present and interpret data they have generated in the laboratory. In the text portion, students cannot simply restate previously learned information (“knowledge telling;” [ 4 , 5 ]) or narrate through the data presented visually. Rather, students must mimic the actions of professional researchers by transforming data into knowledge and structuring their arguments to support specific claims/conclusions. This type of inquiry-based writing encourages active participation in the scientific process, enhancing engagement and learning [ 6 , 7 ].

While science instructors recognize the importance of writing in their courses, many do not provide explicit writing instruction [ 8 ]. Instructors may fear that teaching writing skills diverts time from teaching required science concepts, expect that writing is covered in composition courses, or lack the tools and resources to teach writing [ 8 , 9 , 10 ]. We wanted to support writing in our course without diverting focus from the conceptual and discipline-specific content of the course. We examined available writing resources (e.g., books, websites) and found substantial resources regarding the macro structure of the report (e.g., describing the sections and broad organization of the lab reports, [ 11 , 12 ]. We also found resources for sentence level support related to emphasis and voice [ 13 ]. However, these resources do not give students explicit guidance as to how to write about quantitative information. Thus, it is not surprising that many students struggle to both construct appropriate quantitative evidence statements and express them in writing [ 14 ].

There are, however, a few important resources that explore the structure of writing about quantitative information. Each describe comparisons as a primary mode of providing quantitative evidence, (e.g., The lifespan of cells grown in the presence of drug is 25% shorter than the lifespan of control cells .). In her book about writing about numbers, Miller discusses “quantitative comparisons” as a fundamental skill in quantitative writing [ 15 ]. Jessica Polito states that many disciplines use comparisons as the basis of quantitative evidence statements that support conclusions [ 14 ], and Grawe uses the presence of a comparison as a measure of sophisticated quantitative writing [ 16 ]. We focused on these types of comparative evidence statements and called them Quantitative Comparative statements (QC). We found this type of statement was commonly used to describe data in the scientific literature, and we decided to emphasize the correct construction of these statements in student writing.

We analyzed over a thousand QC statements from student and professional scientific writing to discover the critical elements of a QC statement and the rules that govern those elements. We found that a QC statement needs to have a comparison, a quantitative relational phrase, and at least one contextual element. These essential elements of the QC statement can be thought of as sentence-level syntax. We then developed a metric to measure writing syntax of the QC statement and by proxy, quantitative writing quality. We examined the effectiveness of different approaches to support writing in a course setting and show that practice writing QC statements with feedback can improve student writing. We also investigated how the circumstances of the writing assignment can change the quality of quantitative writing. Together, these data provide insight into how we might improve undergraduate science writing instruction and the clarity of scientific writing.

Methods and materials

Student population and course structure.

We collected data at Santa Clara University (SCU), a private liberal arts university that is a primarily undergraduate institution. Participants were recruited from BIOL25 –Investigations in Cell and Molecular Biology, a lower-division biology course. Prerequisites include a quarter of introductory physiology, a year (3 quarters) of general chemistry and one quarter of organic chemistry. BIOL25 consists of three interactive lecture periods (65 minutes) and one laboratory period (165 minutes) per week. The lecture periods focus on preparing for the laboratory experience, analysis, interpretation, and presentation of data. Laboratory sessions focus on data collection, data analysis and peer feedback activities. During the 10-week quarter, two experimental modules (Enzyme Kinetics and Transcription Regulation) culminate in a lab report. Students organize and communicate their analyzed data in tables and graphs and communicate their conclusions and reasoning in written form. We provide a detailed rubric for the lab reports and a set of explicit instructions for each lab report ( S2 Fig ). In addition, students participate in peer feedback activities with an opportunity to revise prior to submission.

The basic structure of the course was unchanged between 2014 and 2016. The students were distributed among two lecture sections taught by the same instructors and 13 laboratory sections led by 5 different instructors. All students included in this study signed an informed consent form (213 of 214). This study was reviewed and approved by the Santa Clara University Institutional Review Board (project #15-09-700).

Instructional support

General writing feedback (2014–2016)..

In all iterations of the course discussed in this article, students received general writing feedback after each lab report. In each lab report, students wrote paragraphs in response to prompting questions regarding the data. Writing feedback was holistic and included phrases such as “not quantitative”, or “inappropriate comparison,” but was not specific to any type of sentence.

Calculation support (2015–2016).

In 2015 and 2016, students were explicitly introduced to strategies for quantifying relational differences between data points such as percent difference and fold change. Students were given opportunities to practice calculating these values during in class activities prior to writing their lab reports. We stressed that phrases such as more than, drastically higher, and vanishingly small were not quantitative.

Explicit QC statement writing support (2016).

In 2016, we introduced and practiced using quantitative comparative statement as the means to communicate quantitative results. In class, we discussed including an explicit comparison of two conditions and the quantitative relationship between them. Before each lab report, we asked students to write quantitative comparative statements related to the data. We provided formative feedback on the accuracy of the statement and general feedback such as, “not quantitative”, or “inappropriate comparison”. Students in this study were never exposed to the concept of 4C annotation or scoring. We used the scoring strategy exclusively to measure their writing progress.

Identification of quantitative comparative statements (QC)

Quantitative comparative statements are a subset of evidence statements. In native writing (scientific articles or student lab reports), we identified QC statements by the presence of 1) a relational preposition (between, among, etc.), or 2) prepositional phrase ("compared to", "faster/slower than", etc.), 3) a statistical reference (p value), or 4) the presence of quantified change (3 fold, 10% different).

Syntactic elements of QC statements

We examined a corpus of over 1000 QC statements to identify and characterize the essential elements of a QC statement and the rules that govern those elements. Quantitative comparative statements generally take the form of “ The activity of the enzyme is 30% higher in condition X compared to condition Y ”. We identified three critical elements of the quantitative comparative statement: the things being compared (Comparison, condition X and condition Y ), the quantitative relationship between those conditions (Calculation, 30% higher ), and the measurement that gave rise to the compared values (Context, enzyme activity ). Finally, all three elements must be in the same sentence with no redundancy or contradiction (Clarity). These rules are collectively called “4C”.

Syntactic rules for quantitative comparative statements

The Calculation must quantify the relationship between the two compared elements and include both magnitude and direction. Fold change or percent difference are common methods of describing quantitative relationships [ 15 ]. Using absolute or raw values are not sufficient to describe the relationship between the compared elements and are not sufficient. If there is no significant difference between the compared elements, then statistical data must be cited. Context provides additional information about the measurement from which the quantitative comparison was derived, such as growth rate, enzyme activity, etc., or the time at which the comparison was made. The context should be the same for both of the compared elements. Comparisons are usually between like elements (e.g. time vs. time, condition vs. condition) and there should be two and only two in a single sentence. Both compared elements must be explicitly stated so that the reader is not guessing the intended comparison of the writer. A QC statement has Clarity when all three elements are present and in the same sentence. We consider a statement to be “unclear” if it contains inconsistencies or redundancies.

Annotation and scoring of QC statements

We use “annotation” to describe the visual marking of the critical elements of the quantitative comparative statement. We use “scoring” to mean the assignment of a score to a quantitative comparative statement. 4C annotation and 4C scoring do not reflect whether the statement or any of its components are correct, but rather they highlight the syntactic structure of the quantitative comparative statement ( Fig 1 ).

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(A) Original quantitative comparative statement. (B) Identify and box the relational phrase with both magnitude and direction. (C) Circle what the relational phrase refers to (context). (D) Underline the comparison. (E) Fully 4C annotated quantitative comparative statement.

https://doi.org/10.1371/journal.pone.0203109.g001

Annotation process.

We scanned the results sections of published primary journal articles or student lab reports for relational phrases such as faster than, increased, more than, lower than, etc., and drew a box around the relational phrase , or calculation ( Fig 1B ). If the calculation is an absolute value, a raw value, refers to no particular value, or is missing the magnitude or direction, we would strike through the box. Context . Once the relational phrase, or calculation, was identified, we drew a circle around the information, or context , referred to by the relational phrase ( Fig 1C ). Comparison . The relational phrase and the context helped us identify the comparison and we underlined the compared elements ( Fig 1D ).

4C scoring strategy.

To score an annotated statement, a “1” or a “0” is given to each of the three critical components of the quantitative comparative statement. If all the elements are present in a single sentence, there are no redundancies or inconsistencies, a fourth “1” is awarded for clarity. We call this annotation and scoring strategy “4C” to reflect each of the three critical components and the overall clarity of the statement ( Table 1 ).

https://doi.org/10.1371/journal.pone.0203109.t001

Student writing samples

Pre-test/post-test..

In 2016, student writing was assessed using identical pre- and post-tests. The pre-test was administered on the first day of class prior to any writing support. The post-test was administered as part of the final exam. The pre/post assessment consisted of a graph and data table ( S1 Fig ). The prompts asked the students to analyze the data to answer a specific question related to the data and to use quantitative comparative statements.

Student sampling for lab report analysis.

For the lab reports in 2016, we sampled 40 students from a stratified student population (based on overall grade in the course) and 4C scored all of their quantitative comparative statements in each lab report. On average, students wrote 5–6 quantitative comparative statements per results section for a total of over 200 4C scored statements for each lab report. We scored over 100 statements from 17–20 lab reports in 2014 and 2015.

Complexity index

We based complexity on the number of values (data points) students would have to parse to develop a QC statement. The complexity of a given experiment is in part determined by number of conditions tested in an experiment and the different types of measurements used. For example, in lab report #1 (Enzyme Kinetics) students consider 3 experimental conditions (control and two separate variables) and 2 measurements (K m and V max ). Thus we calculated a complexity index of 6 (3 conditions x 2 measurements) for lab report #1. In this measure of complexity index, we assumed that all parameters contributed equally to the complexity of the experiment, and that all parameters were equally likely to be considered by students as they developed their written conclusions. However, by designing specific writing prompts, we could guide students to examine a smaller subset of data points and reduce complexity of the situation. In lab report #1 for example, we can prompt students to consider only the effect of the treatment on a single variable such that they only consider 2 conditions (the control and the single experimental variable described in the prompt) and 2 measurements. Now, students are focused on a subset of data and the complexity of the situation could be considered “4”.

Quantitative comparative statements are universally used to describe data

Having decided to focus on QC statements in student writing, we first wanted to quantify their occurrence in professional writing. We examined the results sections in all the research articles from three issues of pan-scientific journals: Science, Nature, PLOS-One, and PNAS. We identified an average of 7–15 QC statements in each research article, with no significant difference in the mean number of QC statements among the different journals ( Fig 2 , ANOVA, p = 0.194). There was also no difference of the number of QC statements among the different disciplines (Kruskal-Wallis, p = 0.302). Out of the 60 articles examined, we found only one article that did not have a single QC statement to describe the data ( Fig 2 , Nature). These data suggest that QC statements are used in professional forms of quantitative writing to describe data in many different disciplines.

