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9.2 Quantitative research questions

Learning objectives.

Learners will be able to…

• Describe how research questions for exploratory, descriptive, and explanatory quantitative questions differ and how to phrase them
• Identify the differences between and provide examples of strong and weak explanatory research questions

Quantitative descriptive questions

The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, “What is the average student debt load of MSW students?” is a descriptive question—and an important one. We aren’t trying to build a causal relationship here. We’re simply trying to describe how much debt MSW students carry. Quantitative descriptive questions like this one are helpful in social work practice as part of community scans, in which human service agencies survey the various needs of the community they serve. If the scan reveals that the community requires more services related to housing, child care, or day treatment for people with disabilities, a nonprofit office can use the community scan to create new programs that meet a defined community need.

Quantitative descriptive questions will often ask for percentage, count the number of instances of a phenomenon, or determine an average. Descriptive questions may only include one variable, such as ours about student debt load, or they may include multiple variables. Because these are descriptive questions, our purpose is not to investigate causal relationships between variables. To do that, we need to use a quantitative explanatory question.

Quantitative explanatory questions

Most studies you read in the academic literature will be quantitative and explanatory. Why is that? If you recall from Chapter 2, explanatory research tries to build nomothetic causal relationships. They are generalizable across space and time, so they are applicable to a wide audience. The editorial board of a journal wants to make sure their content will be useful to as many people as possible, so it’s not surprising that quantitative research dominates the academic literature.

Structurally, quantitative explanatory questions must contain an independent variable and dependent variable. Questions should ask about the relationship between these variables. The standard format I was taught in graduate school for an explanatory quantitative research question is: “What is the relationship between [independent variable] and [dependent variable] for [target population]?” You should play with the wording for your research question, revising that standard format to match what you really want to know about your topic.

Let’s take a look at a few more examples of possible research questions and consider the relative strengths and weaknesses of each. Table 9.1 does just that. While reading the table, keep in mind that I have only noted what I view to be the most relevant strengths and weaknesses of each question. Certainly each question may have additional strengths and weaknesses not noted in the table. Each of these questions is drawn from student projects in my research methods classes and reflects the work of many students on their research question over many weeks.

Making it more specific

A good research question should also be specific and clear about the concepts it addresses. A student investigating gender and household tasks knows what they mean by “household tasks.” You likely also have an impression of what “household tasks” means. But are your definition and the student’s definition the same? A participant in their study may think that managing finances and performing home maintenance are household tasks, but the researcher may be interested in other tasks like childcare or cleaning. The only way to ensure your study stays focused and clear is to be specific about what you mean by a concept. The student in our example could pick a specific household task that was interesting to them or that the literature indicated was important—for example, childcare. Or, the student could have a broader view of household tasks, one that encompasses childcare, food preparation, financial management, home repair, and care for relatives. Any option is probably okay, as long as the researcher is clear on what they mean by “household tasks.” Clarifying these distinctions is important as we look ahead to specifying how your variables will be measured in Chapter 11.

Table 9.2 contains some “watch words” that indicate you may need to be more specific about the concepts in your research question.

It can be challenging to be this specific in social work research, particularly when you are just starting out your project and still reading the literature. If you’ve only read one or two articles on your topic, it can be hard to know what you are interested in studying. Broad questions like “What are the causes of chronic homelessness, and what can be done to prevent it?” are common at the beginning stages of a research project as working questions. However, moving from working questions to research questions in your research proposal requires that you examine the literature on the topic and refine your question over time to be more specific and clear. Perhaps you want to study the effect of a specific anti-homelessness program that you found in the literature. Maybe there is a particular model to fighting homelessness, like Housing First or transitional housing, that you want to investigate further. You may want to focus on a potential cause of homelessness such as LGBTQ+ discrimination that you find interesting or relevant to your practice. As you can see, the possibilities for making your question more specific are almost infinite.

Quantitative exploratory questions

In exploratory research, the researcher doesn’t quite know the lay of the land yet. If someone is proposing to conduct an exploratory quantitative project, the watch words highlighted in Table 9.2 are not problematic at all. In fact, questions such as “What factors influence the removal of children in child welfare cases?” are good because they will explore a variety of factors or causes. In this question, the independent variable is less clearly written, but the dependent variable, family preservation outcomes, is quite clearly written. The inverse can also be true. If we were to ask, “What outcomes are associated with family preservation services in child welfare?”, we would have a clear independent variable, family preservation services, but an unclear dependent variable, outcomes. Because we are only conducting exploratory research on a topic, we may not have an idea of what concepts may comprise our “outcomes” or “factors.” Only after interacting with our participants will we be able to understand which concepts are important.

Remember that exploratory research is appropriate only when the researcher does not know much about topic because there is very little scholarly research. In our examples above, there is extensive literature on the outcomes in family reunification programs and risk factors for child removal in child welfare. Make sure you’ve done a thorough literature review to ensure there is little relevant research to guide you towards a more explanatory question.

Key Takeaways

• Descriptive quantitative research questions are helpful for community scans but cannot investigate causal relationships between variables.
• Explanatory quantitative research questions must include an independent and dependent variable.
• Exploratory quantitative research questions should only be considered when there is very little previous research on your topic.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

• Identify the type of research you are engaged in (descriptive, explanatory, or exploratory).
• Create a quantitative research question for your project that matches with the type of research you are engaged in.

Preferably, you should be creating an explanatory research question for quantitative research.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Imagine you are studying health care disparities in communities of color and are interested in learning more about culturally appropriate interventions.

• Based on the research question you developed in the previous section, identify what type of research you would be conducting. Is this research project descriptive, explanatory, or exploratory?
• What factors justify your answer?

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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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.

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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3.7 Quantitative Rigour

The extent to which the researchers strive to improve the quality of their study is referred to as rigour. Rigour is accomplished in quantitative research by measuring validity and reliability. 55 These concepts affect the quality of findings and their applicability to broader populations.

Validity refers to the accuracy of a measure. It is the extent to which a study or test accurately measures what it sets out to measure. There are three main types of validity – content, construct and criterion validity.

• Content validity: Content validity examines whether the instrument adequately covers all aspects of the content that it should with respect to the variable under investigation. 56 This type of validity can be assessed through expert judgment and by examining the coverage of items or questions in measure. 56 Face validity is a subset of content validity in which experts are consulted to determine if a measurement tool accurately captures what it is supposed to measure. 56 There are multiple methods for testing content validity – content validity index (CVI) and content validity ratio (CVR). CVI is calculated as the number of experts giving a rating of “very relevant” for each item divided by the total number of experts. Values range from 0 to 1, with items having a CVI score > 0.79 relevant; between 0.70 and 0.79, the item needs revisions, and if the value is below 0.70, the item is eliminated. 57 CVR varies between 1 and −1; a higher score indicates greater agreement among panel members. CVR is calculated as  (Ne – N/2)/(N/2), where Ne is the number of panellists indicating an item as “essential” and N is the total number of panelists. 57 A study by Mousazadeh et al. 2017 investigated the content, face validity and reliability of sociocultural attitude towards appearance questionnaire-3 (SATAQ-3) among female adolescents. 58 To ensure face validity, the questionnaire was given to 25 female adolescents, a psychologist and three nurses, who were required to evaluate the items with respect to problems, ambiguity, relativity, proper terms and grammar, and understandability. For content validity, 15 experts in psychology and nursing were asked to assess the qualitative content validity. To determine the quantitative content validity, the content validity index and content validity ratio were calculated. 58
• Construct validity: A construct is an idea or theoretical concept based on empirical observations that are not directly measurable. An example of a construct could be physical functioning or social anxiety. Thus construct validity determines whether an instrument measures the underlying construct of interest and discriminates it from other related constructs. 55 It is important and expresses the confidence that a particular construct is valid. 55 This type of validity can be assessed using factor analysis or other statistical techniques. For example, Pinar, Rukiye 2005 , evaluated the reliability and construct validity of the SF-36 in Turkish cancer patients. 59 The SF-36 is widely used to measure the quality of life or health status in sick and healthy populations. Principal components factor analysis with varimax rotation confirmed the presence of the seven domains in the SF-36: in the SF-36: physical functioning, role limitations due to physical and emotional problems, mental health, general health perception, bodily pain, social functioning, and vitality. It was concluded that the Turkish version of the SF-36 was a suitable instrument that could be employed in cancer research in Turkey. 59
• Criterion validity: Criterion validity is the relationship between an instrument score and some external criterion. This criterion is considered the “gold standard” and has to be a widely accepted measure that shares the same characteristics as the assessment tool. 55 Determining the validity of a new diagnostic test requires two principal factors – sensitivity and specificity. 60   Sensitivity refers to the probability of detecting those with the disease, while specificity refers to the probability of the test correctly identifying those without the disease. 60 For example, the reverse transcriptase polymerase chain reaction (RT PCR) is the gold standard for testing COVID-19; its results are available at the earliest several hours to days after testing. Rapid antigen tests are diagnostic tools that can be used at the point of care, and the results can be obtained within 30 minutes). 61, 62 Therefore, the validity of these rapid antigen tests was determined against the gold standard. 61, 62 Two published articles that assessed the validity of the rapid antigen test reported sensitivity of 71.43% and 78.3% and specificity of 99.68% and 99.5%, respectively. 61, 62 Thus indicating that the tests were less effective in identifying those who have the disease but highly effective in identifying those who do not have the disease. While it is important to assess the accuracy of the instruments used, it is also imperative to determine if the measure and findings are reliable.

Reliability

Reliability refers to the consistency of a measure. It is the ability of a measure or tests to reproduce a consistent result over time and across different observers. 55 A reliable measurement tool produces consistent results, even when different observers administer the test or when the test is conducted on different occasions. 55, 5 6 Reliability can be assessed by examining test-retest reliability, inter-rater reliability, and internal consistency.

• Test-retest reliability: Test-retest reliability refers to the degree of consistency between the outcomes of the same test or measure taken by the same participants at varying times. It estimates the consistency of measurement repetition. The intraclass correlation coefficient (ICC) is often used to determine test-retest reliability. 56 For example, a study may be conducted to evaluate the reliability of a new tool for measuring pain and might administer the tool to a group of patients at two different time points and compare the results. If the results are consistent across the two-time points, this would indicate that the tool has good test-retest reliability. However, it is important to note that the reliability reduces when the time between administration of the test is extended or too long. An adequate time span between tests should range from 10 to 14 days. 56 The article by Pinar, Rukiye 2005 , demonstrated this by assessing a test–retest stability using intraclass correlation coefficient-ICC. The retest procedure was conducted two weeks after the first test as two weeks was considered to be the optimum re-test interval. 59 This would be sufficiently long for participants to forget their initial responses but not too long that most health domains would change. 59
• Inter-observer (between observers) reliability: is also known as i nter-rater reliability, and it is the level of agreement between two or more observers on the results of an instrument or test. It is the most popular method of determining if two things are equivalent. 55, 56 For example, a study may be conducted to evaluate the reliability of a new tool for measuring depression. This will involve two different raters or observers independently scoring the same patient on the tool and comparing the results. If the results are consistent across the two raters, this would indicate that the tool has excellent inter-rater reliability. The Kappa coefficient is a measure used to assess the agreement between the raters. 56 It can have a maximum value of 1.00; the higher the value, the greater the concordance between the raters. 56
• Internal consistency: Internal consistency refers to the extent to which different items or questions in a test or questionnaire are consistent with one another. It is also known as homogeneity, which indicates whether each component of an instrument measures the same characteristics. 55 This type of reliability can be assessed by calculating Cronbach’s alpha (α) coefficient, which measures the correlation between different items or questions. Cronbach α is expressed as a number between 0 and 1, and a reliability score of 0.7 or above is considered acceptable. 55 For example, Pinar, Rukiye 2005 reported that reliability evaluations of the SF-36 were based on the internal consistency test (Cronbach’s α coefficient). The results showed that Cronbach’s α coefficient for the eight subscales of the SF-36 ranged between 0.79 and 0.90, confirming the internal consistency of the subscales. 59

Now you have an understanding of the quantitative methodology. Use the Padlet below to write a research question that can be answered quantitatively.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Critically Thinking About Qualitative Versus Quantitative Research

What should we do regarding our research questions and methodology.

