Social Sciences Standards

Protocol

1. Research Ethics

1.1 APA Ethics Code

The American Psychological Association Ethical Principles of Psychologists and Code of Conduct (last amended 2017) governs research involving human participants in psychology and related fields. Core principles:

1. Beneficence and nonmaleficence -- strive to benefit participants and take care to do no harm
2. Fidelity and responsibility -- establish relationships of trust; uphold professional standards
3. Integrity -- promote accuracy, honesty, and truthfulness in research
4. Justice -- ensure fair access to and benefit from research contributions
5. Respect for people's rights and dignity -- respect the dignity and worth of all people; protect privacy and confidentiality

Key research-specific standards (Section 8):

• 8.01 Institutional Approval -- obtain IRB or ethics committee approval before conducting research
• 8.02 Informed Consent to Research -- inform participants about the purpose, expected duration, procedures, right to decline or withdraw, foreseeable consequences of declining, potential risks, prospective benefits, limits of confidentiality, incentives, and contact information
• 8.03 Informed Consent for Recording -- obtain explicit consent before recording voices or images
• 8.04 Client/Patient, Student, and Subordinate Research Participants -- protect against adverse consequences of declining or withdrawing
• 8.05 Dispensing with Informed Consent -- permitted only when research would not reasonably cause distress or harm and involves normal educational practices, anonymous surveys, naturalistic observation, or archival data
• 8.06 Offering Inducements -- avoid excessive or inappropriate financial or other inducements
• 8.07 Deception in Research -- permitted only when justified by significant value and when non-deceptive alternatives are not feasible; never deceive about risks; debrief as early as feasible
• 8.08 Debriefing -- provide participants with information about the nature, results, and conclusions; correct any misconceptions; if delayed, take reasonable measures to reduce risk of harm

1.2 ASA Code of Ethics

The American Sociological Association Code of Ethics (revised 2018) establishes parallel standards for sociological research:

1. Professional competence -- engage only in work within the boundaries of competence
2. Integrity -- be honest and transparent in all professional activities
3. Professional and scientific responsibility -- adhere to the highest scientific and professional standards
4. Respect for people's rights, dignity, and diversity -- eliminate bias in professional activities
5. Social responsibility -- apply and make public knowledge in order to contribute to the public good

Key distinctions from APA:

• Greater emphasis on the study of institutions, power structures, and social inequality
• Explicit attention to the researcher's positionality and potential for harm in fieldwork
• Stronger norms around community-based participatory research (CBPR)

1.3 IRB Requirements for Social Science Research

Social science research involving human participants requires Institutional Review Board review. Common review categories:

• Exempt -- minimal risk research using educational tests, surveys, interviews, or observation of public behavior where data cannot identify participants; secondary analysis of existing de-identified data
• Expedited -- minimal risk research using surveys or interviews on sensitive topics; research involving identifiable data that does not qualify for exemption
• Full board -- greater than minimal risk; vulnerable populations (minors, prisoners, pregnant women, cognitively impaired individuals); deception research with more than minimal risk

Essential IRB application components:

1. Study protocol with rationale and research questions
2. Informed consent documents (written or documented waiver)
3. Recruitment materials and strategies
4. Data collection instruments (surveys, interview protocols, observation guides)
5. Data management and security plan (encryption, access controls, retention schedule)
6. Risk assessment with mitigation strategies
7. Debriefing materials (if deception is involved)
8. CITI Program or equivalent training certificates for all investigators
9. Conflict of interest disclosures

1.4 Participant Terminology

Social science research uses participants, not "subjects." This reflects respect for the active role individuals play in research. Additional terminology conventions:

• Use respondents for survey research
• Use informants or interviewees for qualitative interview research
• Use community members or collaborators in participatory research
• Avoid deficit-based language; use person-first or identity-first language as preferred by the community being studied
• Follow APA bias-free language guidelines for age, disability, gender, race/ethnicity, sexual orientation, and socioeconomic status

1.5 Informed Consent and Debriefing

Informed consent requirements:

• Statement that the study involves research and participation is voluntary
• Purpose, expected duration, and procedures described in accessible language
• Right to decline or withdraw at any time without penalty
• Foreseeable risks, discomforts, or adverse effects
• Prospective benefits to participant or society
• Limits of confidentiality (e.g., mandatory reporting requirements)
• Incentive details (amount, timing, proration for partial completion)
• Contact information for the investigator and IRB

Debriefing requirements:

• Provide the true nature of the study, especially if deception was used
• Explain the scientific purpose and expected contributions
• Offer to answer questions
• Provide referral resources if the study addressed sensitive topics (e.g., mental health services)
• Allow participants to withdraw their data after debriefing if deception was involved
• Distribute debriefing form in writing (not verbal only)

2. APA Publication Manual (7th Edition) -- Style and Formatting

2.1 Manuscript Structure

Standard manuscript sections for empirical articles:

1. Title page -- title (no more than 12 words recommended), author names and affiliations, author note (ORCID, disclosures, correspondence)
2. Abstract -- 150--250 words; structured or unstructured depending on journal requirements
3. Introduction -- problem statement, literature review, theoretical framework, research questions/hypotheses
4. Method -- participants (demographics, sampling strategy, sample size justification), materials/measures (with reliability and validity evidence), procedure, data analysis plan
5. Results -- descriptive statistics, primary analyses, secondary/exploratory analyses; do not interpret here
6. Discussion -- summary of findings, relation to prior work, theoretical and practical implications, limitations, future directions
7. References -- APA 7th edition format
8. Tables and Figures -- placed after references (unless journal requires inline placement)
9. Appendices -- supplementary materials (instruments, codebooks, additional analyses)

2.2 Key APA Style Rules

• Use past tense for procedures and results; present tense for discussion of established findings
• Use active voice where possible
• Avoid anthropomorphism (do not attribute human characteristics to non-human entities without justification)
• Use serial (Oxford) comma
• Spell out numbers below 10 unless they represent measurements, statistical values, or appear with units
• Use et al. after first citation for works with three or more authors
• Include DOIs for all references where available
• Use inclusive, bias-free language throughout

2.3 Heading Levels

3. Methodology Conventions

3.1 Survey Design

Scale construction:

• Likert scales -- typically 5-point or 7-point; use consistent anchors (1 = Strongly Disagree to 5 = Strongly Agree)
• 7-point scales generally provide better reliability and variance than 5-point scales
• Always label all scale points (not just endpoints) for improved measurement quality
• Reverse-code items to control for acquiescence bias; report which items were reverse-coded

Reliability:

• Cronbach's alpha -- minimum threshold of .70 for research purposes; .80+ preferred for established scales
• McDonald's omega -- preferred over alpha when assumptions of tau-equivalence are violated (increasingly recommended)
• Test-retest reliability -- appropriate for stable constructs; report the time interval

Validity:

• Content validity -- expert review, cognitive interviews
• Construct validity -- convergent (correlations with theoretically related measures) and discriminant (low correlations with theoretically unrelated measures)
• Criterion validity -- predictive (future outcomes) and concurrent (current outcomes)
• Factor analysis -- confirm the underlying structure (see Section 5.2)

Response quality:

• Include attention check items (e.g., "Please select 'Strongly Agree' for this item")
• Monitor completion time; flag responses significantly below the median
• Report the percentage of incomplete responses and how they were handled

3.2 Experimental Designs

• Between-subjects -- each participant is assigned to one condition; random assignment required for causal inference; check for baseline equivalence
• Within-subjects (repeated measures) -- each participant experiences all conditions; counterbalance order to control for order effects; report counterbalancing method
• Mixed designs -- at least one between-subjects and one within-subjects factor; specify which factors are between and which are within
• Factorial designs -- two or more independent variables; report all main effects and interactions
• Quasi-experimental -- no random assignment; use matching, propensity scores, or statistical controls to address selection bias; acknowledge limitations to causal inference

Randomization:

• Use computer-generated random sequences
• Report the randomization method (simple, block, stratified)
• For online studies, report the platform used and how randomization was implemented

Control conditions:

• Active control (alternative treatment) preferred over no-treatment control when possible
• Waitlist control acceptable when no-treatment comparison is needed
• Report what the control group experienced

3.3 Qualitative Methods

Grounded theory:

• Iterative data collection and analysis; theoretical sampling
• Open coding, axial coding, selective coding (Strauss & Corbin) or initial coding and focused coding (Charmaz)
• Reach theoretical saturation -- new data no longer generate new categories
• Report the tradition followed (classic Glaser, Straussian, constructivist Charmaz)

Thematic analysis:

• Follow the six-phase framework of Braun & Clarke (2006): familiarization, initial coding, theme generation, theme review, theme definition, report writing
• Distinguish between inductive (data-driven) and deductive (theory-driven) approaches
• Report whether semantic or latent themes were identified
• Provide a thematic map showing relationships between themes

Ethnography:

• Extended immersion in the field (typically months to years)
• Participant observation, field notes, interviews
• Reflexivity -- document the researcher's positionality, biases, and influence on the setting
• Thick description -- provide rich, contextualized accounts

Phenomenology:

• Focus on lived experience of a phenomenon
• Interpretive phenomenological analysis (IPA) or descriptive phenomenology (Moustakas)
• Sample sizes typically 3--25 participants
• Report the bracketing or epoche process

Trustworthiness criteria (Lincoln & Guba, 1985):

• Credibility -- prolonged engagement, triangulation, member checking, peer debriefing
• Transferability -- thick description to enable readers to assess applicability
• Dependability -- audit trail, inquiry audit
• Confirmability -- reflexivity, triangulation, audit trail

3.4 Mixed Methods Designs

Core designs:

• Convergent (parallel) -- quantitative and qualitative data collected concurrently; results merged for comparison or integration; use joint displays to present integrated findings
• Explanatory sequential -- quantitative data collected first; qualitative data collected second to explain quantitative results; report how qualitative sampling was informed by quantitative findings
• Exploratory sequential -- qualitative data collected first; findings used to develop a quantitative instrument or intervention; report how qualitative findings shaped quantitative measures

Advanced designs:

• Intervention -- mixed methods embedded within an experimental trial
• Case study -- mixed methods used within a bounded case
• Participatory -- community stakeholders involved in design, data collection, and interpretation

Integration strategies:

• Merging -- compare and contrast quantitative and qualitative findings
• Connecting -- results of one strand inform data collection in the next
• Embedding -- one strand is nested within a larger design of the other type
• Use joint displays (tables or figures) to visualize integration

4. Statistical Reporting

4.1 General Principles

All statistical reporting in social science manuscripts must follow APA conventions:

• Report effect sizes for every test -- never rely on p-values alone
• Report exact p-values to two or three decimal places (e.g., p = .034), not p < .05; use p < .001 only when the exact value is below .001
• Report 95% confidence intervals for all major estimates (means, mean differences, regression coefficients, effect sizes)
• Report sample size (N or n) and degrees of freedom for every statistical test
• Use APA notation: italicize statistical symbols (F, t, p, r, M, SD, N, n, df)
• Zero before the decimal for statistics that can exceed 1 (e.g., M = 0.54); no zero for statistics bounded by -1 to 1 (e.g., p = .034, r = .45)

4.2 Effect Size Measures

Benchmarks from Cohen (1988); use domain-specific norms when available.

4.3 APA-Style Statistical Notation Examples

t-test: t(58) = 2.87, p = .006, d = 0.75, 95% CI [0.22, 1.28]

One-way ANOVA: F(2, 47) = 3.45, p = .041, η² = .13

Repeated-measures ANOVA: F(1.78, 62.30) = 5.12, p = .011, η²_p = .13 (Greenhouse-Geisser corrected)

Chi-square: χ²(3, N = 150) = 11.28, p = .010, V = .27

Correlation: r(98) = .34, p < .001, 95% CI [.15, .50]

Multiple regression coefficient: b = 0.42, SE = 0.12, t(196) = 3.50, p < .001, 95% CI [0.18, 0.66]

Hierarchical linear model (multilevel): b = 0.35, SE = 0.10, t(45.2) = 3.50, p < .001 (Satterthwaite approximation)