The mean (middle vertical line) ± SD are shown. Physical science papers are denoted in red, Biological sciences are in blue, and Social sciences are in green.

https://doi.org/10.1371/journal.pone.0203109.g002

4C scoring used to measure quantitative writing

In 2016, students practiced writing QC statements related to their data and we provided feedback (see Methods ). We measured the effectiveness of the focused writing practice using 4C scoring of QC statements from a pre- and post-test (see Methods and Table 1 ). We observed a 37% increase in student 4C scores on the post-test assessment compared to the pre-test (p < 0.001, Fig 3A ). In addition, we used 4C scoring to interrogate the impact of the writing intervention on each of the required components of the QC statement ( Fig 3B ). We observed improvements in each of the components of QC statements ( Fig 3C ). In the post-test, over 80% of students included a calculation (magnitude and direction), referred explicitly to both items being compared, and referenced the measurement context for their comparison. Only 25% of students produced completely clear statements, meaning that they were not missing any elements, and did not contain redundant or contradictory phrases. Despite the low post-test clarity score, we observed a 40% improvement in students writing completely clear statements in the post-test compared to the pre-test score ( Fig 3C ).

( A) Mean 4C scores of quantitative comparative statements on an identical pre- and post- test. (B) Percent of statements that contain each of the essential components of a QC statement. (C) Percent difference between the pre-test and post-test broken down by essential components of QC statements. (***t-test, p < 0.001) Error bars in A represent Standard Error of the Mean (SEM).

https://doi.org/10.1371/journal.pone.0203109.g003

We next asked if we could measure student learning gains in quantitative writing within the context of a lab report. Students write 2 lab reports per term and we provided varying forms of writing feedback over several iterations of the course (see Methods ). We scored QC statements in two lab reports from 2014 (general writing feedback only), 2015 (general writing feedback and calculation support) and 2016 (general writing feedback, calculation support, and sentence-level writing practice) ( Fig 4A ). There was no appreciable impact on writing quality when we added calculation support to general feedback in 2015 compared to feedback alone in 2014 (t test, p = 0.55, Fig 4A ). However, the addition of sentence-level QC writing support in 2016 resulted in a 22% increase in student mean 4C scores on lab report #1 compared to the same report in 2015 ( Fig 4A , t test, p < 0.05). We noticed the same trends in lab report #2 ( Fig 4B ): general writing feedback and calculation support did not improve scores as compared to general feedback alone (t test, p = 0.88). However, we observed an 80% increase in 4C scores on lab report #2 when we provided sentence-level writing practice compared to feedback alone ( Fig 4B , t test, p < 0.001). The mean 4C scores in each year for each assessment, as well as the forms of writing support employed, are summarized in Table 2 . Overall, these data suggest that sentence-level writing practice with feedback is important in helping students improve the syntax of quantitative writing.

(A) Mean 4C scores of QC statements from lab reports (enzyme kinetics). (B) Mean 4C scores of QC statements from second lab reports (transcriptional regulation). (C) Percent difference between the two lab reports within a given year, broken down by essential components (*p < 0.05, ***p < 0.001) Error bars in A and B represent SEM.

https://doi.org/10.1371/journal.pone.0203109.g004

https://doi.org/10.1371/journal.pone.0203109.t002

We were surprised to find that although the trends in the data were similar between the two lab reports, the mean 4C scores of QC statements in lab report #2 were 40% lower than in lab report #1 in both 2014 and 2015 (t test, p < 0.0001, Fig 4A versus 4B ). We predicted that writing skills would either improve with focused practice, or not change over the course of the quarter. To understand which components of the quantitative comparative statement were differentially impacted in the two lab reports, we calculated the relative frequency with which each component was included in a QC statement. Then, we calculated the difference of those frequencies between the first and second lab report for each year ( Fig 4C ). A column below the x-axis indicates that students made particular mistakes more often in lab report #2 ( Fig 4C ). In 2014, students were able to make comparisons equally well between both lab reports, but students struggled to include a quantitative difference or provide context in their evidence statements ( Fig 4C ). In 2015, in addition to general writing feedback, we also provided instructional support to calculate relative differences. We noted that students were able to incorporate both comparisons and calculations into their QC statements in both reports. However, they often omitted the context ( Fig 4C ). The frequency of mistakes made by students is significantly different between lab report #1 and lab report #2 (Chi squared, p < 0.001). These data suggest that feedback alone is not sufficient to improve quantitative writing. In 2016, we provided targeted practice at the sentence level and observed no significant difference in mean 4C scores between the two lab reports ( Fig 4B , t test, p = 0.0596), suggesting that the writing skills of students did not decrease from one lab report to the next. Additionally, students included the four elements of the QC statement equally well between the two lab reports (Chi squared, p = 0.6530, Fig 4C , 2016). Thus, when students receive targeted, sentence-level writing practice, their ability to write QC statements improves.

Quantitative writing quality is negatively impacted by complexity

We were perplexed as to why quantitative writing syntax (as measured by mean 4C scores) declined in lab report #2 compared to lab report #1 in both 2014 and 2015 ( Fig 4A and 4B ). Because we view the essential components of QC statements as analogous to syntactic rules that govern writing of QC statements, we can apply principles and theories that govern writing skills writ large. Research from writing in English Composition shows that writing ability, as measured by sentence level syntax, deteriorates when the writer is struggling with basic comprehension [ 17 , 18 ]. We hypothesized that students’ ability to write about data also might be negatively impacted when students struggled to comprehend the conceptual system they were asked to interrogate. However, we found no correlation between mean 4C scores and any assessment of conceptual material (data not shown). Nor was there an association between mean 4C scores on the lab reports and the related sections of the final (data not shown). Together, these data suggest that conceptual comprehension does not impact writing of a QC statement.

In addition to conceptual understanding, QC statements require that the writer parse through the data set to select the relevant data points to interrogate. We hypothesized that the number of data points (values) in the data set may negatively impact QC statement syntax. We calculated the complexity of different assignments (see methods ) and plotted mean 4C scores as a function of complexity index. We performed linear regression analysis on those mean 4C scores from writing samples occurring prior to formal writing intervention (2014 and 2015 lab reports, and the 2016 pre-test, Fig 5A , closed circles) and those that occur after specific writing intervention (2016 lab reports and 2016 post-test, Fig 5A , open circles). There is a strong inverse correlation between writing as measured by mean 4C scores and complexity (r 2 = 0.9471 for supported and r 2 = 0.9644 for unsupported writing, Fig 5A ). Moreover, the slopes of the lines generated from the regression analysis of mean 4C scores do not vary significantly despite writing interventions (p = 0.3449). Although the task complexity in 2016 was reduced relative to 2015, the negative impact of complexity on writing persisted. Thus, as the complexity of experimental data sets increases, the ability to write clearly decreases regardless of the writing intervention.

(A) Writing syntax as a function of complexity measured by 4C scoring and reported as either unsupported (closed circles) or supported (open circles) by instructional intervention. Linear regression lines are shown (unsupported, R 2 = 0.9644, supported R 2 = 0.9471). (B) Students were stratified based on overall performance in the course. Statements from students within the group were averaged and reported. Error bars represent SEM.

https://doi.org/10.1371/journal.pone.0203109.g005

Complexity differentially impacts specific populations of students

Part of the developmental process of analytical reasoning is parsing relevant from irrelevant data [ 1 ]. We asked if subpopulations of our students were more capable of parsing information from larger data sets than others. We stratified 2016 students into quartiles based on overall performance in the course. We measured the mean 4C scores from the post-test and both lab reports, and plotted mean 4C score as a function of “constrained” complexity ( Fig 5B ). At lower complexity levels, there is no significant difference between the highest performing students and the lowest performing students (t test, p >0.05). Increasing complexity also had a negative impact on most of our students. However, students in the top quartile were less affected by increased complexity than the lower 75% of the class (t test, p <0.05, Fig 5B ). These data suggest there are students who are developmentally capable of controlling the complexity of the task to focus on the skill of writing.

We set out to help STEM students write more clearly and we focused on writing a specific but universal form of evidence statement, the quantitative comparative statement ([ 14 , 15 ], Fig 2 ). By analyzing text from student lab reports and professional scientific articles, we defined the syntax of quantitative comparative statements ( Fig 1 , Table 1 ). Based on the syntactic rules we established, we scored individual quantitative comparative statements and measured writing quality (Figs 3 – 5 ). Our data show that writing quality (measured by 4C scoring) can be improved with focused practice and feedback (Figs 3 and 4 ). Finally, our data show that the circumstance, i.e., the complexity of the writing task, influences writing quality. For example, writing quality decreased when students interrogated larger data sets (Figs 4 and 5 ), but was improved when students were directed by the writing prompt to focus on a subset of the data ( Fig 5 and data not shown).

Our findings are consistent with previous research in Writing Studies and English Composition showing that syntax suffers when writers are confronted with complex and unfamiliar conceptual material [ 17 , 18 , 19 ]. The Cognitive Process Theory of Writing states that writing is a cognitive endeavor and that three main cognitive activities impact writing, the process of writing (syntax, grammar, spelling, organization, etc.), the task environment (the purpose of the writing task), and knowledge of the writing topic [ 17 , 18 , 19 ]. The theory posits that cognitive overload in any of these areas will negatively impact writing quality [ 17 , 18 ]. Consistent with the theory, our data show that writing quality is a function of explicit writing practice ( Fig 3 ), the size of the data set ( Fig 4A compared to 4B ) and scope of the writing prompts ( Fig 4B 2015 compared to 2016).

Explicit sentence level practice improves writing quality

Our data suggest that practicing isolated sentence construction improves writing quality (Figs 3 and 4 ). In every year of this study, we provided students with generalized feedback about their quantitative comparative statements (e.g., “needs quantitation” or “needs a comparison”) within the context of their lab report. In 2016, students practiced writing a QC statement related to their data but separate from the lab report. Although our feedback was the same, we observed improvement only when the feedback was given to QC statements practiced out of the lab report context ( Fig 4A compared to 4B ). Consistent with our data, the Cognitive Process Theory of Writing predicts that practicing specific syntax will increase fluency, lower the cognitive load on the writer’s working memory, and improve writing [ 17 , 18 ]. Our data are also consistent with research in English Composition demonstrating that when instructors support sentence-level syntax, they observe improved sentence level construction, improved overall composition, and higher level critical thinking [ 20 ]. In addition to improved sentence level syntax, we also observed overall quality of lab reports improved 12% in 2016 compared to the same lab report in 2015 (based on rubric scores, data not shown). If students develop a greater facility with the process of writing by practicing sentence level syntax, they have more cognitive resources available to develop and communicate their reasoning (our data, [ 20 , 21 ]).

Complexity of the writing task affects writing quality

We defined the complexity of the writing assignment as the landscape of information students must sample to interpret and communicate their data. In the case of lab reports, that information is the collected and analyzed data set ( Table 2 ). Students interrogating a larger data set produced lower quality QC statements than when they interrogated a smaller data set (compare lab report #2 to lab report #1 in both 2014 and 2015 cohorts, Fig 4 ). In lab report #2, students not only contended with a larger number of values in the dataset compared to lab report #1, but also with two different measurements. These data are consistent with the Cognitive Process Theory of Writing that suggests that when demands on the writer’s knowledge of the topic increase, the writer cannot devote as many cognitive resources to the task environment or process of writing [ 17 , 18 ]. However, we observed that the negative effect of experimental complexity on writing quality can be mitigated by writing prompts that focus students on a smaller, specific subset of the data ( Fig 5A ). More focused writing prompts and smaller data sets reduce the task environment of the assignment and allow more cognitive load to be devoted to the process of writing.

