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Quantitative Research – Methods, Types and Analysis

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Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Affiliation.

• 1 Humanalysis, Inc., Saratoga Springs, NY 12866, USA. [email protected]
• PMID: 20598692
• DOI: 10.1016/j.ijnurstu.2010.06.004

Generalization, which is an act of reasoning that involves drawing broad inferences from particular observations, is widely-acknowledged as a quality standard in quantitative research, but is more controversial in qualitative research. The goal of most qualitative studies is not to generalize but rather to provide a rich, contextualized understanding of some aspect of human experience through the intensive study of particular cases. Yet, in an environment where evidence for improving practice is held in high esteem, generalization in relation to knowledge claims merits careful attention by both qualitative and quantitative researchers. Issues relating to generalization are, however, often ignored or misrepresented by both groups of researchers. Three models of generalization, as proposed in a seminal article by Firestone, are discussed in this paper: classic sample-to-population (statistical) generalization, analytic generalization, and case-to-case transfer (transferability). Suggestions for enhancing the capacity for generalization in terms of all three models are offered. The suggestions cover such issues as planned replication, sampling strategies, systematic reviews, reflexivity and higher-order conceptualization, thick description, mixed methods research, and the RE-AIM framework within pragmatic trials.

Copyright 2010 Elsevier Ltd. All rights reserved.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

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

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

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

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

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• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
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Table of contents

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

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

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

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

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

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

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

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

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

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

• Replication

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

• Direct comparisons of results

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

• Large samples

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

• Hypothesis testing

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

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

• Superficiality

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

• Narrow focus

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

• Structural bias

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

• Lack of context

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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

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

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Quantitative Research Methods

• By qualitasresearch

Quantitative research methods play a fundamental role in the social sciences, providing systematic approaches for collecting, analyzing, and interpreting numerical data to understand social phenomena. Whether you’re a student embarking on a research project or a professional seeking to expand your methodological toolkit, understanding quantitative research methods is essential for conducting rigorous and insightful studies. In this beginner’s guide, we’ll explore the basics of quantitative research methods, including its key principles, techniques, and applications.

What is Quantitative Research?

Quantitative research is a systematic and objective approach to studying social phenomena through the collection and analysis of numerical data. This method emphasizes measurement, quantification, and statistical analysis to uncover patterns, relationships, and trends within populations. Quantitative research seeks to generalize findings to larger populations and test hypotheses derived from theoretical frameworks.

Key Principles of Quantitative Research

• Standardization: Quantitative research relies on standardized measurement instruments and procedures to ensure consistency and comparability across data collection efforts.
• Objectivity: Quantitative research strives for objectivity and impartiality in data collection, analysis, and interpretation, minimizing researcher bias and subjectivity.
• Generalizability: Quantitative research aims to generalize findings from a sample to a larger population, providing insights that can be applied beyond the specific context of the study.
• Statistical Analysis: Quantitative research employs statistical techniques to analyze and interpret numerical data, identifying patterns, correlations, and associations among variables.

Common Quantitative Research Methods

• Surveys: Surveys involve the administration of structured questionnaires to collect data from a sample of participants, allowing researchers to quantify attitudes, behaviors, and preferences.
• Experiments: Experiments manipulate independent variables to observe their effects on dependent variables under controlled conditions, allowing researchers to establish cause-and-effect relationships.
• Secondary Data Analysis: Secondary data analysis involves the use of existing datasets collected by other researchers or organizations, allowing researchers to analyze data for new insights or test hypotheses.
• Observational Studies: Observational studies involve systematic observation and recording of behaviors, events, or phenomena in natural settings, providing insights into patterns and trends.

Steps in Conducting Quantitative Research

• Formulating Research Questions: Begin by formulating clear and specific research questions or hypotheses that can be tested using quantitative methods.
• Designing the Study: Design the study by selecting appropriate research methods, sampling techniques, and measurement instruments to address research questions.
• Data Collection: Collect data using standardized procedures and measurement instruments, ensuring reliability and validity of measurements.
• Data Analysis: Analyze data using statistical techniques such as descriptive statistics, inferential statistics, and multivariate analysis to identify patterns and relationships.
• Interpreting Findings: Interpret findings in the context of research questions and theoretical frameworks, drawing conclusions based on statistical evidence.

Applications of Quantitative Research

• Market Research: Quantitative research is used to measure consumer preferences, behaviors, and market trends, informing marketing strategies and product development.
• Public Opinion Polling: Quantitative research is used to gauge public attitudes, opinions, and perceptions on social and political issues through surveys and polls.
• Healthcare Research: Quantitative research is used to assess the effectiveness of medical treatments, interventions, and health programs, informing healthcare policy and practice.
• Educational Research: Quantitative research is used to evaluate educational interventions, assess student performance, and identify factors influencing academic achievement.

Quantitative research methods offer powerful tools for studying social phenomena, providing systematic approaches for collecting, analyzing, and interpreting numerical data. By understanding the principles, techniques, and applications of quantitative research, beginners can embark on research projects with confidence, contributing valuable insights to their respective fields. Whether you’re exploring consumer behavior, assessing program effectiveness, or investigating social trends, quantitative research methods offer a robust framework for generating evidence-based knowledge and advancing understanding of the social world.

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Generalizability and Transferability

In this chapter, we discuss generalizabililty, transferability, and the interrelationship between the two. We also explain how these two aspects of research operate in different methodologies, demonstrating how researchers may apply these concepts throughout the research process.

Generalizability Overview

Generalizability is applied by researchers in an academic setting. It can be defined as the extension of research findings and conclusions from a study conducted on a sample population to the population at large. While the dependability of this extension is not absolute, it is statistically probable. Because sound generalizability requires data on large populations, quantitative research -- experimental for instance -- provides the best foundation for producing broad generalizability. The larger the sample population, the more one can generalize the results. For example, a comprehensive study of the role computers play in the writing process might reveal that it is statistically probable that students who do most of their composing on a computer will move chunks of text around more than students who do not compose on a computer.

