Ahmed, S. K. (2024). The pillars of trustworthiness in qualitative research. Journal of Medicine, Surgery, and Public Health, 2, 100051. https://doi.org/10.1016/j.glmedi.2024.100051

Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748), 305–307. https://doi.org/10.1038/d41586-019-00857-9

Arseniev-Koehler, A. (2024). Theoretical foundations and limits of word embeddings: What types of meaning can they capture? Sociological Methods & Research, 53(4), 1753–1793. https://doi.org/10.1177/00491241221140142

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037//0022-3514.51.6.1173

Bartoš, F., Maier, M., Wagenmakers, E.‑J., Nippold, F., Doucouliagos, H., Ioannidis, J. P. A., Otte, W. M., Sladekova, M., Deresssa, T. K., Bruns, S. B., Fanelli, D., & Stanley, T. D. (2024). Footprint of publication selection bias on meta-analyses in medicine, environmental sciences, psychology, and economics. Research Synthesis Methods, 15(3), 500–511. https://doi.org/10.1002/jrsm.1703

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01

Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus Open, 2, 8–14. https://doi.org/10.1016/j.npls.2016.01.001

Biggs, R., Preiser, R., Vos, A. de, Schlüter, M., Maciejewski, K., & Clements, H. (Eds.). (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems. Routledge. https://doi.org/10.4324/9781003021339

Blair, G., Coppock, A., & Humphreys, M. (2023). Research design in the social sciences: Declaration, diagnosis, and redesign. Princeton University Press.

Braga, A. A., Turchan, B. S., Papachristos, A. V., & Hureau, D. M. (2019). Hot spots policing and crime reduction: An update of an ongoing systematic review and meta-analysis. Journal of Experimental Criminology, 15(3), 289–311. https://doi.org/10.1007/s11292-019-09372-3

Braun, V., & Clarke, V. (2021). Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Counselling and Psychotherapy Research, 21(1), 37–47. https://doi.org/10.1002/capr.12360

Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201–216. https://doi.org/10.1080/2159676X.2019.1704846

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE.

Braun, V., & Clarke, V. (2023). Is thematic analysis used well in health psychology? A critical review of published research, with recommendations for quality practice and reporting. Health Psychology Review, 17(4), 695–718. https://doi.org/10.1080/17437199.2022.2161594

Brohinsky, J., Marquart, C., Wang, J., Ruis, A. R., & Shaffer, D. W. (2021). Trajectories in Epistemic Network Analysis. In A. R. Ruis & S. B. Lee (Eds.), Communications in Computer and Information Science. Advances in Quantitative Ethnography (Vol. 1312, pp. 106–121). Springer International Publishing. https://doi.org/10.1007/978-3-030-67788-6_8

Brown, T. A. (2015). Confirmatory factor analysis for applied research. The Guilford Press.

Bruckmaier, G., Krauss, S., Binder, K., Hilbert, S., & Brunner, M. (2021). Tversky and Kahneman’s Cognitive Illusions: Who Can Solve Them, and Why? Frontiers in Psychology, 12, 584689. https://doi.org/10.3389/fpsyg.2021.584689

Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56(3), 1391–1412. https://doi.org/10.1007/s11135-021-01182-y

Campbell, K., Orr, E., Durepos, P., Nguyen, L., Li, L., Whitmore, C., Gehrke, P., Graham, L., & Jack, S. (2021). Reflexive Thematic Analysis for Applied Qualitative Health Research. The Qualitative Report. Advance online publication. https://doi.org/10.46743/2160-3715/2021.5010

Carlson, R., Bauer, J., & Manning, C. D. (2025). A New Pair of GloVes. https://doi.org/10.48550/arXiv.2507.18103

Carlson, S. (1985). A double-blind test of astrology. Nature, 318(6045), 419–425. https://doi.org/10.1038/318419a0

Chang, H.‑Y., & Tsai, C.‑C. (2023). Epistemic Network Analysis of Students’ Drawings to Investigate Their Conceptions of Science Learning with Technology. Journal of Science Education and Technology. Advance online publication. https://doi.org/10.1007/s10956-022-10026-9

Charmaz, K. (2025). Constructing grounded theory. Introducing qualitative methods. SAGE.