Model for writing quality as a function of complexity

Interestingly, the writing quality of students who finished the course with higher final grades (top quartile) was more resistant to increases in complexity compared to their classmates ( Fig 5B ). These data are consistent with the ideas of McCutchen who posits that as writers become more expert in their field, they have more cognitive resources to devote to clear communication. McCutchen suggests that expert writers have 1) more knowledge of their discipline, 2) more familiarity with the genres of science writing (task environment), and 3) more practice with the process of writing [ 19 ]. Based on research in Writing Studies, the Cognitive Process Theory of Writing, and the data presented here, we developed a predictive model of the impact of complexity (cognitive load) on writing quality ( Fig 6 ). We have hypothesized a linear model in which any increase in complexity negatively impacts writing quality ( Fig 6A ) and a “breakpoint” model in which writers maintain a constant level of writing quality at lower complexity levels writing quality but decline at higher levels of complexity ( Fig 6B ). We hypothesize that our top performing students have moved into a more expert space in the model by developing strategies to parse a complex task environment and ignore irrelevant information. Effectively, these skills allow them to minimize the impact of complexity on their cognitive load and maintain their writing quality even in the face of complex data sets ( Fig 5B ).

(A) Simple linear model of the relationship between writing quality and complexity (cognitive load). (B) Model of the relationship between writing quality and complexity in which low complexity has minimal impact on writing quality but higher complexity negatively impacts writing quality.

https://doi.org/10.1371/journal.pone.0203109.g006

4C instruction as a writing intervention

In addition to altering the writing assignment to decrease cognitive load on the students, we also think it will be important to provide students with syntactic structures at the sentence level. In this study, we did not use 4C annotation as an instructional intervention so that 4C scoring would be a more objective measure of writing quality. But, subsequent to this study, we and others have used 4C annotation as an instructional tool and found that student writing improves dramatically (data not shown). Although some argue that using overly structured or templated sentences can stifle creativity, providing basic structure does not necessarily lead to pedantic writing [ 22 ]. A commonly used text in college writing, “They say, I say,” determined that providing templates for constructing opinions and arguments gives students a greater ability to express their thoughts [ 23 ]. Specifically, weaker writers who lack intuitive understanding of how to employ these writing structures benefit from the use of explicit templates, while more advanced writers already employ these writing structures in a fluid and nuanced manner [ 23 ].

4C template as a foundation of quantitative writing

As students become more expert writers and write more complex and sophisticated sentences, they may choose to deviate from the proscribed sentence structure and make editorial decisions about the elements of the quantitative comparison in the context of their argument [ 23 ]. In fact, when we examined the 4C scores of quantitative comparative statements in published literature, we found that, on average, professional scientists write comparisons that are missing one of the three elements (4C score = 1.89 +/- 0.05, n = 281). The expert writer may eliminate an element of the evidence statement because he/she presumes a more sophisticated audience is capable of inferring the missing element from prior knowledge or within the context of the argument. Or, the author may provide all elements of quantitative comparison in their argument but not within a single sentence.

Helping students become expert writers

Based on our research, we think novice writers should write for novice readers and include all of the syntactic elements of a QC statement. As students develop their professional voice, the 4C template will serve as a touchstone to frame their quantitative arguments, and the editorial choices they make will depend on the sophistication of their audience. Students will write clear arguments even if those elements no longer reside within the rigid structure of a single QC statement with a perfect 4C score. We are confident that by supporting student writing at the level of syntax, we are building a solid foundation that will give students greater capacity for reasoning in the face of increasing experimental complexity.

Supporting information

S1 fig. pre test / post test..

Example of the pre- and post-test used to assess the ability to interpret graphical and tabular data and write a quantitative comparative statement.

https://doi.org/10.1371/journal.pone.0203109.s001

S2 Fig. Lab Report Rubric.

A detailed rubric provides students with explicit guidance for each lab report. This rubric corresponds with the experiment exploring enzyme kinetics of β-galactosidase.

https://doi.org/10.1371/journal.pone.0203109.s002

Acknowledgments

The authors thank Dr. Jessica Santangelo for critical feedback on the manuscript and unwavering support for this project. This study was initially developed as part of the Biology Scholars Program (Research Residency) through the American Society for Microbiology and the National Science Foundation (T.R.)

• 1. American Association for the Advancement of Science. Vision and change in undergraduate biology education: a call to action. Brewer Cand Smith D., Eds. American Association for the Advancement of Science. 2011. 1–100. http://visionandchange.org/files/2013/11/aaas-VISchange-web1113.pdf
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• 23. Graff G, Cathy Birkenstein. They say / I say: the moves that matter in academic writing. New York: W.W. Norton & Co.; 2010.

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6 points to consider when planning quantitative market research

There are many things to consider when building your quantitative research – like how to define your target audience, how to select the best list of questions, how to ensure your survey format is easy to follow, all while keeping your overall research objectives, project timing, and budgetary constraints in mind. Read the list below to understand more about quantitative market research tips to consider when developing quantitative surveys.

1. Confirm that quantitative research really is the best approach given your objective

It’s important to understand the difference between quantitative and qualitative research before embarking on your research. While qualitative research is exploratory in nature, quantitative research is confirmatory, methodical, numbers-based and consists mostly of close-ended questions. It is used in all phases of research and across all types of sectors, but is best used for predicting consumer behaviour based on a fixed stimulus (for example A/B testing), or for gauging interest in a specific product, service or feature.

Quantitative research is often used to validate findings from qualitative research or as a benchmark for further exploratory work. If the focus of your research is to explore customer emotions and motivations in a broader sense, or to hear from respondents in a less structured way where they have the ability to elaborate on a topic, a qualitative approach to the research may be more appropriate.

2. Be sure that your sample is representative and that you speak to enough people to have confidence in your findings

This may sound obvious, but it is important the people you survey are representative of the market you are hoping to target. Think carefully about how you structure the initial questions in your questionnaire (e.g. screener) and how you determine who qualifies to continue.

For example, if your client is a milk-alternatives manufacturer interested in understanding perceptions around some new product packaging, you’d want to address the questionnaire to those who currently purchase alternative milk products, or those would seriously consider the purchase of alternative milk products. You may wish to do some initial research on the demographics of current buyers of milk alternatives, so that you can use that to determine the demographic targets to include in your survey sample. For example, if you find that 50% of current buyers of alternative milk products are aged 18-34 years old then you may want to ensure that 18-34 year olds make up 50% of your sample.

In addition to your sample being representative of your target audience, when setting up your research it’s also important to consider how many people you need to survey in order for your findings to be reliable (replicable in the real-world). Online calculators are available to estimate the sample size needed to achieve a given level of confidence in the results, but a common rule-of-thumb for any kind of quantitative research is to try to collect a minimum of 50 instances of any given answer that is important to you; anything less than that is usually brought to the attention of the client/reader when presented as a finding.

3. Be sure you have clearly defined research goals and understand how each part of your survey/questionnaire will help address each goal

Another crucial quantitative market research tip is structuring your quantitative research is understanding how each question helps address the research objectives. Unlike qualitative research which often evolves in structure and design while the research is being conducted (e.g. in the course of speaking to respondents), quantitative requires you understand exactly what kind of output you are seeking before you begin.

As a researcher, it can be hard to be selective with the questions that you ask, but keep in mind both time restrictions (a good rule-of-thumb here is that you can ask 20-30 simple questions in a 10-minute survey) and the possibility of respondent fatigue if you ask questions that are too repetitive in form or content.

A good way to ensure you’re being smart with what you include is to tie every question back to your list of objectives. The more meaningful and goal orientated your questions are, the fewer questions you’ll need. If you find there are questions you can’t tie back, then you may want to remove them from your survey.

You should also avoid the trap of being too ambitious in the number of objectives for a single survey – it’s advisable to limit each survey to answering a maximum of three questions that you have about your business. Including any more than three objectives may lead to confused findings, and increases the risk of fatigue among respondents.

4. Be sure to ask your questions in a way that makes your survey be easy to understand and complete

This goes without saying, but it’s extremely important to keep your questions as simple as possible and word them so they are easily understood by your respondents. Generally speaking, be as concise as possible, avoid ambiguous and leading questions, but also include instructions on how a question should be interpreted and answered (even simple directions such as ‘select one answer’ and ‘select all that apply’ can make a real difference).

Question wording should use professional language, but any words or jargon that may not be easily recognisable by your audience should include an explanation as well. To test if your survey is simple and unambiguous, you might consider asking friends or family members to test it for you rather than colleagues.

5. Be realistic with the time allowed for data collection

Researchers will often underestimate or misquote the amount of time needed for quantitative research. This is often a result of wanting to please stakeholders by turning work around quickly. As a general guide, allow a minimum of one week in field for any quantitative research and at least two weeks for harder-to-reach audiences (particularly B2B). Allowing sufficient time makes it more likely that you will achieve the minimum number of response needed, and also allows the team to review the initial data from a ‘soft launch’ (which typically aims to collect responses from 10% of the final sample) and make any revisions to the survey if needed before proceeding.

6. Have a good understanding of your survey data output and how you’ll use it for next steps within your research programme

  It’s a good idea to visualise what your data output will look like once you’re done with the fieldwork. This will help understand the time needed for data analysis as well as any other data processing components necessary for final delivery. For example, if the survey data output is an Excel spreadsheet, which it often is, it will be important that the Excel file can be used to build survey tables. You will want to consider which software you’ll use to process the data tables and check compatibility with any vendors you’re using for this process.

The ways in which you plan to report on data findings will also help you understand what formats you’ll require with the data output. For example, if you’re hoping to include heat map images in your deliverable, check that your survey tool can generate and export this type of chart and if not, build in time to create them manually.

FieldworkHub frequently partners with clients on quantitative research projects. We offer survey development, hosting and management, data collection, plus analytics and reporting where needed. Get in touch with us today if you’re interested in exploring quantitative opportunities with us – we’d love to hear from you!

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Research Writing: The 5-Step Approach

What is research writing? 

Research writing involves f inding a topic, i dentifying a problem, g athering research, and l ogically presenting the evidence u sing scholarly writing conventions.  

How to improve research writing skills?

Implement a plan before and during the process to develop your research writing skills by following the five-step process.

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Fundamental writing skills for researchers, part 1 introduction and snapshot of writing (6:31).

Everyone is capable of being a good writer, even without any innate skill. A snapshot of research writing is given, from presenting a research question in context of current knowledge to interpreting your findings. In other words, moving from general to specific, then specific to general. It's important to be a careful and intentional writer. It's not about writing, it's about readability. Focusing on your readers and their needs helps make your message clear.

Part 2 Making Meaning Clear (6:31)

"Going-to-the-Caribbean writing" is boring, dense, and generally not reader-friendly because it lacks transitions, logic, and concern for reader understanding. An example of "Caribbean writing," along with a more reader-friendly revision, is provided. Good writing clearly communicates meaning to readers by always keeping their needs in mind.

Part 3 Writing Myths (4:20)

The impulse to impress readers with complex sentences and pretentious words is regrettably common in research writing. Writing to impress seeks validation for the writer rather than comprehension for the reader. Revision is always needed because ideas don’t flow logically from the writer's mind to the page.

Part 4 How Readers Read and Respond (7:19)

There are several levels of a reader's response to a piece of writing. The writer is responsible for the reader’s experience in everything from visual appeal and organization to readability and tone. The purpose of research writing is to convey your data and interpretations of that data while convincing your reader that your perspective is valid. Critique your writing by continually keeping your reader in mind.

Part 5 Helping Your Audience Interpret Your Meaning (11:58)

Your role as writer is to make sense—to make your meaning clear to the reader. Use punctuation, grammar, and other language conventions as road signs to help your reader interpret your writing. Basic vocabulary and simple sentence construction is sufficient, even for winning the Nobel Prize in Literature. But your audience may vary, and that takes very careful planning on your part.