Transferability Overview

Transferability is applied by the readers of research. Although generalizability usually applies only to certain types of quantitative methods, transferability can apply in varying degrees to most types of research . Unlike generalizability, transferability does not involve broad claims, but invites readers of research to make connections between elements of a study and their own experience. For instance, teachers at the high school level might selectively apply to their own classrooms results from a study demonstrating that heuristic writing exercises help students at the college level.

Interrelationships

Generalizability and transferability are important elements of any research methodology, but they are not mutually exclusive: generalizability, to varying degrees, rests on the transferability of research findings. It is important for researchers to understand the implications of these twin aspects of research before designing a study. Researchers who intend to make a generalizable claim must carefully examine the variables involved in the study. Among these are the sample of the population used and the mechanisms behind formulating a causal model. Furthermore, if researchers desire to make the results of their study transferable to another context, they must keep a detailed account of the environment surrounding their research, and include a rich description of that environment in their final report. Armed with the knowledge that the sample population was large and varied, as well as with detailed information about the study itself, readers of research can more confidently generalize and transfer the findings to other situations.

Generalizability

Generalizability is not only common to research, but to everyday life as well. In this section, we establish a practical working definition of generalizability as it is applied within and outside of academic research. We also define and consider three different types of generalizability and some of their probable applications. Finally, we discuss some of the possible shortcomings and limitations of generalizability that researchers must be aware of when constructing a study they hope will yield potentially generalizable results.

In many ways, generalizability amounts to nothing more than making predictions based on a recurring experience. If something occurs frequently, we expect that it will continue to do so in the future. Researchers use the same type of reasoning when generalizing about the findings of their studies. Once researchers have collected sufficient data to support a hypothesis, a premise regarding the behavior of that data can be formulated, making it generalizable to similar circumstances. Because of its foundation in probability, however, such a generalization cannot be regarded as conclusive or exhaustive.

While generalizability can occur in informal, nonacademic settings, it is usually applied only to certain research methods in academic studies. Quantitative methods allow some generalizability. Experimental research, for example, often produces generalizable results. However, such experimentation must be rigorous in order for generalizable results to be found.

An example of generalizability in everyday life involves driving. Operating an automobile in traffic requires that drivers make assumptions about the likely outcome of certain actions. When approaching an intersection where one driver is preparing to turn left, the driver going straight through the intersection assumes that the left-turning driver will yield the right of way before turning. The driver passing through the intersection applies this assumption cautiously, recognizing the possibility that the other driver might turn prematurely.

American drivers also generalize that everyone will drive on the right hand side of the road. Yet if we try to generalize this assumption to other settings, such as England, we will be making a potentially disastrous mistake. Thus, it is obvious that generalizing is necessary for forming coherent interpretations in many different situations, but we do not expect our generalizations to operate the same way in every circumstance. With enough evidence we can make predictions about human behavior, yet we must simultaneously recognize that our assumptions are based on statistical probability.

Consider this example of generalizable research in the field of English studies. A study on undergraduate instructor evaluations of composition instructors might reveal that there is a strong correlation between the grade students are expecting to earn in a course and whether they give their instructor high marks. The study might discover that 95% of students who expect to receive a "C" or lower in their class give their instructor a rating of "average" or below. Therefore, there would be a high probability that future students expecting a "C" or lower would not give their instructor high marks. However, the results would not necessarily be conclusive. Some students might defy the trend. In addition, a number of different variables could also influence students' evaluations of an instructor, including instructor experience, class size, and relative interest in a particular subject. These variables -- and others -- would have to be addressed in order for the study to yield potentially valid results. However, even if virtually all variables were isolated, results of the study would not be 100% conclusive. At best, researchers can make educated predictions of future events or behaviors, not guarantee the prediction in every case. Thus, before generalizing, findings must be tested through rigorous experimentation, which enables researchers to confirm or reject the premises governing their data set.

Considerations

There are three types of generalizability that interact to produce probabilistic models. All of them involve generalizing a treatment or measurement to a population outside of the original study. Researchers who wish to generalize their claims should try to apply all three forms to their research, or the strength of their claims will be weakened (Runkel & McGrath, 1972).

In one type of generalizability, researchers determine whether a specific treatment will produce the same results in different circumstances. To do this, they must decide if an aspect within the original environment, a factor beyond the treatment, generated the particular result. This will establish how flexibly the treatment adapts to new situations. Higher adaptability means that the treatment is generalizable to a greater variety of situations. For example, imagine that a new set of heuristic prewriting questions designed to encourage freshman college students to consider audience more fully works so well that the students write thoroughly developed rhetorical analyses of their target audiences. To responsibly generalize that this heuristic is effective, a researcher would need to test the same prewriting exercise in a variety of educational settings at the college level, using different teachers, students, and environments. If the same positive results are produced, the treatment is generalizable.

A second form of generalizability focuses on measurements rather than treatments. For a result to be considered generalizable outside of the test group, it must produce the same results with different forms of measurement. In terms of the heuristic example above, the findings will be more generalizable if the same results are obtained when assessed "with questions having a slightly different wording, or when we use a six-point scale instead of a nine-point scale" (Runkel & McGrath, 1972, p.46).

A third type of generalizability concerns the subjects of the test situation. Although the results of an experiment may be internally valid, that is, applicable to the group tested, in many situations the results cannot be generalized beyond that particular group. Researchers who hope to generalize their results to a larger population should ensure that their test group is relatively large and randomly chosen. However, researchers should consider the fact that test populations of over 10,000 subjects do not significantly increase generalizability (Firestone,1993).

Potential Limitations

No matter how carefully these three forms of generalizability are applied, there is no absolute guarantee that the results obtained in a study will occur in every situation outside the study. In order to determine causal relationships in a test environment, precision is of utmost importance. Yet if researchers wish to generalize their findings, scope and variance must be emphasized over precision. Therefore, it becomes difficult to test for precision and generalizability simultaneously, since a focus on one reduces the reliability of the other. One solution to this problem is to perform a greater number of observations, which has a dual effect: first, it increases the sample population, which heightens generalizability; second, precision can be reasonably maintained because the random errors between observations will average out (Runkel and McGrath, 1972).