Chen, F. F. (2007). Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

Chen, F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008). An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models. Sociological Methods & Research, 36(4), 462–494. https://doi.org/10.1177/0049124108314720

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

Clauser, B. E., & Bunch, M. B. (Eds.). (2021). The history of educational measurement: Key advancements in theory, policy, and practice. Routledge.

Cole, G. G. (2021). The Grievance Studies Affair; One Funeral at a Time: A Reply to Pluckrose, Lindsay, and Boghossian. Sociological Methods & Research, 50(4), 1937–1945. https://doi.org/10.1177/00491241211009949

Conaghan, C., Daly, E., & Ryan, L. (2024). Reflexive thematic analysis of interviews with postprimary educators’ experience and attitudes toward concussion. Psychology in the Schools, 61(9), 3714–3727. https://doi.org/10.1002/pits.23257

Coutts, J. J., & Hayes, A. F. (2023). Questions of value, questions of magnitude: An exploration and application of methods for comparing indirect effects in multiple mediator models. Behavior Research Methods, 55(7), 3772–3785. https://doi.org/10.3758/s13428-022-01988-0

Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE.

Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966

Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press.

Csíkos, C. (2020). A neveléstudomány kutatásmódszertanának alapjai. ELTE.

Dean, G. O., & Kelly, I. W. (2003). Is astrology relevant to consciousness and psi? Journal of Consciousness Studies, 10(6-7), 175–198.

Di Paola, N., Chari, S., Iannacci, F., & Kraus, S. (2025). Configurational theory in business and management research: Status quo and guidelines for the application of qualitative comparative analysis (QCA). Technological Forecasting and Social Change, 211, 123907. https://doi.org/10.1016/j.techfore.2024.123907

Dijkstra, T. K., & Henseler, J. (2015). Consistent Partial Least Squares Path Modeling1. MIS Quarterly, 39(2), 297–316. https://doi.org/10.25300/MISQ/2015/39.2.02

Doval, E., Viladrich, C., & Angulo-Brunet, A. (2023). Coefficient Alpha: The Resistance of a Classic. Psicothema, 35(1), 5–20. https://doi.org/10.7334/psicothema2022.321

Dunivin, Z. O. (2025). Scaling hermeneutics: a guide to qualitative coding with LLMs for reflexive content analysis. EPJ Data Science, 14(1). https://doi.org/10.1140/epjds/s13688-025-00548-8

Duşa, A. (2019). QCA with R. Springer. https://doi.org/10.1007/978-3-319-75668-4

Dybro Liengaard, B. (2024). Measurement invariance testing in partial least squares structural equation modeling. Journal of Business Research, 177, 114581. https://doi.org/10.1016/j.jbusres.2024.114581

Efron, B., & Hastie, T. (2021). Computer Age Statistical Inference, Student Edition. Cambridge University Press. https://doi.org/10.1017/9781108914062

Eisenstein, J. (2019). Introduction to natural language processing. Adaptive computation and machine learning. The MIT Press.

Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x

Espregueira-Mendes, J., Karlsson, J., Musahl, V., & Ayeni, O. R. (Eds.). (2025). Orthopaedic Sports Medicine. Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-65430-6

Feyerabend, P. (2010). Against method. Verso.

Finlay, L. (2021). Thematic Analysis. European Journal for Qualitative Research in Psychotherapy(11), 103–116. https://doi.org/10.24377/EJQRP.article3062

Fiss, P. C., Cambré, B., & Marx, A. (Eds.). (2013). Configurational Theory and Methods in Organizational Research. Emerald Group Publishing Limited. https://doi.org/10.1108/rso

Folliard, K. J., Crozier, K., & Kamble, M. M. W. (2024). A very, very lonely, unmagical time. The lived experience of perinatal anxiety: A longitudinal interpretative phenomenological analysis. Midwifery, 136, 104070. https://doi.org/10.1016/j.midw.2024.104070

Forer, B. R. (1949). The fallacy of personal validation; a classroom demonstration of gullibility. Journal of Abnormal Psychology, 44(1), 118–123. https://doi.org/10.1037/h0059240

Gardazi, N. M., Daud, A., Malik, M. K., Bukhari, A., Alsahfi, T., & Alshemaimri, B. (2025). BERT applications in natural language processing: a review. Artificial Intelligence Review, 58(6). https://doi.org/10.1007/s10462-025-11162-5

Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press. https://doi.org/10.1017/9781139161879

Geremew, Y. M., Huang, W.‑J., & Hung, K. (2024). Fuzzy-Set Qualitative Comparative Analysis as a Mixed-Method and Analysis Technique: A Comprehensive Systematic Review. Journal of Travel Research, 63(1), 3–26. https://doi.org/10.1177/00472875231168619

Gliner, J. A., Morgan, G. A., & Leech, N. L. (2017). Research methods in applied settings: An integrated approach to design and analysis. Routledge Taylor & Francis Group.

