chapter 4 quantitative and qualitative research

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

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Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

Chapter 4. Finding a Research Question and Approaches to Qualitative Research

We’ve discussed the research design process in general and ways of knowing favored by qualitative researchers.  In chapter 2, I asked you to think about what interests you in terms of a focus of study, including your motivations and research purpose.  It might be helpful to start this chapter with those short paragraphs you wrote about motivations and purpose in front of you.  We are now going to try to develop those interests into actual research questions (first part of this chapter) and then choose among various “traditions of inquiry” that will be best suited to answering those questions.  You’ve already been introduced to some of this (in chapter 1), but we will go further here.

Developing a Research Question

Research questions are different from general questions people have about the social world.  They are narrowly tailored to fit a very specific issue, complete with context and time boundaries.  Because we are engaged in empirical science and thus use “data” to answer our questions, the questions we ask must be answerable by data.  A question is not the same as stating a problem.  The point of the entire research project is to answer a particular question or set of questions.  The question(s) should be interesting, relevant, practical, and ethical.  Let’s say I am generally interested in the problem of student loan debt.  That’s a good place to start, but we can’t simply ask,

General question: Is student loan debt really a problem today?

How could we possibly answer that question? What data could we use? Isn’t this really an axiological (values-based) question? There are no clues in the question as to what data would be appropriate here to help us get started. Students often begin with these large unanswerable questions. They are not research questions. Instead, we could ask,

Poor research question: How many people have debt?

This is still not a very good research question. Why not? It is answerable, although we would probably want to clarify the context. We could add some context to improve it so that the question now reads,

Mediocre research question: How many people in the US have debt today? And does this amount vary by age and location?

Now we have added some context, so we have a better idea of where to look and who to look at. But this is still a pretty poor or mediocre research question. Why is that? Let’s say we did answer it. What would we really know? Maybe we would find out that student loan debt has increased over time and that young people today have more of it. We probably already know this. We don’t really want to go through a lot of trouble answering a question whose answer we already have. In fact, part of the reason we are even asking this question is that we know (or think) it is a problem. Instead of asking what you already know, ask a question to which you really do not know the answer. I can’t stress this enough, so I will say it again: Ask a question to which you do not already know the answer . The point of research is not to prove or make a point but to find out something unknown. What about student loan debt is still a mystery to you? Reviewing the literature could help (see chapter 9). By reviewing the literature, you can get a good sense of what is still mysterious or unknown about student loan debt, and you won’t be reinventing the wheel when you conduct your research. Let’s say you review the literature, and you are struck by the fact that we still don’t understand the true impact of debt on how people are living their lives. A possible research question might be,

Fair research question: What impact does student debt have on the lives of debtors?

Good start, but we still need some context to help guide the project. It is not nearly specific enough.

Better research question: What impact does student debt have on young adults (ages twenty-five to thirty-five) living in the US today?

Now we’ve added context, but we can still do a little bit better in narrowing our research question so that it is both clear and doable; in other words, we want to frame it in a way that provides a very clear research program:

Optimal research question: How do young adults (ages twenty-five to thirty-five) living in the US today who have taken on $30,000 or more in student debt describe the impact of their debt on their lives in terms of finding/choosing a job, buying a house, getting married, and other major life events?

Now you have a research question that can be answered and a clear plan of how to answer it. You will talk to young adults living in the US today who have high debt loads and ask them to describe the impacts of debt on their lives. That is all now in the research question. Note how different this very specific question is from where we started with the “problem” of student debt.

Take some time practicing turning the following general questions into research questions:

• What can be done about the excessive use of force by police officers?
• Why haven’t societies taken firmer steps to address climate change?
• How do communities react to / deal with the opioid epidemic?
• Who has been the most adversely affected by COVID?
• When did political polarization get so bad?

Hint: Step back from each of the questions and try to articulate a possible underlying motivation, then formulate a research question that is specific and answerable.

It is important to take the time to come up with a research question, even if this research question changes a bit as you conduct your research (yes, research questions can change!). If you don’t have a clear question to start your research, you are likely to get very confused when designing your study because you will not be able to make coherent decisions about things like samples, sites, methods of data collection, and so on. Your research question is your anchor: “If we don’t have a question, we risk the possibility of going out into the field thinking we know what we’ll find and looking only for proof of what we expect to be there. That’s not empirical research (it’s not systematic)” ( Rubin 2021:37 ).

Researcher Note

How do you come up with ideas for what to study?

I study what surprises me. Usually, I come across a statistic that suggests something is common that I thought was rare. I tend to think it’s rare because the theories I read suggest it should be, and there’s not a lot of work in that area that helps me understand how the statistic came to be. So, for example, I learned that it’s common for Americans to marry partners who grew up in a different class than them and that about half of White kids born into the upper-middle class are downwardly mobile. I was so shocked by these facts that they naturally led to research questions. How do people come to marry someone who grew up in a different class? How do White kids born near the top of the class structure fall?

—Jessi Streib, author of The Power of the Past and Privilege Lost

What if you have literally no idea what the research question should be? How do you find a research question? Even if you have an interest in a topic before you get started, you see the problem now: topics and issues are not research questions! A research question doesn’t easily emerge; it takes a lot of time to hone one, as the practice above should demonstrate. In some research designs, the research question doesn’t even get clearly articulated until the end of data collection . More on that later. But you must start somewhere, of course. Start with your chosen discipline. This might seem obvious, but it is often overlooked. There is a reason it is called a discipline. We tend to think of “sociology,” “public health,” and “physics” as so many clusters of courses that are linked together by subject matter, but they are also disciplines in the sense that the study of each focuses the mind in a particular way and for particular ends. For example, in my own field, sociology, there is a loosely shared commitment to social justice and a general “sociological imagination” that enables its practitioners to connect personal experiences to society at large and to historical forces. It is helpful to think of issues and questions that are germane to your discipline. Within that overall field, there may be a particular course or unit of study you found most interesting. Within that course or unit of study, there may be an issue that intrigued you. And finally, within that issue, there may be an aspect or topic that you want to know more about.

When I was pursuing my dissertation research, I was asked often, “Why did you choose to study intimate partner violence among Native American women?” This question is necessary, and each time I answered, it helped shape me into a better researcher. I was interested in intimate partner violence because I am a survivor. I didn’t have intentions to work with a particular population or demographic—that came from my own deep introspection on my role as a researcher. I always questioned my positionality: What privileges do I hold as an academic? How has public health extracted information from institutionally marginalized populations? How can I build bridges between communities using my position, knowledge, and power? Public health as a field would not exist without the contributions of Indigenous people. So I started hanging out with them at community events, making friends, and engaging in self-education. Through these organic relationships built with Native women in the community, I saw that intimate partner violence was a huge issue. This led me to partner with Indigenous organizations to pursue a better understanding of how Native survivors of intimate partner violence seek support.

—Susanna Y. Park, PhD, mixed-methods researcher in public health and author of “How Native Women Seek Support as Survivors of Intimate Partner Violence: A Mixed-Methods Study”

One of the most exciting and satisfying things about doing academic research is that whatever you end up researching can become part of the body of knowledge that we have collectively created. Don’t make the mistake of thinking that you are doing this all on your own from scratch. Without even being aware of it, no matter if you are a first-year undergraduate student or a fourth-year graduate student, you have been trained to think certain questions are interesting. The very fact that you are majoring in a particular field or have signed up for years of graduate study in a program testifies to some level of commitment to a discipline. What we are looking for, ideally, is that your research builds on in some way (as extension, as critique, as lateral move) previous research and so adds to what we, collectively, understand about the social world. It is helpful to keep this in mind, as it may inspire you and also help guide you through the process. The point is, you are not meant to be doing something no one has ever thought of before, even if you are trying to find something that does not exactly duplicate previous research: “You may be trying to be too clever—aiming to come up with a topic unique in the history of the universe, something that will have people swooning with admiration at your originality and intellectual precociousness. Don’t do it. It’s safer…to settle on an ordinary, middle-of-the-road topic that will lend itself to a nicely organized process of project management. That’s the clever way of proceeding.… You can always let your cleverness shine through during the stages of design, analysis, and write-up. Don’t make things more difficult for yourself than you need to do” ( Davies 2007:20 ).

Rubin ( 2021 ) suggests four possible ways to develop a research question (there are many more, of course, but this can get you started). One way is to start with a theory that interests you and then select a topic where you can apply that theory. For example, you took a class on gender and society and learned about the “glass ceiling.” You could develop a study that tests that theory in a setting that has not yet been explored—maybe leadership at the Oregon Country Fair. The second way is to start with a topic that interests you and then go back to the books to find a theory that might explain it. This is arguably more difficult but often much more satisfying. Ask your professors for help—they might have ideas of theories or concepts that could be relevant or at least give you an idea of what books to read. The third way is to be very clever and select a question that already combines the topic and the theory. Rubin gives as one example sentencing disparities in criminology—this is both a topic and a theory or set of theories. You then just have to figure out particulars like setting and sample. I don’t know if I find this third way terribly helpful, but it might help you think through the possibilities. The fourth way involves identifying a puzzle or a problem, which can be either theoretical (something in the literature just doesn’t seem to make sense and you want to tackle addressing it) or empirical (something happened or is happening, and no one really understands why—think, for example, of mass school shootings).

Once you think you have an issue or topic that is worth exploring, you will need to (eventually) turn that into a good research question. A good research question is specific, clear, and feasible .

Specific . How specific a research question needs to be is somewhat related to the disciplinary conventions and whether the study is conceived inductively or deductively. In deductive research, one begins with a specific research question developed from the literature. You then collect data to test the theory or hypotheses accompanying your research question. In inductive research, however, one begins with data collection and analysis and builds theory from there. So naturally, the research question is a bit vaguer. In general, the more closely aligned to the natural sciences (and thus the deductive approach), the more a very tight and specific research question (along with specific, focused hypotheses) is required. This includes disciplines like psychology, geography, public health, environmental science, and marine resources management. The more one moves toward the humanities pole (and the inductive approach), the more looseness is permitted, as there is a general belief that we go into the field to find what is there, not necessarily what we imagine we are looking for (see figure 4.2). Disciplines such as sociology, anthropology, and gender and sexuality studies and some subdisciplines of public policy/public administration are closer to the humanities pole in this sense.

Regardless of discipline and approach, however, it is a good idea for beginning researchers to create a research question as specific as possible, as this will serve as your guide throughout the process. You can tweak it later if needed, but start with something specific enough that you know what it is you are doing and why. It is more difficult to deal with ambiguity when you are starting out than later in your career, when you have a better handle on what you are doing. Being under a time constraint means the more specific the question, the better. Questions should always specify contexts, geographical locations, and time frames. Go back to your practice research questions and make sure that these are included.

Clear . A clear research question doesn’t only need to be intelligible to any reader (which, of course, it should); it needs to clarify any meanings of particular words or concepts (e.g., What is excessive force?). Check all your concepts to see if there are ways you can clarify them further—for example, note that we shifted from impact of debt to impact of high debt load and specified this as beginning at $30,000. Ideally, we would use the literature to help us clarify what a high debt load is or how to define “excessive” force.

Feasible . In order to know if your question is feasible, you are going to have to think a little bit about your entire research design. For example, a question that asks about the real-time impact of COVID restrictions on learning outcomes would require a time machine. You could tweak the question to ask instead about the long-term impacts of COVID restrictions, as measured two years after their end. Or let’s say you are interested in assessing the damage of opioid abuse on small-town communities across the United States. Is it feasible to cover the entire US? You might need a team of researchers to do this if you are planning on on-the-ground observations. Perhaps a case study of one particular community might be best. Then your research question needs to be changed accordingly.

Here are some things to consider in terms of feasibility:

• Is the question too general for what you actually intend to do or examine? (Are you specifying the world when you only have time to explore a sliver of that world?)
• Is the question suitable for the time you have available? (You will need different research questions for a study that can be completed in a term than one where you have one to two years, as in a master’s program, or even three to eight years, as in a doctoral program.)
• Is the focus specific enough that you know where and how to begin?
• What are the costs involved in doing this study, including time? Will you need to travel somewhere, and if so, how will you pay for it?
• Will there be problems with “access”? (More on this in later chapters, but for now, consider how you might actually find people to interview or places to observe and whether gatekeepers exist who might keep you out.)
• Will you need to submit an application proposal for your university’s IRB (institutional review board)? If you are doing any research with live human subjects, you probably need to factor in the time and potential hassle of an IRB review (see chapter 8). If you are under severe time constraints, you might need to consider developing a research question that can be addressed with secondary sources, online content, or historical archives (see chapters 16 and 17).

In addition to these practicalities, you will also want to consider the research question in terms of what is best for you now. Are you engaged in research because you are required to be—jumping a hurdle for a course or for your degree? If so, you really do want to think about your project as training and develop a question that will allow you to practice whatever data collection and analysis techniques you want to develop. For example, if you are a grad student in a public health program who is interested in eventually doing work that requires conducting interviews with patients, develop a research question and research design that is interview based. Focus on the practicality (and practice) of the study more than the theoretical impact or academic contribution, in other words. On the other hand, if you are a PhD candidate who is seeking an academic position in the future, your research question should be pitched in a way to build theoretical knowledge as well (the phrasing is typically “original contribution to scholarship”).

The more time you have to devote to the study and the larger the project, the more important it is to reflect on your own motivations and goals when crafting a research question (remember chapter 2?). By “your own motivations and goals,” I mean what interests you about the social world and what impact you want your research to have, both academically and practically speaking. Many students have secret (or not-so-secret) plans to make the world a better place by helping address climate change, pointing out pressure points to fight inequities, or bringing awareness to an overlooked area of concern. My own work in graduate school was motivated by the last of these three—the not-so-secret goal of my research was to raise awareness about obstacles to success for first-generation and working-class college students. This underlying goal motivated me to complete my dissertation in a timely manner and then to further continue work in this area and see my research get published. I cared enough about the topic that I was not ready to put it away. I am still not ready to put it away. I encourage you to find topics that you can’t put away, ever. That will keep you going whenever things get difficult in the research process, as they inevitably will.

On the other hand, if you are an undergraduate and you really have very little time, some of the best advice I have heard is to find a study you really like and adapt it to a new context. Perhaps you read a study about how students select majors and how this differs by class ( Hurst 2019 ). You can try to replicate the study on a small scale among your classmates. Use the same research question, but revise for your context. You can probably even find the exact questions I  used and ask them in the new sample. Then when you get to the analysis and write-up, you have a comparison study to guide you, and you can say interesting things about the new context and whether the original findings were confirmed (similar) or not. You can even propose reasons why you might have found differences between one and the other.