Part 6 Giving Structure to Your Writing (6:24)

Paragraphs, topic sentences, and transitions provide the structure of your writing. Mastering these building blocks is the key to being able to clearly communicate your thinking to your reader. The topic sentence is the king or queen of the sentence and each line of the paragraph should support or elaborate upon that main thought. Transitions are used to help the reader move from one thought to the next, whether within a sentence, from sentence to sentence, or from paragraph to paragraph.

Part 7 Writing as a Logical Process (10:07)

Writing is a logical process, and a sentence is like a mathematical formula. Using levels of generality allows you to move from general to specific levels of detail. Sometimes you'll need to use more words to make your meaning clear to the reader. A piece of writing is not clear simply because it is brief.

Download the Logical Puzzles Handout

Part 8 Making Meaning Clear (9:13)

Logic doesn't flow naturally from mind to paper. You are responsible for writing a clear topic sentence and supporting it in a logical way. Transitions point out to the reader the logical connections between ideas, and order is important. Outlining will help you write effectively and more efficiently.

Part 9 Outlining (8:12)

Planning your writing will save you a great deal of time. Again, levels of generality come into play here, as does the structure of a paragraph. But don't focus on the skeleton of an outline, emphasize the content as you coordinate and subordinate your ideas. When you create an outline, step back and analyze it critically. You need to impose logic on your writing, then crystallize your logic by making specific connections.

Part 10 Headings, Figures, Rhythm, and Length (4:15)

Headings and subheadings used consistently help your reader see the structure of your writing. Tables, figures, and charts are powerful aids to making your meaning clear. But don't just present them to your reader; interpret their significance. Finally, you’ll also improve readability by varying the length and construction of your sentences.

Successfully Writing about Quantitative Research (or Anything)

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To successfully play ball, you need to know the rules of the game, how to think like a champion, and have heart. Notice I did not mention natural, inborn talent. Clearly, talent helps; but it is not sufficient for success.

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Fallon, M. (2016). Successfully Writing about Quantitative Research (or Anything). In: Writing up Quantitative Research in the Social and Behavioral Sciences. Teaching Writing. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6300-609-5_3

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10 Quantitative Skills and How to Develop Them

• Updated December 25, 2023
• Published August 8, 2023

Are you looking to learn more about Quantitative skills? In this article, we discuss Quantitative skills in more detail and give you tips about how you can develop and improve them.

What are Quantitative skills?

Quantitative skills refer to the ability to work with numerical data, perform mathematical calculations, and analyze information using quantitative methods. These skills are crucial in various fields, including but not limited to science, engineering, finance, economics, data science, and social sciences. Here are some key aspects of quantitative skills:

Mathematics

Data analysis.

• Critical Thinking

Modeling and Simulation

Problem solving, computer programming, financial analysis, economics and econometrics, research and surveys, data visualization.

Developing and honing quantitative skills can greatly enhance your problem-solving abilities and increase marketability across various industries and academic disciplines.

Top 10 Quantitative Skills

Below we discuss the top 10 Quantitative skills. Each skill is discussed in more detail, and we will also give you tips on improving them.

Mathematics is a fundamental quantitative skill that forms the bedrock of various disciplines and problem-solving processes. It encompasses various mathematical concepts, including arithmetic, algebra, calculus, geometry, and statistics. With a solid understanding of mathematics, you can work with numerical data, make accurate calculations, and analyze complex patterns and relationships.

How to Improve Mathematics

Improving your mathematical skills involves consistent practice and a growth mindset. Revisiting basic concepts, such as arithmetic operations and algebraic equations, to build a strong foundation. As you progress, delve into more advanced topics like calculus and statistics to understand quantitative analysis better. Embrace challenges and problem-solving exercises to enhance your critical thinking abilities, and seek out resources like textbooks, online courses, or tutorials to reinforce your knowledge.

Practical application is essential for strengthening your mathematical skills. Engage in real-world problems and projects that require quantitative analysis. Work with data sets, interpret graphs, and tackle mathematical modeling tasks. Collaborate with others and seek feedback to gain different perspectives and learn new approaches to problem-solving. The more you immerse yourself in mathematical applications, the more proficient and confident you will become in handling quantitative challenges across various fields. Remember, with determination and perseverance. You can continually improve your mathematical skills and unlock a world of opportunities in the data-driven landscape.

Data analysis is a vital quantitative skill that involves collecting, cleaning, organizing, and interpreting data to extract valuable insights and make informed decisions. It encompasses various techniques, including statistical methods, data visualization, and data mining. Mastering data analysis empowers you to uncover patterns, trends, and correlations within datasets, enabling you to draw meaningful conclusions and address complex problems.

How to Improve Data Analysis

To improve your data analysis skills, familiarize yourself with various data manipulation and cleaning techniques. Learn how to handle missing data, remove outliers, and transform data into a usable format. Next, dive into statistical concepts such as hypothesis testing, regression analysis, and descriptive statistics. Understanding these methods will help you draw accurate conclusions from data and support your decision-making process.

Practice is key to honing your data analysis skills. Seek out real-world datasets and work on projects that require data analysis. Engage in data-driven research, participate in data analysis competitions, or collaborate on data projects with others. Leveraging data analysis tools and software like Python, R, or Excel will also aid in gaining hands-on experience. Continuously challenge yourself to tackle increasingly complex datasets and problems, and seek feedback from peers or mentors to refine your analytical techniques. By combining theoretical knowledge with practical experience, you will become a proficient data analyst, capable of extracting valuable insights from data and driving evidence-based decision-making in diverse domains.

Critical thinking is a foundational quantitative skill that involves analyzing, evaluating, and synthesizing information objectively to make reasoned decisions and solve problems effectively. It encompasses logical reasoning, questioning assumptions, and considering different perspectives. Mastering critical thinking empowers you to approach complex issues with a clear and open mind, make well-informed choices, and overcome challenges more efficiently.

How to Improve Critical Thinking

To improve your critical thinking skills, start by practicing active reading and engaging with diverse sources of information. Question the author’s arguments, identify biases, and assess the validity of the evidence presented. Cultivate a habit of seeking alternative viewpoints to broaden your understanding of complex topics and strengthen your ability to evaluate arguments objectively.

Engaging in thought-provoking discussions and debates can also sharpen your critical thinking skills. Participate in group discussions or join forums where ideas are exchanged and challenged. Defend your viewpoints logically and be receptive to constructive criticism. Through this process, you’ll develop the ability to analyze different perspectives and refine your own arguments.

Additionally, solve puzzles, riddles, and brain-teasers regularly to enhance problem-solving abilities. These activities stimulate your mind and encourage creative thinking, essential in critical thinking. Embrace intellectual curiosity, be open to learning from various disciplines, and continuously question assumptions and conclusions. By consistently practicing critical thinking, you’ll become more adept at making informed decisions, solving complex problems, and navigating the challenges of a data-rich world.

Related :  Quantitative Analyst vs. Data Scientist – What’s The Difference?

Modeling and simulation is a powerful quantitative skill that involves creating mathematical or computational models to represent real-world systems and processes. These models help you understand and analyze complex phenomena, make predictions, and simulate different scenarios to gain insights into how the system behaves under various conditions. Mastering modeling and simulation empower you to solve complex problems, optimize processes, and make data-driven decisions in diverse fields.

How to Improve Modeling and Simulation

To improve your modeling and simulation skills, start by gaining a strong foundation in mathematics, especially in calculus, differential equations, and linear algebra. These mathematical concepts are the building blocks of many modeling techniques. Familiarize yourself with relevant software and programming languages like Python, MATLAB, or simulation-specific tools. Practice implementing models and simulations with real data to understand how they apply to specific situations and improve your technical proficiency.

Study and analyze existing models and simulations in your area of interest. By examining how experts have approached similar problems, you can learn valuable insights and adapt their approaches to your own work. Engage in projects that require creating models and simulations and challenge yourself to develop innovative ways to represent complex systems. Collaborate with professionals in your field or join simulation-focused communities to share knowledge and receive feedback on your work. With dedication and continuous learning, you can enhance your modeling and simulation skills and contribute to cutting-edge research and problem-solving in various domains.

Problem-solving is a fundamental quantitative skill that involves the ability to approach challenges methodically, analyze them, and devise effective solutions. It encompasses critical thinking, data analysis, and decision-making to tackle complex issues across various domains. Mastering problem-solving empowers you to identify problems, break them down into manageable parts, and apply quantitative and qualitative methods to reach well-reasoned conclusions.

How to Improve Problem-Solving

To improve your problem-solving skills, embrace a growth mindset and view challenges as opportunities to learn and grow. Analyze problems systematically by breaking them into smaller components and understanding the relationships between them. Practice active brainstorming to generate multiple solutions and evaluate each option’s feasibility and potential outcomes.

Foster collaboration and seek diverse perspectives by discussing problems with colleagues or mentors. Working in teams can provide valuable insights and different problem-solving approaches. Continuously seek opportunities to apply your problem-solving skills in academic studies, professional work, or personal projects. Embrace failures as learning experiences and use feedback to refine your problem-solving strategies. As you encounter new problems, keep track of your approach, document the steps you take, and reflect on the effectiveness of your solutions. Over time, your problem-solving skills will strengthen, and you will become a resourceful and confident solver of complex quantitative challenges.

Related :  Problem-Solving Interview Questions & Answers

Computer programming is a crucial quantitative skill that involves writing instructions in programming languages to create software, applications, and algorithms. It allows you to automate tasks, manipulate data, and implement complex quantitative models. Mastering computer programming empowers you to turn ideas into reality and leverage the power of technology to solve a wide range of quantitative problems.

How to Improve Computer Programming

To improve your computer programming skills, select a programming language that aligns with your goals and interests. Popular languages like Python, R, or Java offer robust capabilities for quantitative tasks. Begin with the basics, such as learning syntax, variables, and control structures. As you gain confidence, progress to more advanced topics like functions, object-oriented programming, and data structures.

Engage in hands-on projects to apply your programming skills. Work on real-world problems, tackle coding challenges and develop small applications or scripts. Collaborate with others in coding communities or join open-source projects to gain exposure to different coding styles and problem-solving approaches. Seek feedback from peers or mentors to improve your code quality and efficiency. Embrace continuous learning by exploring online tutorials, coding boot camps, or advanced courses in your chosen programming language. As you persistently practice and refine your programming abilities, you’ll become adept at using this quantitative skill to create innovative solutions and contribute to various quantitative domains.

Financial analysis is a vital quantitative skill that involves examining financial data, statements, and economic trends to evaluate the financial health and performance of individuals, companies, or organizations. It encompasses skills like ratio analysis, cash flow analysis, and risk assessment. Mastering financial analysis empowers you to make informed investment decisions, assess business profitability, and manage financial risks effectively.

How to Improve Financial Analysis

To improve your financial analysis skills, familiarize yourself with financial statements like balance sheets, income statements, and cash flow statements. Learn how to interpret these documents and extract meaningful information about a company’s financial position and performance. Practice calculating and interpreting financial ratios to assess a business’s liquidity, profitability, and leverage.