Posted January 26, 2022 | Reviewed by Davia Sills

• Neither a quantitative nor a qualitative methodology is the right way to approach every scientific question.
• Rather, the nature of the question determines which methodology is best suited to address it.
• Often, researchers benefit from a mixed approach that incorporates both quantitative and qualitative methodologies.

As a researcher who has used a wide variety of methodologies, I understand the importance of acknowledging that we, as researchers, do not pick the methodology; rather, the research question dictates it. So, you can only imagine how annoyed I get when I hear of undergraduates designing their research projects based on preconceived notions, like "quantitative is more straightforward," or "qualitative is easier." Apart from the fact that neither of these assertions is actually the case, these young researchers are blatantly missing one of the foundational steps of good research: If you are interested in researching a particular area, you must get to know the area (i.e., through reading) and then develop a question based on that reading.

The nature of the question will dictate the most appropriate methodological approach.

I’ve debated with researchers in the past who are "exclusively" qualitative or "exclusively" quantitative. Depending on the rationale for their exclusivity, I might question a little deeper, learn something, and move on, or I might debate further. Sometimes, I throw some contentious statements out to see what the responses are like. For example, "Qualitative research, in isolation, is nothing but glorified journalism . " This one might not be new to you. Yes, qualitative is flawed, but so, too, is quantitative.

Let's try this one: "Numbers don’t lie, just the researchers who interpret them." If researchers are going to have a pop at qual for subjectivity, why don’t they recognize the same issues in quant? The numbers in a results section may be objectively correct, but their meaningfulness is only made clear through the interpretation of the human reporting them. This is not a criticism but is an important observation for those who believe in the absolute objectivity of quantitative reporting. The subjectivity associated with this interpretation may miss something crucial in the interpretation of the numbers because, hey, we’re only human.

With that, I love quantitative research, but I’m not unreasonable about it. Let’s say we’ve evaluated a three-arm RCT—the new therapeutic intervention is significantly efficacious, with a large effect, for enhancing "x" in people living with "y." One might conclude that this intervention works and that we must conduct further research on it to further support its efficacy—this is, of course, a fine suggestion, consistent with good research practice and epistemological understanding.

However, blindly recommending the intervention based on the interpretation of numbers alone might be suspect—think of all the variables that could be involved in a 4-, 8-, 12-, or 52-week intervention with human participants. It would be foolish to believe that all variables were considered—so, here is a fantastic example of where a qualitative methodology might be useful. At the end of the intervention, a researcher might decide to interview a random 20 percent of the cohort who participated in the intervention group about their experience and the program’s strengths and weaknesses. The findings from this qualitative element might help further explain the effects, aid the initial interpretation, and bring to life new ideas and concepts that had been missing from the initial interpretation. In this respect, infusing a qualitative approach at the end of quantitative analysis has shown its benefits—a mixed approach to intervention evaluation is very useful.

What about before that? Well, let’s say I want to develop another intervention to enhance "z," but there’s little research on it, and that which has been conducted isn’t of the highest quality; furthermore, we don’t know about people’s experiences with "z" or even other variables associated with it.

To design an intervention around "z" would be ‘jumping the gun’ at best (and a waste of funds). It seems that an exploration of some sort is necessary. This is where qualitative again shines—giving us an opportunity to explore what "z" is from the perspective of a relevant cohort(s).

Of course, we cannot generalize the findings; we cannot draw a definitive conclusion as to what "z" is. But what the findings facilitate is providing a foundation from which to work; for example, we still cannot say that "z" is this, that, or the other, but it appears that it might be associated with "a," "b" and "c." Thus, future research should investigate the nature of "z" as a particular concept, in relation to "a," "b" and "c." Again, a qualitative methodology shows its worth. In the previous examples, a qualitative method was used because the research questions warranted it.

Through considering the potentially controversial statements about qual and quant above, we are pushed into examining the strengths and weaknesses of research methodologies (regardless of our exclusivity with a particular approach). This is useful if we’re going to think critically about finding answers to our research questions. But simply considering these does not let poor research practice off the hook.

For example, credible qualitative researchers acknowledge that generalizability is not the point of their research; however, that doesn’t stop some less-than-credible researchers from presenting their "findings" as generalizable as possible, without actually using the word. Such practices should be frowned upon—so should making a career out of strictly using qualitative methodology in an attempt to find answers core to the human condition. All these researchers are really doing is spending a career exploring, yet never really finding anything (despite arguing to the contrary, albeit avoiding the word "generalize").

The solution to this problem, again, is to truly listen to what your research question is telling you. Eventually, it’s going to recommend a quantitative approach. Likewise, a "numbers person" will be recommended a qualitative approach from time to time—flip around the example above, and there’s a similar criticism. Again, embrace a mixed approach.

What's the point of this argument?

I conduct both research methodologies. Which do I prefer? Simple—whichever one helps me most appropriately answer my research question.

Do I have problems with qualitative methodologies? Absolutely—but I have issues with quantitative methods as well. Having these issues is good—it means that you recognize the limitations of your tools, which increases the chances of you "fixing," "sharpening" or "changing out" your tools when necessary.

So, the next time someone speaks with you about labeling researchers as one type or another, ask them why they think that way, ask them which they think you are, and then reflect on the responses alongside your own views of methodology and epistemology. It might just help you become a better researcher.

Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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Module 3 Chapter 4: Overview of Quantitative Study Variables

The first thing we need to understand is the nature of variables and how variables are used in a study’s design to answer the study questions. In this chapter you will learn:

• different types of variables in quantitative studies,
• issues surrounding the unit of analysis question.

Understanding Quantitative Variables

The root of the word variable  is related to the word “vary,” which should help us understand what variables might be. Variables are elements, entities, or factors that can change (vary); for example, the outdoor temperature, the cost of gasoline per gallon, a person’s weight, and the mood of persons in your extended family are all variables. In other words, they can have different values under different conditions or for different people.

We use variables to describe features or factors of interest. Examples might include the number of members in different households, the distance to healthful food sources in different neighborhoods, the ratio of social work faculty to students in a BSW or MSW program, the proportion of persons from different racial/ethnic groups incarcerated, the cost of transportation to receive services from a social work program, or the rate of infant mortality in different counties. In social work intervention research, variables might include characteristics of the intervention (intensity, frequency, duration) and outcomes associated with the intervention.

Demographic Variables . Social workers are often interested in what we call demographic variables .  Demographic variables are used to describe characteristics of a population, group, or sample of the population. Examples of frequently applied demographic variables are

• national origin,
• religious affiliation,
• sexual orientation,
• marital/relationship status,
• employment status,
• political affiliation,
• geographical location,
• education level, and

At a more macro level, the demographics of a community or organization often includes its size; organizations are often measured in terms of their overall budget.

Independent and Dependent Variables . A way that investigators think about study variables has important implications for a study design. Investigators make decisions about having them serve as either  independent variables  or as dependent variables . This distinction is not something inherent to a variable, it is based on how the investigator chooses to define each variable. Independent variables are the ones you might think of as the manipulated “input” variables, while the dependent variables are the ones where the impact or “output” of that input variation would be observed.

Intentional manipulation of the “input” (independent) variable is not always involved. Consider the example of a study conducted in Sweden examining the relationship between having been the victim of child maltreatment and later absenteeism from high school: no one intentionally manipulated whether the children would be victims of child maltreatment (Hagborg, Berglund, & Fahlke, 2017). The investigators hypothesized that naturally occurring differences in the input variable (child maltreatment history) would be associated with systematic variation in a specific outcome variable (school absenteeism). In this case, the independent variable was a history of being the victim of child maltreatment, and the dependent variable was the school absenteeism outcome. In other words, the independent variable is hypothesized by the investigator to cause variation or change in the dependent variable. This is what it might look like in a diagram where “ x ” is the independent variable and “ y ” is the dependent variable (note: you saw this designation earlier, in Chapter 3, when we discussed cause and effect logic):

For another example, consider research indicating that being the victim of child maltreatment is associated with a higher risk of substance use during adolescence (Yoon, Kobulsky, Yoon, & Kim, 2017). The independent variable in this model would be having a history of child maltreatment. The dependent variable would be risk of substance use during adolescence. This example is even more elaborate because it specifies the pathway by which the independent variable (child maltreatment) might impose its effects on the dependent variable (adolescent substance use). The authors of the study demonstrated that post-traumatic stress (PTS) was a link between childhood abuse (physical and sexual) and substance use during adolescence.

Take a moment to complete the following activity.

Types of Quantitative Variables

There are other meaningful ways to think about variables of interest, as well. Let’s consider different features of variables used in quantitative research studies. Here we explore quantitative variables as being categorical, ordinal, or interval in nature. These features have implications for both measurement and data analysis.

Categorical Variables. Some variables can take on values that vary, but not in a meaningful numerical way. Instead, they might be defined in terms of the categories which are possible. Logically, these are called categorical variables . Statistical software and textbooks sometimes refer to variables with categories as nominal variables . Nominal can be thought of in terms of the Latin root “nom” which means “name,” and should not be confused with number. Nominal means the same thing as categorical in describing variables. In other words, categorical or nominal variables are identified by the names or labels of the represented categories. For example, the color of the last car you rode in would be a categorical variable: blue, black, silver, white, red, green, yellow, or other are categories of the variable we might call car color.

What is important with categorical variables is that these categories have no relevant numeric sequence or order. There is no numeric difference between the different car colors, or difference between “yes” or “no” as the categories in answering if you rode in a blue car. There is no implied order or hierarchy to the categories “Hispanic or Latino” and “Not Hispanic or Latino” in an ethnicity variable; nor is there any relevant order to categories of variables like gender, the state or geographical region where a person resides, or whether a person’s residence is owned or rented.

If a researcher decided to use numbers as symbols related to categories in such a variable, the numbers are arbitrary—each number is essentially just a different, shorter name for each category. For example, the variable gender could be coded in the following ways, and it would make no difference, as long as the code was consistently applied.

Race and ethnicity. One of the most commonly explored categorical variables in social work and social science research is the demographic referring to a person’s racial and/or ethnic background. Many studies utilize the categories specified in past U.S. Census Bureau reports. Here is what the U.S. Census Bureau has to say about the two distinct demographic variables, race and ethnicity ( https://www.census.gov/mso/www/training/pdf/race-ethnicity-onepager.pdf ):

What is race? The Census Bureau defines race as a person’s self-identification with one or more social groups. An individual can report as White, Black or African American, Asian, American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, or some other race. Survey respondents may report multiple races. What is ethnicity? Ethnicity determines whether a person is of Hispanic origin or not. For this reason, ethnicity is broken out into two categories, Hispanic or Latino and Not Hispanic or Latino. Hispanics may report as any race.

In other words, the Census Bureau defines two categories for the variable called ethnicity (Hispanic or Latino and Not Hispanic or Latino), and seven categories for the variable called race. While these variables and categories are often applied in social science and social work research, they are not without criticism.

Based on these categories, here is what is estimated to be true of the U.S. population in 2016:

Dichotomous variables. There exists a special category of categorical variable with implications for certain statistical analyses. Categorical variables comprised of exactly two options, no more and no fewer are called dichotomous variables . One example was the U.S. Census Bureau dichotomy of Hispanic/Latino and Non-Hispanic/Non-Latino ethnicity. For another example, investigators might wish to compare people who complete treatment with those who drop out before completing treatment. With the two categories, completed or not completed, this treatment completion variable is not only categorical, it is dichotomous. Variables where individuals respond “yes” or “no” are also dichotomous in nature.

The past tradition of treating gender as either male or female is another example of a dichotomous variable. However, very strong arguments exist for no longer treating gender in this dichotomous manner: a greater variety of gender identities are demonstrably relevant in social work for persons whose identity does not align with the dichotomous (also called binary) categories of man/woman or male/female. These include categories such as agender, androgynous, bigender, cisgender, gender expansive, gender fluid, gender questioning, queer, transgender, and others.