Another way of thinking about research questions is to explicitly tie them to the type of purpose of your study. Of course, this means being very clear about what your ultimate purpose is! Marshall and Rossman ( 2016 ) break down the purpose of a study into four categories: exploratory, explanatory, descriptive, and emancipatory ( 78 ). Exploratory purpose types include wanting to investigate little-understood phenomena, or identifying or discovering important new categories of meaning, or generating hypotheses for further research. For these, research questions might be fairly loose: What is going on here? How are people interacting on this site? What do people talk about when you ask them about the state of the world? You are almost (but never entirely) starting from scratch. Be careful though—just because a topic is new to you does not mean it is really new. Someone else (or many other someones) may already have done this exploratory research. Part of your job is to find this out (more on this in “What Is a ‘Literature Review’?” in chapter 9). Descriptive purposes (documenting and describing a phenomenon) are similar to exploratory purposes but with a much clearer goal (description). A good research question for a descriptive study would specify the actions, events, beliefs, attitudes, structures, and/or processes that will be described.

Most researchers find that their topic has already been explored and described, so they move to trying to explain a relationship or phenomenon. For these, you will want research questions that capture the relationships of interest. For example, how does gender influence one’s understanding of police brutality (because we already know from the literature that it does, so now we are interested in understanding how and why)? Or what is the relationship between education and climate change denialism? If you find that prior research has already provided a lot of evidence about those relationships as well as explanations for how they work, and you want to move the needle past explanation into action, you might find yourself trying to conduct an emancipatory study. You want to be even more clear in acknowledging past research if you find yourself here. Then create a research question that will allow you to “create opportunities and the will to engage in social action” ( Marshall and Rossman 2016:78 ). Research questions might ask, “How do participants problematize their circumstances and take positive social action?” If we know that some students have come together to fight against student debt, how are they doing this, and with what success? Your purpose would be to help evaluate possibilities for social change and to use your research to make recommendations for more successful emancipatory actions.

Recap: Be specific. Be clear. Be practical. And do what you love.

Choosing an Approach or Tradition

Qualitative researchers may be defined as those who are working with data that is not in numerical form, but there are actually multiple traditions or approaches that fall under this broad category. I find it useful to know a little bit about the history and development of qualitative research to better understand the differences in these approaches. The following chart provides an overview of the six phases of development identified by Denzin and Lincoln ( 2005 ):

Table 4.1. Six Phases of Development

There are other ways one could present the history as well. Feminist theory and methodologies came to the fore in the 1970s and 1980s and had a lot to do with the internal critique of more positivist approaches. Feminists were quite aware that standpoint matters—that the identity of the researcher plays a role in the research, and they were ardent supporters of dismantling unjust power systems and using qualitative methods to help advance this mission. You might note, too, that many of the internal disputes were basically epistemological disputes about how we know what we know and whether one’s social location/position delimits that knowledge. Today, we are in a bountiful world of qualitative research, one that embraces multiple forms of knowing and knowledge. This is good, but it means that you, the student, have more choice when it comes to situating your study and framing your research question, and some will expect you to signal the choices you have made in any research protocols you write or publications and presentations.

Creswell’s ( 1998 ) definition of qualitative research includes the notion of distinct traditions of inquiry: “Qualitative research is an inquiry process of understanding based on distinct methodological traditions of inquiry that explore a social or human problem. The research builds complex,   holistic pictures, analyzes words, reports detailed views of informants , and conducted the study in a natural setting” (15; emphases added). I usually caution my students against taking shelter under one of these approaches, as, practically speaking, there is a lot of mixing of traditions among researchers. And yet it is useful to know something about the various histories and approaches, particularly as you are first starting out. Each tradition tends to favor a particular epistemological perspective (see chapter 3), a way of reasoning (see “ Advanced: Inductive versus Deductive Reasoning ”), and a data-collection technique.

There are anywhere from ten to twenty “traditions of inquiry,” depending on how one draws the boundaries. In my accounting, there are twelve, but three approaches tend to dominate the field.

Ethnography

Ethnography was developed from the discipline of anthropology, as the study of (other) culture(s). From a relatively positivist/objective approach to writing down the “truth” of what is observed during the colonial era (where this “truth” was then often used to help colonial administrators maintain order and exploit people and extract resources more effectively), ethnography was adopted by all kinds of social science researchers to get a better understanding of how groups of people (various subcultures and cultures) live their lives. Today, ethnographers are more likely to be seeking to dismantle power relations than to support them. They often study groups of people that are overlooked and marginalized, and sometimes they do the obverse by demonstrating how truly strange the familiar practices of the dominant group are. Ethnography is also central to organizational studies (e.g., How does this institution actually work?) and studies of education (e.g., What is it like to be a student during the COVID era?).

Ethnographers use methods of participant observation and intensive fieldwork in their studies, often living or working among the group under study for months at a time (and, in some cases, years). I’ve called this “deep ethnography,” and it is the subject of chapter 14. The data ethnographers analyze are copious “field notes” written while in the field, often supplemented by in-depth interviews and many more casual conversations. The final product of ethnographers is a “thick” description of the culture. This makes reading ethnographies enjoyable, as the goal is to write in such a way that the reader feels immersed in the culture.

There are variations on the ethnography, such as the autoethnography , where the researcher uses a systematic and rigorous study of themselves to better understand the culture in which they find themselves. Autoethnography is a relatively new approach, even though it is derived from one of the oldest approaches. One can say that it takes to heart the feminist directive to “make the personal political,” to underscore the connections between personal experiences and larger social and political structures. Introspection becomes the primary data source.

Grounded Theory

Grounded Theory holds a special place in qualitative research for a few reasons, not least of which is that nonqualitative researchers often mistakenly believe that Grounded Theory is the only qualitative research methodology . Sometimes, it is easier for students to explain what they are doing as “Grounded Theory” because it sounds “more scientific” than the alternative descriptions of qualitative research. This is definitely part of its appeal. Grounded Theory is the name given to the systematic inductive approach first developed by Glaser and Strauss in 1967, The Discovery of Grounded Theory: Strategies for Qualitative Research . Too few people actually read Glaser and Strauss’s book. It is both groundbreaking and fairly unremarkable at the same time. As a historical intervention into research methods generally, it is both a sharp critique of positivist methods in the social sciences (theory testing) and a rejection of purely descriptive accounts-building qualitative research. Glaser and Strauss argued for an approach whose goal was to construct (middle-level) theories from recursive data analysis of nonnumerical data (interviews and observations). They advocated a “constant comparative method” in which coding and analysis take place simultaneously and recursively. The demands are fairly strenuous. If done correctly, the result is the development of a new theory about the social world.

So why do I call this “fairly unremarkable”? To some extent, all qualitative research already does what Glaser and Strauss ( 1967 ) recommend, albeit without denoting the processes quite so specifically. As will be seen throughout the rest of this textbook, all qualitative research employs some “constant comparisons” through recursive data analyses. Where Grounded Theory sets itself apart from a significant number of qualitative research projects, however, is in its dedication to inductively building theory. Personally, I think it is important to understand that Glaser and Strauss were rejecting deductive theory testing in sociology when they first wrote their book. They were part of a rising cohort who rejected the positivist mathematical approaches that were taking over sociology journals in the 1950s and 1960s. Here are some of the comments and points they make against this kind of work:

Accurate description and verification are not so crucial when one’s purpose is to generate theory. ( 28 ; further arguing that sampling strategies are different when one is not trying to test a theory or generalize results)

Illuminating perspectives are too often suppressed when the main emphasis is verifying theory. ( 40 )

Testing for statistical significance can obscure from theoretical relevance. ( 201 )

Instead, they argued, sociologists should be building theories about the social world. They are not physicists who spend time testing and refining theories. And they are not journalists who report descriptions. What makes sociologists better than journalists and other professionals is that they develop theory from their work “In their driving efforts to get the facts [research sociologists] tend to forget that the distinctive offering of sociology to our society is sociological theory, not research description” ( 30–31 ).

Grounded Theory’s inductive approach can be off-putting to students who have a general research question in mind and a working hypothesis. The true Grounded Theory approach is often used in exploratory studies where there are no extant theories. After all, the promise of this approach is theory generation, not theory testing. Flying totally free at the start can be terrifying. It can also be a little disingenuous, as there are very few things under the sun that have not been considered before. Barbour ( 2008:197 ) laments that this approach is sometimes used because the researcher is too lazy to read the relevant literature.

To summarize, Glaser and Strauss justified the qualitative research project in a way that gave it standing among the social sciences, especially vis-à-vis quantitative researchers. By distinguishing the constant comparative method from journalism, Glaser and Strauss enabled qualitative research to gain legitimacy.

So what is it exactly, and how does one do it? The following stages provide a succinct and basic overview, differentiating the portions that are similar to/in accordance with qualitative research methods generally and those that are distinct from the Grounded Theory approach:

Step 1. Select a case, sample, and setting (similar—unless you begin with a theory to test!).

Step 2. Begin data collection (similar).

Step 3. Engage data analysis (similar in general but specificity of details somewhat unique to Grounded Theory): (1) emergent coding (initial followed by focused), (2) axial (a priori) coding , (3) theoretical coding , (4) creation of theoretical categories; analysis ends when “theoretical saturation ” has been achieved.

Grounded Theory’s prescriptive (i.e., it has a set of rules) framework can appeal to beginning students, but it is unnecessary to adopt the entire approach in order to make use of some of its suggestions. And if one does not exactly follow the Grounded Theory rulebook, it can mislead others if you tend to call what you are doing Grounded Theory when you are not:

Grounded theory continues to be a misunderstood method, although many researchers purport to use it. Qualitative researchers often claim to conduct grounded theory studies without fully understanding or adopting its distinctive guidelines. They may employ one or two of the strategies or mistake qualitative analysis for grounded theory. Conversely, other researchers employ grounded theory methods in reductionist, mechanistic ways. Neither approach embodies the flexible yet systematic mode of inquiry, directed but open-ended analysis, and imaginative theorizing from empirical data that grounded theory methods can foster. Subsequently, the potential of grounded theory methods for generating middle-range theory has not been fully realized ( Charmaz 2014 ).

Phenomenology

Where Grounded Theory sets itself apart for its inductive systematic approach to data analysis, phenomenologies are distinct for their focus on what is studied—in this case, the meanings of “lived experiences” of a group of persons sharing a particular event or circumstance. There are phenomenologies of being working class ( Charlesworth 2000 ), of the tourist experience ( Cohen 1979 ), of Whiteness ( Ahmed 2007 ). The phenomenon of interest may also be an emotion or circumstance. One can study the phenomenon of “White rage,” for example, or the phenomenon of arranged marriage.

The roots of phenomenology lie in philosophy (Husserl, Heidegger, Merleau-Ponty, Sartre) but have been adapted by sociologists in particular. Phenomenologists explore “how human beings make sense of experience and transform experience into consciousness, both individually and as shared meaning” ( Patton 2002:104 ).

One of the most important aspects of conducting a good phenomenological study is getting the sample exactly right so that each person can speak to the phenomenon in question. Because the researcher is interested in the meanings of an experience, in-depth interviews are the preferred method of data collection. Observations are not nearly as helpful here because people may do a great number of things without meaning to or without being conscious of their implications. This is important to note because phenomenologists are studying not “the reality” of what happens at all but an articulated understanding of a lived experience. When reading a phenomenological study, it is important to keep this straight—too often I have heard students critique a study because the interviewer didn’t actually see how people’s behavior might conflict with what they say (which is, at heart, an epistemological issue!).

In addition to the “big three,” there are many other approaches; some are variations, and some are distinct approaches in their own right. Case studies focus explicitly on context and dynamic interactions over time and can be accomplished with quantitative or qualitative methods or a mixture of both (for this reason, I am not considering it as one of the big three qualitative methods, even though it is a very common approach). Whatever methods are used, a contextualized deep understanding of the case (or cases) is central.

Critical inquiry is a loose collection of techniques held together by a core argument that understanding issues of power should be the focus of much social science research or, to put this another way, that it is impossible to understand society (its people and institutions) without paying attention to the ways that power relations and power dynamics inform and deform those people and institutions. This attention to power dynamics includes how research is conducted too. All research fundamentally involves issues of power. For this reason, many critical inquiry traditions include a place for collaboration between researcher and researched. Examples include (1) critical narrative analysis, which seeks to describe the meaning of experience for marginalized or oppressed persons or groups through storytelling; (2) participatory action research, which requires collaboration between the researcher and the research subjects or community of interest; and (3) critical race analysis, a methodological application of Critical Race Theory (CRT), which posits that racial oppression is endemic (if not always throughout time and place, at least now and here).

Do you follow a particular tradition of inquiry? Why?

Shawn Wilson’s book, Research Is Ceremony: Indigenous Research Methods , is my holy grail. It really flipped my understanding of research and relationships. Rather than thinking linearly and approaching research in a more canonical sense, Wilson shook my world view by drawing me into a pattern of inquiry that emphasized transparency and relational accountability. The Indigenous research paradigm is applicable in all research settings, and I follow it because it pushes me to constantly evaluate my position as a knowledge seeker and knowledge sharer.

Autoethnography takes the researcher as the subject. This is one approach that is difficult to explain to more quantitatively minded researchers, as it seems to violate many of the norms of “scientific research” as understood by them. First, the sample size is quite small—the n is 1, the researcher. Two, the researcher is not a neutral observer—indeed, the subjectivity of the researcher is the main strength of this approach. Autoethnographies can be extremely powerful for their depth of understanding and reflexivity, but they need to be conducted in their own version of rigor to stand up to scrutiny by skeptics. If you are skeptical, read one of the excellent published examples out there—I bet you will be impressed with what you take away. As they say, the proof is in the pudding on this approach.

Advanced: Inductive versus Deductive Reasoning

There has been a great deal of ink shed in the discussion of inductive versus deductive approaches, not all of it very instructive. Although there is a huge conceptual difference between them, in practical terms, most researchers cycle between the two, even within the same research project. The simplest way to explain the difference between the two is that we are using deductive reasoning when we test an existing theory (move from general to particular), and we are using inductive reasoning when we are generating theory (move from particular to general). Figure 4.2 provides a schematic of the deductive approach. From the literature, we select a theory about the impact of student loan debt: student loan debt will delay homeownership among young adults. We then formulate a hypothesis based on this theory: adults in their thirties with high debt loads will be less likely to own homes than their peers who do not have high debt loads. We then collect data to test the hypothesis and analyze the results. We find that homeownership is substantially lower among persons of color and those who were the first in their families to graduate from college. Notably, high debt loads did not affect homeownership among White adults whose parents held college degrees. We thus refine the theory to match the new findings: student debt loads delay homeownership among some young adults, thereby increasing inequalities in this generation. We have now contributed new knowledge to our collective corpus.