Transferability

Transferability describes the process of applying the results of research in one situation to other similar situations. In this section, we establish a practical working definition of transferability as it's applied within and outside of academic research. We also outline important considerations researchers must be aware of in order to make their results potentially transferable, as well as the critical role the reader plays in this process. Finally, we discuss possible shortcomings and limitations of transferability that researchers must be aware of when planning and conducting a study that will yield potentially transferable results.

Transferability is a process performed by readers of research. Readers note the specifics of the research situation and compare them to the specifics of an environment or situation with which they are familiar. If there are enough similarities between the two situations, readers may be able to infer that the results of the research would be the same or similar in their own situation. In other words, they "transfer" the results of a study to another context. To do this effectively, readers need to know as much as possible about the original research situation in order to determine whether it is similar to their own. Therefore, researchers must supply a highly detailed description of their research situation and methods.

Results of any type of research method can be applied to other situations, but transferability is most relevant to qualitative research methods such as ethnography and case studies. Reports based on these research methods are detailed and specific. However, because they often consider only one subject or one group, researchers who conduct such studies seldom generalize the results to other populations. The detailed nature of the results, however, makes them ideal for transferability.

Transferability is easy to understand when you consider that we are constantly applying this concept to aspects of our daily lives. If, for example, you are an inexperienced composition instructor and you read a study in which a veteran writing instructor discovered that extensive prewriting exercises helped students in her classes come up with much more narrowly defined paper topics, you could ask yourself how much the instructor's classroom resembled your own. If there were many similarities, you might try to draw conclusions about how increasing the amount of prewriting your students do would impact their ability to arrive at sufficiently narrow paper topics. In doing so, you would be attempting to transfer the composition researcher's techniques to your own classroom.

An example of transferable research in the field of English studies is Berkenkotter, Huckin, and Ackerman's (1988) study of a graduate student in a rhetoric Ph.D. program. In this case study, the researchers describe in detail a graduate student's entrance into the language community of his academic program, and particularly his struggle learning the writing conventions of this community. They make conclusions as to why certain things might have affected the graduate student, "Nate," in certain ways, but they are unable to generalize their findings to all graduate students in rhetoric Ph.D. programs. It is simply one study of one person in one program. However, from the level of detail the researchers provide, readers can take certain aspects of Nate's experience and apply them to other contexts and situations. This is transferability. First-year graduate students who read the Berkenhotter, Huckin, and Ackerman study may recognize similarities in their own situation while professors may recognize difficulties their students are having and understand these difficulties a bit better. The researchers do not claim that their results apply to other situations. Instead, they report their findings and make suggestions about possible causes for Nate's difficulties and eventual success. Readers then look at their own situation and decide if these causes may or may not be relevant.

When designing a study researchers have to consider their goals: Do they want to provide limited information about a broad group in order to indicate trends or patterns? Or do they want to provide detailed information about one person or small group that might suggest reasons for a particular behavior? The method they choose will determine the extent to which their results can be transferred since transferability is more applicable to certain kinds of research methods than others.

Thick Description: When writing up the results of a study, it is important that the researcher provide specific information about and a detailed description of her subject(s), location, methods, role in the study, etc. This is commonly referred to as "thick description" of methods and findings; it is important because it allows readers to make an informed judgment about whether they can transfer the findings to their own situation. For example, if an educator conducts an ethnography of her writing classroom, and finds that her students' writing improved dramatically after a series of student-teacher writing conferences, she must describe in detail the classroom setting, the students she observed, and her own participation. If the researcher does not provide enough detail, it will be difficult for readers to try the same strategy in their own classrooms. If the researcher fails to mention that she conducted this research in a small, upper-class private school, readers may transfer the results to a large, inner-city public school expecting a similar outcome.

The Reader's Role: The role of readers in transferability is to apply the methods or results of a study to their own situation. In doing so, readers must take into account differences between the situation outlined by the researcher and their own. If readers of the Berkenhotter, Huckin, and Ackerman study are aware that the research was conducted in a small, upper-class private school, but decide to test the method in a large inner-city public school, they must make adjustments for the different setting and be prepared for different results.

Likewise, readers may decide that the results of a study are not transferable to their own situation. For example, if a study found that watching more than 30 hours of television a week resulted in a worse GPA for graduate students in physics, graduate students in broadcast journalism may conclude that these results do not apply to them.

Readers may also transfer only certain aspects of the study and not the entire conclusion. For example, in the Berkenhotter, Huckin, and Ackerman study, the researchers suggest a variety of reasons for why the graduate student studied experienced difficulties adjusting to his Ph.D. program. Although composition instructors cannot compare "Nate" to first-year college students in their composition class, they could ask some of the same questions about their own class, offering them insight into some of the writing difficulties the first-year undergraduates are experiencing. It is up to readers to decide what findings are important and which may apply to their own situation; if researchers fulfill their responsibility to provide "thick description," this decision is much easier to make.

Understanding research results can help us understand why and how something happens. However, many researchers believe that such understanding is difficult to achieve in relation to human behaviors which they contend are too difficult to understand and often impossible to predict. "Because of the many and varied ways in which individuals differ from each other and because these differences change over time, comprehensive and definitive experiments in the social sciences are not possible...the most we can ever realistically hope to achieve in educational research is not prediction and control but rather only temporary understanding" (Cziko, 1993, p. 10).

Cziko's point is important because transferability allows for "temporary understanding." Instead of applying research results to every situation that may occur in the future, we can apply a similar method to another, similar situation, observe the new results, apply a modified version to another situation, and so on. Transferability takes into account the fact that there are no absolute answers to given situations; rather, every individual must determine their own best practices. Transferring the results of research performed by others can help us develop and modify these practices. However, it is important for readers of research to be aware that results cannot always be transferred; a result that occurs in one situation will not necessarily occur in a similar situation. Therefore, it is critical to take into account differences between situations and modify the research process accordingly.

Although transferability seems to be an obvious, natural, and important method for applying research results and conclusions, it is not perceived as a valid research approach in some academic circles. Perhaps partly in response to critics, in many modern research articles, researchers refer to their results as generalizable or externally valid. Therefore, it seems as though they are not talking about transferability. However, in many cases those same researchers provide direction about what points readers may want to consider, but hesitate to make any broad conclusions or statements. These are characteristics of transferable results.