Ordinal Variables.  Unlike these categorical variables, sometimes a variable’s categories do have a logical numerical sequence or order. Ordinal, by definition, refers to a position in a series. Variables with numerically relevant categories are called ordinal variables.  For example, there is an implied order of categories from least-to-most with the variable called educational attainment. The U.S. Census data categories for this ordinal variable are:

• 1st-4thgrade
• 5th-6thgrade
• 7th-8thgrade
• high school graduate
• some college, no degree
• associate’s degree, occupational
• associate’s degree academic
• bachelor’s degree
• master’s degree
• professional degree
• doctoral degree

In looking at the 2016 Census Bureau estimate data for this variable, we can see that females outnumbered males in the category of having attained a bachelor’s degree: of the 47,718,000 persons in this category, 22,485,000 were male and 25,234,000 were female. While this gendered pattern held for those receiving master’s degrees, the pattern was reversed for receiving doctoral degrees: more males than females obtained this highest level of education. It is also interesting to note that females outnumbered males at the low end of the spectrum: 441,000 females reported no education compared to 374,000 males.

Here is another example of using ordinal variables in social work research: when individuals seek treatment for a problem with alcohol misuse, social workers may wish to know if this is their first, second, third, or whatever numbered serious attempt to change their drinking behavior. Participants enrolled in a study comparing treatment approaches for alcohol use disorders reported that the intervention study was anywhere from their first to eleventh significant change attempt (Begun, Berger, Salm-Ward, 2011). This change attempt variable has implications for how social workers might interpret data evaluating an intervention that was not the first try for everyone involved.

Rating scales.  Consider a different but commonly used type of ordinal variable: rating scales. Social, behavioral, and social work investigators often ask study participants to apply a rating scale to describe their knowledge, attitudes, beliefs, opinions, skills, or behavior. Because the categories on such a scale are sequenced (most to least or least to most), we call these ordinal variables.

Examples include having participants rate:

• how much they agree or disagree with certain statements (not at all to extremely much);
• how often they engage in certain behaviors (never to always);
• how often they engage in certain behaviors (hourly, daily, weekly, monthly, annually, or less often);
• the quality of someone’s performance (poor to excellent);
• how satisfied they were with their treatment (very dissatisfied to very satisfied)
• their level of confidence (very low to very high).

Interval Variables.  Still other variables take on values that vary in a meaningful numerical fashion. From our list of demographic variables, age is a common example. The numeric value assigned to an individual person indicates the number of years since a person was born (in the case of infants, the numeric value may indicate days, weeks, or months since birth). Here the possible values for the variable are ordered, like the ordinal variables, but a big difference is introduced: the nature of the intervals between possible values. With interval variables  the “distance” between adjacent possible values are equal. Some statistical software packages and textbooks use the term  scale variable : this is exactly the same thing as what we call an interval variable.

For example, in the graph below, the 1 ounce difference between this person consuming 1 ounce or 2 ounces of alcohol (Monday, Tuesday) is exactly the same as the 1 ounce difference between consuming 4 ounces or 5 ounces (Friday, Saturday). If we were to diagram the possible points on the scale, they would all be equidistant; the interval between any two points is measured in standard units (ounces, in this example).

With ordinal variables, such as a rating scales, no one can say for certain that the “distance” between the response options of “never” and “sometimes” is the same as the “distance” between “sometimes” and “often,” even if we used numbers to sequence these response options. Thus, the rating scale remains ordinal, not interval.

What might become a tad confusing is that certain statistical software programs, like SPSS, refer to an interval variable as a “scale” variable. Many variables used in social work research are both ordered and have equal distances between points. Consider for example, the variable of birth order. This variable is interval because:

• the possible values are ordered (e.g., the third-born child came after the first- and second-born and before the fourth-born), and
• the “distances” or intervals are measured in equivalent one-person units.

Continuous variables.  There exists a special type of numeric interval variable that we call continuous variables.  A variable like age might be treated as a continuous variable. Age is ordinal in nature, since higher numbers mean something in relation to smaller numbers. Age also meets our criteria for being an interval variable if we measure it in years (or months or weeks or days) because it is ordinal and there is the same “distance” between being 15 and 30 years old as there is between being 40 and 55 years old (15 calendar years). What makes this a continuous variable is that there are also possible, meaningful “fraction” points between any two intervals. For example, a person can be 20½ (20.5) or 20¼ (20.25) or 20¾ (20.75) years old; we are not limited to just the whole numbers for age. By contrast, when we looked at birth order, we cannot have a meaningful fraction of a person between two positions on the scale.

The Special Case of Income . One of the most abused variables in social science and social work research is the variable related to income. Consider an example about household income (regardless of how many people are in the household). This variable could be categorical (nominal), ordinal, or interval (scale) depending on how it is handled.

Categorical Example:  Depending on the nature of the research questions, an investigator might simply choose to use the dichotomous categories of “sufficiently resourced” and “insufficiently resourced” for classifying households, based on some standard calculation method. These might be called “poor” and “not poor” if a poverty line threshold is used to categorize households. These distinct income variable categories are not meaningfully sequenced in a numerical fashion, so it is a categorical variable.

Ordinal Example:  Categories for classifying households might be ordered from low to high. For example, these categories for annual income are common in market research:

• Less than $25,000.
• $25,000 to $34,999.
• $35,000 to $49,999.
• $50,000 to $74,999.
• $75,000 to $99,999.
• $100,000 to $149,999.
• $150,000 to $199,999.
• $200,000 or more.

Notice that the categories are not equally sized—the “distance” between pairs of categories are not always the same. They start out in about $10,000 increments, move to $25,000 increments, and end up in about $50,000 increments.

Interval Example . If an investigator asked study participants to report an actual dollar amount for household income, we would see an interval variable. The possible values are ordered and the interval between any possible adjacent units is $1 (as long as dollar fractions or cents are not used). Thus, an income of $10,452 is the same distance on a continuum from $9,452 and $11,452—$1,000 either way.

The Special Case of Age .  Like income, “age” can mean different things in different studies. Age is usually an indicator of “time since birth.” We can calculate a person’s age by subtracting a date of birth variable from the date of measurement (today’s date minus date of birth). For adults, ages are typically measured in years where adjacent possible values are distanced in 1-year units: 18, 19, 20, 21, 22, and so forth. Thus, the age variable could be a continuous type of interval variable.

However, an investigator might wish to collapse age data into ordered categories or age groups. These still would be ordinal, but might no longer be interval if the increments between possible values are not equivalent units. For example, if we are more interested in age representing specific human development periods, the age intervals might not be equal in span between age criteria. Possibly they might be:

• Infancy (birth to 18 months)
• Toddlerhood (18 months to 2 ½ years)
• Preschool (2 ½ to 5 years)
• School age (6 to 11 years)
• Adolescence (12 to 17 years)
• Emerging Adulthood (18 to 25 years)
• Adulthood (26 to 45 years)
• Middle Adulthood (46 to 60 years)
• Young-Old Adulthood (60 to 74 years)
• Middle-Old Adulthood (75 to 84 years)
• Old-Old Adulthood (85 or more years)

Age might even be treated as a strictly categorical (non-ordinal) variable. For example, if the variable of interest is whether someone is of legal drinking age (21 years or older), or not. We have two categories—meets or does not meet legal drinking age criteria in the United States—and either one could be coded with a “1” and the other as either a “0” or “2” with no difference in meaning.

What is the “right” answer as to how to measure age (or income)? The answer is “it depends.” What it depends on is the nature of the research question: which conceptualization of age (or income) is most relevant for the study being designed.

Alphanumeric Variables.  Finally, there are data which do not fit into any of these classifications. Sometimes the information we know is in the form of an address or telephone number, a first or last name, zipcode, or other phrases. These kinds of information are sometimes called alphanumeric variables . Consider the variable “address” for example: a person’s address might be made up of numeric characters (the house number) and letter characters (spelling out the street, city, and state names), such as 1600 Pennsylvania Ave. NW, Washington, DC, 20500.

Actually, we have several variables present in this address example:

• the street address: 1600 Pennsylvania Ave.
• the city (and “state”): Washington, DC
• the zipcode: 20500.

This type of information does not represent specific quantitative categories or values with systematic meaning in the data. These are also sometimes called “string” variables in certain software packages because they are made up of a string of symbols. To be useful for an investigator, such a variable would have to be converted or recoded into meaningful values.

A Note about Unit of Analysis

An important thing to keep in mind in thinking about variables is that data may be collected at many different levels of observation. The elements studied might be individual cells, organ systems, or persons. Or, the level of observation might be pairs of individuals, such as couples, brothers and sisters, or parent-child dyads. In this case, the investigator may collect information about the pair from each individual, but is looking at each pair’s data. Thus, we would say that the unit of analysis  is the pair or dyad, not each individual person. The unit of analysis could be a larger group, too: for example, data could be collected from each of the students in entire classrooms where the unit of analysis is classrooms in a school or school system. Or, the unit of analysis might be at the level of neighborhoods, programs, organizations, counties, states, or even nations. For example, many of the variables used as indicators of food security at the level of communities, such as affordability and accessibility, are based on data collected from individual households (Kaiser, 2017). The unit of analysis in studies using these indicators would be the communities being compared. This distinction has important measurement and data analysis implications.

A Reminder about Variables versus Variable Levels

A study might be described in terms of the number of variable categories, or levels, that are being compared. For example, you might see a study described as a 2 X 2 design—pronounced as a two by two design. This means that there are 2 possible categories for the first variable and 2 possible categories for the other variable—they are both dichotomous variables. A study comparing 2 categories of the variable “alcohol use disorder” (categories for meets criteria, yes or no) with 2 categories of the variable “illicit substance use disorder” (categories for meets criteria, yes or no) would have 4 possible outcomes (mathematically, 2 x 2=4) and might be diagrammed like this (data based on proportions from the 2016 NSDUH survey, presented in SAMHSA, 2017):

Reading the 4 cells in this 2 X 2 table tells us that in this (hypothetical) survey of 540 individuals, 500 did not meet criteria for either an alcohol or illicit substance use disorder (No, No); 26 met criteria for an alcohol use disorder only (Yes, No); 10 met criteria for an illicit substance use disorder only (No, Yes), and 4 met criteria for both an alcohol and illicit substance use disorder (Yes, Yes). In addition, with a little math applied, we can see that a total of 30 had an alcohol use disorder (26 + 4) and 14 had an illicit substance use disorder (10 + 4). And, we can see that 40 had some sort of substance use disorder (26 + 10 + 4).

Thus, when you see a study design description that looks like two numbers being multiplied, that is essentially telling you how many categories or levels of each variable there are and leads you to understand how many cells or possible outcomes exist. A 3 X 3 design has 9 cells, a 3 X 4 design has 12 cells, and so forth. This issue becomes important once again when we discuss sample size in Chapter 6.

Interactive Excel Workbook Activities

Complete the following Workbook Activity:

• SWK 3401.3-4.1 Beginning Data Entry

Chapter Summary

In summary, investigators design many of their quantitative studies to test hypotheses about the relationships between variables. Understanding the nature of the variables involved helps in understanding and evaluating the research conducted. Understanding the distinctions between different types of variables, as well as between variables and categories, has important implications for study design, measurement, and samples. Among other topics, the next chapter explores the intersection between the nature of variables studied in quantitative research and how investigators set about measuring those variables.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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1.15: Chapter 15 Quantitative Analysis Inferential Statistics

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• Page ID 84791

• William Pelz
• Herkimer College via Lumen Learning

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Inferential statistics are the statistical procedures that are used to reach conclusions about associations between variables. They differ from descriptive statistics in that they are explicitly designed to test hypotheses. Numerous statistical procedures fall in this category, most of which are supported by modern statistical software such as SPSS and SAS. This chapter provides a short primer on only the most basic and frequent procedures; readers are advised to consult a formal text on statistics or take a course on statistics for more advanced procedures.

Basic Concepts

British philosopher Karl Popper said that theories can never be proven, only disproven. As an example, how can we prove that the sun will rise tomorrow? Popper said that just because the sun has risen every single day that we can remember does not necessarily mean that it will rise tomorrow, because inductively derived theories are only conjectures that may or may not be predictive of future phenomenon. Instead, he suggested that we may assume a theory that the sun will rise every day without necessarily proving it, and if the sun does not rise on a certain day, the theory is falsified and rejected. Likewise, we can only reject hypotheses based on contrary evidence but can never truly accept them because presence of evidence does not mean that we may not observe contrary evidence later. Because we cannot truly accept a hypothesis of interest (alternative hypothesis), we formulate a null hypothesis as the opposite of the alternative hypothesis, and then use empirical evidence to reject the null hypothesis to demonstrate indirect, probabilistic support for our alternative hypothesis.