Goretzko, D., Siemund, K., & Sterner, P. (2024). Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis. Educational and Psychological Measurement, 84(1), 123–144. https://doi.org/10.1177/00131644231163813

Greckhamer, T., Furnari, S., Fiss, P. C., & Aguilera, R. V. (2018). Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization, 16(4), 482–495. https://doi.org/10.1177/1476127018786487

Greckhamer, T., Misangyi, V. F., & Fiss, P. C. (2013). The Two QCAs: From a Small-N to a Large-N Set Theoretic Approach. In P. C. Fiss, B. Cambré, & A. Marx (Eds.), Configurational Theory and Methods in Organizational Research (Vol. 38, pp. 49–75). Emerald Group Publishing Limited. https://doi.org/10.1108/S0733-558X(2013)0000038007

Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press.

Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. https://doi.org/10.48550/arXiv.2203.05794

Groskurth, K., Bluemke, M., & Lechner, C. M. (2024). Why we need to abandon fixed cutoffs for goodness-of-fit indices: An extensive simulation and possible solutions. Behavior Research Methods, 56(4), 3891–3914. https://doi.org/10.3758/s13428-023-02193-3

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hajtman, E., & Hajtman, B. (2018). Gyakorló példák a pszichológus hallgatók statisztikai tantárgyainak anyagához. Akadémiai Kiadó. https://doi.org/10.1556/9789634542513

Hak, T., Jaspers, F., & Dul, J. (2013). The Analysis of Temporally Ordered Configurations: Challenges and Solutions. In P. C. Fiss, B. Cambré, & A. Marx (Eds.), Configurational Theory and Methods in Organizational Research (Vol. 38, pp. 107–127). Emerald Group Publishing Limited. https://doi.org/10.1108/S0733-558X(2013)0000038009

Hankar, M., Kasri, M., & Beni-Hssane, A. (2025). A comprehensive overview of topic modeling: Techniques, applications and challenges. Neurocomputing, 628, 129638. https://doi.org/10.1016/j.neucom.2025.129638

Hardwicke, T. E., Thibault, R. T., Clarke, B., Moodie, N., Crüwell, S., Schiavone, S. R., Handcock, S. A., Nghiem, K. an, Mody, F., Eerola, T., & Vazire, S. (2024). Prevalence of Transparent Research Practices in Psychology: A Cross-Sectional Study of Empirical Articles Published in 2022. Advances in Methods and Practices in Psychological Science, 7(4), Article 25152459241283477. https://doi.org/10.1177/25152459241283477

Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. The Guilford Press.

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2022). Multilevel and Longitudinal Modeling with IBM SPSS. Routledge. https://doi.org/10.4324/9780367824273

Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312(5782), 1900–1902. https://doi.org/10.1126/science.1128898

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hill, C. E., & Knox, S. (2021). Essentials of consensual qualitative research. American Psychological Association. https://doi.org/10.1037/0000215-000

Hove, D. T., Jorgensen, T. D., & van der Ark, L. A. (2024). Updated guidelines on selecting an intraclass correlation coefficient for interrater reliability, with applications to incomplete observational designs. Psychological Methods, 29(5), 967–979. https://doi.org/10.1037/met0000516

Howard, M. C., Boudreaux, M., Cogswell, J., Manix, K. G., & Oglesby, M. T. (2025). A literature review of model fit and model comparisons with confirmatory factor analysis: Formalizing the informal in organizational science. Applied Psychology, 74(1), Article e12592. https://doi.org/10.1111/apps.12592

Hsieh, H.‑F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Hunyadi, L., & Vita, L. (2019). Statisztika I. Akadémiai Kiadó. https://doi.org/10.1556/9789634542216

Hurlbert, S. H. (1984). Pseudoreplication and the Design of Ecological Field Experiments. Ecological Monographs, 54(2), 187–211. https://doi.org/10.2307/1942661

Husserl, E. (2012). Ideas: General introduction to pure phenomenology. Routledge.