The inductive approach is contrasted in figure 4.3. Here, we did not begin with a preexisting theory or previous literature but instead began with an observation. Perhaps we were conducting interviews with young adults who held high amounts of debt and stumbled across this observation, struck by how many were renting apartments or small houses. We then noted a pattern—not all the young adults we were talking to were renting; race and class seemed to play a role here. We would then probably expand our study in a way to be able to further test this developing theory, ensuring that we were not seeing anomalous patterns. Once we were confident about our observations and analyses, we would then develop a theory, coming to the same place as our deductive approach, but in reverse.

A third form of reasoning, abductive (sometimes referred to as probabilistic reasoning) was developed in the late nineteenth century by American philosopher Charles Sanders Peirce. I have included some articles for further reading for those interested.

Among social scientists, the deductive approach is often relaxed so that a research question is set based on the existing literature rather than creating a hypothesis or set of hypotheses to test. Some journals still require researchers to articulate hypotheses, however. If you have in mind a publication, it is probably a good idea to take a look at how most articles are organized and whether specific hypotheses statements are included.

Table 4.2. Twelve Approaches. Adapted from Patton 2002:132-133.

Further Readings

The following readings have been examples of various approaches or traditions of inquiry:

Ahmed, Sara. 2007. “A Phenomenology of Whiteness.” Feminist Theory 8(2):149–168.

Charlesworth, Simon. 2000. A Phenomenology of Working-Class Experience . Cambridge: Cambridge University Press.*

Clandinin, D. Jean, and F. Michael Connelly. 2000. Narrative Inquiry: Experience and Story in Qualitative Research . San Francisco: Jossey-Bass.

Cohen, E. 1979. “A Phenomenology of Tourist Experiences.” Sociology 13(2):179–201.

Cooke, Bill, and Uma Kothari, eds. 2001. Participation: The New Tyranny? London: Zed Books. A critique of participatory action.

Corbin, Juliet, and Anselm Strauss. 2008. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 3rd ed. Thousand Oaks, CA: SAGE.

Crabtree, B. F., and W. L. Miller, eds. 1999. Doing Qualitative Research: Multiple Strategies . Thousand Oaks, CA: SAGE.

Creswell, John W. 1997. Qualitative Inquiry and Research Design: Choosing among Five Approaches. Thousand Oaks, CA: SAGE.

Glaser, Barney G., and Anselm Strauss. 1967. The Discovery of Grounded Theory: Strategies for Qualitative Research . New York: Aldine.

Gobo, Giampetro, and Andrea Molle. 2008. Doing Ethnography . Thousand Oaks, CA: SAGE.

Hancock, Dawson B., and Bob Algozzine. 2016. Doing Case Study Research: A Practical Guide for Beginning Research . 3rd ed. New York: Teachers College Press.

Harding, Sandra. 1987. Feminism and Methodology . Bloomington: Indiana University Press.

Husserl, Edmund. (1913) 2017. Ideas: Introduction to Pure Phenomenology . Eastford, CT: Martino Fine Books.

Rose, Gillian. 2012. Visual Methodologies . 3rd ed. London: SAGE.

Van der Riet, M. 2009. “Participatory Research and the Philosophy of Social Science: Beyond the Moral Imperative.” Qualitative Inquiry 14(4):546–565.

Van Manen, Max. 1990. Researching Lived Experience: Human Science for an Action Sensitive Pedagogy . Albany: State University of New York.

Wortham, Stanton. 2001. Narratives in Action: A Strategy for Research and Analysis . New York: Teachers College Press.

Inductive, Deductive, and Abductive Reasoning and Nomothetic Science in General

Aliseda, Atocha. 2003. “Mathematical Reasoning vs. Abductive Reasoning: A Structural Approach.” Synthese 134(1/2):25–44.

Bonk, Thomas. 1997. “Newtonian Gravity, Quantum Discontinuity and the Determination of Theory by Evidence.” Synthese 112(1):53–73. A (natural) scientific discussion of inductive reasoning.

Bonnell, Victoria E. 1980. “The Uses of Theory, Concepts and Comparison in Historical Sociology.” C omparative Studies in Society and History 22(2):156–173.

Crane, Mark, and Michael C. Newman. 1996. “Scientific Method in Environmental Toxicology.” Environmental Reviews 4(2):112–122.

Huang, Philip C. C., and Yuan Gao. 2015. “Should Social Science and Jurisprudence Imitate Natural Science?” Modern China 41(2):131–167.

Mingers, J. 2012. “Abduction: The Missing Link between Deduction and Induction. A Comment on Ormerod’s ‘Rational Inference: Deductive, Inductive and Probabilistic Thinking.’” Journal of the Operational Research Society 63(6):860–861.

Ormerod, Richard J. 2010. “Rational Inference: Deductive, Inductive and Probabilistic Thinking.” Journal of the Operational Research Society 61(8):1207–1223.

Perry, Charner P. 1927. “Inductive vs. Deductive Method in Social Science Research.” Southwestern Political and Social Science Quarterly 8(1):66–74.

Plutynski, Anya. 2011. “Four Problems of Abduction: A Brief History.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 1(2):227–248.

Thompson, Bruce, and Gloria M. Borrello. 1992. “Different Views of Love: Deductive and Inductive Lines of Inquiry.” Current Directions in Psychological Science 1(5):154–156.

Tracy, Sarah J. 2012. “The Toxic and Mythical Combination of a Deductive Writing Logic for Inductive Qualitative Research.” Qualitative Communication Research 1(1):109–141.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A person who introduces the researcher to a field site’s culture and population.  Also referred to as guides.  Used in ethnography .

A form of research and a methodological tradition of inquiry in which the researcher uses self-reflection and writing to explore personal experiences and connect this autobiographical story to wider cultural, political, and social meanings and understandings.  “Autoethnography is a research method that uses a researcher's personal experience to describe and critique cultural beliefs, practices, and experiences” ( Adams, Jones, and Ellis 2015 ).

The philosophical framework in which research is conducted; the approach to “research” (what practices this entails, etc.).  Inevitably, one’s epistemological perspective will also guide one’s methodological choices, as in the case of a constructivist who employs a Grounded Theory approach to observations and interviews, or an objectivist who surveys key figures in an organization to find out how that organization is run.  One of the key methodological distinctions in social science research is that between quantitative and qualitative research.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A later stage coding process used in Grounded Theory in which data is reassembled around a category, or axis.

A later stage-coding process used in Grounded Theory in which key words or key phrases capture the emergent theory.

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

A methodological tradition of inquiry that focuses on the meanings held by individuals and/or groups about a particular phenomenon (e.g., a “phenomenology of whiteness” or a “phenomenology of first-generation college students”).  Sometimes this is referred to as understanding “the lived experience” of a particular group or culture.  Interviews form the primary tool of data collection for phenomenological studies.  Derived from the German philosophy of phenomenology (Husserl 1913; 2017).

The number of individuals (or units) included in your sample

A form of reasoning which employs a “top-down” approach to drawing conclusions: it begins with a premise or hypothesis and seeks to verify it (or disconfirm it) with newly collected data.  Inferences are made based on widely accepted facts or premises.  Deduction is idea-first, followed by observations and a conclusion.  This form of reasoning is often used in quantitative research and less often in qualitative research.  Compare to inductive reasoning .  See also abductive reasoning .

A form of reasoning that employs a “bottom-up” approach to drawing conclusions: it begins with the collection of data relevant to a particular question and then seeks to build an argument or theory based on an analysis of that data.  Induction is observation first, followed by an idea that could explain what has been observed.  This form of reasoning is often used in qualitative research and seldom used in qualitative research.  Compare to deductive reasoning .  See also abductive reasoning .

An “interpretivist” form of reasoning in which “most likely” conclusions are drawn, based on inference.  This approach is often used by qualitative researchers who stress the recursive nature of qualitative data analysis.  Compare with deductive reasoning and inductive reasoning .

A form of social science research that generally follows the scientific method as established in the natural sciences.  In contrast to idiographic research , the nomothetic researcher looks for general patterns and “laws” of human behavior and social relationships.  Once discovered, these patterns and laws will be expected to be widely applicable.  Quantitative social science research is nomothetic because it seeks to generalize findings from samples to larger populations.  Most qualitative social science research is also nomothetic, although generalizability is here understood to be theoretical in nature rather than statistical .  Some qualitative researchers, however, espouse the idiographic research paradigm instead.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Quantitative and Qualitative Research: An Overview of Approaches

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In Chap. 1 , the nature and scope of research were outlined and included an overview of quantitative and qualitative research and a brief description of research designs. In this chapter, both quantitative and qualitative research will be described in a little more detail with respect to essential features and characteristics. Furthermore, the research designs used in each of these approaches will be reviewed. Finally, this chapter will conclude with examples of published quantitative and qualitative research in medical imaging and radiation therapy.

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4 Gathering and Analyzing Qualitative Data

Gathering and analyzing qualitative data.

As the role of clinician researchers expands beyond the bedside, it is important to consider the possibilities of inquiry beyond the quantitative approach. In contrast to the quantitative approach, qualitative methodology is highly inductive and relies on the background and interpretation of the researcher to derive meaning from the gathering and analytic processes central to qualitative inquiry.

Chapter 4: Learning Objectives

As you explore the research opportunities central to your interests to consider whether qualitative component would enrich your work, you’ll be able to:

• Define what qualitative research is
• Compare qualitative and quantitative approaches
• Describe the process of creating themes from recurring ideas gleaned from narrative interviews

What Is Qualitative Research?

Quantitative researchers typically start with a focused research question or hypothesis, collect a small amount of numerical data from a large number of individuals, describe the resulting data using statistical techniques, and draw general conclusions about some large population. Although this method is by far the most common approach to conducting empirical research in fields such as respiratory care and other clinical fields, there is an important alternative called qualitative research. Qualitative research originated in the disciplines of anthropology and sociology but is now used to study psychological topics as well. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques, such as grounded theory, thematic analysis, critical discourse analysis, or interpretative phenomenological analysis. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants.

Consider, for example, a study by researcher Per Lindqvist and his colleagues, who wanted to learn how the families of teenage suicide victims cope with their loss (Lindqvist, Johansson, & Karlsson, 2008). They did not have a specific research question or hypothesis, such as, What percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To address this question, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected.

The Purpose of Qualitative Research

The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior. This method is how we know that people have a strong tendency to obey authority figures, for example, and that female undergraduate students are not substantially more talkative than male undergraduate students. But while quantitative research is good at providing precise answers to specific research questions, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And quantitative research is not very good at communicating what it is actually like to be a member of a particular group in a particular situation.

But the relative weaknesses of quantitative research are the relative strengths of qualitative research. Qualitative research can help researchers to generate new and interesting research questions and hypotheses. The research of Lindqvist and colleagues, for example, suggests that there may be a general relationship between how unexpected a suicide is and how consumed the family is with trying to understand why the teen committed suicide. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Among qualitative researchers, this depth is often referred to as “thick description” (Geertz, 1973) .

Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the “lived experience” of the research participants. Lindqvist and colleagues, for example, describe how all the families spontaneously offered to show the interviewer the victim’s bedroom or the place where the suicide occurred—revealing the importance of these physical locations to the families. It seems unlikely that a quantitative study would have discovered this detail. The table below lists some contrasts between qualitative and quantitative research

Data Collection and Analysis in Qualitative Research

Data collection approaches in qualitative research are quite varied and can involve naturalistic observation, participant observation, archival data, artwork, and many other things. But one of the most common approaches, especially for psychological research, is to conduct interviews. Interviews in qualitative research can be unstructured—consisting of a small number of general questions or prompts that allow participants to talk about what is of interest to them—or structured, where there is a strict script that the interviewer does not deviate from. Most interviews are in between the two and are called semi-structured interviews, where the researcher has a few consistent questions and can follow up by asking more detailed questions about the topics that come up. Such interviews can be lengthy and detailed, but they are usually conducted with a relatively small sample. The unstructured interview was the approach used by Lindqvist and colleagues in their research on the families of suicide victims because the researchers were aware that how much was disclosed about such a sensitive topic should be led by the families, not by the researchers.

Another approach used in qualitative research involves small groups of people who participate together in interviews focused on a particular topic or issue, known as focus groups. The interaction among participants in a focus group can sometimes bring out more information than can be learned in a one- on-one interview. The use of focus groups has become a standard technique in business and industry among those who want to understand consumer tastes and preferences. The content of all focus group interviews is usually recorded and transcribed to facilitate later analyses. However, we know from social psychology that group dynamics are often at play in any group, including focus groups, and it is useful to be aware of those possibilities. For example, the desire to be liked by others can lead participants to provide inaccurate answers that they believe will be perceived favorably by the other participants. The same may be said for personality characteristics. For example, highly extraverted participants can sometimes dominate discussions within focus groups.

Data Analysis in Qualitative Research

Although quantitative and qualitative research generally differ along several important dimensions (e.g., the specificity of the research question, the type of data collected), it is the method of data analysis that distinguishes them more clearly than anything else. To illustrate this idea, imagine a team of researchers that conducts a series of unstructured interviews with people recovering from alcohol use disorder to learn about the role of their religious faith in their recovery. Although this project sounds like qualitative research, imagine further that once they collect the data, they code the data in terms of how often each participant mentions God (or a “higher power”), and they then use descriptive and inferential statistics to find out whether those who mention God more often are more successful in abstaining from alcohol. Now it sounds like quantitative research. In other words, the quantitative-qualitative distinction depends more on what researchers do with the data they have collected than with why or how they collected the data.

But what does qualitative data analysis look like? Just as there are many ways to collect data in qualitative research, there are many ways to analyze data. Here we focus on one general approach called grounded theory (Glaser & Strauss, 1967) . This approach was developed within the field of sociology in the 1960s and has gradually gained popularity in psychology. Remember that in quantitative research, it is typical for the researcher to start with a theory, derive a hypothesis from that theory, and then collect data to test that specific hypothesis. In qualitative research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this analysis in stages. First, they identify ideas that are repeated throughout the data. Then they organize these ideas into a smaller number of broader themes. Finally, they write a theoretical narrative—an interpretation of the data in terms of the themes that they have identified. This theoretical narrative focuses on the subjective experience of the participants and is usually supported by many direct quotations from the participants themselves.