A second problem with testing hypothesized relationships in social science research is that the dependent variable may be influenced by an infinite number of extraneous variables and it is not plausible to measure and control for all of these extraneous effects. Hence, even if two variables may seem to be related in an observed sample, they may not be truly related in the population, and therefore inferential statistics are never certain or deterministic, but always probabilistic.

How do we know whether a relationship between two variables in an observed sample is significant, and not a matter of chance? Sir Ronald A. Fisher, one of the most prominent statisticians in history, established the basic guidelines for significance testing. He said that a statistical result may be considered significant if it can be shown that the probability of it being rejected due to chance is 5% or less. In inferential statistics, this probability is called the p-value , 5% is called the significance level (α), and the desired relationship between the p-value and α is denoted as: p≤0.05. The significance level is the maximum level of risk that we are willing to accept as the price of our inference from the sample to the population. If the p-value is less than 0.05 or 5%, it means that we have a 5% chance of being incorrect in rejecting the null hypothesis or having a Type I error. If p>0.05, we do not have enough evidence to reject the null hypothesis or accept the alternative hypothesis.

We must also understand three related statistical concepts: sampling distribution, standard error, and confidence interval. A sampling distribution is the theoretical distribution of an infinite number of samples from the population of interest in your study. However, because a sample is never identical to the population, every sample always has some inherent level of error, called the standard error . If this standard error is small, then statistical estimates derived from the sample (such as sample mean) are reasonably good estimates of the population. The precision of our sample estimates is defined in terms of a confidence interval (CI). A 95% CI is defined as a range of plus or minus two standard deviations of the mean estimate, as derived from different samples in a sampling distribution. Hence, when we say that our observed sample estimate has a CI of 95%, what we mean is that we are confident that 95% of the time, the population parameter is within two standard deviations of our observed sample estimate. Jointly, the p-value and the CI give us a good idea of the probability of our result and how close it is from the corresponding population parameter.

General Linear Model

Most inferential statistical procedures in social science research are derived from a general family of statistical models called the general linear model (GLM). A model is an estimated mathematical equation that can be used to represent a set of data, and linear refers to a straight line. Hence, a GLM is a system of equations that can be used to represent linear patterns of relationships in observed data.

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18 10. Quantitative sampling

Chapter Outline

• The sampling process (25 minute read time)
• Sampling approaches for quantitative research (15 minute read time)
• Sample quality (24 minute read time)

Content warning: examples contain references to addiction to technology, domestic violence and batterer intervention, cancer, illegal drug use, LGBTQ+ discrimination, binge drinking, intimate partner violence among college students, child abuse, neocolonialism and Western hegemony.

10.1 The sampling process

Learning Objectives

Learners will be able to…

• Decide where to get your data and who you might need to talk to
• Evaluate whether it is feasible for you to collect first-hand data from your target population
• Describe the process of sampling
• Apply population, sampling frame, and other sampling terminology to sampling people your project’s target population

One of the things that surprised me most as a research methods professor is how much my students struggle with understanding sampling. It is surprising because people engage in sampling all the time. How do you learn whether you like a particular food, like BBQ ribs? You sample them from different restaurants! Obviously, social scientists put a bit more effort and thought into the process than that, but the underlying logic is the same. By sampling a small group of BBQ ribs from different restaurants and liking most of them, you can conclude that when you encounter BBQ ribs again, you will probably like them. You don’t need to eat all of the BBQ ribs in the world to come to that conclusion, just a small sample. [1] Part of the difficulty my students face is learning sampling terminology, which is the focus of this section.

Who is your study about and who should you talk to?

At this point in the research process, you know what your research question is. Our goal in this chapter is to help you understand how to find the people (or documents) you need to study in order to find the answer to your research question. It may be helpful at this point to distinguish between two concepts. Your unit of analysis is the entity that you wish to be able to say something about at the end of your study (probably what you’d consider to be the main focus of your study). Your unit of observation is the entity (or entities) that you actually observe, measure, or collect in the course of trying to learn something about your unit of analysis.

It is often the case that your unit of analysis and unit of observation are the same. For example, we may want to say something about social work students (unit of analysis), so we ask social work students at our university to complete a survey for our study (unit of observation). In this case, we are observing individuals , i.e. students, so we can make conclusions about individual s .

On the other hand, our unit of analysis and observation can differ. We could sample social work students to draw conclusions about organizations or universities. Perhaps we are comparing students at historically black colleges and universities (HBCUs) and primarily white institutions (PWIs). Even though our sample was made up of individual students from various colleges (our unit of observation), our unit of analysis was the university as an organization. Conclusions we made from individual-level data were used to understand larger organizations.

Similarly, we could adjust our sampling approach to target specific student cohorts. Perhaps we wanted to understand the experiences of black social work students in PWIs. We could choose either an individual unit of observation by selecting students, or a group unit of observation by studying the National Association of Black Social Workers .

Sometimes the units of analysis and observation differ due to pragmatic reasons. If we wanted to study whether being a social work student impacted family relationships, we may choose to study family members of students in social work programs who could give us information about how they behaved in the home. In this case, we would be observing family members to draw conclusions about individual students.

In sum, there are many potential units of analysis that a social worker might examine, but some of the most common include i ndividuals, groups, and organizations. Table 10.1 details examples identifying the units of observation and analysis in a hypothetical study of student addiction to electronic gadgets.

First-hand vs. second-hand knowledge

Your unit of analysis will be determined by your research question. Specifically, it should relate to your target population. Your unit of observation, on the other hand, is determined largely by the method of data collection you use to answer that research question. Let’s consider a common issue in social work research: understanding the effectiveness of different social work interventions. Who has first-hand knowledge and who has second-hand knowledge? Well, practitioners would have first-hand knowledge about implementing the intervention. For example, they might discuss with you the unique language they use help clients understand the intervention. Clients, on the other hand, have first-hand knowledge about the impact of those interventions on their lives. If you want to know if an intervention is effective, you need to ask people who have received it!

Unfortunately, student projects run into pragmatic limitations with sampling from client groups. Clients are often diagnosed with severe mental health issues or have other ongoing issues that render them a vulnerable population at greater risk of harm. Asking a person who was recently experiencing suicidal ideation about that experience may interfere with ongoing treatment. Client records are also confidential and cannot be shared with researchers unless clients give explicit permission. Asking one’s own clients to participate in the study creates a dual relationship with the client, as both clinician and researcher, and dual relationship have conflicting responsibilities and boundaries.

Obviously, studies are done with social work clients all the time. But for student projects in the classroom, it is often required to get second-hand information from a population that is less vulnerable. Students may instead choose to study clinicians and how they perceive the effectiveness of different interventions. While clinicians can provide an informed perspective, they have less knowledge about personally receiving the intervention. In general, researchers prefer to sample the people who have first-hand knowledge about their topic, though feasibility often forces them to analyze second-hand information instead.

Population: Who do you want to study?

In social scientific research, a  population   is the cluster of people you are most interested in.  It is often the “who” that you want to be able to say something about at the end of your study. While populations in research may be rather large, such as “the American people,” they are more typically  more specific than that. For example, a large study for which the population of interest is the American people will likely specify which American people, such as adults over the age of 18 or citizens or legal permanent residents. Based on your work in Chapter 2, you should have a target population identified in your working question. That might be something like “people with developmental disabilities” or “students in a social work program.”

It is almost impossible for a researcher to gather data from their entire population of interest. This might sound surprising or disappointing until you think about the kinds of research questions that social workers typically ask. For example, let’s say we wish to answer the following question: “How does gender impact attendance in a batterer intervention program?” Would you expect to be able to collect data from all people in batterer intervention programs across all nations from all historical time periods? Unless you plan to make answering this research question your entire life’s work (and then some), I’m guessing your answer is a resounding no. So, what to do? Does not having the time or resources to gather data from every single person of interest mean having to give up your research interest?

• What is your population, the people you want to make conclusions about?
• Do your unit of analysis and unit of observation differ or are they the same?
• Can you ethically and practically get first-hand information from the people most knowledgeable about the topic, or will you rely on second-hand information from less vulnerable populations?

Setting: Where will you go to get your data?

While you can’t gather data from everyone, you can find some people from your target population to study. The first rule of sampling is: go where your participants are. You will need to figure out where you will go to get your data. For many student researchers, it is their agency, their peers, their family and friends, or whoever comes across students’ social media posts or emails asking people to participate in their study.

Each setting (agency, social media) limits your reach to only a small segment of your target population who has the opportunity to be a part of your study. This intermediate point between the overall population and the sample of people who actually participate in the researcher’s study is called a sampling frame . A sampling frame is a list of people from which you will draw your sample.

But where do you find a sampling frame? Answering this question is the first step in conducting human subjects research. Social work researchers must think about locations or groups in which your target population gathers or interacts. For example, a study on quality of care in nursing homes may choose a local nursing home because it’s easy to access. The sampling frame could be all of the residents of the nursing home. You would select your participants for your study from the list of residents. Note that this is a real list. That is, an administrator at the nursing home would give you a list with every resident’s name or ID number from which you would select your participants. If you decided to include more nursing homes in your study, then your sampling frame could be all the residents at all the nursing homes who agreed to participate in your study.

Let’s consider some more examples. Unlike nursing home patients, cancer survivors do not live in an enclosed location and may no longer receive treatment at a hospital or clinic. For social work researchers to reach participants, they may consider partnering with a support group that services this population. Perhaps there is a support group at a local church survivors may attend. Without a set list of people, your sampling frame would simply be the people who showed up to the support group on the nights you were there. Similarly, if you posted an advertisement in an online peer-support group for people with cancer, your sampling frame is the people in that group.

More challenging still is recruiting people who are homeless, those with very low income, or those who belong to stigmatized groups. For example, a research study by Johnson and Johnson (2014) [2] attempted to learn usage patterns of “bath salts,” or synthetic stimulants that are marketed as “legal highs.” Users of “bath salts” don’t often gather for meetings, and reaching out to individual treatment centers is unlikely to produce enough participants for a study, as the use of bath salts is rare. To reach participants, these researchers ingeniously used online discussion boards in which users of these drugs communicate. Their sampling frame included everyone who participated in the online discussion boards during the time they collected data. Another example might include using a flyer to let people know about your study, in which case your sampling frame would be anyone who walks past your flyer wherever you hang it—usually in a strategic location where you know your population will be.

In conclusion, sampling frames can be a real list of people like the list of faculty and their ID numbers in a university department, which allows you to clearly identify who is in your study and what chance they have of being selected. However, not all sampling frames allow you to be so specific. It is also important to remember that accessing your sampling frame must be practical and ethical, as we discussed in section 2.3 and in Chapter 8. For studies that present risks to participants, approval from gatekeepers and the university’s institutional review board (IRB) is needed.

Criteria: What characteristics must your participants have/not have?

Your sampling frame is not just everyone in the setting you identified. For example, if you were studying MSW students who are first-generation college students, you might select your university as the setting, but not everyone in your program is a first-generation student. You need to be more specific about which characteristics or attributes individuals either must have or cannot have before they participate in the study. These are known as inclusion and exclusion criteria, respectively.

Inclusion criteria are the characteristics a person must possess in order to be included in your sample. If you were conducting a survey on LGBTQ+ discrimination at your agency, you might want to sample only clients who identify as LGBTQ+. In that case, your inclusion criteria for your sample would be that individuals have to identify as LGBTQ+.

Comparably,  exclusion criteria are characteristics that disqualify a person from being included in your sample. In the previous example, you could think of cisgenderism and heterosexuality as your exclusion criteria because no person who identifies as heterosexual or cisgender would be included in your sample. Exclusion criteria are often the mirror image of inclusion criteria. However, there may be other criteria by which we want to exclude people from our sample. For example, we may exclude clients who were recently discharged or those who have just begun to receive services.

Recruitment: How will you ask people to participate in your study?