4.4 Null Hypothesis Significance Testing and Alternatives

• Always interpret p-values in context of effect sizes and confidence intervals
• A non-significant p-value does not demonstrate "no effect" -- discuss power and the width of the confidence interval
• Consider Bayesian alternatives where appropriate: report Bayes factors (BF10) and interpret using Jeffreys' (1961) guidelines (BF10 > 3 = moderate evidence, > 10 = strong evidence)
• Consider equivalence testing (TOST procedure) when the goal is to demonstrate that an effect is practically negligible
• Report power analyses -- specify the software used (G*Power, R pwr package), the target effect size, alpha level, and desired power (typically .80 or .90)

5. Common Analytical Frameworks

5.1 Likert Scale Analysis

The ordinal vs. interval debate:

• Ordinal treatment -- use nonparametric tests (Mann-Whitney U, Kruskal-Wallis, Spearman's rho) for individual items
• Interval treatment -- composite scores from multi-item scales (summed or averaged) can generally be treated as approximately interval; parametric tests are acceptable when distributions are approximately normal
• Report the approach chosen and cite methodological justification (e.g., Norman, 2010 for interval treatment of summed scores; Jamieson, 2004 for ordinal caution)
• Always report the number of items, response anchors, and reliability of composite scores

5.2 Factor Analysis

Exploratory Factor Analysis (EFA):

1. Assess sampling adequacy: Kaiser-Meyer-Olkin (KMO) > .60; Bartlett's test of sphericity significant
2. Determine the number of factors: parallel analysis (preferred), scree plot, eigenvalues > 1 (Kaiser criterion -- use cautiously as it tends to over-extract)
3. Choose extraction method: principal axis factoring (PAF) for non-normal data; maximum likelihood (ML) for normal data
4. Choose rotation: oblique (promax or direct oblimin) when factors are expected to correlate; orthogonal (varimax) only when factors are theoretically uncorrelated
5. Interpret factor loadings: retain items with loadings > .40 on the primary factor and < .30 on cross-loadings
6. Report the pattern matrix (for oblique rotation) or rotated factor matrix (for orthogonal rotation), eigenvalues, and variance explained

Confirmatory Factor Analysis (CFA):

1. Specify the measurement model based on theory or prior EFA results
2. Report model fit indices: χ²/df (< 3), CFI (> .95), TLI (> .95), RMSEA (< .06, with 90% CI), SRMR (< .08)
3. Report standardized factor loadings (all should be > .40, ideally > .50)
4. Assess convergent validity: Average Variance Extracted (AVE) > .50
5. Assess discriminant validity: AVE for each factor exceeds the squared inter-factor correlation (Fornell-Larcker criterion)
6. Report modification indices only if theoretically justified modifications are made; document all post-hoc modifications

5.3 Structural Equation Modeling (SEM)

• Combine measurement model (CFA) with structural model (path analysis) in a single framework
• Minimum sample size: 200 or 10--20 observations per estimated parameter (Kline, 2015)
• Report the same fit indices as CFA: χ², df, CFI, TLI, RMSEA (with 90% CI), SRMR
• Report standardized and unstandardized path coefficients with standard errors and p-values
• Use bootstrapping (minimum 5,000 samples) for indirect effects and non-normal data
• Test alternative models and compare fit using Δχ² test, ΔCFI (< .01 for equivalent fit), AIC, or BIC
• Report the software and estimator used (e.g., Mplus with MLR, lavaan in R with WLSMV for ordinal data)

5.4 Mediation and Moderation Analysis

Mediation:

• Use the PROCESS macro (Hayes, 2022) or SEM for mediation analysis
• Report the total effect (c), direct effect (c'), and indirect effect (ab)
• Use bootstrapped confidence intervals (5,000--10,000 samples) for the indirect effect -- do not use the Sobel test (it assumes normality of the indirect effect, which is rarely met)
• Report the completely standardized indirect effect for comparability
• For multiple mediators, report specific indirect effects and total indirect effect

Moderation:

• Center or standardize continuous predictors before creating interaction terms
• Report the interaction term coefficient and probe the interaction at meaningful values of the moderator (e.g., -1 SD, mean, +1 SD or specific substantive values)
• Plot the interaction with simple slopes; report simple slope coefficients and significance
• For categorical moderators, report pairwise comparisons of slopes across groups
• Use the Johnson-Neyman technique to identify the exact value of the moderator at which the effect transitions between significant and non-significant