Stay updated on economic and financial market trends to understand their impact on financial analysis. Follow news and market reports and analyze how economic indicators influence financial data. Engage in case studies and financial modeling exercises to simulate real-world scenarios and strengthen your analytical abilities. Seek internships or work opportunities in finance-related roles to gain practical experience and exposure to financial analysis in a professional setting. Seek feedback from experienced financial analysts and mentors to refine your skills and build confidence in your financial analysis capabilities. With dedication and continuous learning, you can become a proficient financial analyst capable of providing valuable insights and recommendations in the dynamic world of finance.

Economics and econometrics are valuable quantitative skills that study economic systems, behavior, and trends. Furthermore, Economics deals with understanding how individuals, businesses, and governments make choices to allocate resources to satisfy their needs and wants. Econometrics involves applying statistical and mathematical methods to economic data to develop and test economic models. Mastering economics and econometrics empower you to analyze economic phenomena, forecast trends, and evaluate policy impacts.

How to Improve Economics and Econometrics

To improve your skills in economics and econometrics, start by building a strong foundation in economic principles, theories, and concepts. Understand the fundamental factors influencing supply and demand, market structures, and economic growth. As you progress, familiarize yourself with statistical techniques commonly used in econometrics, such as regression analysis, time-series analysis, and hypothesis testing.

Engage in economic research and data analysis projects to gain hands-on experience. Utilize economic databases, access publicly available economic data, and practice applying econometric methods to analyze the data. Consider taking specialized courses or pursuing advanced degrees in economics or econometrics to deepen your knowledge and expertise. Collaborate with professors, researchers, or peers to receive feedback on your work and exchange ideas. Embrace interdisciplinary approaches by integrating knowledge from related fields such as finance, international relations, or environmental studies. By continuously challenging yourself to apply economic principles and econometric methods to real-world problems, you’ll become a skilled economist capable of contributing valuable to economic research and policy analysis.

These are essential quantitative skills for gathering and analyzing academic, business, or social data. Research involves designing studies, formulating hypotheses, and collecting data through various methods such as surveys, experiments, or observations. Surveys are specific data collection tools that involve asking a targeted group of individuals questions to gather information about their opinions, behaviors, or preferences. Mastering research and surveys empower you to obtain valuable insights, draw meaningful conclusions, and contribute to evidence-based decision-making.

How to Improve Research and Surveys

To improve your skills in research and surveys, start by learning about research methodologies and survey design. Understand the different types of research approaches, sampling techniques, and data collection methods. Practice creating survey questionnaires that are clear, unbiased, and effectively capture the information you need. Consider using online survey platforms to distribute surveys and analyze the responses efficiently.

Emphasize the importance of ethics in research and surveys. Familiarize yourself with ethical guidelines for conducting research involving human subjects, ensuring confidentiality, and obtaining informed consent. Participate in research projects or volunteer to assist with surveys to gain practical experience. Collaborate with experienced researchers or survey specialists to learn from their expertise and receive feedback on your own work. Continuously review and improve your research and survey techniques based on feedback and evolving best practices. By refining your skills and adhering to rigorous research standards, you’ll become a proficient researcher capable of conducting insightful studies and providing valuable contributions to your field of interest.

Data visualization is a crucial quantitative skill that involves presenting data in graphical or visual formats to convey complex information in a clear and intuitive manner. It encompasses various visualization techniques such as charts, graphs, maps, and infographics. Mastering data visualization empowers you to communicate data-driven insights effectively, enabling others to understand trends, patterns, and relationships within datasets more easily.

How to Improve Data Visualization

To improve your data visualization skills, start by understanding the principles of effective data visualization. Learn about different types of charts and graphs and when to use each to best represent your data. Practice using data visualization tools like Tableau, Excel, or Python libraries (e.g., Matplotlib, Seaborn) to create compelling visualizations. Experiment with different color schemes, fonts, and design elements to enhance the visual appeal and clarity of your visualizations.

Seek inspiration from existing data visualization examples and data-driven stories. Analyze how other professionals and data journalists present complex information visually and learn from their techniques. Participate in data visualization challenges or competitions to challenge yourself and receive feedback from a broader audience. Collaborate with peers or mentors in data-related fields to exchange ideas and insights. By continuously practicing data visualization and incorporating feedback into your work, you’ll develop the skills to create impactful visualizations that effectively communicate data insights and aid decision-making in diverse domains.

Quantitative Skills Conclusion

In conclusion, developing quantitative skills is paramount in today’s data-driven world. Whether you are a student, a professional, or an aspiring researcher, honing these skills can significantly enhance your problem-solving abilities and boost your career prospects. Working with numbers, analyzing data, and making informed decisions based on quantitative evidence is highly valued across various fields and industries.

Improving these skills requires dedication, practice, and a growth mindset. Embrace challenges and seek opportunities to apply quantitative techniques in your academic or professional projects. Use online courses, tutorials, and resources to reinforce your knowledge and learn new methodologies. Collaborate with others to gain different perspectives and approaches to problem-solving. Seek feedback from mentors or experts to refine your techniques and continue to grow.

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Quantitative Research

What is quantitative research.

Quantitative research is a systematic, objective, and data-driven approach used to gather and analyze numerical information. It focuses on obtaining quantifiable data and applying statistical methods to derive meaningful insights. In simple terms, quantitative research involves the collection and interpretation of numerical data to understand and explore relationships, patterns, and trends.

In quantitative research, data is collected through structured methods such as surveys, experiments, or systematic observations. This data is then analyzed using statistical techniques, which allow researchers to identify patterns, test hypotheses, and draw reliable conclusions. The results of quantitative research provide measurable evidence for supporting or disproving theories, making predictions, and informing decision-making processes.

Researchers employ various statistical tools and software to analyze the data and ensure the accuracy and validity of their findings. They apply mathematical models, perform calculations, and use graphs or charts to present their results clearly.

Quantitative research is widely used in many fields, including social sciences, marketing, business, psychology, and healthcare. It enables researchers to explore large datasets, generalize findings to larger populations, and make evidence-based decisions.

By following a structured and rigorous methodology, quantitative research provides objective and systematic insights into phenomena, helping us understand the world around us in a logical and measurable way.

The Importance of Assessing Quantitative Research Skills

Assessing a candidate's ability in quantitative research is crucial for making informed hiring decisions. Here's why:

Data-driven Decision Making : Quantitative research skills enable professionals to analyze numerical data accurately and derive meaningful insights. By assessing a candidate's quantitative research abilities, you ensure that your organization has individuals who can effectively analyze data to inform decision-making processes.

Accurate Analysis : Proficiency in quantitative research ensures that candidates can interpret and analyze data accurately. This skill allows them to identify patterns, trends, and relationships within data sets, providing valuable insights that contribute to the success of your organization's projects and initiatives.

Informing Strategy : Quantitative research skills provide the foundation for strategic decision-making. Candidates with a strong understanding of quantitative research can utilize data to identify market trends, customer behavior patterns, and potential areas for growth, helping your organization stay ahead of the competition.

Problem Solving : Strong quantitative research skills equip individuals with the ability to solve complex problems through data analysis. Assessing candidates' proficiency in quantitative research ensures that you are hiring individuals who can approach challenges analytically and develop innovative solutions based on data-driven insights.

Effective Resource Allocation : Organizations can benefit from individuals who can optimize resource allocation based on quantitative research findings. Assessing candidates' abilities in this area ensures that you have individuals who can make informed decisions on resource allocation, maximizing efficiency and productivity within your organization.

By assessing candidates' abilities in quantitative research, organizations can ensure they have individuals who can analyze data accurately, inform decision-making processes, and contribute to the success of their projects and initiatives. Evaluate candidates' quantitative research skills effectively with Alooba's comprehensive assessment platform.

Assessing Quantitative Research Skills with Alooba

Alooba's assessment platform offers effective ways to evaluate candidates' proficiency in quantitative research. Here are some of the test types that can be utilized for this purpose:

Concepts & Knowledge Test : This multiple-choice test allows you to assess candidates' understanding of fundamental concepts and principles related to quantitative research. It measures their knowledge of statistical methods, data analysis techniques, and research design, providing insights into their theoretical understanding of quantitative research.

Written Response Test : The written response test allows candidates to demonstrate their ability to explain and analyze quantitative research concepts in writing. By asking candidates to provide written responses or essays on relevant topics, you can evaluate their critical thinking skills and their ability to articulate complex ideas related to quantitative research.

Alooba's assessment platform enables you to customize both the Concepts & Knowledge and Written Response tests according to the specific skills and knowledge required for quantitative research roles within your organization. The platform provides automatic grading for objective questions, ensuring efficient evaluation and providing detailed results to help you make informed hiring decisions.

With Alooba's assessment tools, you can accurately evaluate candidates' quantitative research skills, ensuring that you select individuals who possess the necessary knowledge and ability to excel in quantitative research roles. Streamline your hiring process and assess candidates effectively with Alooba's comprehensive assessment platform.

Topics Covered in Quantitative Research

Quantitative research encompasses various subtopics that delve into different aspects of data analysis and numerical interpretation. Here are some key topics covered within the realm of quantitative research:

Statistical Analysis : Quantitative research involves the application of statistical methods to analyze numerical data. This includes descriptive statistics, inferential statistics, correlation analysis, regression analysis, and hypothesis testing. Statistical analysis enables researchers to draw conclusions and make predictions based on data patterns observed.

Research Design : Quantitative research requires a well-defined research design to ensure the validity and reliability of the study. Researchers must carefully plan the sample selection, data collection methods, and data measurement techniques. Understanding research design principles helps in designing studies that can yield accurate and generalizable outcomes.

Survey Design : Surveys are commonly used in quantitative research to collect data from a large number of participants. Designing effective survey questions and survey instruments is a critical skill in quantitative research. This includes creating unbiased and reliable survey items, selecting appropriate response formats, and ensuring data integrity.

Data Collection Methods : Quantitative research utilizes various methods for collecting data, such as experiments, questionnaires, structured interviews, and systematic observations. Researchers must understand the strengths and limitations of each method and choose the most suitable approach for their research objectives.

Data Visualization : Effective data visualization is vital in quantitative research to communicate findings clearly. Researchers use graphs, charts, and other visual representations to present numerical data in a visually appealing and understandable manner. This helps in highlighting patterns and trends and makes it easier for stakeholders to grasp the insights.

Ethical Considerations : Ethical considerations play a significant role in quantitative research. Researchers must adhere to ethical guidelines while collecting, analyzing, and reporting data. These guidelines ensure participant confidentiality, informed consent, data security, and the responsible use of information.

Exploring these topics within quantitative research equips researchers with the necessary knowledge and skills to conduct rigorous and meaningful analyses. With Alooba's assessment platform, you can evaluate candidates' understanding of these subtopics and their ability to apply quantitative research principles effectively in practice.

Applications of Quantitative Research

Quantitative research finds applications across various fields and industries. Its data-driven approach and analytical methods contribute to evidence-based decision-making. Here are some common applications of quantitative research:

Market Research : In the realm of marketing, quantitative research helps businesses understand consumer behavior, market trends, and preferences. It enables organizations to analyze customer data, conduct surveys, and perform statistical analysis to inform marketing strategies, product development, and target audience segmentation.

Social Sciences : Quantitative research plays a crucial role in social sciences by providing empirical evidence for studying human behavior, societal trends, and public opinions. It helps researchers collect and analyze data on a large scale, facilitating the identification of patterns, correlations, and factors influencing social phenomena.