Once you have a location and list of people from which to select, all that is left is to reach out to your participants. Recruitment refers to the process by which the researcher informs potential participants about the study and asks them to participate in the research project. Recruitment comes in many different forms. If you have ever received a phone call asking for you to participate in a survey, someone has attempted to recruit you for their study. Perhaps you’ve seen print advertisements on buses, in student centers, or in a newspaper. I’ve received many emails that were passed around my school asking for participants, usually for a graduate student project. (As an aside, researchers sometimes speak of “research karma.” If you participate in others’ research studies, they will participate in yours.)  As we learn more about specific types of sampling, make sure your recruitment strategy makes sense with your sampling approach. For example, if you put up a flyer in the student health office to recruit student athletes for your study, you may not be targeting your recruitment efforts to settings where your target population is likely to see your recruitment materials.

Recruiting human participants

Sampling is the first time in which you will contact potential study participants. Before you start this process, it is important to make sure you have approval from your university’s institutional review board as well as any gatekeepers at the locations in which you plan to conduct your study. As we discussed in section 10.1, the first rule of sampling is to go where your participants are. If you are studying domestic violence, reach out to local shelters, advocates, or service agencies. Gatekeepers will be necessary to gain access to your participants. For example, a gatekeeper can forward your recruitment email across their employee email list. Review our discussion of gatekeepers in Chapter 2 before proceeding with contacting potential participants as part of recruitment.

Recruitment can take many forms. You may show up at a staff meeting to ask for volunteers. You may send a company-wide email. Each step of this process should be vetted by the IRB as well as other stakeholders and gatekeepers. You will also need to set reasonable expectations for how many reminders you will send to the person before moving on. Generally, it is a good idea to give people a little while to respond, though reminders are often accompanied by an increase in participation. Pragmatically, it is a good idea for you to think through each step of the recruitment process and how much time it will take to complete it.

For example, as a graduate student, I conducted a study of state-level disabilities administrators in which I was recruiting a sample of very busy people and had no financial incentives to offer them for participating in my study. It helped for my research team to bring on board a well-known agency as a research partner, allowing them to review and offer suggestions on our survey and interview questions. This collaborative process took time and had to be completed before sampling could start. Once sampling commenced, I pulled contact names from my collaborator’s database and public websites, and set a weekly schedule of email and phone contacts. I would contact the director once via email. Ten days later, I would follow up via email and by leaving a voicemail with their administrative support staff. Ten days after that, I would reach out to state administrators in a different office via email and then again via phone, if needed. The process took months to complete and required a complex Excel tracking document.

Recruitment will also expose your participants to the informed consent information you prepared. For students going through the IRB, there are templates you will have to follow in order to get your study approved. For students whose projects unfold under the supervision of their department, rather than the IRB, you should check with your professor on what the expectations are for getting participant consent. In the aforementioned study, I used our IRB’s template to create a consent form but did not include a signature line. The IRB allowed me to collect my data without a signature, as there was little risk of harm from the study. It was imperative to review consent information before completing the survey and interview with participants. Only when the participant is totally clear on the purpose, risks and benefits, confidentiality protections, and other information detailed in Chapter 8, can you ethically move forward with including them in your sample.

Sampling available documents

As with sampling humans, sampling documents centers around the question: which documents are the most relevant to your research question, in that which will provide you first-hand knowledge. Common documents analyzed in student research projects include client files, popular media like film and music lyrics, and policies from service agencies. In a case record review, the student would create exclusion and inclusion criteria based on their research question. Once a suitable sampling frame of potential documents exists, the researcher can use probability or non-probability sampling to select which client files are ultimately analyzed.

Sampling documents must also come with consent and buy-in from stakeholders and gatekeepers. Assuming you have approval to conduct your study and access to the documents you need, the process of recruitment is much easier than in studies sampling humans. There is no informed consent process with documents, though research with confidential health or education records must be done in accordance with privacy laws such as the Health Insurance Portability and Accountability Act and the Family Educational Rights and Privacy Act . Barring any technical or policy obstacles, the gathering of documents should be easier and less time consuming than sampling humans.

Sample: Who actually participates in your study?

Once you find a sampling frame from which you can recruit your participants and decide which characteristics you will  include  and   exclude, you will recruit people using a specific sampling approach, which we will cover in Section 10.2. At the end, you’re left with the group of people you successfully recruited from your sampling frame to participate in your study, your sample . If you are a participant in a research project—answering survey questions, participating in interviews, etc.—you are part of the sample in that research project.

Visualizing sampling terms

Sampling terms can be a bit daunting at first. However, with some practice, they will become second nature. Let’s walk through an example from a research project of mine. I collected data for a research project related to how much it costs to become a licensed clinical social worker (LCSW) in each state. Becoming an LCSW is necessary to work in private clinical practice and is used by supervisors in human service organizations to sign off on clinical charts from less credentialed employees, and to provide clinical supervision. If you are interested in providing clinical services as a social worker, you should become familiar with the licensing laws in your state.

Using Figure 10.1 as a guide, my population is clearly clinical social workers, as these are the people about whom I want to draw conclusions. The next step inward would be a sampling frame. Unfortunately, there is no list of every licensed clinical social worker in the United States. I could write to each state’s social work licensing board and ask for a list of names and addresses, perhaps even using a Freedom of Information Act request if they were unwilling to share the information. That option sounds time-consuming and has a low likelihood of success. Instead, I tried to figure out a convenient setting social workers are likely to congregate. I considered setting up a booth at a National Association of Social Workers (NASW) conference and asking people to participate in my survey. Ultimately, this would prove too costly, and the people who gather at an NASW conference may not be representative of the general population of clinical social workers. I finally discovered the NASW membership email list, which is available to advertisers, including researchers advertising for research projects. While the NASW list does not contain every clinical social worker, it reaches over one hundred thousand social workers regularly through its monthly e-newsletter, a large proportion of social workers in practice, so the setting was likely to draw a representative sample. To gain access to this setting from gatekeepers, I had to provide paperwork showing my study had undergone IRB review and submit my measures for approval by the mailing list administrator.

Once I gained access from gatekeepers, my setting became the members of the NASW membership list. I decided to recruit 5,000 participants because I knew that people sometimes do not read or respond to email advertisements, and I figured maybe 20% would respond, which would give me around 1,000 responses. Figuring out my sample size was a challenge, because I had to balance the costs associated with using the NASW newsletter. As you can see on their pricing page , it would cost money to learn personal information about my potential participants, which I would need to check later in order to determine if my population was representative of the overall population of social workers. For example, I could see if my sample was comparable in race, age, gender, or state of residence to the broader population of social workers by comparing my sample with information about all social workers published by NASW. I presented my options to my external funder as:

• I could send an email advertisement to a lot of people (5,000), but I would know very little about them and they would get only one advertisement.
• I could send multiple advertisements to fewer people (1,000) reminding them to participate, but I would also know more about them by purchasing access to personal information.
• I could send multiple advertisements to fewer people (2,500), but not purchase access to personal information to minimize costs.

In your project, there is no expectation you purchase access to anything, and if you plan on using email advertisements, consider places that are free to access like employee or student listservs. At the same time, you will need to consider what you can know or not know about the people who will potentially be in your study, and I could collect any personal information we wanted to check representativeness in the study itself. For this reason, we decided to go with option #1. When I sent my email recruiting participants for the study, I specified that I only wanted to hear from social workers who were either currently receiving or recently received clinical supervision for licensure—my inclusion criteria. This was important because many of the people on the NASW membership list may not be licensed or license-seeking social workers. So, my sampling frame was the email addresses on the NASW mailing list who fit the inclusion criteria for the study, which I figured would be at least a few thousand people. Unfortunately, only 150 licensed or license-seeking clinical social workers responded to my recruitment email and completed the survey. You will learn in Section 10.3 why this did not make for a very good sample.

From this example, you can see that sampling is a process. The process flows sequentially from figuring out your target population, to thinking about where to find people from your target population, to figuring out how much information you know about potential participants, and finally to selecting recruiting people from that list to be a part of your sample. Through the sampling process, you must consider where people in your target population are likely to be and how best to get their attention for your study. Sampling can be an easy process, like calling every 100th name from the phone book, or challenging, like standing every day for a few weeks in an area in which people who are homeless gather for shelter. In either case, your goal is to recruit enough people who will participate in your study so you can learn about your population.

What about sampling non-humans?

Many student projects do not involve recruiting and sampling human subjects. Instead, many research projects will sample objects like client charts, movies, or books. The same terms apply, but the process is a bit easier because there are no humans involved. If a research project involves analyzing client files, it is unlikely you will look at every client file that your agency has. You will need to figure out which client files are important to your research question. Perhaps you want to sample clients who have a diagnosis of reactive attachment disorder. You would have to create a list of all clients at your agency (setting) who have reactive attachment disorder (your inclusion criteria) then use your sampling approach (which we will discuss in the next section) to select which client files you will actually analyze for your study (your sample). Recruitment is a lot easier because, well, there’s no one to convince but your gatekeepers, the managers of your agency. However, researchers who publish chart reviews must obtain IRB permission before doing so.

Key Takeaways

• The first rule of sampling is to go where your participants are. Think about virtual or in-person settings in which your target population gathers. Remember that you may have to engage gatekeepers and stakeholders in accessing many settings, and that you will need to assess the pragmatic challenges and ethical risks and benefits of your study.
• Consider whether you can sample documents like agency files to answer your research question. Documents are much easier to “recruit” than people!
• Researchers must consider which characteristics are necessary for people to have (inclusion criteria) or not have (exclusion criteria), as well as how to recruit participants into the sample.
• Social workers can sample individuals, groups, or organizations.
• Sometimes the unit of analysis and the unit of observation in the study differ. In student projects, this is often true as target populations may be too vulnerable to expose to research whose potential harms may outweigh the benefits.
• One’s recruitment method has to match one’s sampling approach, as will be explained in the next chapter.
• Based on what you know right now, how representative of your population are potential participants in the setting?
• How much information can you reasonably know about potential participants before you recruit them?
• Are there gatekeepers and what kinds of concerns might they have?
• Are there any stakeholders that may be beneficial to bring on board as part of your research team for the project?
• What interests might stakeholders and gatekeepers bring to the project and would they align with your vision for the project?
• What ethical issues might you encounter if you sampled people in this setting?
• For the settings you’ve identified, how might you recruit participants?
• Identify your inclusion criteria and exclusion criteria, and assess whether you have enough information on whether people in each setting will meet them.

10.2 Sampling approaches for quantitative research

• Determine whether you will use probability or non-probability sampling, given the strengths and limitations of each specific sampling approach
• Distinguish between approaches to probability sampling and detail the reasons to use each approach

Sampling in quantitative research projects is done because it is not feasible to study the whole population, and researchers hope to take what we learn about a small group of people (your sample) and apply it to a larger population. There are many ways to approach this process, and they can be grouped into two categories—probability sampling and non-probability sampling. Sampling approaches are inextricably linked with recruitment, and researchers should ensure that their proposal’s recruitment strategy matches the sampling approach.

Probability sampling approaches use a random process, usually a computer program, to select participants from the sampling frame so that everyone has an equal chance of being included. It’s important to note that random means the researcher used a process that is truly random . In a project sampling college students, standing outside of the building in which your social work department is housed and surveying everyone who walks past is not random. Because of the location, you are likely to recruit a disproportionately large number of social work students and fewer from other disciplines. Depending on the time of day, you may recruit more traditional undergraduate students, who take classes during the day, or more graduate students, who take classes in the evenings.

In this example, you are actually using non-probability sampling . Another way to say this is that you are using the most common sampling approach for student projects, availability sampling . Also called convenience sampling, this approach simply recruits people who are convenient or easily available to the researcher. If you have ever been asked by a friend to participate in their research study for their class or seen an advertisement for a study on a bulletin board or social media, you were being recruited using an availability sampling approach.

There are a number of benefits to the availability sampling approach. First and foremost, it is less costly and time-consuming for the researcher. As long as the person you are attempting to recruit has knowledge of the topic you are studying, the information you get from the sample you recruit will be relevant to your topic (although your sample may not necessarily be representative of a larger population). Availability samples can also be helpful when random sampling isn’t practical. If you are planning to survey students in an LGBTQ+ support group on campus but attendance varies from meeting to meeting, you may show up at a meeting and ask anyone present to participate in your study. A support group with varied membership makes it impossible to have a real list–or sampling frame–from which to randomly select individuals. Availability sampling would help you reach that population.