As an example, consider a study by researchers Laura Abrams and Laura Curran, who used the grounded theory approach to study the experience of postpartum depression symptoms among low-income mothers (Abrams & Curran, 2009) . Their data were the result of unstructured interviews with 19 participants. The table below hows the five broad themes the researchers identified and the more specific repeating ideas that made up each of those themes. In their research report, they provide numerous quotations from their participants, such as this one from “Destiny:”

“Well, just recently my apartment was broken into and the fact that his Medicaid for some reason was cancelled so a lot of things was happening within the last two weeks all at one time. So that in itself I don’t want to say almost drove me mad but it put me in a funk….Like I really was depressed. (p. 357)”

Their theoretical narrative focused on the participants’ experience of their symptoms, not as an abstract “affective disorder” but as closely tied to the daily struggle of raising children alone under often difficult circumstances. The table below illustrates the process of creating themes from repeating ideas in the qualitative research gathering and analysis process.

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize qualitative methods on the grounds that they lack objectivity, are difficult to evaluate in terms of reliability and validity, and do not allow generalization to people or situations other than those actually studied. At the same time, some qualitative researchers criticize quantitative methods on the grounds that they overlook the richness of human behavior and experience and instead answer simple questions about easily quantifiable variables.

In general, however, qualitative researchers are well aware of the issues of objectivity, reliability, validity, and generalizability. In fact, they have developed a number of frameworks for addressing these issues (which are beyond the scope of our discussion). And in general, quantitative researchers are well aware of the issue of oversimplification. They do not believe that all human behavior and experience can be adequately described in terms of a small number of variables and the statistical relationships among them. Instead, they use simplification as a strategy for uncovering general principles of human behavior.

Many researchers from both the quantitative and qualitative camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). In fact, the studies by Lindqvist and colleagues and by Abrams and Curran both combined quantitative and qualitative approaches. One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. Again, while a qualitative study might suggest that families who experience an unexpected suicide have more difficulty resolving the question of why, a well-designed quantitative study could test a hypothesis by measuring these specific variables in a large sample. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge and how can they be reconciled?

Using qualitative research can often help clarify quantitative results via triangulation. Trenor, Yu, Waight, Zerda, and Sha (2008) investigated the experience of female engineering students at a university. In the first phase, female engineering students were asked to complete a survey, where they rated a number of their perceptions, including their sense of belonging. Their results were compared across the student ethnicities, and statistically, the various ethnic groups showed no differences in their ratings of their sense of belonging.

One might look at that result and conclude that ethnicity does not have anything to do with one’s sense of belonging. However, in the second phase, the authors also conducted interviews with the students, and in those interviews, many minority students reported how the diversity of cultures at the university enhanced their sense of belonging. Without the qualitative component, we might have drawn the wrong conclusion about the quantitative results.

This example shows how qualitative and quantitative research work together to help us understand human behavior. Some researchers have characterized qualitative research as best for identifying behaviors or the phenomenon whereas quantitative research is best for understanding meaning or identifying the mechanism. However, Bryman (2012) argues for breaking down the divide between these arbitrarily different ways of investigating the same questions.

Key Takeaways

• The qualitative approach is centered on an inductive method of reasoning
• The qualitative approach focuses on understanding phenomenon through the perspective of those experiencing it
• Researchers search for recurring topics and group themes to build upon theory to explain findings
• A mixed methods approach uses both quantitative and qualitative methods to explain different aspects of a phenomenon, processes, or practice
• This chapter can be attributed to Research Methods in Psychology by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. This adaptation constitutes the fourth edition of this textbook, and builds upon the second Canadian edition by Rajiv S. Jhangiani (Kwantlen Polytechnic University) and I-Chant A. Chiang (Quest University Canada), the second American edition by Dana C. Leighton (Texas A&M University-Texarkana), and the third American edition by Carrie Cuttler (Washington State University) and feedback from several peer reviewers coordinated by the Rebus Community. This edition is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ↵

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Chapter 2: Psychological Research

Qualitative and Quantitative Approaches to Research

When designing a study, typically, researchers choose a quantitative or qualitative research design. In some cases, a mixed-method approach, including both quantitative and qualitative measures, may be appropriate. Which approach used will develop on the research question and the type of information sought. Quantitative methods may be better for understanding what is happening, while qualitative methods may be better for understanding the hows and why of a phenomenon.

Video 2.2 Types of Research explains the difference between qualitative and quantitative research.

Quantitative Research

Quantitative research typically starts with a focused research question or hypothesis, collects a small amount of data from each of a large number of individuals, describes the resulting data using statistical techniques, and draws general conclusions about some large population. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior; however, it is not nearly as good at  generating   novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation. But the relative weaknesses of quantitative research are the relative strengths of qualitative research.

Qualitative Research

Although quantitative research is by far the most common approach to conducting empirical research in psychology, there is a vital alternative called qualitative research . Qualitative research can help researchers to generate new and interesting research questions and hypotheses. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the  experience   of their research participants. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the ‘lived experience’ of the research participants.

Mixed-Methods

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize that qualitative methods lack objectivity, are challenging to evaluate, and do not allow generalization to other people or situations. At the same time, some qualitative researchers criticize that quantitative methods overlook the richness of behavior and experience, and instead answer simple questions about easily quantifiable variables. However, many researchers from both camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge, and how can they be reconciled?

Video 2.3 What are Qualitative and Quantitative Variables explains the difference between quantitative and qualitative variables that may be used in research.

Becoming Familiar with Research

An excellent way to become more familiar with these research approaches, both quantitative and qualitative, is to look at journal articles, which are written in sections that follow these steps in the scientific process. Most psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract (summary of the article), introduction or literature review, methods explaining how the study was conducted, results of the study, discussion and interpretation of findings, and references.

The aftermath of teenage suicide: a qualitative study of the psychosocial consequences for the supervising family

Per Lindqvist and his colleagues (2008), wanted to learn how the families of teenage suicide victims cope with their loss. They did not have a specific research question or hypothesis, such as, what percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from  their  perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience.

Child and Adolescent Development Copyright © 2023 by Krisztina Jakobsen and Paige Fischer is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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4.3 Qualitative Research Methodologies

Phenomenology is a research approach that seeks to understand the essence of a particular phenomenon through a detailed exploration of individual experiences. It is especially beneficial for exploring personal experiences such as emotions, perceptions, and awareness. As a budding qualitative researcher, it is imperative that you understand the different qualitative methods to enable you to choose the appropriate methods for your research question. In this chapter, we aim to discuss the most common qualitative methodologies which include descriptive, phenomenology, narrative inquiry, case study, ethnography, action research and grounded theory (Figure 4.2).

Descriptive:  A descriptive qualitative study attempts to systematically describe a situation, problem, phenomenon, service or programme. It focuses on discovering the who, what, and where of events or experiences and gaining insights from informants regarding a poorly understood phenomenon. 12 It is also used when more information is required to aid the development and refinement of questionnaires in research projects aiming to gain firsthand knowledge of patients’, relatives’ or professionals’ experiences with a particular topic. 13 This is a good choice for beginner qualitative researchers doing exploratory studies. It uses purposive or convenience sampling, with in-depth interviews as the most common data collection method. 14 Data analysis for this type of qualitative research focuses on a rich descriptive summary of the characteristics (themes) of the phenomena with some interpretation. 14 An example is the study by Cao et al. 2022 that explored the state of education regarding end-of-life care from the perspectives of undergraduate nurses. The findings showed that the undergraduate curriculum related to end-of-life care was disjointed and cultural attitudes toward disease and death impede the undergraduate nurses’ learning and knowledge translation of end-of-life care. 15

Phenomenology is also commonly used in qualitative research, and it is a research approach that seeks to understand the essence of a particular phenomenon through a detailed exploration of individual experiences. It is especially beneficial for exploring personal experiences such as emotions, perceptions, and awareness.that is especially beneficial for exploring personal experiences such as emotions, perceptions, and awareness. 16 It involves in-depth conversations on a specific topic, captures the relationships between people, things, events and situations and describes and explains phenomena from the perspective of those who have experienced it. 17 It explores the dimensions of participants’ experiences. 18 It seeks to understand problems, ideas, and situations in terms of shared understandings and experiences rather than differences. 19 Phenomenological research often employs in-depth, unstructured or semi-structured interviews as a means of data collection. 20 Data analysis typically involves identifying the essential structure or meaning of the experience being studied and then describing it in a way that is understandable to others. The researcher uses a process called the transcendental-phenomenological reduction to bracket off or set aside any preconceived notions of the phenomenon being studied. 21 In this method, researchers use theme analysis to focus on the attributed meaning of participants’ lived experiences rather than influencing findings with their own beliefs. 21 This process allows the researcher to gain a deeper understanding of the phenomenon’s essence as it is lived and experienced by participants. 21 For example, Liao et al. 2021 conducted a study exploring what medical learners experience through narrative medicine and the meanings they ascribe to narrative-based learning. The study identified six themes: feeling hesitation, seeking guidance, shifting roles in narratives, questioning relationships, experiencing transformation, and requesting a safe learning environment. 22

Narrative inquiry: Narrative inquiry is qualitative research that seeks to understand how individuals make meaning of their lives and the world around them through studying their stories and experiences. 23 This qualitative research focuses on marginalised populations, usually individuals or small groups and aims to give voice to their perspective. 24 This approach helps people learn more about the participants’ culture, historical experiences, identity, and lifestyle and is often recorded as a biography, life history, artifacts or traditional story. 25 It captures a wealth of story data, including emotions, beliefs, images, and insights about time. It also considers the relationship between personal experience and the wider social and cultural context. 24 Importantly, it also involves joint investigation and joint meaning-building between participants and researchers. 26 A major benefit of narrative inquiry is that it involves storytelling, and because humans are natural storytellers, the approach makes it easy to elicit stories. 24 Additionally, it facilitates the creation and construction of data through narratives of lived experience and fosters meaning formation, thus providing valuable insight into the complexities of human life, culture, and behavior. 11 This makes it possible to gather in-depth meaning as participants usually reveal themselves in their stories. 27 Narrative inquiry entails collecting data in the form of stories or narratives through interviews, written or visual materials, or other kinds of self-expression. 24 Data analysis in narrative inquiry involves identifying the themes, patterns, and meaning of the stories under consideration and understanding how the stories are formed and related to the individual’s experiences and perspective. 24 An example is the study by Gordon et al. 2015 which explored medical trainees’ experiences of leadership and followership in the interprofessional healthcare workplace. 28 The findings showed that participants most often narrated experiences from the position of follower. 28 Their narratives illustrated many factors that facilitate or inhibit the development of leadership identities. 28 Traditional medical and interprofessional hierarchies persist within the healthcare workplace, and wider healthcare systems can act as barriers to distributed leadership practices. 28

Case Study aids holistic exploration of a phenomenon. It provides powerful stories within social contexts through various data sources. It undertakes the exploration through various lenses to capture continuity and change and reveal multiple facets of the phenomenon. 29 It is an explanatory, descriptive or exploratory analysis of a single case example of a phenomenon. Case study aids researchers in giving a holistic, detailed account of a single case (or more) as it occurs in its real-life context. 30 The purpose of a case study is to understand complex phenomena and to explore new research questions in a real-world setting. 29 There are three main types of qualitative case study design: intrinsic case study, instrumental case study and collective case study. 31 An intrinsic case study is often conducted to learn about a one-of-a-kind phenomenon. 31 This type of case study focuses on a single case or a small number of cases and explores a specific phenomenon or issue in depth. 30,31 The researcher needs to define the phenomenon’s distinctiveness, which separates it from all others. In contrast, the instrumental case study employs a specific instance (some of which may be superior to others) to acquire a more extensive understanding of an issue or phenomenon. 30,31 An instrumental case study uses a single case or a small number of cases to explore a broader research question or problem. 31 The collective case study researches numerous instances concurrently or sequentially to obtain a more comprehensive understanding of a specific subject. 30,31 This type of case study analyses multiple cases to understand a phenomenon or issue from different perspectives. 31 The data collection techniques used in a case study include interviews, observations, or written or visual materials. Data can be collected from various sources, including the case, documents or records, and other relevant individuals. In a case study, data analysis is often inductive, which means that the researcher begins with the data and generates themes, patterns, or insights from it. To examine the data, the researcher may employ a range of approaches, such as coding, memoing, or content analysis. An example of a case study is the study by Lemmen et al. 2021 , which aimed to provide insight into how adopting positive health (PH) in a general practice affects primary care professionals’ (PCP) job satisfaction. 32 The findings of the study identified three themes regarding PCPs’ adoption of PH and job satisfaction, namely adopting and adapting Positive Health, giving substance to Positive Health in practice, and changing financial and organisational structures. 32 Thus, the PCPs adopted PH, which supported PCPs to express, legitimise, and promote their distinctive approach to care work and its value. 32 PH also enabled PCPs to change their financial and organisational structures, freeing time to spend on patients and their own well-being. The changes made by the practice increased the job satisfaction of the PCPs. 32

Ethnography is the study of culture and entails the observation of details of everyday life as they naturally unfold in the real world. It is commonly used in anthropological research focusing on the community 33 . It generally involves researchers directly observing a participant’s natural environment over time. 33 A key feature of ethnography is the fact that natural settings, unadapted for the researchers’ interests, are used. In ethnography, the natural setting or environment is as important as the participants, and such methods have the advantage of explicitly acknowledging that, in the real world, environmental constraints and context influence behaviours and outcomes. 34 Ethnography focuses on the lived culture of a group of people, that is, the knowledge they use to generate and interpret social behaviour. 35 Ethnography often involves a small number of cases or a community, ethnic or social groups. The researcher enters the lived experience of participants in the field and spends considerable time with them to understand their way of life. This research approach increases the strength of the data. 35 An example of ethnographic research is the study by Hinder and Greenhalgh, 2012 . The study sought to produce a richer understanding of how people live with diabetes and why self-management is challenging for some. The study revealed that self-management involved both practical and cognitive tasks (e.g. self-monitoring, menu planning, medication adjustment) and socio-emotional ones (e.g. coping with illness, managing relatives’ input, negotiating access to services or resources). 36 Self-management was hard work and was enabled or constrained by economic, material and socio-cultural conditions within the family, workplace and community. 36 Although this study is old, it provides insight into some of the challenges associated with diabetes. 36 While more devices have helped with diabetes in recent years, some of these challenges may still exist.