Generalizability is actually, as we have seen, quite different from transferability. Unfortunately, confusion surrounding these two terms can lead to misinterpretation of research results. Emphasis on the value of transferable results -- as well as a clear understanding among researchers in the field of English of critical differences between the conditions under which research can be generalized, transferred, or, in some cases, both generalized and transferred -- could help qualitative researchers avoid some of the criticisms launched by skeptics who question the value of qualitative research methods.

Generalizability and Transferability: Synthesis

Generalizability allows us to form coherent interpretations in any situation, and to act purposefully and effectively in daily life. Transferability gives us the opportunity to sort through given methods and conclusions to decide what to apply to our own circumstances. In essence, then, both generalizability and transferability allow us to make comparisons between situations. For example, we can generalize that most people in the United States will drive on the right side of the road, but we cannot transfer this conclusion to England or Australia without finding ourselves in a treacherous situation. It is important, therefore, to always consider context when generalizing or transferring results.

Whether a study emphasizes transferability or generalizability is closely related to the goals of the researcher and the needs of the audience. Studies done for a magazine such as Time or a daily newspaper tend towards generalizability, since the publishers want to provide information relevant to a large portion of the population. A research project pointed toward a small group of specialists studying a similar problem may emphasize transferability, since specialists in the field have the ability to transfer aspects of the study results to their own situations without overt generalizations provided by the researcher. Ultimately, the researcher's subject, audience, and goals will determine the method the researcher uses to perform a study, which will then determine the transferability or generalizability of the results.

A Comparison of Generalizability and Transferability

Although generalizability has been a preferred method of research for quite some time, transferability is relatively a new idea. In theory, however, it has always accompanied research issues. It is important to note that generalizability and transferability are not necessarily mutually exclusive; they can overlap.

From an experimental study to a case study, readers transfer the methods, results, and ideas from the research to their own context. Therefore, a generalizable study can also be transferable. For example, a researcher may generalize the results of a survey of 350 people in a university to the university population as a whole; readers of the results may apply, or transfer, the results to their own situation. They will ask themselves, basically, if they fall into the majority or not. However, a transferable study is not always generalizable. For example, in case studies , transferability allows readers the option of applying results to outside contexts, whereas generalizability is basically impossible because one person or a small group of people is not necessarily representative of the larger population.

Controversy, Worth, and Function

Research in the natural sciences has a long tradition of valuing empirical studies; experimental investigation has been considered "the" way to perform research. As social scientists adapted the methods of natural science research to their own needs, they adopted this preference for empirical research. Therefore, studies that are generalizable have long been thought to be more worthwhile; the value of research was often determined by whether a study was generalizable to a population as a whole. However, more and more social scientists are realizing the value of using a variety of methods of inquiry, and the value of transferability is being recognized.

It is important to recognize that generalizability and transferability do not alone determine a study's worth. They perform different functions in research, depending on the topic and goals of the researcher. Where generalizable studies often indicate phenomena that apply to broad categories such as gender or age, transferability can provide some of the how and why behind these results.

However, there are weaknesses that must be considered. Researchers can study a small group that is representative of a larger group and claim that it is likely that their results are applicable to the larger group, but it is impossible for them to test every single person in the larger group. Their conclusions, therefore, are only valid in relation to their own studies. Another problem is that a non-representative group can lead to a faulty generalization. For example, a study of composition students'; revision capabilities which compared students' progress made during a semester in a computer classroom with progress exhibited by students in a traditional classroom might show that computers do aid students in the overall composing process. However, if it were discovered later that an unusually high number of students in the traditional classrooms suffered from substance abuse problems outside of the classroom, the population studied would not be considered representative of the student population as a whole. Therefore, it would be problematic to generalize the results of the study to a larger student population.

In the case of transferability, readers need to know as much detail as possible about a research situation in order to accurately transfer the results to their own. However, it is impossible to provide an absolutely complete description of a situation, and missing details may lead a reader to transfer results to a situation that is not entirely similar to the original one.

Applications to Research Methods

The degree to which generalizability and transferability are applicable differs from methodology to methodology as well as from study to study. Researchers need to be aware of these degrees so that results are not undermined by over-generalizations, and readers need to ensure that they do not read researched results in such a way that the results are misapplied or misinterpreted.

Applications of Transferability and Generalizability: Case Study

Research Design Case studies examine individuals or small groups within a specific context. Research is typically gathered through qualitative means: interviews, observations, etc. Data is usually analyzed either holistically or by coding methods.

Assumptions In research involving case studies, a researcher typically assumes that the results will be transferable. Generalizing is difficult or impossible because one person or small group cannot represent all similar groups or situations. For example, one group of beginning writing students in a particular classroom cannot represent all beginning student writers. Also, conclusions drawn in case studies are only about the participants being observed. With rare exceptions, case studies are not meant to establish cause/effect relationships between variables. The results of a case study are transferable in that researchers "suggest further questions, hypotheses, and future implications," and present the results as "directions and questions" (Lauer & Asher 32).

Example In order to illustrate the writing skills of beginning college writers, a researcher completing a case study might single out one or more students in a composition classroom and set about talking to them about how they judge their own writing as well as reading actual papers, setting up criteria for judgment, and reviewing paper grades/teacher interpretation.

Results of a Study In presenting the results of the previous example, a researcher should define the criteria that were established in order to determine what the researcher meant by "writing skills," provide noteworthy quotes from student interviews, provide other information depending on the kinds of research methods used (e.g., surveys, classroom observation, collected writing samples), and include possibilities for furthering this type of research. Readers are then able to assess for themselves how the researcher's observations might be transferable to other writing classrooms.

Applications of Transferability and Generalizability: Ethnography

Research Design Ethnographies study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through observer immersion into the culture or group. Research is completed through various methods, which are similar to those of case studies, but since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research. (Jonathon Kozol's "There Are No Children Here" is a good example of this.)