Ivanović, J. (2016). Bevezetés a pedagógiai kutatás módszertanába. Újvidéki Egyetem.

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning. Springer International Publishing. https://doi.org/10.1007/978-3-031-38747-0

Jenkins, R. (2025). Pierre Bourdieu. Routledge.

Jiang, J., & Nguyen, T. (2021). Linear and Generalized Linear Mixed Models and Their Applications. Springer New York. https://doi.org/10.1007/978-1-0716-1282-8

Johnson, S. J., Murty, M. R., & Navakanth, I. (2024). A detailed review on word embedding techniques with emphasis on word2vec. Multimedia Tools and Applications, 83(13), 37979–38007. https://doi.org/10.1007/s11042-023-17007-z

Jurafsky, D., & Martin, J. H. (2025). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models.

Kakamad, F. H., Abdalla, B. A., Abdullah, H. O., Omar, S. S., Mohammed, S. H., Ahmed, S. M., Mohammed, K. K., Hassan, H. A [Hemn A.], Baba, H. O., Ahmed, J. O., Mustafa, M. Q., Omar, D. A., Salih, R. Q., Rahim, H. M., Hussein, D. A., Hassan, M. N., Mikael, T. M., Hassan, H. A [Hunar A.], & Najar, K. A. (2024). Lists of predatory journals and publishers: a review for future refinement. European Science Editing, 50, Article e118119. https://doi.org/10.3897/ese.2024.e118119

Kalkbrenner, M. T. (2024). Choosing Between Cronbach’s Coefficient Alpha, McDonald’s Coefficient Omega, and Coefficient H: Confidence Intervals and the Advantages and Drawbacks of Interpretive Guidelines. Measurement and Evaluation in Counseling and Development, 57(2), 93–105. https://doi.org/10.1080/07481756.2023.2283637

Kaposi, J., & Szőke-Milinte, E. (Eds.). (2022). Kutatási módszerek pedagógusjelölteknek. Pázmány Péter Katolikus Egyetem.

Kassai, S. (2020). Using Interpretative Phenomenological Analysis (IPA) to Assess Recovery Processes : A Qualitative Analysis of Experience and Identity (Vol. 19). L’Harmattan. https://doi.org/10.56037/978-963-414-617-9

King, N. (2019). Interviews in qualitative research. SAGE.

Kline, R. B. (2023). Principles and practice of structural equation modeling. The Guilford Press.

Kontra, J. (2011). A pedagógiai kutatások módszertana: Egyetemi jegyzet. Kaposvári Egyetem.

Koo, T. K., & Li, M. Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

Korbmacher, M., Azevedo, F., Pennington, C. R., Hartmann, H., Pownall, M., Schmidt, K., Elsherif, M., Breznau, N., Robertson, O., Kalandadze, T., Yu, S., Baker, B. J., O’Mahony, A., Olsnes, J. Ø.‑S., Shaw, J. J., Gjoneska, B., Yamada, Y., Röer, J. P., Murphy, J., ... Evans, T. (2023). The replication crisis has led to positive structural, procedural, and community changes. Communications Psychology, 1(1), 3. https://doi.org/10.1038/s44271-023-00003-2

Korstjens, I., & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. The European Journal of General Practice, 24(1), 120–124. https://doi.org/10.1080/13814788.2017.1375092

Krippendorff, K. (2019). Content Analysis: An Introduction to Its Methodology. SAGE. https://doi.org/10.4135/9781071878781

Kuckartz, U., & Rädiker, S. (2023). Qualitative content analysis: Methods, practice and software. SAGE.

Kuhn, T. S. (2012). The structure of scientific revolutions. The University of Chicago Press.

Lagerspetz, M. (2021). “The Grievance Studies Affair” Project: Reconstructing and Assessing the Experimental Design. Science, Technology, & Human Values, 46(2), 402–424. https://doi.org/10.1177/0162243920923087

Lakens, D. (2022). Sample Size Justification. Collabra: Psychology, 8(1), Article 33267. https://doi.org/10.1525/collabra.33267

Lang, C., Siemens, G., Wise, A., & Gasevic, D. (Eds.). (2017). Handbook of Learning Analytics. Society for Learning Analytics Research. https://doi.org/10.18608/hla17

Lennox, C., & Payne-Mann, C. (2025). An Explanation of Path Analysis and Recommendations for Best Practice. Contemporary Accounting Research, Article 1911-3846.70019. Advance online publication. https://doi.org/10.1111/1911-3846.70019