5.5 Multilevel / Hierarchical Linear Modeling (HLM)

When data have a nested structure (students within classrooms, employees within organizations, repeated measures within individuals):

1. Justify the multilevel approach -- calculate the intraclass correlation coefficient (ICC); if ICC > .05, multilevel modeling is warranted
2. Build models sequentially:
   • Null model (intercept only) -- to calculate ICC
   • Random intercept model -- level-1 predictors with random intercepts
   • Random slope model -- allow slopes to vary across clusters
   • Cross-level interaction model -- level-2 predictors moderating level-1 effects
3. Report:
   • Fixed effects: unstandardized coefficients, standard errors, t-values (or z-values), p-values, and 95% CIs
   • Random effects: variance components for intercepts and slopes, and their covariance
   • Model comparison: deviance statistics (-2LL), AIC, BIC; likelihood ratio tests for nested models
   • ICC at each level
   • R² at each level (Snijders & Bosker pseudo-R² or Nakagawa & Schielzeth's marginal and conditional R²)
4. Software reporting -- specify the package (lme4 in R, HLM, Mplus, Stata mixed) and estimation method (ML or REML)

6. Reporting Checklists

6.1 JARS -- Journal Article Reporting Standards (Quantitative)

APA's JARS standards (Appelbaum et al., 2018) specify what to report in quantitative research manuscripts:

Title page and abstract:

• Title reflects the variables and relationships under investigation
• Abstract includes objectives, participants, methods, results (with effect sizes), and conclusions

Introduction:

• Problem statement with significance
• Review of relevant literature with theoretical grounding
• Specific hypotheses or research questions

Method -- Participants:

• Eligibility criteria and sampling method
• Sample size, power analysis, and demographic characteristics
• Attrition rates and reasons

Method -- Measures:

• For each instrument: name, construct measured, number of items, response format, scoring procedure, reliability evidence (in the current sample), and validity evidence
• Psychometric citations

Method -- Procedure:

• Detailed description sufficient for replication
• IRB approval and informed consent procedures
• Data collection setting, dates, and duration

Method -- Data analysis:

• Statistical software and version
• Data screening procedures (missing data, outliers, normality)
• Missing data handling method (listwise deletion, multiple imputation, FIML) with justification
• Alpha level and correction for multiple comparisons (Bonferroni, Holm, Benjamini-Hochberg)
• Analytic strategy mapped to each research question/hypothesis

Results:

• Descriptive statistics for all study variables (means, standard deviations, ranges, or frequencies)
• Correlation matrix for continuous study variables
• Results for each hypothesis/research question with test statistic, degrees of freedom, exact p-value, effect size, and confidence interval
• Post-hoc analyses clearly labeled as exploratory

6.2 JARS-Qual -- Qualitative Article Reporting Standards

Research design:

• Qualitative approach and rationale (e.g., why grounded theory was chosen)
• Researcher description -- training, experience, positionality, relationship to the topic
• Reflexivity statement

Participants/data sources:

• Sampling strategy (purposive, theoretical, snowball, maximum variation)
• Number of participants and rationale for sample size
• Participant characteristics and context
• How participants were recruited and consented

Data collection:

• Form of data (interviews, focus groups, observation, documents, visual data)
• Data collection procedures (interview guide, observation protocol)
• Recording method and transcription process
• Data saturation or sufficiency statement

Analysis:

• Analytic method and procedures
• Coding process (first cycle, second cycle; inductive vs. deductive)
• Software used (NVivo, Atlas.ti, Dedoose, MAXQDA)
• Steps taken to ensure trustworthiness (member checking, peer debriefing, triangulation, audit trail)

Findings:

• Themes or categories with definitions
• Data excerpts (quotes) to illustrate each theme -- sufficient in number and length
• Disconfirming or negative cases discussed
• Relationship among themes (narrative, thematic map, or model)

6.3 JARS-Mixed -- Mixed Methods Reporting Standards

• Rationale for using mixed methods
• Specific mixed methods design named and cited (e.g., convergent design per Creswell & Plano Clark, 2018)
• Timing of quantitative and qualitative strands (concurrent or sequential)
• Priority or weighting of each strand
• Point of integration and integration strategy
• Joint display or other visualization of integrated findings
• Follow JARS for quantitative strand and JARS-Qual for qualitative strand independently
• Report meta-inferences -- conclusions drawn from integrating both strands