Assumptions As with case studies, findings of ethnographies are also considered to be transferable. The main goals of an ethnography are to "identify, operationally define, and interrelate variables" within a particular context, which ultimately produce detailed accounts or "thick descriptions" (Lauer & Asher 39). Unlike a case study, the researcher here discovers many more details. Results of ethnographies should "suggest variables for further investigation" and not generalize beyond the participants of a study (Lauer & Asher 43). Also, since analysts completing this type of research tend to rely on multiple methods to collect information (a practice also referred to as triangulation), their results typically help create a detailed description of human behavior within a particular environment.

Example The Iowa Writing Program has a widespread reputation for producing excellent writers. In order to begin to understand their training, an ethnographer might observe students throughout their degree program. During this time, the ethnographer could examine the curriculum, follow the writing processes of individual writers, and become acquainted with the writers and their work. By the end of a two year study, the researcher would have a much deeper understanding of the unique and effective features of the program.

Results of a Study Obviously, the Iowa Writing Program is unique, so generalizing any results to another writing program would be problematic. However, an ethnography would provide readers with insights into the program. Readers could ask questions such as: what qualities make it strong and what is unique about the writers who are trained within the program? At this point, readers could attempt to "transfer" applicable knowledge and observations to other writing environments.

Applications of Transferability and Generalizability: Experimental Research

Research Design A researcher working within this methodology creates an environment in which to observe and interpret the results of a research question. A key element in experimental research is that participants in a study are randomly assigned to groups. In an attempt to create a causal model (i.e., to discover the causal origin of a particular phenomenon), groups are treated differently and measurements are conducted to determine if different treatments appear to lead to different effects.

Assumptions Experimental research is usually thought to be generalizable. This methodology explores cause/effect relationships through comparisons among groups (Lauer & Asher 152). Since participants are randomly assigned to groups, and since most experiments involve enough individuals to reasonably approximate the populations from which individual participants are drawn, generalization is justified because "over a large number of allocations, all the groups of subjects will be expected to be identical on all variables" (155).

Example A simplified example: Six composition classrooms are randomly chosen (as are the students and instructors) in which three instructors incorporate the use of electronic mail as a class activity and three do not. When students in the first three classes begin discussing their papers through e-mail and, as a result, make better revisions to their papers than students in the other three classes, a researcher is likely to conclude that incorporating e-mail within a writing classroom improves the quality of students' writing.

Results of a Study Although experimental research is based on cause/effect relationships, "certainty" can never be obtained, but rather results are "probabilistic" (Lauer and Asher 161). Depending on how the researcher has presented the results, they are generalizable in that the students were selected randomly. Since the quality of writing improved with the use of e-mail within all three classrooms, it is probable that e-mail is the cause of the improvement. Readers of this study would transfer the results when they sorted out the details: Are these students representative of a group of students with which the reader is familiar? What types of previous writing experiences have these students had? What kind of writing was expected from these students? The researcher must have provided these details in order for the results to be transferable.

Applications of Transferability and Generalizability: Survey

Research Design The goal of a survey is to gain specific information about either a specific group or a representative sample of a particular group. Survey respondents are asked to respond to one or more of the following kinds of items: open-ended questions, true-false questions, agree-disagree (or Likert) questions, rankings, ratings, and so on. Results are typically used to understand the attitudes, beliefs, or knowledge of a particular group.

Assumptions Assuming that care has been taken in the development of the survey items and selection of the survey sample and that adequate response rates have been achieved, surveys results are generalizable. Note, however, that results from surveys should be generalized only to the population from which the survey results were drawn.

Example For instance, a survey of Colorado State University English graduate students undertaken to determine how well French philosopher/critic Jacques Derrida is understood before and after students take a course in critical literary theory might inform professors that, overall, Derrida's concepts are understood and that CSU's literary theory class, E615, has helped students grasp Derrida's ideas.

Results of a Study The generalizability of surveys depends on several factors. Whether distributed to a mass of people or a select few, surveys are of a "personal nature and subject to distortion." Survey respondents may or may not understand the questions being asked of them. Depending on whether or not the survey designer is nearby, respondents may or may not have the opportunity to clarify their misunderstandings.

It is also important to keep in mind that errors can occur at the development and processing levels. A researcher may inadequately pose questions (that is, not ask the right questions for the information being sought), disrupt the data collection (surveying certain people and not others), and distort the results during the processing (misreading responses and not being able to question the participant, etc.). One way to avoid these kinds of errors is for researchers to examine other studies of a similar nature and compare their results with results that have been obtained in previous studies. This way, any large discrepancies will be exposed. Depending on how large those discrepancies are and what the context of the survey is, the results may or may not be generalizable. For example, if an improved understanding of Derrida is apparent after students complete E615, it can be theorized that E615 effectively teaches students the concepts of Derrida. Issues of transferability might be visible in the actual survey questions themselves; that is, they could provide critical background information readers might need to know in order to transfer the results to another context.

The Qualitative versus Quantitative Debate

In Miles and Huberman's 1994 book Qualitative Data Analysis , quantitative researcher Fred Kerlinger is quoted as saying, "There's no such thing as qualitative data. Everything is either 1 or 0" (p. 40). To this another researcher, D. T. Campbell, asserts "all research ultimately has a qualitative grounding" (p. 40). This back and forth banter among qualitative and quantitative researchers is "essentially unproductive" according to Miles and Huberman. They and many other researchers agree that these two research methods need each other more often than not. However, because typically qualitative data involves words and quantitative data involves numbers, there are some researchers who feel that one is better (or more scientific) than the other. Another major difference between the two is that qualitative research is inductive and quantitative research is deductive. In qualitative research, a hypothesis is not needed to begin research. However, all quantitative research requires a hypothesis before research can begin.

Another major difference between qualitative and quantitative research is the underlying assumptions about the role of the researcher. In quantitative research, the researcher is ideally an objective observer that neither participates in nor influences what is being studied. In qualitative research, however, it is thought that the researcher can learn the most about a situation by participating and/or being immersed in it. These basic underlying assumptions of both methodologies guide and sequence the types of data collection methods employed.