Lim, W. M. (2025). What Is Qualitative Research? An Overview and Guidelines. Australasian Marketing Journal, 33(2), 199–229. https://doi.org/10.1177/14413582241264619

Marx, A., & Dusa, A. (2011). Crisp-Set Qualitative Comparative Analysis (csQCA), Contradictions and Consistency Benchmarks for Model Specification. Methodological Innovations Online, 6(2), 103–148. https://doi.org/10.4256/mio.2010.0037

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2024). Designing experiments and analyzing data: A model comparison perspective. Routledge Taylor & Francis Group.

Mayring, P. (2022). Qualitative content analysis: A step-by-step guide. SAGE.

McCammon, L. C., Gillen, P., McLaughlin, D., & Kernohan, W. G. (2025). An Interpretative Phenomenological Analysis That Seeks to Describe and Understand the Personal Experience of Burnout in General Practitioners. Qualitative Health Research, 35(1), 118–131. https://doi.org/10.1177/10497323241260738

McElreath, R. (2020). Statistical Rethinking. Chapman and Hall/CRC. https://doi.org/10.1201/9780429029608

McLevey, J., Scott, J., & Carrington, P. J. (Eds.). (2024). The Sage handbook of social network analysis. SAGE.

McNeish, D., & Dumas, D. (2025). Reliability representativeness: How well does coefficient alpha summarize reliability across the score distribution? Behavior Research Methods, 57(3), 93. https://doi.org/10.3758/s13428-025-02611-8

McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61–88. https://doi.org/10.1037/met0000425

Miles, M. B., Huberman, A. M., & Saldaña, J. (2020). Qualitative data analysis: A methods sourcebook. SAGE.

Neuendorf, K. A. (2017). The Content Analysis Guidebook. SAGE. https://doi.org/10.4135/9781071802878

Ng, C. K. J., Kwan, L. Y. J., & Chan, W. (2024). A Note on Evaluating the Moderated Mediation Effect. Structural Equation Modeling: A Multidisciplinary Journal, 31(2), 340–356. https://doi.org/10.1080/10705511.2023.2201396

Nicmanis, M. (2024). Reflexive Content Analysis: An Approach to Qualitative Data Analysis, Reduction, and Description. International Journal of Qualitative Methods, 23, Article 16094069241236603. https://doi.org/10.1177/16094069241236603

NISO CRediT Working Group. ANSI/NISO Z39.104-2022, CRediT, Contributor Roles Taxonomy. NISO.

Nizza, I. E., Farr, J., & Smith, J. A. (2021). Achieving excellence in interpretative phenomenological analysis (IPA): Four markers of high quality. Qualitative Research in Psychology, 18(3), 369–386. https://doi.org/10.1080/14780887.2020.1854404

Nosek, B. A., Hardwicke, T. E., Moshontz, H., Allard, A., Corker, K. S., Dreber, A., Fidler, F., Hilgard, J., Kline Struhl, M., Nuijten, M. B., Rohrer, J. M., Romero, F., Scheel, A. M., Scherer, L. D., Schönbrodt, F. D., & Vazire, S. (2022). Replicability, Robustness, and Reproducibility in Psychological Science. Annual Review of Psychology, 73, 719–748. https://doi.org/10.1146/annurev-psych-020821-114157

O’Connor, C., & Joffe, H. (2020). Intercoder Reliability in Qualitative Research: Debates and Practical Guidelines. International Journal of Qualitative Methods, 19, Article 1609406919899220. https://doi.org/10.1177/1609406919899220

Oana, I.‑E., Schneider, C. Q., & Thomann, E. (2021). Qualitative Comparative Analysis Using R. Cambridge University Press. https://doi.org/10.1017/9781009006781

Pearl, J. (2013). Causality. Cambridge University Press. https://doi.org/10.1017/CBO9780511803161

Pessoa, A. S. G., Harper, E., Santos, I. S., & Da Gracino, M. C. S. (2019). Using Reflexive Interviewing to Foster Deep Understanding of Research Participants’ Perspectives. International Journal of Qualitative Methods, 18, Article 1609406918825026. https://doi.org/10.1177/1609406918825026

Phillips, D. C. (2014). Encyclopedia of educational theory and philosophy. SAGE.