Education : Quantitative research is employed in educational settings to evaluate the effectiveness of teaching methods, measure learning outcomes, and assess the impact of educational interventions. It allows educators and policymakers to make data-informed decisions for curriculum development, instructional improvement, and educational program evaluation.

Healthcare : In the healthcare industry, quantitative research aids in understanding disease prevalence, treatment efficacy, and patient outcomes. It enables researchers to conduct clinical trials, analyze medical data, and develop evidence-based guidelines for medical practice and healthcare policy.

Finance and Economics : Quantitative research is extensively used in finance and economics for market analysis, risk assessment, and forecasting. It helps financial institutions and economists make informed investment decisions, estimate economic trends, and model complex financial systems.

Environmental Studies : Quantitative research is valuable in environmental studies, where it assists in collecting and analyzing data related to climate change, pollution levels, and natural resource management. It allows researchers to evaluate environmental impacts, study ecosystems, and inform sustainable practices.

By utilizing quantitative research methodologies, organizations and researchers gain valuable insights into complex phenomena, enabling evidence-based decision-making and informed actions within their respective fields. Alooba's comprehensive assessment platform ensures that candidates possess the necessary quantitative research skills required for these applications, allowing organizations to select top talent for their specific domain.

Roles That Require Strong Quantitative Research Skills

Several roles rely on strong quantitative research skills to perform their job effectively. These roles involve rigorous data analysis, evidence-based decision-making, and the ability to interpret numerical information. If you are interested in pursuing a career that emphasizes quantitative research, consider the following roles:

Data Analyst : Data analysts play a crucial role in collecting, analyzing, and interpreting data to identify trends, patterns, and insights. They employ quantitative research techniques to explore datasets, perform statistical analysis, and provide actionable recommendations based on their findings.

Data Scientist : Data scientists leverage quantitative research skills to derive meaningful insights from complex data sets. They use statistical models, machine learning algorithms, and data visualization techniques to uncover patterns and trends, develop predictive models, and solve intricate business problems.

Financial Analyst : Financial analysts rely on quantitative research skills to assess financial data, analyze investment opportunities, and evaluate risk. They use statistical tools and financial models to make informed recommendations on investment strategies, budget planning, and financial forecasting.

Operations Analyst : Operations analysts apply quantitative research methods to optimize business processes, improve efficiency, and reduce costs. They collect and analyze operational data, perform statistical analysis, and identify opportunities for process improvement and resource optimization.

Marketing Analyst : Marketing analysts utilize quantitative research skills to analyze market trends, consumer behavior, and campaign effectiveness. They study quantitative data from various marketing channels, interpret the results, and provide insights to guide marketing strategies and tactics.

User Behaviour Analyst : User behavior analysts focus on understanding and interpreting user interactions with digital platforms and products. They employ quantitative research methods to analyze user data, conduct A/B tests, and uncover user preferences, preferences, and pain points to optimize user experience.

These roles represent just a few examples of the numerous opportunities available for individuals with strong quantitative research skills. By honing your quantitative research abilities, you can excel in careers that require data-driven decision-making and the ability to derive meaningful insights from numerical data. Explore these roles on Alooba's platform to discover the diverse opportunities that align with your interests and skillset.

Associated Roles

Data analyst.

Data Analysts draw meaningful insights from complex datasets with the goal of making better decisions. Data Analysts work wherever an organization has data - these days that could be in any function, such as product, sales, marketing, HR, operations, and more.

Data Engineer

Data Engineers are responsible for moving data from A to B, ensuring data is always quickly accessible, correct and in the hands of those who need it. Data Engineers are the data pipeline builders and maintainers.

Data Scientist

Data Scientists are experts in statistical analysis and use their skills to interpret and extract meaning from data. They operate across various domains, including finance, healthcare, and technology, developing models to predict future trends, identify patterns, and provide actionable insights. Data Scientists typically have proficiency in programming languages like Python or R and are skilled in using machine learning techniques, statistical modeling, and data visualization tools such as Tableau or PowerBI.

Financial Analyst

Financial Analysts are experts in assessing financial data to aid in decision-making within various sectors. These professionals analyze market trends, investment opportunities, and the financial performance of companies, providing critical insights for investment decisions, business strategy, and economic policy development. They utilize financial modeling, statistical tools, and forecasting techniques, often leveraging software like Excel, and programming languages such as Python or R for their analyses.

HR Analysts are integral in managing HR data across multiple systems throughout the employee lifecycle. This role involves designing and launching impactful reports, ensuring data integrity, and providing key insights to support strategic decision-making within the HR function. They work closely with various stakeholders, offering training and enhancing HR data reporting capabilities.

Insights Analyst

Insights Analysts play a pivotal role in transforming complex data sets into actionable insights, driving business growth and efficiency. They specialize in analyzing customer behavior, market trends, and operational data, utilizing advanced tools such as SQL, Python, and BI platforms like Tableau and Power BI. Their expertise aids in decision-making across multiple channels, ensuring data-driven strategies align with business objectives.

Marketing Analyst

Marketing Analysts specialize in interpreting data to enhance marketing efforts. They analyze market trends, consumer behavior, and campaign performance to inform marketing strategies. Proficient in data analysis tools and techniques, they bridge the gap between data and marketing decision-making. Their role is crucial in tailoring marketing efforts to target audiences effectively and efficiently.

Operations Analyst

Operations Analysts are pivotal in improving the efficiency and effectiveness of business processes. They work across various departments, such as supply chain, logistics, and human resources, utilizing their expertise in data analysis and project management. These professionals are adept in extracting and interpreting data, identifying trends, and providing actionable insights to enhance operational performance. They typically employ tools like SQL, Excel, and PowerBI, and are skilled in communication and problem-solving to support decision-making processes.

Product Analyst

Product Analysts utilize data to optimize product strategies and enhance user experiences. They work closely with product teams, leveraging skills in SQL, data visualization (e.g., Tableau), and data analysis to drive product development. Their role includes translating business requirements into technical specifications, conducting A/B testing, and presenting data-driven insights to inform product decisions. Product Analysts are key in understanding customer needs and driving product innovation.

Product Manager

Product Managers are responsible for the strategy, roadmap, and feature definition of a product or product line. They work at the intersection of business, technology, and user experience, focusing on delivering solutions that meet market needs. Product Managers often have a background in business, engineering, or design, and are skilled in areas such as market research, user experience design, and agile methodologies.

Product Owner

Product Owners serve as a vital link between business goals and technical implementation. They work closely with stakeholders to understand and prioritize their needs, translating them into actionable user stories for development teams. Product Owners manage product backlogs, ensure alignment with business objectives, and play a crucial role in Agile and Scrum methodologies. Their expertise in both business and technology enables them to guide the product development process effectively.

User Behaviour Analyst

User Behaviour Analysts focus on analyzing and interpreting user data to improve overall user experience on digital platforms. Their role involves studying user interactions, feedback, and patterns to inform product development and user support strategies. These analysts typically work with large datasets, employing tools like SQL, and techniques in data visualization and statistical analysis. Their insights are crucial in shaping product enhancements and tailoring user communication.

Related Skills

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What are Research Skills? How to Improve Your Skills in Research

Learn strategies and techniques to improve your research skills. Avoid common mistakes and implement proven methods for efficient research. This article offers practical tips to enhance your ability to find and evaluate high-quality information.

Are you struggling to find relevant and reliable information for your research? Do you want to avoid getting lost in a sea of sources and needing help knowing where to start? Improving your research skills is essential for academic success and professional growth.

In today's information age, effectively conducting research has become more important than ever. Whether you are a student, a professional, or simply someone who wants to stay informed, knowing how to find and evaluate information is crucial.

Fortunately, some strategies and techniques can help you improve your research skills and become a more efficient and effective researcher. By avoiding common mistakes and implementing proven methods, you can enhance your ability to find high-quality information and make the most of your research endeavors. This article will explore some practical tips and tricks to help you improve your research skills and achieve better results.

What is Research?

Research is a critical part of learning, problem-solving, and decision-making. It is an essential process used in every field for both the individual and collective’s mutual benefit and success. Research involves systematically gathering data from primary or secondary sources, analyzing it, interpreting it, and communicating its findings to researchers and other interested parties.

Research can be divided into two main categories: quantitative research, which uses numerical data to describe phenomena, and qualitative research, which seeks to understand people's beliefs, opinions, values, or behaviors. Quantitative research often involves applying model-based approaches that can predict outcomes based on observations. It is one of the most powerful methods of discovering information about the world, as it allows for testing hypotheses in a systematic manner. Qualitative research is more exploratory in nature by focusing on understanding the motivations behind what people do or think rather than developing models or producing statistics in order to conclude behavior and relationships between variables. This type of research usually relies more on observation and engagement with people instead of using statistical models.

What are Research Skills?

Research skills are the abilities and talents required to focus on an objective, gather the relevant data linked to it, analyze it using appropriate methods, and accurately communicate the results. Taking part in research indicates that you have acquired knowledge of your subject matter, have digested that knowledge, and processed, evaluated, and analyzed it until you can resolve a problem or answer a query. It is highly beneficial for employers to hire people with strong research skills since they can provide valuable insights and add value to the company’s performance. Therefore, researching effectively has become crucial to securing a job in most industries.

Why Do Research Skills Matter?

Research skills are essential if one intends to succeed in today's competitive world. With technology ever-evolving and a need to stay ahead of the competition, employees who possess research skills can prove invaluable to their employers. These skills include researching, analyzing, and interpreting data and making informed decisions based on that information.

Employers value workers who can quickly develop a thorough understanding of any changes or trends in their field of work through accurate research. Knowing how to assess customer needs, recognize competition, write reports, improve productivity, and advise on investments can also benefit any business. With the help of research skills, companies can uncover ways to adapt their services or products that better serve their customers’ needs while helping them save money at the same time. This makes overall operations more efficient as well as helps a company remain ahead of its competitors.

Essential Research Skills :

Here is a list of essential research skills:

Data Collection

Data collection is an important part of comprehending a certain topic and ensuring reliable information is collected while striving to answer complex questions. Every situation differs, but data collection typically includes surveys, interviews, observations, and existing document reviews. The data collected can be quantitative or qualitative, depending on the nature of the problem at hand. As students advance through university and other educational institutions, they will need to read extensively into a particular field and may even need to undertake comprehensive literature reviews to answer fundamental questions.

The skills acquired through data collection during university are invaluable for future roles and jobs. Gaining experience in understanding complex topics, reading widely on a given subject matter, collecting relevant data, and analyzing findings - all these activities are integral when dealing with any type of project within the corporate sector. Therefore, embarking on various research projects enhances a person's education level and brings about significant professional experience.

Goal-Setting

Setting goals is an important skill for any successful research project. It allows you to stay focused and motivated throughout the process. Goals are also essential in helping with direction: they provide a path to organize our thoughts, narrow our focus, and prioritize the tasks we need to undertake to achieve our desired result. The concept of goal-setting is inherent in most research processes, as everything needs to have something to strive for — whether that’s gaining knowledge about a particular topic or testing a theory.

When it comes to creating and setting goals during the research process, you must have clear and specific objectives in mind from the outset. Writing down your thoughts helps define these objectives, which can inform the data collection process; moreover, thinking about short-term and long-term goals can help you create manageable steps toward achieving them. Learning how to break up larger projects into smaller “mini-goals effectively” can make all the difference when tackling complex investigations — allowing researchers to monitor their progress more easily and culminate results further down the line.