Availability sampling is appropriate for student and smaller-scale projects, but it comes with significant limitations. The purpose of sampling in quantitative research is to generalize from a small sample to a larger population. Because availability sampling does not use a random process to select participants, the researcher cannot be sure their sample is representative of the population they hope to generalize to. Instead, the recruitment processes may have been structured by other factors that may bias the sample to be different in some way than the overall population.

So, for instance, if we asked social work students about their level of satisfaction with the services at the student health center, and we sampled in the evenings, we would get most likely get a biased perspective of the issue. Students taking only night classes are much more likely to commute to school, spend less time on campus, and use fewer campus services. Our results would not represent what all social work students feel about the topic. We might get the impression that no social work student had ever visited the health center, when that is not actually true at all. Sampling bias will be discussed in detail in Section 10.3.

Approaches to probability sampling

What might be a better strategy is getting a list of all email addresses of social work students and randomly selecting email addresses of students to whom you can send your survey. This would be an example of simple random sampling . It’s important to note that you need a real list of people in your sampling frame from which to select your email addresses. For projects where the people who could potentially participate is not known by the researcher, probability sampling is not possible. It is likely that administrators at your school’s registrar would be reluctant to share the list of students’ names and email addresses. Always remember to consider the feasibility and ethical implications of the sampling approach you choose.

Usually, simple random sampling is accomplished by assigning each person, or element , in your sampling frame a number and selecting your participants using a random number generator. You would follow an identical process if you were sampling records or documents as your elements, rather than people. True randomness is difficult to achieve, and it takes complex computational calculations to do so. Although you think you can select things at random, human-generated randomness is actually quite predictable, as it falls into patterns called heuristics . To truly randomly select elements, researchers must rely on computer-generated help. Many free websites have good pseudo-random number generators. A good example is the website Random.org , which contains a random number generator that can also randomize lists of participants. Sometimes, researchers use a table of numbers that have been generated randomly. There are several possible sources for obtaining a random number table. Some statistics and research methods textbooks provide such tables in an appendix.

Though simple, this approach to sampling can be tedious since the researcher must assign a number to each person in a sampling frame. Systematic sampling techniques are somewhat less tedious but offer the benefits of a random sample. As with simple random samples, you must possess a list of everyone in your sampling frame. Once you’ve done that, to draw a systematic sample you’d simply select every k th element on your list. But what is k , and where on the list of population elements does one begin the selection process?

k is your selection interval or the distance between the elements you select for inclusion in your study. To begin the selection process, you’ll need to figure out how many elements you wish to include in your sample. Let’s say you want to survey 25 social work students and there are 100 social work students on your campus. In this case, your selection interval, or  k , is 4. To get your selection interval, simply divide the total number of population elements by your desired sample size. Systematic sampling starts by randomly selecting a number between 1 and  k  to start from, and then recruiting every  kth person. In our example, we may start at number 3 and then select the 7th, 11th, 15th (and so forth) person on our list of email addresses. In Figure 10.2, you can see the researcher starts at number 2 and then selects every third person for inclusion in the sample.

There is one clear instance in which systematic sampling should not be employed. If your sampling frame has any pattern to it, you could inadvertently introduce bias into your sample by using a systemic sampling strategy. (Bias will be discussed in more depth in section 10.3.) This is sometimes referred to as the problem of periodicity. Periodicity refers to the tendency for a pattern to occur at regular intervals.

To stray a bit from our example, imagine we were sampling client charts based on the date they entered a health center and recording the reason for their visit. We may expect more admissions for issues related to alcohol consumption on the weekend than we would during the week. The periodicity of alcohol intoxication may bias our sample towards either overrepresenting or underrepresenting this issue, depending on our sampling interval and whether we collected data on a weekday or weekend.

Advanced probability sampling techniques

Returning again to our idea of sampling student email addresses, one of the challenges in our study will be the different types of students. If we are interested in all social work students, it may be helpful to divide our sampling frame, or list of students, into three lists—one for traditional, full-time undergraduate students, another for part-time undergraduate students, and one more for full-time graduate students—and then randomly select from these lists. This is particularly important if we wanted to make sure our sample had the same proportion of each type of student compared with the general population.

This approach is called stratified random sampling . In stratified random sampling, a researcher will divide the study population into relevant subgroups or strata and then draw a sample from each subgroup, or stratum. Strata is the plural of stratum, so it refers to all of the groups while stratum refers to each group. This can be used to make sure your sample has the same proportion of people from each stratum. If, for example, our sample had many more graduate students than undergraduate students, we may draw incorrect conclusions that do not represent what all social work students experience.

Generally, the goal of stratified random sampling is to recruit a sample that makes sure all elements of the population are included sufficiently that conclusions can be drawn about them. Usually, the purpose is to create a sample that is identical to the overall population along whatever strata you’ve identified. In our sample, it would be graduate and undergraduate students. Stratified random sampling is also useful when a subgroup of interest makes up a relatively small proportion of the overall sample. For example, if your social work program contained relatively few Asian students but you wanted to make sure you recruited enough Asian students to conduct statistical analysis, you could use race to divide people into subgroups or strata and then disproportionately sample from the Asian students to make sure enough of them were in your sample to draw meaningful conclusions. Statistical tests may have a minimum number

Up to this point in our discussion of probability samples, we’ve assumed that researchers will be able to access a list of population elements in order to create a sampling frame. This, as you might imagine, is not always the case. Let’s say, for example, that you wish to conduct a study of health center usage across students at each social work program in your state. Just imagine trying to create a list of every single social work student in the state. Even if you could find a way to generate such a list, attempting to do so might not be the most practical use of your time or resources. When this is the case, researchers turn to cluster sampling. Cluster sampling  occurs when a researcher begins by sampling groups (or clusters) of population elements and then selects elements from within those groups.

Let’s work through how we might use cluster sampling. While creating a list of all social work students in your state would be next to impossible, you could easily create a list of all social work programs in your state. Then, you could draw a random sample of social work programs (your cluster) and then draw another random sample of elements (in this case, social work students) from each of the programs you randomly selected from the list of all programs.

Cluster sampling often works in stages. In this example, we sampled in two stages—(1) social work programs and (2) social work students at each program we selected. However, we could add another stage if it made sense to do so. We could randomly select (1) states in the United States (2) social work programs in that state and (3) individual social work students. As you might have guessed, sampling in multiple stages does introduce a  greater   possibility of error. Each stage is subject to its own sampling problems. But, cluster sampling is nevertheless a highly efficient method.

Jessica Holt and Wayne Gillespie (2008) [3] used cluster sampling in their study of students’ experiences with violence in intimate relationships. Specifically, the researchers randomly selected 14 classes on their campus and then drew a random sub-sample of students from those classes. But you probably know from your experience with college classes that not all classes are the same size. So, if Holt and Gillespie had simply randomly selected 14 classes and then selected the same number of students from each class to complete their survey, then students in the smaller of those classes would have had a greater chance of being selected for the study than students in the larger classes. Keep in mind, with random sampling the goal is to make sure that each element has the same chance of being selected. When clusters are of different sizes, as in the example of sampling college classes, researchers often use a method called probability proportionate to size  (PPS). This means that they take into account that their clusters are of different sizes. They do this by giving clusters different chances of being selected based on their size so that each element within those clusters winds up having an equal chance of being selected.

To summarize, probability samples allow a researcher to make conclusions about larger groups. Probability samples require a sampling frame from which elements, usually human beings, can be selected at random from a list. The use of random selection reduces the error and bias present in non-probability samples, which we will discuss in greater detail in section 10.3, though some error will always remain. In relying on a random number table or generator, researchers can more accurately state that their sample represents the population from which it was drawn. This strength is common to all probability sampling approaches summarized in Table 10.2.

In determining which probability sampling approach makes the most sense for your project, it helps to know more about your population. A simple random sample and systematic sample are relatively similar to carry out. They both require a list all elements in your sampling frame. Systematic sampling is slightly easier in that it does not require you to use a random number generator, instead using a sampling interval that is easy to calculate by hand.

However, the relative simplicity of both approaches is counterweighted by their lack of sensitivity to characteristics of your population. Stratified samples can better account for periodicity by creating strata that reduce or eliminate its effects. Stratified sampling also ensure that smaller subgroups are included in your sample, thereby making your sample more representative of the overall population. While these benefits are important, creating strata for this purpose requires having information about your population before beginning the sampling process. In our social work student example, we would need to know which students are full-time or part-time, graduate or undergraduate, in order to make sure our sample contained the same proportions. Would you know if someone was a graduate student or part-time student, just based on their email address? If the true population parameters are unknown, stratified sampling becomes significantly more challenging.

Common to each of the previous probability sampling approaches is the necessity of using a real list of all elements in your sampling frame. Cluster sampling is different. It allows a researcher to perform probability sampling in cases for which a list of elements is not available or feasible to create. Cluster sampling is also useful for making claims about a larger population (in our previous example, all social work students within a state). However, because sampling occurs at multiple stages in the process, (in our previous example, at the university and student level), sampling error increases. For many researchers, the benefits of cluster sampling outweigh this weaknesses.

Matching recruitment and sampling approach

Recruitment must match the sampling approach you choose in section 10.2. For many students, that will mean using recruitment techniques most relevant to availability sampling. These may include public postings such as flyers, mass emails, or social media posts. However, these methods would not make sense for a study using probability sampling. Probability sampling requires a list of names or other identifying information so you can use a random process to generate a list of people to recruit into your sample. Posting a flyer or social media message means you don’t know who is looking at the flyer, and thus, your sample could not be randomly drawn. Probability sampling often requires knowing how to contact specific participants.  For example, you may do as I did, and contact potential participants via phone and email. Even then, it’s important to note that not everyone you contact will enter your study. We will discuss more about evaluating the quality of your sample in section 10.3.

• Probability sampling approaches are more accurate when the researcher wants to generalize from a smaller sample to a larger population. However, non-probability sampling approaches are often more feasible. You will have to weigh advantages and disadvantages of each when designing your project.
• There are many kinds of probability sampling approaches, though each require you know some information about people who potentially would participate in your study.
• Probability sampling also requires that you assign people within the sampling frame a number and select using a truly random process.
• Identify one of the sampling approaches listed in this chapter that might be appropriate to answering your question and list the strengths and limitations of it.
• Describe how you will recruit your participants and how your plan makes sense with the sampling approach you identified.
• Examine one of the empirical articles from your literature review. Identify what sampling approach they used and how they carried it out from start to finish.

10.3 Sample quality

• Assess whether your sampling plan is likely to produce a sample that is representative of the population you want to draw conclusions about
• Identify the considerations that go into producing a representative sample and determining sample size
• Distinguish between error and bias in a sample and explain the factors that lead to each

Okay, so you’ve chosen where you’re going to get your data (setting), what characteristics you want and don’t want in your sample (inclusion/exclusion criteria), and how you will select and recruit participants (sampling approach and recruitment). That means you are done, right? (I mean, there’s an entire section here, so probably not.) Even if you make good choices and do everything the way you’re supposed to, you can still draw a poor sample. If you are investigating a research question using quantitative methods, the best choice is some kind of probability sampling, but aside from that, how do you know a good sample from a bad sample? As an example, we’ll use a bad sample I collected as part of a research project that didn’t go so well. Hopefully, your sampling will go much better than mine did, but we can always learn from what didn’t work.

Representativeness

A representative sample is, “a sample that looks like the population from which it was selected in all respects that are potentially relevant to the study” (Engel & Schutt, 2011). [4] For my study on how much it costs to get an LCSW in each state, I did not get a sample that looked like the overall population to which I wanted to generalize. My sample had a few states with more than ten responses and most states with no responses. That does not look like the true distribution of social workers across the country. I could compare the number of social workers in each state, based on data from the National Association of Social Workers, or the number of recent clinical MSW graduates from the Council on Social Work Education. More than that, I could see whether my sample matched the overall population of clinical social workers in gender, race, age, or any other important characteristics. Sadly, it wasn’t even close. So, I wasn’t able to use the data to publish a report.