Although there are clear differences between qualitative and quantitative approaches, some researchers maintain that the choice between using qualitative or quantitative approaches actually has less to do with methodologies than it does with positioning oneself within a particular discipline or research tradition. The difficulty of choosing a method is compounded by the fact that research is often affiliated with universities and other institutions. The findings of research projects often guide important decisions about specific practices and policies. The choice of which approach to use may reflect the interests of those conducting or benefitting from the research and the purposes for which the findings will be applied. Decisions about which kind of research method to use may also be based on the researcher's own experience and preference, the population being researched, the proposed audience for findings, time, money, and other resources available (Hathaway, 1995).

Some researchers believe that qualitative and quantitative methodologies cannot be combined because the assumptions underlying each tradition are so vastly different. Other researchers think they can be used in combination only by alternating between methods: qualitative research is appropriate to answer certain kinds of questions in certain conditions and quantitative is right for others. And some researchers think that both qualitative and quantitative methods can be used simultaneously to answer a research question.

To a certain extent, researchers on all sides of the debate are correct: each approach has its drawbacks. Quantitative research often "forces" responses or people into categories that might not "fit" in order to make meaning. Qualitative research, on the other hand, sometimes focuses too closely on individual results and fails to make connections to larger situations or possible causes of the results. Rather than discounting either approach for its drawbacks, though, researchers should find the most effective ways to incorporate elements of both to ensure that their studies are as accurate and thorough as possible.

It is important for researchers to realize that qualitative and quantitative methods can be used in conjunction with each other. In a study of computer-assisted writing classrooms, Snyder (1995) employed both qualitative and quantitative approaches. The study was constructed according to guidelines for quantitative studies: the computer classroom was the "treatment" group and the traditional pen and paper classroom was the "control" group. Both classes contained subjects with the same characteristics from the population sampled. Both classes followed the same lesson plan and were taught by the same teacher in the same semester. The only variable used was the computers. Although Snyder set this study up as an "experiment," she used many qualitative approaches to supplement her findings. She observed both classrooms on a regular basis as a participant-observer and conducted several interviews with the teacher both during and after the semester. However, there were several problems in using this approach: the strict adherence to the same syllabus and lesson plans for both classes and the restricted access of the control group to the computers may have put some students at a disadvantage. Snyder also notes that in retrospect she should have used case studies of the students to further develop her findings. Although her study had certain flaws, Snyder insists that researchers can simultaneously employ qualitative and quantitative methods if studies are planned carefully and carried out conscientiously.

Annotated Bibliography

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A comprehensive review of social scientific research, including techniques for research. The logic behind social scientific research is discussed.

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Describes a case study of a beginning student in a Ph.D. program. Looks at the process of his entry into an academic discourse community.

Black, Susan. (1996). Redefining the teacher's role. Executive Educator,18 (8), 23-26.

Discusses the value of well-trained teacher-researchers performing research in their classrooms. Notes that teacher-research focuses on the particular; it does not look for broad, generalizable principles.

Blank, Steven C. (1984). Practical business research methods . Westport: AVI Publishing Company, Inc.

A comprehensive book of how to set up a research project, collect data, and reach and report conclusions.

Bridges, David. (1993). Transferable Skills: A Philosophical Perspective. Studies in Higher Education 18 (1), 43-51.

Transferability of skills in learning is discussed, focusing on the notions of cross-disciplinary, generic, core, and transferable skills and their role in the college curriculum.

Brookhart, Susan M. & Rusnak, Timothy G. (1993). A pedagogy of enrichment, not poverty: Successful lessons of exemplary urban teachers. Journal of Teacher Education, 44 (1), 17-27.

Reports the results of a study that explored the characteristics of effective urban teachers in Pittsburgh. Suggests that the results may be transferable to urban educators in other contexts.

Bryman, Alan. (1988). Quantity and quality in social research . Boston: Unwin Hyman Ltd.

Butcher, Jude. (1994, July). Cohort and case study components in teacher education research. Paper presented at the annual conference of the Australian Teacher Education Association, Brisbane, Queensland, Australia.

Argues that studies of teacher development will be more generalizable if a broad set of methods are used to collect data, if the data collected is both extensive and intensive, and if the methods used take into account the differences in people and situations being studied.

Carter, Duncan. (1993). Critical thinking for writers: Transferable skills or discipline-specific strategies? Composition Studies/Freshman English News, 21 (1), 86-93.

Questions the context-dependency of critical thinking, and whether critical thinking skills are transferable to writing tasks.

Carter, Kathy. (1993). The place of story in the study of teaching and teacher education. Educational Researcher, 22 (1), 5-12.

Discusses the advantages of story-telling in teaching and teacher education, but cautions instructors, who are currently unfamiliar with story-telling in current pedagogical structures, to be careful in implementing this method in their teaching.

Clonts, Jean G. (1992, January). The concept of reliability as it pertains to data from qualitative studies. Paper presented at the annual meeting of the Southwest Educational Research Association, Houston, TX.

Presents a review of literature on reliability in qualitative studies and defines reliability as the extent to which studies can be replicated by using the same methods and getting the same results. Strategies to enhance reliability through study design, data collection, and data analysis are suggested. Generalizability as an estimate of reliability is also explored.

Connelly, Michael F. & Clandinin D. Jean. (1990). Stories of experience and narrative inquiry. Educational Researcher, 19. (5), 2-14.

Describes narrative as a site of inquiry and a qualitative research methodology in which experiences of observer and observed interact. This form of research necessitates the development of new criteria, which may include apparency, verisimilitude, and transferability (7).

Crocker, Linda & Algina, James. (1986). Introduction to classical & modern test theory. New York: Holt, Rinehart and Winston.

Discusses test theory and its application to psychometrics. Chapters range from general overview of major issues to statistical methods and application.

Cronbach, Lee J. et al. (1967). The dependability of behavioral measurements: multifaceted studies of generalizability. Stanford: Stanford UP.

A technical research report that includes statistical methodology in order to contrast multifaceted generalizability with classical reliability.

Cziko, Gary A. (1992). Purposeful behavior as the control of perception: implications for educational research. Educational Researcher, 21 (9), 10-18. El-Hassan, Karma. (1995). Students' Rating of Instruction: Generalizability of Findings. Studies in Educational Research 21 (4), 411-29.