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer. https://doi.org/10.1007/b98882

Poploski, K. M., Smith, C., & Popchak, A. J. (2025). Biases and Confounders. In J. Espregueira-Mendes, J. Karlsson, V. Musahl, & O. R. Ayeni (Eds.), Orthopaedic Sports Medicine (pp. 1–15). Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-65430-6_116-1

Popper, K. R. (2002). The logic of scientific discovery. Routledge.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879

Preiser, R., García, M. M., Hill, L., & Klein, L. (2021). Qualitative content analysis. In R. Biggs, R. Preiser, A. de Vos, M. Schlüter, K. Maciejewski, & H. Clements (Eds.), The Routledge Handbook of Research Methods for Social-Ecological Systems (pp. 270–281). Routledge. https://doi.org/10.4324/9781003021339-23

Proceedings of the 14th Learning Analytics and Knowledge Conference. (2024). ACM.

Rácz, J., Karsai, S., & Tóth, V. (2023). Kvalitatív pszichológia. ELTE Eötvös Kiadó.

Ragin, C. C. (2000). Fuzzy-set social science. University of Chicago Press.

Ragin, C. C. (2014). The comparative method : moving beyond qualitative and quantitative strategies. University of California Press.

Reid, J. W., Parrish, J., Syed, S. B., & Couch, B. (2024). Finding the Connections: A Scoping Review of Epistemic Network Analysis in Science Education. Journal of Science Education and Technology. Advance online publication. https://doi.org/10.1007/s10956-024-10193-x

Richtig, G., Berger, M., Koeller, M., Richtig, M., Richtig, E., Scheffel, J., Maurer, M., & Siebenhaar, F. (2023). Predatory journals: Perception, impact and use of Beall’s list by the scientific community-A bibliometric big data study. PloS One, 18(7), e0287547. https://doi.org/10.1371/journal.pone.0287547

Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, 109074. https://doi.org/10.1016/j.dib.2023.109074

Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). stm : An R Package for Structural Topic Models. Journal of Statistical Software, 91(2). https://doi.org/10.18637/jss.v091.i02

Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems, 121(12), 2637–2650. https://doi.org/10.1108/IMDS-02-2021-0082

Rooij, M. de, & Weeda, W. (2020). Cross-Validation: A Method Every Psychologist Should Know. Advances in Methods and Practices in Psychological Science, 3(2), 248–263. https://doi.org/10.1177/2515245919898466

Roos, J. M., & Bauldry, S. (2022). Confirmatory Factor Analysis. SAGE. https://doi.org/10.4135/9781071938959

Rosseel, Y. (2012). lavaan : An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2). https://doi.org/10.18637/jss.v048.i02

Rönkkö, M., & Cho, E. (2022). An Updated Guideline for Assessing Discriminant Validity. Organizational Research Methods, 25(1), 6–14. https://doi.org/10.1177/1094428120968614

Ruis, A. R., & Lee, S. B. (Eds.). (2021). Communications in Computer and Information Science. Advances in Quantitative Ethnography. Springer International Publishing. https://doi.org/10.1007/978-3-030-67788-6

Rust, J., Kosinski, M., & Stillwell, D. (2021). Modern psychometrics: The science of psychological assessment. Routledge.

Sántha, K. (2009). Bevezetés a kvalitatív pedagógiai kutatás módszertanába. Eötvös József Kiadó.

Sántha, K. (2015a). Multikódolt adatok kvalitatív elemzése. Eötvös József Kiadó.

Sántha, K. (2015b). Trianguláció a pedagógiai kutatásban. Eötvös József Kiadó.

Sántha, K. (2022). Kvalitatív tartalomelemzés. Eötvös József Kiadó.

Saqr, M., & López-Pernas, S. (Eds.). (2024). Learning Analytics Methods and Tutorials. Springer International Publishing. https://doi.org/10.1007/978-3-031-54464-4

Sarstedt, M., Adler, S. J., Ringle, C. M., Cho, G., Diamantopoulos, A., Hwang, H., & Liengaard, B. D. (2024). Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling. Journal of Product Innovation Management, 41(6), 1100–1117. https://doi.org/10.1111/jpim.12738

Sathyanarayana, S., & Mohanasundaram, T. (2024). Fit Indices in Structural Equation Modeling and Confirmatory Factor Analysis: Reporting Guidelines. Asian Journal of Economics, Business and Accounting, 24(7), 561–577. https://doi.org/10.9734/ajeba/2024/v24i71430

Savalei, V., & Reise, S. P. (2019). Don’t Forget the Model in Your Model-based Reliability Coefficients: A Reply to McNeish. Collabra: Psychology, 5(1), Article 36. https://doi.org/10.1525/collabra.247

Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press.