Critical Thinking

Critical thinking is an integral part of the modern workplace. To succeed, one must be able to look at a situation objectively and make decisions based on evidence. The information examined needs to come from various sources, such as data collection, personal observation, or analysis. The goal should then be to take all this information and form a logical judgment that informs an action plan or idea.

Someone who displays strong critical thinking skills will not just accept proposed ideas at face value but instead can understand how these ideas can be applied and challenged. Accepting something without consideration means making the wrong decision due to a lack of thought. Critical thinkers understand how brainstorming works, assessing all elements before forming any decision. From negotiating with colleagues or customers in adversarial scenarios to analyzing complex documents such as legal contracts in order to review business agreements - critical dedicated apply their knowledge effectively and are able to back up their evaluation with evidence collected from multiple sources.

Observation Skills

Observation skills are necessary for conducting any form of research, whether it be in the workplace or as part of an investigative process. It is important to be able to pick up on the details that might otherwise pass unnoticed, such as inconsistencies in data or irregularities in how something is presented, and to pay careful attention to regulations and procedures that govern the company or environment. This can help researchers to ensure their processes are accurate and reliable.

As well as analyzing what we see around us directly, many research methodologies often involve calculated statistical analyses and calculations. For this reason, it’s important to develop strong observation skills so that the legitimacy of information can be confirmed and checked before conclusions are formed. Improving this skill requires dedication and practice, which could include keeping a journal reflecting on experiences, posing yourself questions about what you have observed, and seeking out opportunities in unfamiliar settings to test your observations.

Detail Orientation

Detail orientation is an important research skill for any scientific endeavor. It allows one to assess a situation or problem in minute detail and make appropriate judgments based on the information gathered. A detail-oriented thinker can easily spot errors, inconsistencies, and vital pieces of evidence, which can help lead to accurate conclusions from the research. Additionally, this skill allows someone to evaluate the quality and accuracy of data recorded during an experiment or project more efficiently to ensure validity.

Spotting small mistakes that may otherwise have been overlooked is a crucial part of conducting detailed research that must be perfected. Individuals aiming for superior outcomes should strive to develop their skill at detecting details by practicing critical analysis techniques, such as breaking down large bodies of information into smaller tasks to identify finer points quickly. Moreover, encouragement should also be made for elaborate comparison and analysis between different pieces of information when solving a complex problem, as it can help provide better insights into problems accurately.

Investigative Skills

Investigative skills are an essential component when it comes to gathering and analyzing data. In a professional setting, it is important to determine the accuracy and validity of different sources of information before making any decisions or articulating ideas. Generally, effective investigation requires collecting different sets of reliable data, such as surveys and interviews with stakeholders, employees, customers, etc. For example, if a company internally assesses possible challenges within its business operations environment, it would need to conduct more profound research involving talking to relevant stakeholders who could provide critical perspectives about the situation.

Data-gathering techniques such as comparison shopping and regulatory reviews have become more commonplace in the industry as people strive for greater transparency and more accurate results. Knowing how to identify reliable sources of information can give individuals a competitive advantage and allow them to make sound decisions based on accurate data. Investing time in learning different investigative skills can help recruiters spot applicants dedicated to acquiring knowledge in this field. Developing these investigative skills is also valuable for those looking for executive positions or starting their own business. By familiarizing themselves with their application process, people can become adept at collecting high-quality data they may use in their research endeavors.

Time Management

Time management is a key skill for any researcher. It's essential to be able to allocate time between different activities so you can effectively plan and structure your research projects. Without good time management, you may find yourself hastily completing tasks or feeling stressed out as you rush to complete an analysis. Ultimately, managing your time allows you to stay productive and ensure that each project is completed with the highest results.

Good time management requires various skills such as planning ahead, prioritizing tasks, breaking down large projects into smaller steps, and even delegating some activities when possible. It also means setting realistic goals for yourself in terms of the amount of research that can be achieved in certain timestamps and learning how to adjust these goals when needed. Becoming mindful of how you spend the same hours each day will propel your productivity and see positive results from your efforts. Time management becomes especially relevant regarding data collection and analysis – it is crucial to understand precisely what kind of resources are needed for each task before diving into the research itself. Knowing how much time should be dedicated to each step is essential for meeting deadlines while still retaining accuracy in the final outcomes of one’s study.

Tips on How to Improve Your Research Skills

Below are some tips that can help in improving your skills in research:

Initiate your project with a structured outline

When embarking on any research project, creating an outline and scope document must first ensure that you remain on the right track. An outline sets expectations for your project by forming a detailed strategy for researching the topic and gathering the necessary data to conclude. It will help you stay organized and break down large projects into more manageable parts. This can help prevent procrastination as each part of the project has its own timeline, making it easier to prioritize tasks accordingly.

Using an outline and scope document also allows for better structure when conducting research or interviews, as it guides which sources are most relevant, what questions need to be answered, and how information should be collected or presented. This ensures that all information received through research or interviews stays within the confines of the chosen topic of investigation. Additionally, it ensures that no important details are overlooked while minimizing the chance that extraneous information gets included in your results. Taking this time upfront prevents potential problems during analysis or reporting of findings later.

Acquire expertise in advanced data collection methods

When it comes to collecting data for research purposes, a range of advanced data collection techniques can be used to maximize your efficiency and accuracy. One such technique is customizing your online search results with advanced search settings. By adding quotation marks and wildcard characters to the terms you are searching for, you are more likely to find the information you need from reliable sources. This can be especially useful if, for instance, you are looking for exact quotes or phrases. Different search engines require different advanced techniques and tactics, so learning these can help you get more specific results from your research endeavors.

Aside from using online searches, another standard methodology when conducting research is accessing primary information through libraries or other public sources. A specific classification system will likely be in place that can help researchers locate the materials needed quickly and easily. Knowing and understanding this system allows one to access information much more efficiently while also giving them ample opportunity to increase their knowledge of various topics by browsing related content in the same category groups. Thus, by learning about advanced data collection techniques for both online and offline sources, researchers can make substantial progress in their studies more efficiently.

Validate and examine the reliability of your data sources

Collecting reliable information for research can be a challenge, especially when relying on online sources. It is essential to remember that not all sources are created equal, and some sites may contain false or inaccurate data. It is, therefore important to verify and analyze the data before using it as part of your research.

One way to start verifying and analyzing your sources is to cross-reference material from one source with another. This may help you determine if particular facts or claims are accurate and, therefore, more valid than others. Additionally, trace where the data is coming from by looking at the author or organization behind it so that you can assess their expertise in a particular field and authority on the topic at hand. Once these steps have been completed, you can confidently use this trusted information for your project.

Structure your research materials

Organizing your research materials is an integral part of any research process. When you’re conducting a project or study and trying to find the most relevant information, you can become overwhelmed with all the data available. It’s important to separate valid from invalid materials and to categorize research materials by subject for easy access later on. Bookmarking websites on a computer or using a digital asset management tool are two effective methods for organizing research information.

When researching, it’s critical to remember that some sources have limited value and may be outside the scope of your topic. Recognizing reliable material versus trustworthy resources can be complex in this sea of information. However, sorting data into appropriate categories can help narrow down what is necessary for producing valid conclusions. This method of classifying information helps ensure that vital documents aren't overlooked during the organization process as they are placed in folders shortcutted for quick access within one centralized source whenever needed. Separating valuable sources also makes it easier to reference later on when writing reports or giving presentations - material won't get lost among irrelevant data, and conclusions will be backed by sound evidence.

Enhance your research and communication capabilities

Developing research and communication skills is essential for succeeding academically and professionally in the modern world. The key to improving these skills lies in rigorous practice, which can begin with small projects such as resolving common issues or completing a research task that can be made into a personal project. One way to do this is to volunteer for research projects at work and gain experience under the guidance of experienced researchers. This will improve your research skills and help you develop communication skills when working with others on the project. Another option is to turn a personal project into a research task. For example, if you plan on taking a holiday soon, you could create an objective method to select the best destination by conducting online research on destinations and making informed decisions based on thorough analysis. Practicing in this way enables you to complete any research task confidently and communicate efficiently with ease.

How to Articulate Research Skills on Your Resume

Research projects require commitment and perseverance, making it an important skill to include on a resume. Even if you have had limited research experience throughout your education or previous job, including this in your resume assesses these qualities to potential employers. It's important to consider the extent of your research experience when deciding how to add this part of your background to your resume. If you have been involved with multiple in-depth research projects, it might be best to highlight this by including it as its own section. On the other hand, if the amount of research you have completed is more limited, then try including it in the skills section instead.

When adding research experience and accomplishments into either section of your resume, be sure to emphasize any specific roles or contributions you made during the process instead of just describing the project itself. Furthermore, remember to quantify any successes where possible - this showcases both communication and technical proficiency strengths, which can help make your resume stand out even more. By properly articulating research skills within a resume, employers will likely be more interested in what job seekers have accomplished in their careers.

How to Apply Research Skills Effectively in Your Workplace

Research skills are an invaluable set of abilities to bring to your workplace. To make sure you use them properly, a good place to start is by taking time to plan the project you have been assigned. Whether it’s writing a report or analyzing data, mapping out what tasks you need to do and how long they should take helps to understand the project timeline better. This also makes setting aside dedicated time for research easier too.

To ensure that the decisions made are sound and informed, reading up on the subject area related to the project remains one of the premier ways of doing this. This will help to ensure that any problems arising can be solved quickly and effectively, as well as provide answers before any decisions are actually put into practice. By arming yourself with knowledge gathered through reading about a particular topic, it can give you more confidence when formulating plans or strategies in which direction to take your work in.

Final Thoughts

Research skills are increasingly important in the modern world, and gaining proficiency in this area can significantly benefit a person's career. Research skills are essential for success in many different roles and fields, including those within business and industry, education, science, and medicine. Developing a deep understanding of research allows us to identify problems better and critically evaluate potential solutions. It also bolsters our problem-solving abilities as we work to find creative solutions that meet our efforts' objectives.

By improving your research capabilities, you can impress employers during an application process or when joining a team at work. Research skills are considered soft skills by potential employers since they signal that you have attention to detail while simultaneously demonstrating your ability to learn new things quickly. Employers regard these skills highly, making them one of the key graduate career skills recruiters seek. Furthermore, being able to add ‘research skills’ to your CV will be looked upon favorably by employers and help drive up your employability significantly. Demonstrating that you possess these sought-after traits makes it easier for recruiters to give you the opportunity you've been looking for, so it's worth investing the time into developing these life-long learning tools today.

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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 organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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10 Simple Ways to Improve Your Research Paper Writing Skills

When your topic for research is complex you definitely want to study it with all possible precision to get a good mark and your teacher's admiration for your work. Yet, what should you do if writing papers and examining various issues is not an easy job for you? How can you eliminate failure to do this assignment correctly?

Right now, we are going to tell you about a number of useful recommendations which will help you prepare a stunning research paper . Are you ready for a short workshop on academic writing? Great! Then let's start!

1. Work out a schedule

Break the whole work into parts and assume how much time you will need for each of them. For example, it may look like:

Collecting information-1-2 days

Analyzing retrieved data-1 day

Working on the body of your assignment -2-3 days

Editing a draft of the paper-2 days

Completing the final version-1-2 days

Revising your assignment-1 day

Such a well-devised plan will help you organize the writing process better. Yet, keep in mind that working on some parts of your study may take a bit longer than you think. Thus, you should have a couple of extra days or even a week to finish it.