Action Research involves a cyclical process of planning, action, observation, and reflection to improve practice or address a problem. It attempts to understand and improve the world via change. 37 The goal of action research is to generate new knowledge and understanding about a specific issue while at the same time taking action to improve the situation. 37 Action research is guided by the desire to take action, so it is not a design. A type of action research is participatory action research. 38 At its core, this is a collaborative, self-reflective enquiry undertaken by researchers and participants to understand and improve upon the practices in which they participate and the situations in which they find themselves. 38 The goal is for the participant to be an equal partner with the researcher. 39 The reflective process is inextricably tied to action, impacted by knowledge of history, culture, and the local context, and is rooted in social connections. 38 It is an inquiry process used to understand and improve complex social systems, such as organisations, communities, or classrooms. 40 Participatory action research draws on qualitative methods such as interviews and observation to inquire about ways to improve the quality of practice. 41 The study by Doherty and O’Brien, 2021 explored midwives’ understandings of burnout, professionally and personally, in the context of contemporary maternity care in Ireland. 42 Multiple factors influenced midwives’ views and understandings of burnout. PAR provided a platform for midwives to examine their ideas and views on burnout with the collaborative support of their midwifery colleagues, via cycles of action and reflection, which is necessary to develop and maintain change. Midwives characterised burnout as continuous stress and tiredness, with an accompanying decline in their coping capacities, motivation, empathy, and/or efficacy. Burnout is unique to the person and is primarily induced and irrevocably tied to excessive workload in midwifery. 42

Grounded theory first described by Glaser and Strauss in 1967, is a framework for qualitative research that suggests that theory must derive from data, unlike other forms of research, which suggest that data should be used to test theory. 43 It is a qualitative research process that entails developing theories based on evidence that has been collected from the participants. 43 Grounded theory may be particularly valuable when little or nothing is known or understood about a problem, situation, or context. 44 The main purpose is to develop a theory that explains patterns and correlations in data and may be utilised to understand and predict the phenomenon under investigation. This method often entails gathering data through interviews, focus groups, questionnaires, surveys, transcripts, letters, government reports, papers, grey literature, music, artefacts, videos, blogs and memos, then analysing it to identify patterns and relationships. 45 Data is analysed via inductive analysis; the researcher starts with observations and data and then builds hypotheses and insights based on the data. In addition, a continual comparison technique is employed, which entails comparing data repeatedly to identify patterns and themes. 46 Furthermore, open, axial and selective coding is used. Open coding divides data into smaller chunks and classifies them based on their qualities and relationships. 47 In axial coding, links between categories and their subcategories are examined with respect to data. 47 Through “selective coding,” all categories are brought together around a “core” category, and categories requiring further explanation include descriptive information. This type of coding is more likely to occur in the final stages of study. 47 An example is the study by Malau-Aduli et al., 2020 ; the study had two main aims – (1) to identify the factors that influence an International Medical Graduate’s (IMG) decision to remain working in regional, rural, and remote areas; (2) to develop a theory, grounded in the data, to explain how these factors are prioritised, evaluated and used to inform a decision to remain working in RRR areas. 48   The findings revealed that the IMG decision-making process involved a complex, dynamic, and iterative process of balancing life goals based on life stage. Many factors were considered when assessing the balance of three primary life goals: satisfaction with work, family, and lifestyle. Another example is the study by Akosah-Twumasi et al. 2020 which explored the perceived role of sub-Saharan African migrant parents living in Australia in the career decision-making processes of their adolescent children. 49 The study showed that the majority of SSA immigrant parents continued to parent in the same manner as they did back home. 49 Interestingly, some parents modified their parenting approaches due to their perceptions of the host nation. 49 However, due to their apparent lack of educational capacity to educate their children, other parents who would otherwise be authoritative turned into trustworthy figures. 49

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.

This paper is in the following e-collection/theme issue:

Published on 23.4.2024 in Vol 26 (2024)

This is a member publication of University of Oxford (Jisc)

Empowering School Staff to Support Pupil Mental Health Through a Brief, Interactive Web-Based Training Program: Mixed Methods Study

Authors of this article:

Original Paper

• Emma Soneson 1, 2 , PhD   ; 
• Emma Howarth 3 , PhD   ; 
• Alison Weir 4, 5 , MA, MSc   ; 
• Peter B Jones 2 * , PhD   ; 
• Mina Fazel 1 * , DM  

1 Department of Psychiatry, University of Oxford, Oxford, United Kingdom

2 Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

3 School of Psychology, University of East London, London, United Kingdom

4 Faculty of Education, University of Cambridge, Cambridge, United Kingdom

5 Howard Community Academy, Anglian Learning multi-academy trust, Bury St Edmunds, United Kingdom

*these authors contributed equally

Corresponding Author:

Emma Soneson, PhD

Department of Psychiatry

University of Oxford

Warneford Lane

Oxford, OX3 7JX

United Kingdom

Phone: 44 1865 613127

Email: [email protected]

Background: Schools in the United Kingdom and elsewhere are expected to protect and promote pupil mental health. However, many school staff members do not feel confident in identifying and responding to pupil mental health difficulties and report wanting additional training in this area.

Objective: We aimed to explore the feasibility of Kognito’s At-Risk for Elementary School Educators , a brief, interactive web-based training program that uses a simulation-based approach to improve school staff’s knowledge and skills in supporting pupil mental health.

Methods: We conducted a mixed methods, nonrandomized feasibility study of At-Risk for Elementary School Educators in 6 UK primary schools. Our outcomes were (1) school staff’s self-efficacy and preparedness to identify and respond to pupil mental health difficulties, (2) school staff’s identification of mental health difficulties and increased risk of mental health difficulties, (3) mental health support for identified pupils (including conversations about concerns, documentation of concerns, in-class and in-school support, and referral and access to specialist mental health services), and (4) the acceptability and practicality of the training. We assessed these outcomes using a series of questionnaires completed at baseline (T1), 1 week after the training (T2), and 3 months after the training (T3), as well as semistructured qualitative interviews. Following guidance for feasibility studies, we assessed quantitative outcomes across time points by comparing medians and IQRs and analyzed qualitative data using reflexive thematic analysis.

Results: A total of 108 teachers and teaching assistants (TAs) completed T1 questionnaires, 89 (82.4%) completed T2 questionnaires, and 70 (64.8%) completed T3 questionnaires; 54 (50%) completed all 3. Eight school staff members, including teachers, TAs, mental health leads, and senior leaders, participated in the interviews. School staff reported greater confidence and preparedness in identifying and responding to mental health difficulties after completing the training. The proportion of pupils whom they identified as having mental health difficulties or increased risk declined slightly over time (median T1 =10%; median T2 =10%; median T3 =7.4%), but findings suggested a slight increase in accuracy compared with a validated screening measure (the Strengths and Difficulties Questionnaire). In-school mental health support outcomes for identified pupils improved after the training, with increases in formal documentation and communication of concerns as well as provision of in-class and in-school support. Referrals and access to external mental health services remained constant. The qualitative findings indicated that school staff perceived the training as useful, practical, and acceptable.

Conclusions: The findings suggest that brief, interactive web-based training programs such as At-Risk for Elementary School Educators are a feasible means to improve the identification of and response to mental health difficulties in UK primary schools. Such training may help address the high prevalence of mental health difficulties in this age group by helping facilitate access to care and support.

Introduction

In recent years, there has been an increased emphasis on the role of schools in supporting children’s mental health [ 1 - 3 ]. This enhanced focus has been driven in large part by an apparent increase in mental health difficulties (including behavioral, social, and emotional difficulties) present in school-aged populations [ 4 - 6 ]—a concern that became increasingly prominent in the context of the COVID-19 pandemic and the associated school closures and social distancing measures [ 7 , 8 ]. There is also a growing recognition of the many unique advantages of using the school setting to promote and protect pupil mental health [ 9 ]. First, most lifetime disorders begin during the schooling years [ 10 ], which suggests that schools may be an ideal setting for early identification and intervention. Second, schools have access to most children, meaning that they are an important component of any public health approach to address child mental health difficulties [ 11 - 14 ]. Third, schools benefit from prolonged engagement with pupils, which can facilitate the implementation of mental health promotion and prevention strategies as well as support and interventions for pupils with identified mental health needs [ 12 ]. Finally, mental health support in schools is often more accessible to families than other types of support [ 15 ].

However, while school staff are increasingly expected to support children’s mental health [ 1 ], many do not feel prepared to do so [ 16 - 19 ] due in part to receiving limited training and supervision in this area [ 20 ]. Therefore, improving school staff’s confidence and preparedness are important considerations for supporting them in taking an expanded role in pupil mental health [ 21 ]. Most schools offer some form of mental health training [ 22 , 23 ], but many staff members believe that they could benefit from additional training [ 18 - 20 , 24 - 26 ]. One area where staff training may be particularly beneficial is the identification of and first response to pupils who have mental health difficulties or who are believed to be at increased risk of developing them. However, although there is evidence suggesting that school staff, parents, and practitioners see such training as an acceptable, feasible, and potentially useful way to support pupil mental health [ 20 , 27 - 29 ], empirical evidence for the effectiveness of such training is limited and focuses primarily on intermediate outcomes (eg, staff knowledge and confidence) rather than downstream outcomes (eg, accurate identification, access to support, and mental health outcomes) [ 30 , 31 ]. Furthermore, there are several potential barriers to implementing training programs in schools, including time, cost, and resource requirements [ 28 ].

At-Risk for Elementary School Educators : A Brief, Interactive Web-Based Training

Training programs that address these barriers may be beneficial for supporting schools in identifying and responding to pupil mental health difficulties. Brief, interactive web-based training programs are a particularly promising avenue as they have the potential to be more affordable, flexible, and scalable than other training formats. One such training is At-Risk for Elementary School Educators (hereinafter, At-Risk ), a virtual simulation-based program developed by the American company Kognito [ 32 ]. The program, which has been completed by >125,000 teachers in the United States, aims to improve pupil mental health by “[building] awareness, knowledge, and skills about mental health, and [preparing] users to lead real-life conversations with pupils, parents, and caregivers about their concerns and available support” [ 33 ].

The program addresses many common implementation barriers to school-based mental health training. For example, At-Risk only requires approximately 1 hour to complete, which is much shorter than many other available training programs [ 31 , 34 ]. This comparatively low time commitment may address the concern that training programs are overly time intensive and, thus, make the training more feasible for busy schools [ 28 , 34 , 35 ]. The web-based format of At-Risk may also address concerns about school-based mental health programs being resource intensive [ 28 ]. Nearly all school mental health training programs documented in the literature are face-to-face sessions led by external facilitators [ 34 , 36 ], with only a few examples of web-based training [ 37 - 39 ]. For schools with limited budgets, programs requiring external facilitators can prove unsustainable and have limited scalability. In terms of financial resources, the costs of At-Risk vary depending on the number of licenses purchased, but the maximum cost is approximately £22 (US $30) per user, a price point that is feasible for many UK schools. In the United States, there have been many examples of bulk purchases at the district or state level that have made the training even more affordable per teacher. In many areas, the training is even free at point of use due to state- or district-wide licensing agreements [ 40 ].

To date, 3 randomized studies have examined the effectiveness and acceptability of At-Risk among samples of American teachers [ 17 , 41 ] and teachers in training [ 42 ] across school years. Each study found high satisfaction ratings, with between 75% and 85% of participants rating the training as useful, well constructed, relevant, and easy to use, and nearly all (88%-95%) reporting that they would recommend it to colleagues. The training also improved teachers’ self-rated preparedness, self-efficacy, and likelihood of identifying and discussing concerns about pupils’ mental health and referring them to appropriate support when needed. These improvements were reflected in the teachers’ behaviors—compared with teachers in the control group , those who completed At-Risk self-reported significantly more helping behaviors (eg, identifying psychological distress, discussing concerns with pupils and parents, and consulting with parents about options for care and support) and gatekeeping behaviors (ie, connecting pupils with care and support) after the training and at 3 months after the training. The findings of these studies indicate that At-Risk may help improve teachers’ ability to identify and respond to pupil mental health needs and lead to positive behavior change in terms of discussing concerns and facilitating access to care and support.

At-Risk in a UK Context: Considerations for Transportability

These 3 studies suggest that At-Risk may be a promising intervention for improving children’s mental health; however, there is still much to be learned about the training’s effectiveness, feasibility, and acceptability. Furthermore, to date, no evaluation of the training has been conducted outside the United States. There is increasing focus on the influence of context on the effectiveness of complex interventions [ 43 - 48 ], and while some interventions have shown success in terms of transportability [ 48 ], other interventions that have evidence of effectiveness in one context have demonstrated null or even negative effects in another [ 46 ]. Furthermore, information that could inform “transportability” is often not collected as part of evaluations [ 44 ], making it difficult to determine the likelihood of success in a new setting.

There are many contextual differences between the United States and the United Kingdom that could mean that school-based interventions developed in one country may not translate well to the other. Cross-country differences in education systems and (mental) health services are particularly relevant to this study. Differences in the education system include the length and content of initial teacher training, the number and roles of teaching assistants (TAs), and school funding structures. There are also key differences in the structure and availability of school-based mental health provision. In the United States, schools often have staff whose sole or at least main responsibility is mental health, such as school psychologists. While these roles are becoming more common in the United Kingdom with the implementation of the Green Paper recommendations [ 1 ], in most UK primary schools, mental health is included within the broader roles of the special educational needs coordinator (SENCo) and pastoral team. Finally, differences in the wider health care systems across the countries also mean that the process and outcomes of external referrals to specialist mental health services vary across settings, another fact that may influence the transportability of school-based interventions such as At-Risk.

Given these uncertainties regarding intervention transportability, additional evaluation of At-Risk is needed to understand whether it is a potentially useful and feasible tool to improve the identification of and response to mental health difficulties in UK primary schools. To explore the potential value of the training in this new context, we conducted a mixed methods feasibility study of At-Risk in 6 UK primary schools covering pupils aged 4 to 11 years . We aimed to examine the influence of At-Risk on staff confidence and preparedness, identification of pupils with mental health difficulties or increased risk of developing mental health difficulties, mental health support outcomes for identified children, and intervention acceptability and practicality.

Study Design

We used a mixed methods, nonrandomized, pretest-posttest study design to explore the feasibility of At-Risk in UK primary schools. While feasibility studies are acknowledged as a key stage of intervention design and evaluation [ 49 , 50 ], there is no universally agreed-upon definition of a feasibility study [ 50 , 51 ]. Therefore, we focused on 3 criteria from the guidance by Bowen et al [ 52 ]: acceptability, practicality, and limited effectiveness testing.