Schoemann, A. M., Moore, E. W. G., & Yagiz, G. (2025). How and why to follow best practices for testing mediation models with missing data. International Journal of Psychology : Journal International De Psychologie, 60(1), e13257. https://doi.org/10.1002/ijop.13257

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

Shaffer, D. W. (2017). Quantitative ethnography. Cathcart Press.

Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A Tutorial on Epistemic Network Analysis: Analyzing the Structure of Connections in Cognitive, Social, and Interaction Data. Journal of Learning Analytics, 3(3), 9–45. https://doi.org/10.18608/jla.2016.33.3

Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., Frank, K., Rupp, A. A., & Mislevy, R. (2009). Epistemic Network Analysis: A Prototype for 21st-Century Assessment of Learning. International Journal of Learning and Media, 1(2), 33–53. https://doi.org/10.1162/ijlm.2009.0013

Shaffer, D. W., & Ruis, A. R. (2017). Epistemic Network Analysis: A Worked Example of Theory-Based Learning Analytics. In C. Lang, G. Siemens, A. Wise, & D. Gasevic (Eds.), Handbook of Learning Analytics (pp. 175–187). Society for Learning Analytics Research. https://doi.org/10.18608/hla17.015

Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662–1677. https://doi.org/10.1108/EJM-08-2020-0636

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.‑H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. https://doi.org/10.1037/0033-2909.86.2.420

Siebert-Evenstone, A. L., Arastoopour Irgens, G., Collier, W., Swiecki, Z., Ruis, A. R., & Williamson Shaffer, D. (2017). In Search of Conversational Grain Size: Modeling Semantic Structure using Moving Stanza Windows. Journal of Learning Analytics, 4(3). https://doi.org/10.18608/jla.2017.43.7

Silver, C., & Lewins, A. (2014). Using Software in Qualitative Research: A Step-by-Step Guide. SAGE. https://doi.org/10.4135/9781473906907

Skaaning, S.‑E. (2011). Assessing the Robustness of Crisp-set and Fuzzy-set QCA Results. Sociological Methods & Research, 40(2), 391–408. https://doi.org/10.1177/0049124111404818

Smith, J. A. (2011). Evaluating the contribution of interpretative phenomenological analysis. Health Psychology Review, 5(1), 9–27. https://doi.org/10.1080/17437199.2010.510659

Smith, J. A. (2024). Travelling in time: Using interpretative phenomenological analysis (IPA) to examine temporal aspects of the personal experience of issues in health and well-being. European Journal of Psychotherapy & Counselling, 26(3-4), 235–252. https://doi.org/10.1080/13642537.2024.2388663

Smith, J. A., Flowers, P., & Larkin, M. (2022). Interpretative phenomenological analysis: Theory, method and research. SAGE.

Smith, J. A., & Nizza, I. E. (2022). Essentials of interpretative phenomenological analysis. American Psychological Association.

Stapley, E., O’Keeffe, S., & Midgley, N. (2022). Developing Typologies in Qualitative Research: The Use of Ideal-type Analysis. International Journal of Qualitative Methods, 21, Article 16094069221100633. https://doi.org/10.1177/16094069221100633

Stroup, W. W., Ptukhina, M., & Garai, J. (2024). Generalized Linear Mixed Models. Chapman and Hall/CRC. https://doi.org/10.1201/9780429092060

Sun, Y., Hsieh, J., & Gao, X. (2025). Integrating Qualitative Comparative Analysis With Reflexive Thematic Analysis in Theme Development. International Journal of Qualitative Methods, 24, Article 16094069251318749. https://doi.org/10.1177/16094069251318749

Swiecki, Z., Ruis, A. R., Farrell, C., & Shaffer, D. W. (2020). Assessing individual contributions to Collaborative Problem Solving: A network analysis approach. Computers in Human Behavior, 104, 105876. https://doi.org/10.1016/j.chb.2019.01.009

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2019). Using multivariate statistics. Pearson.