• Will the sample of people (or documents) look like the population to which you want to generalize?
• Specifically, what characteristics are important in determining whether a sample is representative of the population? How do these characteristics relate to your research question?
• Consider returning to this question once you have completed the sampling process and evaluate whether the sample in your study was similar to what you designed in this section.

Many of my students erroneously assume that using a probability sampling technique will guarantee a representative sample. This is not true.  Engel and Schutt (2011) identify that probability sampling increases the chance of representativeness; however, it does not guarantee that the sample will be representative. If a representative sample is important to your study, it would be best to use a sampling approach that allows you to control the proportion of specific characteristics in your sample. For instance, stratified random sampling allows you to control the distribution of specific variables of interest within your sample. However, that requires knowing information about your participants before you hand them surveys or expose them to an experiment.

In my study, if I wanted to make sure I had a certain number of people from each state (state being the strata), making the proportion of social workers from each state in my sample similar to the overall population, I would need to know which email addresses were from which states. That was not information I had. So, instead I conducted simple random sampling and randomly selected 5,000 of 100,000 email addresses on the NASW list. There was less of a guarantee of representativeness, but whatever variation existed between my sample and the population would be due to random chance. This would not be true for an availability or convenience sample. While these sampling approaches are common for student projects, they come with significant limitations in that variation between the sample and population is due to factors other than chance. We will discuss these non-random differences later in the chapter when we talk about bias. For now, just remember that the representativeness of a sample is helped by using random sampling, though it is not a guarantee.

• Before you start sampling, do you know enough about your sampling frame to use stratified random sampling, which increases the potential of getting a representative sample?
• Do you have enough information about your sampling frame to use another probability sampling approach like simple random sampling or cluster sampling?
• If little information is available on which to select people, are you using availability sampling? Remember that availability sampling is okay if it is the only approach that is feasible for the researcher, but it comes with significant limitations when drawing conclusions about a larger population.

Assessing representativeness should start prior to data collection. I mentioned that I drew my sample from the NASW email list, which (like most organizations) they sell to advertisers when companies or researchers need to advertise to social workers. How representative of my population is my sampling frame? Well, the first question to ask is what proportion of my sampling frame would actually meet my exclusion and inclusion criteria. Since my study focused specifically on clinical social workers, my sampling frame likely included social workers who were not clinical social workers, like macro social workers or social work managers. However, I knew, based on the information from NASW marketers, that many people who received my recruitment email would be clinical social workers or those working towards licensure, so I was satisfied with that. Anyone who didn’t meet my inclusion criteria and opened the survey would be greeted with clear instructions that this survey did not apply to them.

At the same time, I should have assessed whether the demographics of the NASW email list and the demographics of clinical social workers more broadly were similar. Unfortunately, this was not information I could gather. I had to trust that this was likely to going to be the best sample I could draw and the most representative of all social workers.

• Before you start, what do you know about your setting and potential participants?
• You want to avoid throwing out half of the surveys you get back because the respondents aren’t a part of your target population. This is a common error I see in student proposals.

Many of you will sample people from your agency, like clients or staff. Let’s say you work for a children’s mental health agency, and you wanted to study children who have experienced abuse. Walking through the steps here might proceed like this:

• Think about or ask your coworkers how many of the clients at your agency have experienced this issue. If it’s common, then clients at your agency would probably make a good sampling frame for your study. If not, then you may want to adjust your research question or consider a different agency to sample. You could also change your target population to be more representative with your sample. For example, while your agency’s clients may not be representative of all children who have survived abuse, they may be more representative of abuse survivors in your state, region, or county. In this way, you can draw conclusions about a smaller population, rather than everyone in the world who is a victim of child abuse.
• Think about those characteristics that are important for individuals in your sample to have or not have. Obviously, the variables in your research question are important, but so are the variables related to it. Take a look at the empirical literature on your topic. Are there different demographic characteristics or covariates that are relevant to your topic?
• All of this assumes that you can actually access information about your sampling frame prior to collecting data. This is a challenge in the real world. Even if you ask around your office about client characteristics, there is no way for you to know for sure until you complete your study whether it was the most representative sampling frame you could find. When in doubt, go with whatever is feasible and address any shortcomings in sampling within the limitations section of your research report. A good project is a done project.
• While using a probability sampling approach helps with sample representativeness, it does not guarantee it. Due to random variation, samples may differ across important characteristics. If you can feasibly use a probability sampling approach, particularly stratified random sampling, it will help make your sample more representative of the population.
• Even if you choose a sampling frame that is representative of your population and use a probability sampling approach, there is no guarantee that the sample you are able to collect will be representative. Sometimes, people don’t respond to your recruitment efforts. Other times, random chance will mean people differ on important characteristics from your target population. ¯\_(ツ)_/¯

In agency-based samples, the small size of the pool of potential participants makes it very likely that your sample will not be representative of a broader target population. Sometimes, researchers look for specific outcomes connected with sub-populations for that reason. Not all agency-based research is concerned with representativeness, and it is still worthwhile to pursue research that is relevant to only one location as its purpose is often to improve social work practice.

Sample size

Let’s assume you have found a representative sampling frame, and that you are using one of the probability sampling approaches we reviewed in section 10.2. That should help you recruit a representative sample, but how many people do you need to recruit into your sample? As with many questions about sample quality, students should keep feasibility in mind. The easiest answer I’ve given as a professor is, “as many as you can, without hurting yourself.” While your quantitative research question would likely benefit from hundreds or thousands of respondents, that is not likely to be feasible for a student who is working full-time, interning part-time, and in school full-time. Don’t feel like your study has to be perfect, but make sure you note any limitations in your final report.

To the extent possible, you should gather as many people as you can in your sample who meet your criteria. But why? Let’s think about an example you probably know well. Have you ever watched the TV show Family Feud ? Each question the host reads off starts with, “we asked 100 people…” Believe it or not,  Family Feud uses simple random sampling to conduct their surveys the American public. Part of the challenge on  Family Feud is that people can usually guess the most popular answers, but those answers that only a few people chose are much harder. They seem bizarre, and are more difficult to guess. That’s because 100 people is not a lot of people to sample. Essentially, Family Feud is trying to measure what the answer is for all 327 million people in the United States by asking 100 of them. As a result, the weird and idiosyncratic responses of a few people are likely to remain on the board as answers, and contestants have to guess answers fewer and fewer people in the sample provided. In a larger sample, the oddball answers would likely fade away and only the most popular answers would be represented on the game show’s board.

In my ill-fated study of clinical social workers, I received 87 complete responses. That is far below the hundred thousand licensed or license-eligible clinical social workers. Moreover, since I wanted to conduct state-by-state estimates, there was no way I had enough people in each state to do so. For student projects, samples of 50-100 participants are more than enough to write a paper (or start a game show), but for projects in the real world with real-world consequences, it is important to recruit the appropriate number of participants. For example, if your agency conducts a community scan of people in your service area on what services they need, the results will inform the direction of your agency, which grants they apply for, who they hire, and its mission for the next several years. Being overly confident in your sample could result in wasted resources for clients.

So what is the right number? Theoretically, we could gradually increase the sample size so that the sample approaches closer and closer to the total size of the population (Bhattacherjeee, 2012). [5] But as we’ve talked about, it is not feasible to sample everyone. How do we find that middle ground? To answer this, we need to understand the sampling distribution . Imagine in your agency’s survey of the community, you took three different probability samples from your community, and for each sample, you measured whether people experienced domestic violence. If each random sample was truly representative of the population, then your rate of domestic violence from the three random samples would be about the same and equal to the true value in the population.

But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, the rate of domestic violence you measure may be slightly different from sample to sample. Think about the sample you collect as existing on a distribution of infinite possible samples. Most samples you collect will be close to the population mean but many will not be. The degree to which they differ is associated with how much the subject you are sampling about varies in the population. In our example, samples will vary based on how varied the incidence of domestic violence is from person to person. The difference between the domestic violence rate we find and the rate for our overall population is called the sampling error .

An easy way to minimize sampling error is to increase the number of participants in your sample, but in actuality, minimizing sampling error relies on a number of factors outside of the scope of a basic student project. You can see this online textbook for more examples on sampling distributions or take an advanced methods course at your university, particularly if you are considering becoming a social work researcher. Increasing the number of people in your sample also increases your study’s power , or the odds you will detect a significant relationship between variables when one is truly present in your sample. If you intend on publishing the findings of your student project, it is worth using a power analysis to determine the appropriate sample size for your project. You can follow this excellent video series from the Center for Open Science on how to conduct power analyses using free statistics software. A faculty members who teaches research or statistics could check your work. You may be surprised to find out that there is a point at which you adding more people to your sample will not make your study any better.

Honestly, I did not do a power analysis for my study. Instead, I asked for 5,000 surveys with the hope that 1,000 would come back. Given that only 87 came back, a power analysis conducted after the survey was complete would likely to reveal that I did not have enough statistical power to answer my research questions. For your projects, try to get as many respondents as you feasibly can, but don’t worry too much about not reaching the optimal amount of people to maximize the power of your study unless you goal is to publish something that is generalizable to a large population.

A final consideration is which statistical test you plan to use to analyze your data. We have not covered statistics yet, though we will provide a brief introduction to basic statistics in this textbook. For now, remember that some statistical tests have a minimum number of people that must be present in the sample in order to conduct the analysis. You will complete a data analysis plan before you begin your project and start sampling, so you can always increase the number of participants you plan to recruit based on what you learn in the next few chapters.

• How many people can you feasibly sample in the time you have to complete your project?

One of the interesting things about surveying professionals is that sometimes, they email you about what they perceive to be a problem with your study. I got an email from a well-meaning participant in my LCSW study saying that my results were going to be biased! She pointed out that respondents who had been in practice a long time, before clinical supervision was required, would not have paid anything for supervision. This would lead me to draw conclusions that supervision was cheap, when in fact, it was expensive. My email back to her explained that she hit on one of my hypotheses, that social workers in practice for a longer period of time faced fewer costs to becoming licensed. Her email reinforced that I needed to account for the impact of length of practice on the costs of licensure I found across the sample. She was right to be on the lookout for bias in the sample.

One of the key questions you can ask is if there is something about your process that makes it more likely you will select a certain type of person for your sample, making it less representative of the overall population. In my project, it’s worth thinking more about who is more likely to respond to an email advertisement for a research study. I know that my work email and personal email filter out advertisements, so it’s unlikely I would even see the recruitment for my own study (probably something I should have thought about before using grant funds to sample the NASW email list). Perhaps an older demographic that does not screen advertisements as closely, o r those whose NASW account was linked to a personal email with fewer junk filters would be more likely to respond. To the extent I made conclusions about clinical social workers of all ages based on a sample that was biased towards older social workers, my results would be biased. This is called selection bias , or the degree to which people in my sample differ from the overall population.

Another potential source of bias here is nonresponse bias . Because people do not often respond to email advertisements (no matter how well-written they are), my sample is likely to be representative of people with characteristics that make them more likely to respond. They may have more time on their hands to take surveys and respond to their junk mail. To the extent that the sample is comprised of social workers with a lot of time on their hands (who are those people?) my sample will be biased and not representative of the overall population.

It’s important to note that both bias and error describe how samples differ from the overall population. Error describes random variations between samples, due to chance. Using a random process to recruit participants into a sample means you will have random variation between the sample and the population. Bias creates variance between the sample and population in a specific direction, such as towards those who have time to check their junk mail. Bias may be introduced by the sampling method used or due to conscious or unconscious bias introduced by the researcher (Rubin & Babbie, 2017). [6] A researcher might select people who “look like good research participants,” in the process transferring their unconscious biases to their sample. They might exclude people from the sampling from who “would not do well with the intervention.” Careful researchers can avoid these, but unconscious and structural biases can be challenging to root out.

• Identify potential sources of bias in your sample and brainstorm ways you can minimize them, if possible.