Issues of dimensionality, validity, reliability, and generalizability of students' ratings of instruction are discussed in relation to a study in which 610 college students who evaluated their instructors on the Teacher Effectiveness Scale.

Feingold, Alan. (1994). Gender differences in variability in intellectual abilities: a cross-cultural perspective. Sex Roles: A Journal of Research 20 (1-2), 81-93.

Feingold conducts a cross-cultural quantitative review of contemporary findings of gender differences in variability in verbal, mathematical, and spatial abilities to assess the generalizability of U.S. findings that males are more variable than females in mathematical and spatial abilities, and the sexes are equally variable in verbal ability.

Firestone,William A. (1993). Alternative arguments for generalizing from data as applied to qualitative research. Educational Researcher, 22 (4), 16-22.

Focuses on generalization in three areas of qualitative research: sample to population extrapolation, analytic generalization, and case-to-case transfer (16). Explains underlying principles, related theories, and criteria for each approach.

Fyans, Leslie J. (Ed.). (1983). Generalizability theory: Inferences and practical applications. In New Directions for Testing and Measurement: Vol. 18. San Francisco: Jossey-Bass.

A collection of articles on generalizability theory. The goal of the book is to present different aspects and applications of generalizability theory in a way that allows the reader to apply the theory.

Hammersley, Martyn. (Ed.). (1993). Social research: Philosophy, politics and practice. Newbury Park, CA: Sage Publications.

A collection of articles that provide an overview of positivism; includes an article on increasing the generalizability of qualitative research by Janet Ward Schofield.

Hathaway, R. (1995). Assumptions underlying quantitative and qualitative research: Implications for institutional research. Research in higher education, 36 (5), 535-562.

Hathaway says that the choice between using qualitative or quantitative approaches is less about methodology and more about aligning oneself with particular theoretical and academic traditions. He concluded that the two approaches address questions in very different ways, each one having its own advantages and drawbacks.

Heck, Ronald H., Marcoulides, George A. (1996). . Research in the Teaching of English 22 (1), 9-44.

Hipps, Jerome A. (1993). Trustworthiness and authenticity: Alternate ways to judge authentic assessments. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA.

Contrasts the foundational assumptions of the constructivist approach to traditional research and the positivist approach to authentic assessment in relation to generalizability and other research issues.

Howe, Kenneth & Eisenhart, Margaret. (1990). Standards for qualitative (and quantitative) research: A prolegomenon. Educational Researcher, 19 (4), 2-9.

Huang, Chi-yu, et al. (1995, April). A generalizability theory approach to examining teaching evaluation instruments completed by students. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

Presents the results of a study that used generalizability theory to investigate the reasons for variability in a teacher and course evaluation mechanism.

Hungerford, Harold R. et al. (1992). Investigating and Evaluating Environmental Issues and Actions: Skill Development Modules .

A guide designed to teach students how to investigate and evaluate environmental issues and actions. The guide is presented in six modules including information collecting and surveys, questionnaires, and opinionnaires.

Jackson, Philip W. (1990). The functions of educational research. Educational Researcher 19 (7), 3-9. Johnson, Randell G. (1993, April). A validity generalization study of the multiple assessment and program services test. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA.

Presents results of study of validity reports of the Multiple Assessment and Program Services Test using quantitative analysis to determine the generalizability of the results.

Jones, Elizabeth A & Ratcliff, Gary. (1993). Critical thinking skills for college students. (National Center on Postsecondary Teaching, Learning, and Asessment). University Park, PA.

Reviews research literature exploring the nature of critical thinking; discusses the extent to which critical thinking is generalizable across disciplines.

Karpinski, Jakub. (1990). Causality in Sociological Research . Boston: Kluwer Academic Publishers.

Discusses causality and causal analysis in terms of sociological research. Provides equations and explanations.

Kirsch, Irwin S. & Jungeblut, Ann. (1995). Using large-scale assessment results to identify and evaluate generalizable indicators of literacy. (National Center on Adult Literacy, Publication No. TR94-19). Philadelphia, PA.

Reports analysis of data collected during an extensive literacy survey in order to help understand the different variables involved in literacy proficiency. Finds that literacy skills can be predicted across large, heterogeneous populations, but not as effectively across homogeneous populations.

Lauer, Janice M. & Asher, J. William. (1988). Composition research: empirical designs. New York: Oxford Press.

Explains the selection of subjects, formulation of hypotheses or questions, data collection, data analysis, and variable identification through discussion of each design.

LeCompte, Margaret & Goetz, Judith Preissle. (1982). Problems of reliability and validity in ethnographic research. Review of Educational Research, 52 (1), 31-60.

Concentrates on educational research and ethnography and shows how to better take reliability and validity into account when doing ethnographic research.

Marcoulides, George; Simkin, Mark G. (1991). Evaluating student papers: the case for peer review. Journal of Education for Business 67 (2), 80-83.

A preprinted evaluation form and generalizability theory are used to judge the reliability of student grading of their papers.

Maxwell, Joseph A. (1992). Understanding and validity in qualitative research. Harvard Educational Review, 62 (3), 279-300.

Explores the five types of validity used in qualitative research, including generalizable validity, and examines possible threats to research validity.

McCarthy, Christine L. (1996, Spring). What is "critical thinking"? Is it generalizable? Educational Theory, 46 217-239.

Reviews, compares and contrasts a selection of essays from Stephen P. Norris' book The Generalizability of Critical Thinking: Multiple Perspectives on an Education Ideal in order to explore the diversity of the topic of critical thinking.

Miles, Matthew B. & Huberman, A. Michael. (1994). Qualitative data analysis. Thousand Oaks: Sage Publications.

A comprehensive review of data analysis. Subjects range from collecting data to producing an actual report.

Minium, Edward W. & King, M. Bruce, & Bear, Gordon. (1993). Statistical reasoning in psychology and education . New York: John Wiley & Sons, Inc.

A textbook designed to teach students about statistical data and theory.

Moss, Pamela A. (1992). Shifting conceptions of validity in educational measurement: Implications for performance assessment. Review of Educational Research, 62 (3), 229-258. Nachmias, David & Nachmias, Chava . (1981). Research methods in the social sciences. New York: St. Martin's Press.