Intervention: At-Risk for Elementary School Educators

At-Risk is a web-based training that is delivered individually and requires only a log-in and internet connection. Using a simulation-based teaching model, the training aims to (1) improve mental health awareness and knowledge, (2) empower users to approach pupils about what they have noticed, (3) impart skills to have meaningful conversations with pupils and parents, and (4) train users to refer pupils to further support. The diagram in Figure 1 illustrates how the training might lead to improved mental health outcomes for pupils.

The simulation begins with an introduction by a virtual coach, who defines and explains how to recognize the warning signs of psychological distress and specific mental health difficulties and provides guidance and practical advice for discussing and acting upon concerns. Users then practice 2 virtual scenarios. The first scenario involves a fifth-grade (UK Year 6; ages of 10-11 years) teacher speaking with the parent of a pupil showing signs of behavioral difficulties. The second involves a third-grade (UK Year 4; ages of 8-9 years) teacher speaking with a pupil showing signs of emotional difficulties. During the conversations, users choose what to say via drop-down menus organized into categories (eg, “bring up concerns” or “ask a question”) and phrases (eg, “Mia sometimes seems a little agitated in class”). Throughout the conversation, users receive feedback through a “comfort bar” (based on how the pupil or parent perceives the conversation), opportunities to “see” the thoughts of the pupil or parent, and suggestions from the virtual coach.

Importantly, there is no one “right” way to conduct the conversations, and several approaches can lead to a positive outcome. Throughout the conversation, users can “undo” actions to backtrack after receiving an undesirable response or to explore what the response would have been had they chosen another option. At the end of each conversation, the pupil or parent provides feedback on the conversation. The training finishes with a short segment on connecting pupils with further support.

For this feasibility study, we used an unmodified version of the training (ie, the standard training designed for American schools, not tailored to the UK context) provided free of cost by Kognito. The potential need for adaptation and tailoring was an important consideration that we explicitly examined as part of our exploration of the acceptability and practicality of At-Risk in this new setting.

Recruitment

We originally sought to purposively sample 5 primary schools from Cambridgeshire or Norfolk that (1) had a higher-than-average proportion of pupils eligible for free school meals or (2) were located in an area in the top tertile of deprivation as measured using the Index of Multiple Deprivation [ 53 ], which we calculated with the publicly available Schools, pupils and their characteristics data [ 54 ]. We emailed headteachers, SENCos, and mental health leads from 131 candidate schools in September and October 2019 about participating in the study. To increase recruitment, we contacted additional schools in January 2020 for a study start date of March 2020. However, the study was suspended in March 2020 due to the in-person school closures associated with the onset of the COVID-19 pandemic. As some of the participating schools dropped out due to the pandemic, we reopened recruitment for a January 2021 study start date. In this round, we did not restrict participation by the 2 deprivation criteria described previously (ie, free school meal eligibility and Index of Multiple Deprivation), so any UK-based mainstream primary school was eligible to participate. The January 2021 start date was again delayed by the pandemic, but there was no subsequent recruitment.

Teachers and TAs

Schools were responsible for recruiting individual teachers and TAs to participate in the training. We encouraged schools to invite all teachers and TAs to participate, but schools made a variety of decisions in this regard. Three schools (schools D, E, and F) had all staff complete the training during inset (in-service training) days or other designated times, 2 schools (schools A and C) had staff volunteer to participate, and 1 school (school B) selected 2 to 3 staff members in each year group to participate.

Measures and Materials

School characteristics.

The characteristics of the participating schools, including school type, school sex (ie, whether they were single or mixed sex), urbanicity, head count, area-level deprivation, level of free school meal eligibility, ethnic composition, and proportion of pupils with special educational needs, were obtained from publicly available data from the Department for Education [ 54 , 55 ].

Teacher and TA Identification Form

The purpose of the Teacher and TA Identification Form ( Multimedia Appendix 1 ) was to understand which pupils participants would identify as having mental health difficulties or an increased risk of developing mental health difficulties. As systematic reviews in this area have identified no suitable questionnaires [ 28 , 30 ], we developed a bespoke questionnaire, which was reviewed by a school staff advisory group to ensure accuracy and relevance. The questionnaire begins with instructions, including explanations and examples of what is meant by “mental health difficulties or risk for mental health difficulties.” Full definitions are provided in Multimedia Appendix 1 , but in brief, “mental health difficulties” are described as “behavioural and social-emotional problems” regardless of formal diagnosis, and “risk for mental health difficulties” is described as experiences that increase the chance of a child developing mental health difficulties in the future.

For all pupils in their class, participants first indicated whether they thought a pupil had mental health difficulties or increased risk. If yes, they answered 9 subsequent questions about mental health support outcomes. The first four outcomes were about communication of concerns, namely whether they had (1) formally documented their concerns with the school, (2) communicated concerns to the SENCo, pastoral care lead, or mental health lead, (3) communicated concerns to another member of the school staff, or (4) communicated concerns to the child or their parents. The next five outcomes pertained to the provision of mental health support, namely whether the pupil (5) received in-class support; (6) received in-school support or had an in-house support plan; (7) had documented social, emotional, and mental health (SEMH) status (a type of special educational need focused on mental health difficulties); (8) had been referred to external mental health services; or (9) had access to external mental health services.

Strengths and Difficulties Questionnaire

The teacher-report Strengths and Difficulties Questionnaire (SDQ) [ 56 - 59 ] served as the comparator for findings about teachers’ and TAs’ identification of pupils. The SDQ includes 25 positive and negative psychological attributes across 5 scales: emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behavior. The first 4 scales add up to a Total Difficulties Score (0-40, with higher scores representing greater difficulties). The SDQ has demonstrated acceptable psychometric properties in primary school samples [ 60 ]. It is important to note that the SDQ is not an exact comparator as it measures a narrower concept than the Teacher and TA Identification Form (which also includes increased risk ). However, this comparison could potentially yield valuable information regarding feasibility .

Pre- and Posttraining Surveys

Kognito uses pre- and posttraining surveys to assess their training. These surveys (based on the validated Gatekeeper Behavior Scale [ 61 ]) explore teachers’ self-efficacy in identifying and responding to mental health difficulties and whether their attitudes, self-efficacy, or practice have changed since completing the At-Risk training. The posttraining survey also includes questions on perceptions of the training’s impact. We independently (ie, with no input from Kognito) reviewed the merits of these questionnaires and decided to use them in this study because (1) they covered relevant and useful concepts related to our aims and (2) using them increased comparability to the other 3 US-based studies of At-Risk . We slightly adapted the surveys to make them more relevant to the UK context ( Multimedia Appendix 2 ).

Interview Schedules

For the pretraining interviews with SENCos and mental health leads, we developed a topic guide about current practice ( Multimedia Appendix 3 ) with the specific purpose of creating Mental Health Resource Maps for each school (refer to the Procedures section). The main topics pertained to formal and informal procedures for when staff members suspect that a child might have mental health difficulties or increased risk, as well as the types of support available.

For the posttraining interviews with teachers, TAs, and strategic stakeholders (ie, those with key leadership roles, including senior leadership teams [SLTs], school governors, and SENCos and mental health leads), we developed 3 separate topic guides ( Multimedia Appendix 3 ), which were informed by our research questions, systematic reviews [ 28 , 30 ], and the Consolidated Framework for Implementation Research [ 62 , 63 ]. For teachers and TAs who completed At-Risk and strategic stakeholders, interview topics included the acceptability of the training, the practicality of implementing it in schools, the utility of further refinement and testing, possible harms associated with the training (if any), and suggestions for adaptations. For teachers and TAs who did not complete the training, topics included reasons for not completing it, barriers to acceptability and practicality, and suggestions for adaptations.

Interviews With SENCos and Mental Health Leads

We conducted a pretraining interview with each school’s SENCo or mental health lead to develop a “Mental Health Resource Map” with information on referral processes and available support. These maps served an ethical purpose by ensuring that pupils identified as potentially having mental health difficulties would have the best possible chance of being linked to care and support.

Completing At-Risk

Schools’ timelines for the study varied due to the pandemic and other commitments. School D completed the training in December 2020; school E completed the training in March 2021; schools B, C, and F completed the training in May 2021; and school A completed the training in June 2021. At baseline (T1), participants completed a Teacher and TA Identification Form and the pretraining survey. They then completed the At-Risk training. We encouraged schools to designate specific time for the training, which 3 schools (schools D, E, and F) did. One week after training (T2), participants were asked to complete a second Teacher and TA Identification Form and the posttraining survey. Three months after the training or at the end of the school year (whichever came first; T3), participants completed a third Teacher and TA Identification Form as well as SDQs for all pupils. All questionnaires were completed on the University of Cambridge Qualtrics platform (Qualtrics International Inc).

Feedback Provision

After T2, we provided all SENCos and mental health leads but not teachers or TAs with feedback regarding which children had been identified as having mental health difficulties or increased risk. After T3, we provided SDQ scores for each child as well as whole-class distributions (where available). This feedback was provided to ensure the ethical conduct of the study.

Interviews With Teachers, TAs, and Strategic Stakeholders

We aimed to recruit at least 3 teachers or TAs who completed the training per school, 3 to 5 teachers or TAs who had not completed the training across all schools, and up to 3 strategic stakeholders per school for posttraining semistructured interviews. Schools contacted staff members directly with an invitation to complete a virtual interview.

Quantitative Outcomes

Analytical samples.

For the main analysis, participants were included if they (1) completed at least the pretraining (T1) questionnaires and the training itself and (2) had what we judged to be a typical number of children they regularly worked with. For the latter criterion, given that the average UK primary school class size is approximately 27 to 28 pupils [ 64 ], we excluded teachers and TAs who worked with <10 children (as we suspected this would not be a random selection of pupils and would therefore influence aggregate identification rates) and those who worked with >60 children (as we believed that it would be difficult for a teacher or TA to know >2 classes’ worth of children well enough to make accurate judgments about their mental health).

Teacher and TA Self-Efficacy and Preparedness

To assess teachers’ and TAs’ preparedness, self-efficacy, and perceptions of training impact, we calculated the absolute and relative frequencies of responses to the pre- and posttraining surveys. Participants were eligible for inclusion in this analysis only if they had pretraining (T1) data.

Identification Outcomes

On the basis of the Teacher and TA Identification Forms, we calculated the number and percentage of pupils in each class whom teachers and TAs perceived as having mental health difficulties or increased risk at each time point. We summarized these across all participants using medians and IQRs.

We then calculated SDQ scores, which we compared with responses from the Teacher and TA Identification Form by calculating (1) the median and IQR for the percentage of children identified by participants who did not have elevated SDQ scores and (2) the median and IQR for the percentage of children with elevated SDQ scores who were not identified by participants. To be included in these analyses, participants had to have completed all 3 time points. For the first outcome, they had to have completed an SDQ for all children they identified in the Teacher and TA Identification Form . For the second outcome, they had to have completed SDQs for at least 80% of their class. Where it was possible to match pupil IDs between teachers and TAs, we pooled SDQ data such that, if one participant did not meet the inclusion criteria themselves, they could still be included if the SDQ data were available from another staff member working with the same children.

Mental Health Support Outcomes

Finally, for each time point, we calculated medians and IQRs for the proportion of identified children with each of the 9 mental health support outcomes (refer to the Teacher and TA Identification Form section for the outcomes) .

Sensitivity Analyses

We also conducted 2 post hoc sensitivity analyses. The first sensitivity analysis excluded all participants from school D. When we prepared feedback for school D (the first school to complete the training), we learned that most participants at the school had misinterpreted the Teacher and TA Identification Form. We edited the form and instructions accordingly to address this issue, but therefore, school D participants completed a slightly different form than the other schools. The second sensitivity analysis was a complete case analysis intended to explore observed differences in outcomes according to whether participants had completed all 3 time points. For the analysis of outcomes pertaining to preparedness, self-efficacy, and perceptions of training impact, we included all participants who completed the surveys at least at T1 and T2.

Statistical Analysis

For all quantitative outcomes, we focused on preliminary, descriptive comparisons across the 3 time points and did not perform any formal hypothesis testing. This aligns with established recommendations for feasibility studies, which generally lack the statistical power necessary for a clear interpretation of hypothesis-testing results [ 65 - 68 ]. We conducted all quantitative analyses in R (version 4.0.3; R Foundation for Statistical Computing) [ 69 ] except for the comparison of Teacher and TA Identification Forms and SDQ scores, for which we used Microsoft Excel (Microsoft Corp). We created all plots using the ggplot2 [ 70 ] and likert packages [ 71 ]. To score the SDQs, we used the freely available R code on the Youthinmind website [ 72 ].

Qualitative Outcomes

We considered 3 analysis approaches for the interview and qualitative questionnaire data: content analysis [ 73 ], framework analysis [ 74 ], and reflexive thematic analysis [ 75 , 76 ]. We initially decided to use content analysis for the survey comments and reflexive thematic analysis for the interviews; however, as we familiarized ourselves with the data, we realized that there was significant overlap between the survey comments and interviews and decided that analyzing them separately was not a useful distinction. As our main aim was to generate insights into the program and its future potential, we decided to use the 6-phase reflexive thematic analysis by Braun and Clarke [ 76 ] for all qualitative data due to its flexibility and ability to generate themes both inductively and deductively. ES developed the initial themes, and MF and EH helped clarify and enrich them. ES and MF worked together to name and refine the themes before the final write-up. We managed and coded all qualitative data in ATLAS.ti (version 9.1.3; ATLAS.ti Scientific Software Development GmbH) and additionally created manual thematic maps to better visualize and understand patterns between our data.

Ethical Considerations

This study was approved by the University of Cambridge Psychology Research Ethics Committee (PRE 2019.076). We obtained active informed consent from all teachers and TAs who took part in the study. We used an opt-out model for parental consent whereby parents received (directly from the schools via their preferred communication routes) an information sheet detailing study aims, procedures, how data would be used, and the right to opt their child out of participation. Parents had 2 weeks to opt their child out of the study by returning a hard copy of the opt-out form or emailing or calling the school. Schools kept track of all opt-outs and instructed teachers and TAs not to include these children in their forms. All quantitative data were collected using anonymous pupil and staff identifiers generated by the participating schools, and all qualitative data were deidentified before analysis, with identifiable information stored on secure servers at the University of Cambridge. Teachers and TAs received £20 (approximately US $28) vouchers for completing the training and questionnaires for at least 2 of the 3 time points and an additional £10 (approximately US $14) for taking part in an interview. School staff members who created the anonymous identifiers received £10 (approximately US $14) vouchers to thank them for their time.