Critical considerations

Think back to you undergraduate degree. Did you ever participate in a research project as part of an introductory psychology or sociology course? Social science researchers on college campuses have a luxury that researchers elsewhere may not share—they have access to a whole bunch of (presumably) willing and able human guinea pigs. But that luxury comes at a cost—sample representativeness. One study of top academic journals in psychology found that over two-thirds (68%) of participants in studies published by those journals were based on samples drawn in the United States (Arnett, 2008). [7] Further, the study found that two-thirds of the work that derived from US samples published in the Journal of Personality and Social Psychology was based on samples made up entirely of American undergraduate students taking psychology courses.

These findings certainly raise the question: What do we actually learn from social science studies and about whom do we learn it? That is exactly the concern raised by Joseph Henrich and colleagues (Henrich, Heine, & Norenzayan, 2010), [8] authors of the article “The Weirdest People in the World?” In their piece, Henrich and colleagues point out that behavioral scientists very commonly make sweeping claims about human nature based on samples drawn only from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies, and often based on even narrower samples, as is the case with many studies relying on samples drawn from college classrooms. As it turns out, robust findings about the nature of human behavior when it comes to fairness, cooperation, visual perception, trust, and other behaviors are based on studies that excluded participants from outside the United States and sometimes excluded anyone outside the college classroom (Begley, 2010). [9] This certainly raises questions about what we really know about human behavior as opposed to US resident or US undergraduate behavior. Of course, not all research findings are based on samples of WEIRD folks like college students. But even then, it would behoove us to pay attention to the population on which studies are based and the claims being made about those to whom the studies apply.

Another thing to keep in mind is that just because a sample may be representative in all respects that a researcher thinks are relevant, there may be relevant aspects that didn’t occur to the researcher when she was drawing her sample. You might not think that a person’s phone would have much to do with their voting preferences, for example. But had pollsters making predictions about the results of the 2008 presidential election not been careful to include both cell phone-only and landline households in their surveys, it is possible that their predictions would have underestimated Barack Obama’s lead over John McCain because Obama was much more popular among cell phone-only users than McCain (Keeter, Dimock, & Christian, 2008). [10] This is another example of bias.

Putting it All Together

So how do we know how good our sample is or how good the samples gathered by other researchers are? While there might not be any magic or always-true rules we can apply, there are a couple of things we can keep in mind as we read the claims researchers make about their findings.

First, remember that sample quality is determined only by the sample actually obtained, not by the sampling method itself. A researcher may set out to administer a survey to a representative sample by correctly employing a random sampling approach with impeccable recruitment materials. But, if only a handful of the people sampled actually respond to the survey, the researcher should not make claims like their sample went according to plan.

Another thing to keep in mind, as demonstrated by the preceding discussion, is that researchers may be drawn to talking about implications of their findings as though they apply to some group other than the population actually sampled. Whether the sampling frame does not match the population or the sample and population differ on important criteria, the resulting sampling error can lead to bad science.

We’ve talked previously about the perils of generalizing social science findings from graduate students in the United States and other Western countries to all cultures in the world, imposing a Western view as the right and correct view of the social world. As consumers of theory and research, it is our responsibility to be attentive to this sort of (likely unintentional) bait and switch. And as researchers, it is our responsibility to make sure that we only make conclusions from samples that are representative. A larger sample size and probability sampling can improve the representativeness and generalizability of the study’s findings to larger populations, though neither are guarantees.

Finally, keep in mind that a sample allowing for comparisons of theoretically important concepts or variables is certainly better than one that does not allow for such comparisons. In a study based on a nonrepresentative sample, for example, we can learn about the strength of our social theories by comparing relevant aspects of social processes. We talked about this as theory-testing in Chapter 5.

At their core, questions about sample quality should address who has been sampled, how they were sampled, and for what purpose they were sampled. Being able to answer those questions will help you better understand, and more responsibly interpret, research results. For your study, keep the following questions in mind.

• Are your sample size and your sampling approach appropriate for your research question?
• How much do you know about your sampling frame ahead of time? How will that impact the feasibility of different sampling approaches?
• What gatekeepers and stakeholders are necessary to engage in order to access your sampling frame?
• Are there any ethical issues that may make it difficult to sample those who have first-hand knowledge about your topic?
• Does your sampling frame look like your population along important characteristics? Once you get your data, ask the same question of the sample you successfully recruit.
• What about your population might make it more difficult or easier to sample?
• Are there steps in your sampling procedure that may bias your sample to render it not representative of the population?
• If you want to skip sampling altogether, are there sources of secondary data you can use? Or might you be able to answer you questions by sampling documents or media, rather than people?
• The sampling plan you implement should have a reasonable likelihood of producing a representative sample. Student projects are given more leeway with nonrepresentative samples, and this limitation should be discussed in the student’s research report.
• Researchers should conduct a power analysis to determine sample size, though quantitative student projects should endeavor to recruit as many participants as possible. Sample size impacts representativeness of the sample, its power, and which statistical tests can be conducted.
• The sample you collect is one of an infinite number of potential samples that could have been drawn. To the extent the data in your sample varies from the data in the entire population, it includes some error or bias. Error is the result of random variations. Bias is systematic error that pushes the data in a given direction.
• Even if you do everything right, there is no guarantee that you will draw a good sample. Flawed samples are okay to use as examples in the classroom, but the results of your research would have limited applicability to the community and society.
• Historically, samples were drawn from dominant groups and generalized to all people. This shortcoming is a limitation of some social science literature and should be considered a colonialist scientific practice.
• I clearly need a snack. ↵
• Johnson, P. S., & Johnson, M. W. (2014). Investigation of “bath salts” use patterns within an online sample of users in the United States. Journal of psychoactive drugs ,  46 (5), 369-378. ↵
• Holt, J. L., & Gillespie, W. (2008). Intergenerational transmission of violence, threatened egoism, and reciprocity: A test of multiple psychosocial factors affecting intimate partner violence.  American  Journal of Criminal Justice, 33 , 252–266. ↵
• Engel, R. & Schutt (2011). The practice of research in social work (2nd ed.) . California: SAGE ↵
• Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices . Retrieved from: https://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1002&context=oa_textbooks ↵
• Rubin, C. & Babbie, S. (2017). Research methods for social work (9th edition) . Boston, MA: Cengage. ↵
• Arnett, J. J. (2008). The neglected 95%: Why American psychology needs to become less American. American Psychologist , 63, 602–614. ↵
• Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences , 33, 61–135. ↵
• Newsweek magazine published an interesting story about Henrich and his colleague’s study: Begley, S. (2010). What’s really human? The trouble with student guinea pigs. Retrieved from http://www.newsweek.com/2010/07/23/what-s-really-human.html ↵
• Keeter, S., Dimock, M., & Christian, L. (2008). Calling cell phones in ’08 pre-election polls. The Pew Research Center for the People and the Press . Retrieved from  http://people-press.org/files/legacy-pdf/cell-phone-commentary.pdf ↵

entity that a researcher wants to say something about at the end of her study (individual, group, or organization)

the entities that a researcher actually observes, measures, or collects in the course of trying to learn something about her unit of analysis (individuals, groups, or organizations)

the larger group of people you want to be able to make conclusions about based on the conclusions you draw from the people in your sample

the list of people from which a researcher will draw her sample

the people or organizations who control access to the population you want to study

Inclusion criteria are general requirements a person must possess to be a part of your sample.

characteristics that disqualify a person from being included in a sample

the process by which the researcher informs potential participants about the study and attempts to get them to participate

the group of people you successfully recruit from your sampling frame to participate in your study

sampling approaches for which a person’s likelihood of being selected from the sampling frame is known

sampling approaches for which a person’s likelihood of being selected for membership in the sample is unknown

researcher gathers data from whatever cases happen to be convenient or available

(as in generalization) to make claims about a large population based on a smaller sample of people or items

selecting elements from a list using randomly generated numbers

the units in your sampling frame, usually people or documents

selecting every kth element from your sampling frame

the tendency for a pattern to occur at regular intervals

dividing the study population into subgroups based on a characteristic (or strata) and then drawing a sample from each subgroup

the characteristic by which the sample is divided in stratified random sampling

a sampling approach that begins by sampling groups (or clusters) of population elements and then selects elements from within those groups

in cluster sampling, giving clusters different chances of being selected based on their size so that each element within those clusters has an equal chance of being selected

"a sample that looks like the population from which it was selected in all respects that are potentially relevant to the study" (Engel & Schutt, 2011)

the set of all possible samples you could possibly draw for your study

the odds you will detect a significant relationship between variables when one is truly present in your sample

The bias that occurs when those who respond to your request to participate in a study are different from those who do not respond to you request to participate in a study.

Graduate research methods in social work Copyright © 2020 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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When Does a Researcher Choose a Quantitative, Qualitative, or Mixed Research Approach?

• Published: 26 November 2021
• Volume 53 , pages 113–131, ( 2022 )

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• Feyisa Mulisa   ORCID: orcid.org/0000-0002-0738-6554 1  

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In educational studies, the paradigm war over quantitative and qualitative research approaches has raged for more than half a century. The focus in the late twentieth century was on the distinction between the two approaches, and the motivation was to retain one of the approaches’ supremacy. Since the early twenty-first century, there has been a growing interest in situating in the middle position and combining both approaches into a single study or a series of studies. Despite these signs of progress, when it comes to using the appropriate research approach at the right time, beginner educational researchers remain perplexed. This paper, therefore, provides useful guidelines that facilitate the choice of quantitative, qualitative, or mixed research approaches in educational inquiry. To achieve this objective, this article comprises three distinct and underlying areas of interest, which have been structured into three sections. The first section highlights the distinctions between quantitative and qualitative research approaches. The second section discusses the paradigm views that underpin the choice of a particular research approach. Finally, an effort has been made to determine the appropriate time to opt for any of the research approaches that facilitate successful educational investigations. Since truth and the means used to discover it are both dynamic, it is also essential to foresight innovative approaches to research with distinguishing features of applications to educational research.

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Mulisa, F. When Does a Researcher Choose a Quantitative, Qualitative, or Mixed Research Approach?. Interchange 53 , 113–131 (2022). https://doi.org/10.1007/s10780-021-09447-z

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Received : 29 March 2021

Accepted : 18 November 2021

Published : 26 November 2021

Issue Date : March 2022

DOI : https://doi.org/10.1007/s10780-021-09447-z

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Marketing Research: Planning, Process, Practice

Student resources, multiple choice quizzes.

Try these quizzes to test your understanding.

1. There exist two types of research designs only.

2. Which of the following are research design options?

• quantitative, qualitative, critical studies or a mixture of each of these
• style and looks
• depth and scope

3. A mixed research design ______.

• includes people of different cultures
• includes researchers from different backgrounds
• makes use of both quantitative and qualitative information to draw conclusions

4. Incommensurability is ______.

• inability to measure and compare different views/theories
• when you do not have measurement instruments
• when a particular phenomenon cannot be measured

5. Mixed design research is particularly suited to market research because marketers need both quantitative and qualitative information.

6. Triangulation, complementarity, development, problem clarification and relevance are examples of ______.

• problems associated with mixed design approach
• advantages of using mixed design approach
• neither of these

7. Triangulation means ______.

• to use three methods to understand the problem
• to use more than one method to compensate for weaknesses of each one

8. Complementarity means ______.

• making use of different types of information at different levels
• choosing the same information but presented differently

9. Development occurs when research may lead to more clarifications but also more questions that may require different types of information.

10. Before selecting the research design, it is important to consider the type of phenomenon being studied and the possible characteristics of the research design.

11. Action research is more interested in improving objectivity than solving human problems.

12. Action research is primarily interested in resolving practical problems.

13. Action research is not interested in complex problems or ethical issues.

14. Identify which of the following stages are seen to reflect the right chronology of paradigms.

• Positivism, Interpretivism, Critical theoretical, Action research
• Action research, Interpretivism, Critical theoretical, Positivism
• Critical theoretical, Interpretivism, Positivism, Action research
• Positivism, Interpretivism, Action research, Critical theoretical

15. You may carry out an action research without addressing the issue of cycles.

16. The following are action research cycle steps: Reflecting, Planning, Acting, Observing.

17. Case studies are only useful if we are dealing with a new phenomenon.

18. When selecting a research design a phenomenon should be ______.

• communicable

19. Exploratory research is the opposite of ______.

• longitudinal

20. Case studies can be divided according to type. What is not a valid type to position a case study?

• revelatory case
• representative case
• retrospective case