6.4 CONSORT -- Consolidated Standards of Reporting Trials

For social science experiments (especially randomized controlled trials in clinical psychology, education, or behavioral interventions):

Key CONSORT items for social science research:

1. Structured abstract with trial design, methods, results, and conclusions
2. Scientific background and explanation of rationale
3. Specific objectives or hypotheses
4. Description of trial design (parallel, crossover, factorial) including allocation ratio
5. Eligibility criteria for participants
6. Settings and locations of data collection
7. Interventions described in sufficient detail for replication (consider TIDieR checklist)
8. Pre-specified primary and secondary outcomes with measurement time points
9. Sample size determination (power analysis with assumptions)
10. Randomization: sequence generation, allocation concealment, implementation
11. Blinding: who was blinded and how (where applicable)
12. Statistical methods for comparing groups
13. Participant flow diagram (enrollment, allocation, follow-up, analysis)
14. Baseline characteristics of each group
15. Number of participants analyzed per group (intention-to-treat and per-protocol if applicable)
16. Results for primary and secondary outcomes with effect sizes and confidence intervals
17. Harms or unintended effects
18. Limitations, generalizability, and interpretation
19. Registration number and name of trial registry
20. Where the full protocol can be accessed

Checklist

Ethics and Compliance

• Study protocol reviewed and approved by IRB or ethics committee
• Approval number documented in manuscript (Method section)
• Informed consent obtained from all participants (or documented waiver)
• APA Ethics Code or ASA Code of Ethics compliance stated in Method
• CITI Program or equivalent training completed by all investigators
• Debriefing provided to all participants (especially if deception was used)
• Participants referred to as "participants" (not "subjects") throughout

APA Style

• Manuscript formatted per APA Publication Manual 7th edition
• Title page includes author note with ORCID, disclosures, correspondence address
• Abstract is 150--250 words
• Headings follow APA levels (centered bold, flush left bold, etc.)
• Bias-free, person-first language used throughout
• References include DOIs where available

Method

• Participant demographics reported (age, gender, race/ethnicity, education, or other relevant characteristics)
• Sampling method and recruitment strategy described
• Sample size justification provided (power analysis with software, effect size, alpha, power)
• All measures described with reliability evidence in the current sample
• Procedure described in sufficient detail for replication
• Data analysis plan specified (including missing data handling and multiple comparison corrections)

Statistical Reporting

• Descriptive statistics reported for all study variables
• Effect sizes reported for every inferential test
• Exact p-values reported (not p < .05 unless p < .001)
• 95% confidence intervals reported for major estimates
• N and degrees of freedom reported for every test
• APA-style notation used: italicized test statistics, proper formatting

Qualitative Reporting (if applicable)

• Qualitative approach named and justified
• Researcher positionality and reflexivity statement included
• Data saturation or sufficiency addressed
• Trustworthiness strategies described (member checking, triangulation, audit trail)
• Sufficient data excerpts provided to support each theme

Mixed Methods (if applicable)

• Mixed methods design named and cited
• Timing, priority, and integration strategy described
• Joint display or integration visualization included
• Meta-inferences discussed

Reporting Standards

• Appropriate JARS checklist completed (JARS, JARS-Qual, or JARS-Mixed)
• CONSORT checklist completed if reporting a randomized trial
• Reporting checklist submitted with manuscript

References

• American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000
• American Psychological Association. (2017). Ethical principles of psychologists and code of conduct. https://www.apa.org/ethics/code
• American Sociological Association. (2018). Code of ethics and policies and procedures of the ASA Committee on Professional Ethics. https://www.asanet.org/code-ethics
• American Sociological Association. (2019). American Sociological Association style guide (6th ed.). https://www.asanet.org/publishing/asa-style-guide
• Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 3--25. https://doi.org/10.1037/amp0000191
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suarez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 26--46. https://doi.org/10.1037/amp0000151
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77--101. https://doi.org/10.1191/1478088706qp063oa
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
• Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.
• Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
• Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
• Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE.
• Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332. https://doi.org/10.1136/bmj.c332
• Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). SAGE.