Participants

A total of 6 schools participated in this study (Table S1 in Multimedia Appendix 4 [ 40 ]). Among these 6 schools, there were 4 (67%) from Cambridgeshire and 1 (17%) each from Greater London and Merseyside; 5 (83%) were located in urban areas and 1 (17%) was located in a rural area. All but 1 school (5/6, 83%) were situated in areas of above-average deprivation, and 50% (3/6) of the schools had a higher-than-average proportion of pupils eligible for free school meals. In total, 67% (4/6) of the schools had a high proportion of White pupils (>80%), and 33% (2/6) of the schools were more diverse, with approximately 20% of pupils from Black, Black British, Caribbean, or African backgrounds (school B) or Asian or Asian British backgrounds (school E).

A total of 108 teachers and TAs completed the T1 questionnaires and the training itself, 89 (82.4%) completed the T2 questionnaires, and 70 (64.8%) completed the T3 questionnaires ( Table 1 ), with 54 (50%) having completed all 3. After excluding those teachers and TAs who did not meet the inclusion criteria for the analyses, the final analytical samples were as follows:

• Main analysis of identification and mental health support outcomes: n=97 at T1, n=75 at T2, and n=57 at T3.
• Main analysis of preparedness, self-efficacy, and training impact outcomes: n=107 at T1 and n=83 at T2.
• Main analysis comparing identification outcomes with SDQ scores: n=28 and n=25 (refer to the following section).
• Complete case sensitivity analysis: n=51 at T1, T2, and T3.
• Sensitivity analysis excluding all teachers and TAs from school D: n=70 at T1, n=54 at T2, and n=41 at T3.

Compared with the 2019-2020 national workforce statistics for teachers and TAs working in state-funded nursery and primary schools [ 77 ], our sample had a similar proportion of women (81/89, 91% in our sample vs 90.9% nationally) and a slightly higher proportion of White staff members (82/89, 92% in our sample vs 90.5% nationally).

A total of 7.4% (8/108) of school staff members from 67% (4/6) of the schools completed an interview ( Table 2 ).

a N=89 because this information was collected only at T2.

b N NA =4 (number with missing data for this question).

c Percentages add up to >100 because some participants had multiple roles.

d SENCo: special educational needs coordinator.

e N NA =7 (number with missing data for this question).

a PSHE: Personal, Social, Health and Economic.

b SENCo: special educational needs coordinator.

c TA: teaching assistant.

d HLTA: higher-level teaching assistant.

Pretest-posttest changes suggested that participating in the training was beneficial for the staff and that they had positive perceptions of the training. Findings regarding preparedness ( Figure 2 ) suggest improvements across all domains of recognizing and acting upon concerns about pupils’ mental health, particularly in terms of using key communication strategies and working with parents. Findings regarding self-efficacy ( Figure 3 ) suggest that participants were more confident in their abilities to discuss their concerns about pupils’ mental health after the training than before. Again, the largest changes were observed in discussing concerns with parents and applying key communication strategies. Finally, findings regarding teachers’ and TAs’ perceptions of the impact of applying the skills of the training ( Figure 4 ) suggest that they were generally positive about the possible effects of the training on pupil outcomes (ie, attendance and academic success), teacher-pupil rapport, and the classroom environment. The results from the complete case analysis ( Multimedia Appendix 5 ) were nearly identical to those of the main analysis (all differences were ≤3 percentage points in magnitude).

In terms of how many pupils were identified as having mental health difficulties or increased risk, participants identified similar proportions of their pupils before and immediately after the training and then fewer over time. The median percentage of pupils whom participants believed had mental health difficulties or increased risk was 10% (IQR 6.7%-18.2%) at T1, 10% (IQR 4.5%-16.7%) at T2, and 7.4% (IQR 5.0%-16.7%) at T3. The directions of change were similar for both sensitivity analyses (whereby teachers and TAs identified fewer children over time), with slight differences. For the sensitivity analysis excluding school D ( Multimedia Appendix 6 ), the percentages were slightly (approximately 2 percentage points) higher. For the complete case analysis, the decrease was also notable 1 week after the training, decreasing from 10% (IQR 6.7%-17.3%) at T1 to 8% (IQR 3.9%-16.7%) at T2 and 7.4% (IQR 5.7%-16.7%) at T3.

In terms of the accuracy of identification, it seems that teachers and TAs became slightly more accurate over time in comparison to pupils’ SDQ scores (although it is important to acknowledge the limitations described in the Methods section regarding questionnaire comparability). The median percentage of children identified by participants who did not have elevated SDQ scores was 40% (IQR 0%-50%) at T1, 27.2% (IQR 0%-50%) at T2, and 25% (IQR 0%-50%) at T3. The median percentage of children with elevated SDQ scores who were not identified by participants was 68.8% (IQR 42.9%-87.5%) at T1, 66.7% (IQR 50%-88.2%) at T2, and 57.1% (IQR 33.3%-87.5%) at T3. In the sensitivity analysis excluding school D, the results were similar (typically within 5 percentage points); one small difference was that the median percentage of children identified by teachers and TAs who did not have elevated SDQ scores was 0% (IQR 0%-50%) at T2. The results of the complete case analysis were identical to those of the main analysis.

Overall, the findings suggest that the training may be beneficial for facilitating conversations and access to school-based support (but not external support) for pupils with identified mental health difficulties or increased risk. Figure 5 presents the findings for the 9 mental health support outcomes among identified children across the 3 study time points. As with before the training, there was typically a wide variation in outcomes.

A comparison across time points suggests that participants formally documented their concerns and spoke with the SENCo, pastoral lead, or mental health lead for a greater proportion of identified pupils after the training than before. For example, at T1, teachers and TAs documented concerns for a median of 50% (IQR 0%-100%) of identified pupils; this increased to 56.3% (IQR 4.2%-100%) at T2 and 75.7% (IQR 0%-100%) at T3. The equivalent statistics for speaking with the SENCo, pastoral lead, or mental health lead were a median of 66.7% (IQR 16.7%-100%) at T1, 75% (IQR 50.0%-100%) at T2, and 95.5% (IQR 50.0%-100%) at T3. There was no change in speaking with another staff member, but this was because nearly all participants did so across all time points. Finally, the percentage of pupils whom teachers and TAs spoke with (or whose parents they spoke with) also increased after the training, with a median of 33.3% (IQR 0%-87.5%) at T1, 61.9% (IQR 0%-100%) at T2, and 50% (IQR 0%-100%) at T3.

A comparison across time points also suggests increases in school-based support for identified children after the training compared with before. The median percentage of pupils identified by teachers and TAs who received in-class support increased from 75% (IQR 35.4%-100%) at T1 to 100% at T2 and T3 (IQR 50%-100% and 66.7%-100%, respectively). There was a more modest increase in the receipt of in-school support or in-house support plans, with a median of 40% (IQR 0%-71.4%) of identified pupils receiving them at T1 compared with 50% at T2 and T3 (IQR 3.6%-100% and 8.3%-81.4%, respectively). There was very little change in documented SEMH status or referral or access to specialist mental health services. For each of these outcomes, the median percentage of identified pupils was 0% across time points.

The findings from the sensitivity analyses were similar to those of the main analysis in terms of direction, although improvements across time in the complete case analysis ( Multimedia Appendix 5 ) tended to be more modest than for the main analysis.

Acceptability and Practicality

Quantitative findings.

Quantitative data from the posttraining survey showed that participants were generally positive about the training. Of the 83 participants who completed the survey, 53 (64%) rated it as “good” and 13 (16%) rated it as “very good.” An additional 17% (14/83) rated it as “fair,” 2% (2/83) rated it as “poor”, and 1% (1/83) as “very poor.” A total of 84% (70/83) of the teachers and TAs said that the scenarios in the training were relevant to them. Finally, most participants (74/83, 89%) would recommend the training to other educators.

Qualitative Findings

Qualitative data also suggested that school staff generally found the training practical and acceptable. We generated three themes from our survey and interview data:

• Individual fit: positive perceptions, self-efficacy, and change.
• Institutional fit: alignment with school values and context.
• Taking it forward: improvements and implementation.

Additional findings on possible harms are presented in Multimedia Appendix 7 .

Individual Fit: Positive Perceptions, Self-Efficacy, and Change

In general, participants perceived the program to be a “good fit” with their personal philosophies and practice. Regarding the training itself, many appreciated the included scenarios, particularly in terms of their relevance to their practice. The format of the training—primarily that it was web-based and required active role-play—was also viewed as useful, engaging, and novel and might have contributed to its perceived usefulness. For example, one teacher commented:

The interactive elements of the training were brilliant and something which I have never encountered before! [Survey respondent (SR) 56; school E]

One teacher and well-being lead described:

I think it definitely made you think. [...] you had to really think about what was being said and the response that you would give, reflecting back on sort of the knowledge that they’d given you beforehand, so I thought that was good. [Interviewee 1]

Other participants suggested that opportunities to practice skills during the training improved the likelihood of using those skills in day-to-day practice.

Participants also believed that they had learned a lot from the training, especially in terms of skills and strategies. These included but were not limited to the skills within the At-Risk “EASING” strategy (check your Emotions, Ask for permission, be Specific, use I statements, keep it Neutral, and show Genuine curiosity). Importantly, there was evidence that participants had also applied new skills. Several participants described having new conversations with pupils or parents facilitated by the skills and strategies from the training. For example, one teacher described:

It was that permission thing [...] I wanted to ask [a child] about his home life [...] and kind of he just cried and didn’t want to speak about it anymore, and then when I asked him if we were OK to talk about it, he said, “Actually no, because I think I’m going to cry again,” so then we left it. And then he came to me the following week, and [...] said, “Can we talk about it now?” [...] so actually me asking that, it was the wrong time for him to talk about it, he wasn’t ready, he would have just been emotional, and wouldn’t have been able to get his words out, and actually the week after, him coming to me and saying, “Can we have a little chat,” works perfectly [...] And now we’re more aware of his situation. [Interviewee 2]

This skill seems to have enabled this pupil to have this conversation with the teacher in a manner (in terms of time, place, and identified person) that suited him. Other participants provided similar examples, referencing how skills from the training had facilitated better outcomes.

However, it is important to note that the perceived usefulness of the training varied. Most notably, some participants indicated that their previous training or role made the training less impactful. Illustratively, when asked how the training had impacted their practice, one TA responded:

Having previously received similar training, due to my role, I do not have any recent cases where the training would have changed the way I carried out discussions. [SR 60; school E]

Institutional Fit: Alignment With School Values and Context

Sustainable school-based programs should also align with the values of the school more broadly. Participants often referenced the importance of schools’ prioritization of pupil mental health. For example, one teacher described:

[Mental health is] a conversation which is constantly ongoing and trying to constantly better our practices and make sure we’re looking after them as best as we can and spotting things as best we can as well. [Interviewee 3]

This description demonstrates how prioritizing mental health can promote the critical evaluation of related school practices as well as the additional provision of training opportunities. In many cases, support from the SLT led to formal recognition of pupil mental health within school policies or plans. One strategic stakeholder explained:

I think because our school have well-being and mental health as such a focus, SLT are very supportive of doing things like this and they’re very accommodating. So when I said we had the training and people were going to have to take part in the training, it was very flexible, although they had other ones lined up, they were quite happy to move things around to make things work. And I think, the fact it is such a priority in our school definitely makes that easier. [Interviewee 1]

In this school, mental health and well-being were one of three main school priorities. As indicated previously, direction setting from the SLT is key to ensuring momentum and impetus. However, as others noted, it is important that support from the SLT is genuine rather than being “just another tick box” (Interviewee 4) exercise.

Another facet of institutional fit pertained to the practical aspects of the training. Schools are time- and resource-limited settings, so mental health training needs to fit within this context. The format of At-Risk, especially its flexibility and relatively low time requirements, was viewed as beneficial, with comments such as “For the amount of time [...] I got a huge amount from it” (Interviewee 4). Others made direct comparisons with other training courses. For example, one higher-level TA had previously completed a 1-day, in-person training course with a similar purpose to that of At-Risk. While she preferred the in-person training, she listed the benefits of both types:

[In the in-person training] you can then query and question to your trainer, so you’ve got that interaction, so that obviously isn’t there, is it, on the computer one. [...] if I was looking from a management point of view, I would say, budgetary, I’m sure it’s cheaper [...] to use [At-Risk], not just cheaper as in [...] money, [...] but also cheaper in time [...] So probably if I was looking [...] with my management hat on, I would say the computer-based [training] would get the same message, or similar message, across for a wider audience for probably a cheaper cost. [Interviewee 7]

In terms of efficiency, this participant highlighted the favorable input-to-output ratio of At-Risk , which could allow more staff members to participate in training. This quote also highlights that schools could use At-Risk flexibly. For example, schools might assign staff members to different training programs based on their roles and previous experience, with more intensive, in-person training for staff members with more significant mental health roles and At-Risk for those with fewer responsibilities or less experience.

Taking It Forward: Improvements and Implementation

Participants offered key insights into how to take the training forward in terms of both changes to the training itself and how best to implement it, primarily by tailoring it to the UK context. In terms of language, there was some reference to the American accent, but more so, participants highlighted the need to adapt some of the terminology and signposting resources to reflect UK support systems. They also made suggestions about additional training that could be useful with different topics (such as bullying) and age groups (particularly for younger children).

In addition to improvements and adaptations to the training itself, participants illustrated the importance of implementation. A common theme was that, to maximize impact, the training should include follow-up discussions or live workshopping. One teacher suggested:

I think some kind of “live” element to conclude the training—to have a “real” person to ask questions to as part of a group video chat could have been useful. Also, maybe to ask advice about particular scenarios that we may have found ourselves in in the past. [SR 56; school E]

By facilitating greater engagement and critical thinking, a live element could enhance the impact of the training and potentially make At-Risk more acceptable to those who generally prefer face-to-face training. Participants indicated that someone internal, for example, the SENCo, would be best placed to lead a live element and would enable staff to practice role-playing based on situations and scenarios specific to each school.