2. Make an outline

This is highly effective for many reasons. First, in such a way, you can build a structure for your paper. Secondly, you will become better-motivated, since you will know for sure what information you already have and what you still need to find.

3. Conduct your own investigation

Be an academic detective. As soon as you formulate the main issue of your research, do your own study: ask your teacher questions connected with your topic or talk to experts who are competent in the subject you are learning about. Also, you can analyze as many relevant sources as you can find. Such a comprehensive approach will help make your own expertise accurate and deep.

4. Write a thesis statement

Place it at the beginning of your research. It will express the key idea of your study and clarify what problem you are going to solve due to your investigation. You can make it catchy or thought-provoking. In any case, a thesis sentence will become the core of your paper.

5. Use credible sources

Take information from credible books and articles. They shouldn't be older than 4-5 years. Though, it could be a good idea to use earlier publications if you want to highlight some tendencies in a particular area.

6. Provide unbiased arguments 

Choose objective data and opinions to support the main ideas of your work. Compare different points of view and suggest your own vision of the issue. Such an approach will help you acquire analytical skills as well as valuable experience as an entry-level scientist.

7. Make the text of your paper coherent

Write all the paragraphs in a logical sequence. Stick to the topic and try not to skip from one idea to another abruptly. Also, present your thoughts and arguments consequently to increase the accuracy of your research.

8. Add citations, diagrams, or tables

Suitable quotes of authoritative experts and scholars will raise the credibility of your paper. Additionally, they will create vital links between the experience of other specialists and your own findings. Yet, make sure you cite their works according to the popular formatting styles in order not to be accused of plagiarism. Also, using diagrams and tables in your research paper will help to organize the information you present and make your assignment look more logical.

9. Complete a profound summary

When you come to the last part of your paper, focus on concluding your research precisely. There's no need to add any new details or facts at this stage. Rather than that, reiterate the crucial findings you have discovered while investigating the problem.

10. Edit your paper and refine its content

After your research paper is finished, it is time to review it. Check and edit the text carefully. Take no less than three rounds to revise it properly and not to omit grammatical or stylistic errors. In addition, double-check the word choice and the rules of the paper format you have chosen.

Although research papers are not easy to complete, we are sure you will cope with this task in a perfect way by following our recommendations. But if you prefer to buy a research paper online customized to your needs instead, a professional team of ENL writers will readily prepare any task for you. 

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Resume Worded   |  Resume Skills

Skill profile, quantitative researcher, improve your resume's success rate by using these quantitative researcher skills and keywords ..

• Hard Skills and Keywords for your Quantitative Researcher Resume
• ATS Scan : Compare Your Resume To These Skills
• How To Add Skills
• Quantitative Researcher More Resume Templates

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Frequently asked questions.

• 3. Effective Action Verbs for your Resume

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Looking for keywords for a specific job search for your job title here., © 2024 resume worded. all rights reserved., quantitative researcher resume keywords and skills (hard skills).

Here are the keywords and skills that appear most frequently on recent Quantitative Researcher job postings. In other words, these are the most sought after skills by recruiters and hiring managers. Add keywords directly into your resume's work experiences , education or Skills section . Remember that every job is different. Instead of including all keywords on your resume, identify those that are most relevant to the job you're applying to. Use the free Targeted Resume tool to help with this.

• Machine Learning
• Quantitative Finance
• Python (Programming Language)
• R (Programming Language)
• Quantitative Research
•  Find out what your resume's missing
• Statistical Modeling
• Data Science
• Quantitative Investing
• Deep Learning
• Quantitative Analytics
• Time Series Analysis
• Financial Modeling
• Data Analysis
• Data Mining

Resume Skills: Programming

•  Match your resume to these skills

Resume Skills: Statistical Techniques

• Regression Analysis
• Distribution Testing
• Hypothesis Testing
• Monte Carlo Techniques
• Risk Modelling
• Risk Analysis

Resume Skills: Big Data Tools

Resume skills: ai and machine learning.

• scikit-learn

  Where on my resume do I add these buzzwords? Add keywords directly into your resume's work experiences , education or projects. Alternatively, you can also include a Skills section where you can list your technical skills in order of your proficiency. Only include these technical skills or keywords into your resume if you actually have experience with them.

   Does your resume contain all the right skills? Paste in your resume in the AI Resume Scan ↓ section below and get an instant score.

Compare Your Resume To These Quantitative Researcher Skills (ATS Scan)

Paste your resume below and our AI will identify which keywords are missing from your resume from the list above (and what you need to include). Including the right keywords will help you get past Applicant Tracking Systems (i.e. resume screeners) which may scan your resume for keywords to see if you're a match for the job.

Sample Quantitative Researcher Resume Examples: How To Include These Skills

Add keywords directly into your resume's work experiences , education or skills section , like we've shown in the examples below. use the examples below as inspiration..

  Where on my resume do I add these buzzwords? Add keywords directly into your resume's work experiences , education or projects. Only include these technical skills or keywords into your resume if you actually have experience with them.

How do I add skills to a Quantitative Researcher resume?

Go through the Quantitative Researcher posting you're applying to, and identify hard skills the company is looking for. For example, skills like Python (Programming Language), Quantitative Finance and Machine Learning are possible skills. These are skills you should try to include on your resume.

Add other common skills from your industry - such as R (Programming Language), MATLAB and Statistics - into your resume if they're relevant.

Incorporate skills - like Time Series Analysis, C++ and Algorithms - into your work experience too. This shows hiring managers that you have practical experience with these tools, techniques and skills.

Consider including a section in your resume dedicated to your research experience. On Quantitative Researcher resumes, hiring managers want to see research projects which you led or where involved with, and their outcomes.

Try to add the exact job title, Quantitative Researcher, somewhere into your resume to get past resume screeners. See the infographic for how to do this.

Word Cloud for Quantitative Researcher Skills & Keywords

The following word cloud highlights the most popular keywords that appear on Quantitative Researcher job descriptions. The bigger the word, the more frequently it shows up on employer's job postings. If you have experience with these keywords, include them on your resume.

Get your Resume Instantly Checked, For Free

Upload your resume and we'll spot the issues in it before an actual quantitative researcher recruiter sees it. for free., quantitative researcher resume templates.

Here are examples of proven resumes in related jobs and industries, approved by experienced hiring managers. Use them as inspiration when you're writing your own resume. You can even download and edit the resume template in Google Docs.

Resume Example Professional

An effective Description of the templates...

Download this resume template

This resume template is suitable for experienced hires or mid-level hires. The education contains two examples of an education experiences, but only include one (your most recent one) if you're a senior level employee.

Tips on why this template works

   makes great use of space.

It strikes the right balance between white space and content, and doesn't waste space on unnecessary images and icons. Remember, recruiters aren't looking at how creative you are when it comes to your template. Your content is core and should be the focus.

   Strong resume bullet points

This job seeker uses resume bullet points that uses strong action verbs, and most importantly, contain numbers that demonstrate the significance of their accomplishments.

Resume Example Highlights (Free)

This template is clean, readable by resume screeners, and is effective at calling out key accomplishments and projects from specific work experiences. This would be useful if you have been at a company for a while, or been in a consulting-type of role, and want to point hiring managers to your most impressive accomplishments.

   Strong action verbs

Action verbs are important on your resume are vital. They evoke strong imagery to your reader, and this resume does an excellent job by using words such as “spearheaded,” “managed,” and “drove.” These words will help you to put your achievements in perspective, in conjunction with measurable results. Use action verbs relating to the skills you want to highlight.

   Specific examples of finished projects

Many of your accomplishments will involve your responsibilities in your employer's high-level projects. Recruiters want to see what you’ve completed in previous roles -- such as the Operations Improvement Project and new iPhone app launch highlighted in this resume. The numbers make your experience real, rather than a vague “oversaw several teams for a project.” What did you do specifically? Be specific.

Resume Example Modern Two-Column

This two column resume template has been designed and created in Google Docs, and puts an emphasis on a skills section. You can download it in Word, or edit it directly in Google Docs.

   Prioritize work experience, while including other key sections

The two-column in this Google Docs resume template prioritizes the work experience sections, while maximizing the content into the resume. Not all two column templates are ATS-compatible, but this one is when it is saved as PDF and passed through a resume screener.

   Includes a strong Skills section

Skills sections are a great way to include specific keywords and skills that you have, that haven't been included in other parts of your resume. This helps you get past resume screeners that scan your resume for specific keywords.

Resume Example Clean Modern

If you're a job seeker with a few years of experience under your belt, use a template like this one. It's simple, effective at highlighting our work experience, and minimizes the emphasis on the education section (the dates are omitted which is good to prevent ageism, especially if you graduated more than 10 years ago).

   Professionally-designed template

Minimal templates like this one are exactly what mid-to-senior level recruiters want to see - it shows professionalism, focuses on accomplishments, and makes full use of each page.

   Resume summary highlights key accomplishments

The first rule about including a resume summary is that it does not repeat accomplishments mentioned elsewhere on the resume. This resume stresses new software engineering and leadership skills right at the top of the resume, and includes an award too. If you include a summary, try to include a mix of both technical accomplishments (e.g. projects you developed or led), as well as career-related accomplishments (e.g. being promoted).

Resume Example Entry-Level (Free)

Use this Google Docs template if you're a student, recent graduate, or a career changer. Right out of college, you may not have much experience in the field. To supplement that, use your experience in clubs and activities, volunteering, projects, and useful coursework to help highlight your knowledge on the subject.

   Emphasis on education

If you're an entry-level job seeker that has recently completed education (or in the process of completing a degree), you should prioritize your education and include it first. This Google Docs template does this.

   University projects relevant to the job

If you're an entry level job seeker (or a career-changer), you may not have enough work experience to fill up your resume. This is where class projects and university projects come in. This template has a section dedicated to projects, which you can use to talk about volunteering, class projects, or personal projects relevant to the job.

Resume Example Concise

This is a suitable Google Docs resume template for all kinds of roles, including senior, entry-level and mid-level. Note how the focus is the work experience section only, and the education section is limited. This is what you should do if you graduated a while ago.

   Use a skills section to highlight specific keywords

To get past resume screeners and Applicant Tracking Systems, use a skills section that includes specific skills the job is looking for. This is an easy way to tailor your resume.

   Keep you education short, if you're a mid to senior level job seeker

What skills should you add to a Quantitative Researcher resume?

Some effective Quantitative Researcher skills you can add to your resume include:

Target your Resume to a Job Description

While the keywords above are a good indication of what skills you need on your resume, you should try to find additional keywords that are specific to the job. To do this, use the free Targeted Resume tool. It analyzes the job you are applying to and finds the most important keywords you need on your resume. It is personalized to your resume, and is the best way to ensure your resume will pass the automated resume filters. Start targeting your resume

Most resumes get auto-rejected because of small, simple errors. These errors are easy to miss but can be costly in your job search. If you want to make sure your resume is error-free, upload it to Score My Resume for a free resume review. You'll get a score so you know where your resume stands, as well as actionable feedback to improve it. Get a free resume review

Scan your skills and keywords.

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Find out what keywords recruiters search for. These keywords will help you beat resume screeners (i.e. the Applicant Tracking System).

   get a resume score., find out how effective your resume really is. you'll get access to our confidential resume review tool which will tell you how recruiters see your resume..

Thank you for the checklist! I realized I was making so many mistakes on my resume that I've now fixed. I'm much more confident in my resume now.