Tan, Y., Swiecki, Z., Ruis, A. R., & Shaffer, D. (2024). Epistemic Network Analysis and Ordered Network Analysis in Learning Analytics. In M. Saqr & S. López-Pernas (Eds.), Learning Analytics Methods and Tutorials (pp. 569–636). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-54464-4_18

Tang, J., Liu, G., Bai, J., & Jiang, J [Junrong] (2024). The impacts of peer assessment on critical thinking competence: An epistemic network analysis. Journal of Hospitality, Leisure, Sport & Tourism Education, 35, 100515. https://doi.org/10.1016/j.jhlste.2024.100515

Taylor, N., Maduesesi, O., Vasiliou, V. S., & Thompson, A. R. (2025). The experience of living with vitiligo in Nigeria: A participatory Interpretative Phenomenological Analysis. Journal of Health Psychology, 30(5), 1120–1135. https://doi.org/10.1177/13591053241261684

Terry, G., & Hayfield, N. (2021). Essentials of thematic analysis. American Psychological Association. https://doi.org/10.1037/0000238-000

Tinto, V. (2017). Through the Eyes of Students. Journal of College Student Retention: Research, Theory & Practice, 19(3), 254–269. https://doi.org/10.1177/1521025115621917

Tomán, E. (2024). Genetikai örökség – családi test: Egy öröklődő betegség embodiment-tapasztalata. Alkalmazott Pszichológia, 26. https://doi.org/10.17627/ALKPSZICH.2024.4.37

Trainor, L. R., & Bundon, A. (2021). Developing the craft: reflexive accounts of doing reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 13(5), 705–726. https://doi.org/10.1080/2159676X.2020.1840423

Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124

Vargha, A. (2015). Matematikai statisztika - Pszichológiai, nyelvészeti és biológiai alkalmazásokkal. Pólya.

Vargha, A. (2019). Többváltozós statisztika dióhéjban. Pólya.

Vargha, A. (2020). Normális vagy? És ha nem? - Statisztikai módszerek nem normális eloszlású változókkal pszichológiai kutatásokban. Pólya.

Viberg, O., Baars, M., Mello, R. F., Weerheim, N., Spikol, D., Bogdan, C., Gasevic, D., & Paas, F. (2024). Exploring the nature of peer feedback: An epistemic network analysis approach. Journal of Computer Assisted Learning, 40(6), 2809–2821. https://doi.org/10.1111/jcal.13035

Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a World Beyond “p < 0.05”. The American Statistician, 73(sup1), 1–19. https://doi.org/10.1080/00031305.2019.1583913

Wen, C., Clough, P., Paton, R., & Middleton, R. (2025). Leveraging large language models for thematic analysis: a case study in the charity sector. AI & SOCIETY. Advance online publication. https://doi.org/10.1007/s00146-025-02487-4

West, B. T., Welch, K. B., Gałecki, A. T., & Gillespie, B. W. (2022). Linear Mixed Models. Chapman and Hall/CRC. https://doi.org/10.1201/9781003181064

Wilcox, R. R. (2021). Introduction to robust estimation and hypothesis testing. Elsevier.

Williams, L. J., Williams, A. R., & O’Boyle, E. H. (2024). Assessment of Path Model Fit: Evidence of Effectiveness and Recommendations for use of the RMSEA-P. Organizational Research Methods, 27(1), 64–87. https://doi.org/10.1177/10944281221124946

Wolf, M. G., & McNeish, D. (2023). Dynamic : An R Package for Deriving Dynamic Fit Index Cutoffs for Factor Analysis. Multivariate Behavioral Research, 58(1), 189–194. https://doi.org/10.1080/00273171.2022.2163476

Zahavi, D. (2021). Applied phenomenology: why it is safe to ignore the epoché. Continental Philosophy Review, 54(2), 259–273. https://doi.org/10.1007/s11007-019-09463-y

Zhang, B., Zhou, Y., & Li, D. (2025). Can Human Reading Validate a Topic Model? Sociological Methodology, 55(1), 59–90. https://doi.org/10.1177/00811750241265336

Zhao, L., Echeverria, V., Swiecki, Z., Yan, L., Alfredo, R., Li, X., Gasevic, D., & Martinez‑Maldonado, R. (2024). Epistemic Network Analysis for End-users: Closing the Loop in the Context of Multimodal Analytics for Collaborative Team Learning. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 90–100). ACM. https://doi.org/10.1145/3636555.3636855