There was also wide acknowledgment that any training had to lie within a strong support system. This began with having a clear referral pathway for identified concerns, which was viewed as important for facilitating access to support. In some cases, teachers and TAs were able to find new ways to support children after completing the training. However, in many cases, participants—and strategic stakeholders in particular—explained that support had not always been readily available. For example, one strategic stakeholder recounted what happened after the training:

A lot of them are people saying to me, “What are you going to do about it?” about different children. And I, because some of our support staff don’t know the sort of route for getting extra support, or they’re really shocked to find actually there’s nothing out there for these children...it’s about what we can do in school, and I think people have been really quite shocked about that. You know, they just presume I can make a phone call and these children will get face-to-face counselling. [Interviewee 5]

This shows the importance of embedding the training within a wider support system, including collaboration with external agencies. However, many interviewees referenced the systemic issues that schools face in helping pupils access specialist support, particularly in terms of the high thresholds and long waiting lists that exist for many external services. While schools may be able to provide beneficial support for children, particularly for those with lower-level difficulties, this indicates an ongoing area of need for schools and their pupils.

Summary of Findings

This study offers the first UK evidence for Kognito’s At-Risk for Elementary School Educators , extending findings from 3 US-based trials and providing needed evidence regarding the potential utility, acceptability, and practicality of brief, interactive web-based mental health training for school staff. Overall, the findings showed that At-Risk is a feasible means of improving the identification of and response to pupil mental health difficulties in UK primary schools. Quantitative findings showed that staff preparedness and self-efficacy in identifying and responding to mental health difficulties increased after the training. Identification rates did not increase (and, in fact, decreased at the 3-month follow-up), but there was some suggestion that teachers’ and TAs’ identification became slightly more accurate in comparison with SDQ scores. Crucially, for those pupils identified as having mental health difficulties or increased risk, in-school mental health support outcomes (ie, documentation or discussion of concerns, conversations with pupils and parents, and in-class and in-school support) increased after the training, but more “downstream” outcomes (ie, documented SEMH status and referral and access to external mental health services) did not. Qualitative findings indicated that participants generally found the training acceptable and practical, with many explaining how they intended to use or had already used the skills they learned to improve their practice. Participants also suggested several useful improvements for the training and its implementation, including making it more relevant to the United Kingdom, adding more scenarios, and including a live element in the implementation of the training.

Findings regarding confidence and preparedness reflect those of the 3 US-based studies of At-Risk [ 17 , 41 , 42 ] and the wider literature surrounding teacher mental health training [ 31 ]. In general, mental health training seems to be effective in improving staff confidence. For example, 2 Australian-based studies [ 37 , 78 ] found that secondary school teachers who completed training felt more confident discussing their concerns and helping pupils with their mental health. Another UK-based study of a psychoeducational training program to improve recognition of depression in secondary schools [ 79 ] found significant pretest-posttest improvements in teacher confidence in their knowledge of symptoms, ability to recognize symptoms, and knowledge about how to speak with pupils about their mental health. However, not all studies have shown an impact, with a prominent UK-based study of mental health first aid training finding no effect on educators’ confidence in helping pupils with their mental health [ 80 ].

The general decrease in the proportion of pupils identified as having mental health difficulties or increased risk stands in contrast to previous studies of At-Risk , which found that school staff identified significantly more pupils of concern after completing the training [ 17 , 41 ]. Evidence of the effect of other training programs on identification is extremely limited [ 30 , 31 , 36 ], and differences in context, training content and delivery, baseline knowledge, and outcome measurement make it difficult to compare findings across studies. Two vignette-based studies showed little effect of either mental health first aid [ 78 ] or psychoeducational [ 81 ] training on identification (although each study also reported high recognition of difficulties before the training), whereas studies focused on real-world identification have shown mixed results [ 79 , 82 ]. However, changes in the proportion of identified pupils must be contextualized within the accuracy of identification. There are consequences of both over- and underidentification [ 83 , 84 ], most notably in terms of inefficient allocation of limited mental health support resources. While comparison with the SDQ suggested that there was some improvement in terms of the accuracy of identification following the training, underidentification remained a substantial challenge, with between one-half and two-thirds of pupils with elevated SDQ scores remaining unidentified by teachers and TAs. The underidentification of children with mental health difficulties in educational settings, particularly for children with internalizing as opposed to externalizing problems [ 85 ], has been well documented in the literature [ 30 ], and it is likely that a combination of identification models is required to address this challenge [ 27 , 29 ].

Promisingly, the training appeared to be useful in terms of connecting pupils with care and support, an outcome not frequently measured in other studies [ 30 , 31 , 34 ]. First, the findings suggested that participants had conversations about or documented concerns for a greater proportion of identified pupils following the training, which reflects findings from previous studies of At-Risk [ 17 , 41 ]. This is a rather unique outcome in the literature as other training evaluations have found no difference between training and control groups in terms of conversations with pupils and colleagues [ 78 ]. Importantly, this study went beyond conversations to include outcomes pertaining to in-school and external support. The increases in in-class and in-school support for identified pupils reflect findings of the UK-based study by Kidger et al [ 80 ] of mental health first aid training and the Australian-based pilot study by Parker et al [ 37 ] of a web-based training program, each of which found a positive effect of the training on helping behaviors. Although in-class and in-school support seemed to increase following the training, it is notable that referrals and access to specialist services did not. There are several plausible explanations for this finding. For example, it is likely that school staff were already aware of children with the most severe mental health difficulties and were confident and able to support newly identified pupils—who might have had lower-level mental health difficulties—within the school setting. However, if the training did lead to the identification of children who might benefit from specialist care, there are many barriers to accessing such support (eg, availability and long waiting lists) that might have influenced these outcomes, as reflected in both the qualitative interviews and the wider literature [ 23 , 86 ].

In addition, quantitative and qualitative findings suggested that the program was a good fit for individuals and schools, which aligns with previous research on the acceptability and perceived need for mental health training for school staff [ 18 , 20 , 27 - 29 , 87 , 88 ]. The training’s format seemed to be a key contributor to its feasibility. With a few exceptions [ 37 , 39 , 89 ], the web-based simulation-based format of At-Risk is unique among training programs and is well aligned with teachers’ preferences. For example, in their focus group study of UK secondary school teachers, Shelemy et al [ 20 ] found that participants wanted engaging, interactive, and concise training that included practical strategies and illustrative case studies, all of which are central to At-Risk . While the authors found that teachers disagreed over the usefulness of web-based training, it is possible that these concerns would have decreased during the COVID-19 pandemic as staff became more accepting of web-based opportunities to learn.

Qualitative findings also demonstrated the importance of school context and culture, which have been highlighted in previous research [ 27 ]. In particular, participants noted the importance of school culture in adopting mental health interventions into regular practice. In their systematic review, Moore et al [ 90 ] identified school culture, values, and policies as key facilitators of sustaining mental health interventions. A related area of focus was support from the SLT. This support is a well-recognized factor contributing to intervention success and sustainability for several reasons, including these leaders’ practical role in communicating about interventions and allocating specific time and resources to them [ 43 , 90 , 91 ]. However, it is important to recognize that mental health training for school staff may be even more needed and impactful in schools where mental health is not as much of a priority.

Limitations

Our mixed methods approach, wide range of outcomes, and diverse sample of participating schools offer rich information regarding the feasibility of At-Risk in the United Kingdom. These strengths notwithstanding, there are also several limitations to consider when interpreting the findings. The nonrandomized design, while common for feasibility studies, prevents any conclusions regarding causality and also limits the exploration of other factors that may have influenced outcomes (eg, providing teachers and TAs with the Mental Health Resource Maps or SENCos and mental health leads with feedback on identified pupils). In terms of recruitment and retention, the study had 50% (54/108) attrition. Several factors may have influenced this, including the increased pressure on school staff due to the COVID-19 pandemic, the timing of the study within the school year, and the requirement to communicate with participants only via the study link person. While we tried to explore the effect of attrition through a complete case sensitivity analysis, we lacked important information on the characteristics of those who dropped out as this information was collected only at T2. Furthermore, we were only able to recruit 8 staff members for the posttraining interviews, which was far below our recruitment target. Low participation rates could again be due to several factors, including the impact of the COVID-19 pandemic or competing priorities. Of note, we were not able to recruit anyone who did not complete the training, any headteachers, or any staff from 2 of the schools (schools A and D). This could mean that we lack viewpoints that may be important for understanding the feasibility and utility of the training.

There were also limitations associated with the study measures. While the Teacher and TA Identification Form was informed by the literature and reviewed by our primary school staff advisory group, its validity and reliability are unknown. In addition, the questionnaire only measured mental health support outcomes for those pupils identified as having mental health difficulties or increased risk. Therefore, we do not have information on those who were not identified. The measure is also based on teacher and TA reports and so may not have complete information about all types of support that pupils receive. Another important limitation pertains to the comparator used to assess the identification outcomes. To understand the potential utility of the training program, it is important to have a robust comparator. While we chose to use the teacher-report SDQ, it would also have been interesting to compare identification outcomes with parent-rated mental health difficulties, particularly in light of the low interrater agreement of common measures of child mental health difficulties [ 92 ]. An even stronger comparator would be to assess the teacher and TA identification outcomes against a clinical interview; however, this was not feasible in this study.

Finally, at the time of writing, At-Risk is currently not available for use as Kognito restructures its offerings. This demonstrates a trend that unfortunately is a common occurrence in the field of mental health, whereby many evidence-based digital tools are not available to potential end users [ 93 ]. Nonetheless, the learnings from this feasibility study offer rich information on what type of content and format may be useful for training programs in this area and, as such, can support further development and evaluation in the field.

Implications for Practice

Studies have consistently demonstrated that school staff would appreciate additional training on how best to support pupil mental health [ 18 , 20 , 87 , 88 ]. However, to be scalable, such programs must be realistic in terms of time, cost, and resource requirements [ 28 , 90 , 91 ]. Contextualized within the wider literature on school-based mental health interventions, the findings from this study suggest that mental health training is a feasible option for upskilling school staff to identify and respond to pupil mental health difficulties. They further highlight several specific factors that might positively contribute to feasibility and scalability, many of which are reflected in the broader literature on mental health training [ 20 , 28 ]. For example, teachers and TAs appreciated that the training actively engaged them in learning and applying new skills and that it used realistic examples to demonstrate the real-world applicability of the training, whereas school leaders identified the relatively low time and cost requirements and flexibility as key factors that could make the training feasible for their school context.

However, this is not to say that there are no implementation barriers associated with At-Risk or similar training programs. While the resources required to implement At-Risk are relatively low compared with other training programs, they must still be considered within the context of other school priorities. As demonstrated in the interviews and the wider literature [ 3 , 43 , 90 , 91 ], support from school leadership is essential for securing the time and budget required to implement a training such as At-Risk , and in schools where mental health is not a priority, there are likely to be many barriers to implementation . Even in schools with strong support from the leadership team, it may be difficult to find the requisite budget, time, and human resources to devote to the training. Finally, as is the case with any school-based mental health intervention, it is important that schools do not take sole responsibility for pupils’ mental health. Active partnership between schools and mental health services is key to ensuring that schools feel empowered and supported in this role [ 21 , 90 , 94 ]. While the schools in this study worked hard to support pupils as best they could, interviewees expressed frustration about the difficulty of accessing external support for children who could benefit from it. This is not an uncommon theme in the wider literature surrounding school-based interventions [ 20 , 23 , 91 ] and is a key consideration for scaling up training programs.

Implications for Future Research

The promising findings of this study suggest that additional research is needed to explore the role of scalable mental health training in supporting schools to protect and promote children’s mental health. On the basis of gaps in the literature, particular areas of interest include training for primary school staff (as most are focused on secondary school staff), web-based training (as opposed to traditional time- and resource-intensive in-person training), and training that takes a “whole school approach” by including all school staff members (rather than only teachers). This final area is especially interesting as findings from this study and others [ 27 ] have highlighted stakeholders’ preference that training programs include all school staff members. While our study jointly analyzed findings for teachers and TAs, future research would do well to consider how the unique roles and perspectives of these professionals—as well as other staff members within the school setting—might influence outcomes. Furthermore, future research should be more inclusive about their choice of outcomes, as too often evaluations of school staff training programs have focused on intermediate outcomes such as knowledge or confidence [ 31 ] without considering more “downstream” outcomes such as access to support. Finally, as demonstrated in our study, there is great value in using mixed methods approaches and including information about wider issues of feasibility and implementation, and studies that take this broader lens can help identify programs that are scalable, sustainable, and effective.

Conclusions

School staff would welcome additional mental health training to enable them to respond to pupil mental health difficulties, but there are many barriers to implementing such training at scale. Therefore, training programs that have relatively low time and resource requirements have great potential to fulfill an unmet need in schools. This mixed methods feasibility study showed that At-Risk for Elementary School Educators —an example of a brief, interactive web-based training program—is a feasible means of empowering school staff to accurately identify and respond to pupil mental health difficulties and increased risk.

Acknowledgments

The authors would like to thank Professor Paul Ramchandani for his input in the design of this study, the Cambridge Mental Health in Schools Advisory Group for sharing their views and advice throughout the study, and the staff at the 6 schools that took part in this study for their time and effort. This study was funded by the UK Research and Innovation Emerging Minds network (grant ES/S004726/2), and the training was provided to the schools free of cost by Kognito. ES was funded by a Gates Cambridge Scholarship (grant OPP1144) for the duration of the study. MF is funded by the National Institute for Health and Care Research (NIHR) Oxford and Thames Valley Applied Research Collaboration at the Oxford Health National Health Service (NHS) Foundation Trust. PBJ is funded by the NIHR (grant 0616-20003). All research in the Cambridge Department of Psychiatry is supported by the NIHR Applied Research Collaboration East of England and the Cambridge Biomedical Research Centre at the Cambridgeshire and Peterborough NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care, or the Bill & Melinda Gates Foundation.

Data Availability

The data sets generated during and analyzed during this study are not publicly available due to restrictions associated with our ethics approvals but are available from the corresponding author on reasonable request.

Conflicts of Interest

Kognito is a for-profit company. After reviewing their questionnaires on preparedness and self-efficacy and having found them rigorous and unbiased, we independently decided to include them as outcomes in our study. Kognito had no role in the study design, analysis, or publication. PBJ was a scientific advisory board member for MSD. All other authors declare no other conflicts of interest.

Teacher and Teaching Assistant Identification Form.

Kognito pre- and posttraining surveys.

Interview topic guides.

School characteristics.

Results from complete case sensitivity analysis.

Results from the sensitivity analysis excluding school D.

Quantitative and qualitative findings pertaining to the potential harms of At-Risk.

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Abbreviations

Edited by T Leung; submitted 24.02.23; peer-reviewed by E Widnall, B Fernandes, J Burns, K Cohen; comments to author 27.08.23; accepted 01.03.24; published 23.04.24.

©Emma Soneson, Emma Howarth, Alison Weir, Peter B Jones, Mina Fazel. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.04.2024.

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