Discusses the foundations of empirical research, data collection, data processing and analysis, inferential methods, and the ethics of social science research.

Nagy, Philip; Jarchow, Elaine McNally. (1981). Estimating variance components of essay ratings in a complex design. Speech/Conference Paper .

This paper discusses variables influencing written composition quality and how they can be best controlled to improve the reliability assessment of writing ability.

Nagy, William E., Herman, Patricia A., & Anderson, Richard C. (1985). Learning word meanings from context: How broadly generalizable? (University of Illinois at Urbana-Champaign. Center for the Study of Reading, Technical Report No. 347). Cambridge, MA: Bolt, Beranek and Newman.

Reports the results of a study that investigated how students learn word meanings while reading from context. Claims that the study was designed to be generalized.

Naizer, Gilbert. (1992, January). Basic concepts in generalizability theory: A more powerful approach to evaluating reliability. Presented at the annual meeting of the Southwest Educational Research Association, Houston, TX.

Discusses how a measurement approach called generalizability theory (G-theory) is an important alternative to the more classical measurement theory that yields less useful coefficients. G-theory is about the dependability of behavioral measurements that allows the simultaneous estimation of multiple sources of error variance.

Newman, Isadore & Macdonald, Suzanne. (1993, May). Interpreting qualitative data: A methodological inquiry. Paper presented at the annual meeting of the Ohio Academy of Science, Youngstown, OH.

Issues of consistency, triangulation, and generalizability are discussed in relation to a qualitative study involving graduate student participants. The authors refute Polkinghorne's views of the generalizability of qualitative research, arguing that quantitative research is more suitable for generalizability.

Norris, Stephen P. (Ed.). (1992). The generalizability of critical thinking: multiple perspectives on an education ideal. New York: Teachers College Press. A set of essays from a variety of disciplines presenting different perspectives on the topic of the generalizability of critical thinking. The authors refer and respond to each other. Peshkin, Alan. (1993). The goodness of qualitative research. Educational Researcher, 22 (2), 23-29.

Discusses how effective qualitative research can be in obtaining desired results and concludes that it is an important tool scholars can use in their explorations. The four categories of qualitative research--description, interpretation, verification, and evaluation--are examined.

Rafilson, Fred. (1991, July). The case for validity generalization.

Describes generalization as a quantitative process. Briefly discusses theory, method, examples, and applications of validity generalization, emphasizing unseen local methodological problems.

Rhodebeck, Laurie A. The structure of men's and women's feminist orientations: feminist identity and feminist opinion. Gender & Society 10 (4), 386-404.

This study considers two problems: the extent to which feminist opinions are distinct from feminist identity and the generalizability of these separate constructs across gender and time.

Runkel, Philip J. & McGrath, E. Joseph. (1972). Research on human behavior: A systematic guide to method. New York: Holt, Rinehart and Winston, Inc.

Discusses how researchers can utilize their experiences of human behavior and apply them to research in a systematic and explicit fashion.

Salomon, Gavriel. (1991). Transcending the qualitative-quantitative debate: The analytic and systemic approaches to educational research. Educational Researcher, 20 (6), 10-18.

Examines the complex issues/variables involved in studies. Two types of approaches are explored: an Analytic Approach, which assumes internal and external issues, and a Systematic Approach, in which each component affects the whole. Also discusses how a study can never fully measure how much x affects y because there are so many inter-relations. Knowledge is applied differently within each approach.

Schrag, Francis. (1992). In defense of positivist research paradigms. Educational Researcher, 21 (5), 5-8.

Positivist critics Elliot Eisner, Fredrick Erikson, Henry Giroux, and Thomas Popkewitz are logically committed to propositions that can be tested only by means of positivist research paradigms. A definition of positivism is gathered through example. Overall, it is concluded that educational research need not aspire to be practical.

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Discusses managerial approaches to conducting research in organizations. Provides understandable definitions and explanations of such methods as sampling and data analysis and interpretation.

Shadish, William R. (1995). The logic of generalization: five principles common to experiments and ethnographies. American Journal of Community Psychology 23 (3), 419-29.

Both experiments and ethnographies are highly localized, so they are often criticized for lack of generalizability. This article describes a logic of generalization that may help solve such problems.

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Snyder, I. (1995). Multiple perspectives in literacy research: Integrating the quantitative and qualitative. Language and Education, 9 (1), 45-59.

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A small section on the application of generalizability in regards to case studies.

Citation Information

Jeffrey Barnes, Kerri Conrad, Christof Demont-Heinrich, Mary Graziano, Dawn Kowalski, Jamie Neufeld, Jen Zamora, and Mike Palmquist. (1994-2024). Generalizability and Transferability. The WAC Clearinghouse. Colorado State University. Available at https://wac.colostate.edu/repository/writing/guides/.

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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.

Sampling Techniques for Quantitative Research

• First Online: 27 October 2022

Cite this chapter

• Moniruzzaman Sarker   ORCID: orcid.org/0000-0003-3595-5838 4 &
• Mohammed Abdulmalek AL-Muaalemi 5  

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In quantitative research, collecting data from an entire population of a study is impractical in many instances. It squanders resources like time and money which can be minimized by choosing suitable sampling techniques between probability and non-probability methods. The chapter outlines a brief idea about the different categories of sampling techniques with examples. Sensibly selecting among the sampling techniques allows the researcher to generalize the findings to a specific study context. Although probability sampling is more appealing to draw a representative sample, non-probability sampling techniques also enable the researcher to generalize the findings upon implementing the sampling strategy wisely. Moreover, adopting probability sampling techniques is not feasible in many situations. The chapter suggests selecting sampling techniques should be guided by research objectives, study scope, and availability of sampling frame rather than looking at the nature of sampling techniques.

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Assistant Professor of Marketing, Southamton Malysia Business School, University of Southamton Malysia, Johor Bahru, Malaysia

Moniruzzaman Sarker

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Moniruzzaman Sarker, AL-Muaalemi, M.A. (2022). Sampling Techniques for Quantitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_15

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