Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-26T01:40:23.380Z Has data issue: false hasContentIssue false
  • English
  • Français

Configural frequency trees

Published online by Cambridge University Press:  10 March 2021

Wolfgang Wiedermann Open the ORCID record for Wolfgang Wiedermann [Opens in a new window] ,
Keith C. Herman ,
Wendy Reinke  and
Alexander von Eye
Wolfgang Wiedermann*
Affiliation:
Department of Educational, School & Counseling Psychology and Missouri Prevention Science Institute, University of Missouri, Columbia, USA
Keith C. Herman
Affiliation:
Department of Educational, School & Counseling Psychology and Missouri Prevention Science Institute, University of Missouri, Columbia, USA
Wendy Reinke
Affiliation:
Department of Educational, School & Counseling Psychology and Missouri Prevention Science Institute, University of Missouri, Columbia, USA
Alexander von Eye
Affiliation:
Department of Psychology, Michigan State University, East Lansing, MI, USA
*
Author for Correspondence: Wolfgang Wiedermann, Statistics, Measurement, and Evaluation in Education, Department of Educational, School, and Counseling Psychology, College of Education, and Missouri Prevention Science Institute, University of Missouri, 13B Hill Hall, Columbia, MO, 65211, USA; E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Although variable-oriented analyses are dominant in developmental psychopathology, researchers have championed a person-oriented approach that focuses on the individual as a totality. This view has methodological implications and various person-oriented methods have been developed to test person-oriented hypotheses. Configural frequency analysis (CFA) has been identified as a prime method for a person-oriented analysis of categorical data. CFA searches for configurations in cross-classifications and asks whether the number of observed cases is larger (CFA type) or smaller (CFA antitype) than expected under a probability model. The present study introduces a combination of CFA and model-based recursive partitioning (MOB) to test for type/antitype heterogeneity in the population. MOB CFA is well suited to detect complex moderation processes and can distinguish between subpopulation and population types/antitypes. Model specifications are discussed for first-order CFA and prediction CFA. Results from two simulation studies suggest that MOB CFA is able to detect moderation processes with high accuracy. Two empirical examples are given from school mental health research for illustrative purposes. The first example evaluates heterogeneity in student behavior types/antitypes, the second example focuses on the effect of a teacher classroom management intervention on student behavior. An implementation of the approach is provided in R.

Keywords

configural frequency analysismodel-based recursive partitioningmoderationperson-oriented researchregression trees
Type
Regular Article
Information
Development and Psychopathology , Volume 34 , Issue 4 , October 2022 , pp. 1585 - 1603
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley & Sons.CrossRefGoogle Scholar
Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821. doi:10.2307/2951764CrossRefGoogle Scholar
Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 40, 57–87.CrossRefGoogle ScholarPubMed
Bergman, L. R. (2001). A person approach in research on adolescence: Some methodological challenges. Journal of Adolescent Research, 16, 28–53. doi:10.1177/0743558401161004CrossRefGoogle Scholar
Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319. doi:10.1017/S095457949700206XCrossRefGoogle ScholarPubMed
Bergman, L. R., Magnusson, D., & El Khouri, B. M. (2003). Studying individual development in an interindividual context: A person-oriented approach. Mahwah, NJ: Lawrence Erlbaum Associates.CrossRefGoogle Scholar
Biglan, A., Flay, B. R., Embry, D. D., & Sandler, I. N. (2012). The critical role of nurturing environments for promoting human well-being. American Psychologist, 67, 257–271. doi:10.1037/a0026796Google ScholarPubMed
Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140. doi:10.1023/A:1018054314350CrossRefGoogle Scholar
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. L. (1984). Classification and regression trees. Belmont: Wadsworth.Google Scholar
Cairns, R. B. (1979). Social development: The origins and plasticity of interchanges. San Francisco: Freeman.Google Scholar
Cairns, R. B., Elder, G. H., & Costello, E. J. (Eds.). (1996). Developmental science (1st ed.). Cambridge University Press. doi:10.1017/CBO9780511571114CrossRefGoogle Scholar
Christensen, R. (1997). Log-linear models and logistic regression (2nd ed.). New York: Springer.Google Scholar
Cicchetti, D., & Schneider-Rosen, K. (1986). An organization approach to childhood depression. In Rutter, M., Izard, C., & Read, P. (Eds.), Depression in young people, clinical and developmental perspectives (pp. 71–134). New York: Guilford Press.Google Scholar
Cicchetti, D., & Toth, S. L. (1995). Developmental psychopathology and disorders of affect: Risk, disorder, and adaptation. In Cicchetti, D., & Cohen, D. J. (Eds.), Developmental psychopathology: Risk, disorder, and adaptation (Vol. 2, pp. 369–420). New York: Wiley & Sons.Google Scholar
Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7, 249–253. doi:10.1177/014662168300700301CrossRefGoogle Scholar
Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis for the social, behavioral, and health sciences. New York: Wiley and Sons.Google Scholar
Daukantaitė, D., Lundh, L.-G., & Wångby-Lundh, M. (2019). Association of direct and indirect aggression and victimization with self-harm in young adolescents: A person-oriented approach. Development and Psychopathology, 31, 727–739. doi:10.1017/S0954579418000433CrossRefGoogle ScholarPubMed
DeCoster, J., Iselin, A.-M. R., & Gallucci, M. (2009). A conceptual and empirical examination of justifications for dichotomization. Psychological Methods, 14, 349–366. doi:10.1037/a0016956CrossRefGoogle ScholarPubMed
Dishion, T. J., & Patterson, G. R. (2006). The development and ecology of antisocial behavior in children and adolescents. In Cicchetti, D., & Cohen, D. J. (Eds.), Developmental psychopathology (vol. 3): Risk, disorder, and adaption (2nd ed., pp. 503–541). Hoboken: Wiley & Sons.Google Scholar
Dunnett, C. W., & Tamhane, A. C. (1992). A step-up multiple test procedure. Journal of the American Statistical Association, 87, 162–170. doi:10.1080/01621459.1992.10475188Google Scholar
Dusseldorp, E., & Van Mechelen, I. (2014). Qualitative interaction trees: A tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine, 33, 219–237. doi:10.1002/sim.5933CrossRefGoogle ScholarPubMed
Fokkema, M., Smits, N., Zeileis, A., Hothorn, T., & Kelderman, H. (2018). Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees. Behavior Research Methods, 50, 2016–2034. doi:10.3758/s13428-017-0971-xCrossRefGoogle ScholarPubMed
Fokkema, M., & Strobl, C. (2020). Fitting prediction rule ensembles to psychological research data: An introduction and tutorial. Psychological Methods, doi:10.1037/met0000256CrossRefGoogle ScholarPubMed
Ford, D. H., & Lerner, R. M. (1992). Developmental systems theory. Newbury Park: Sage.Google Scholar
Freund, Y., & Schapire, R. E. (1995). A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting. European Conference on Computational Learning Theory.CrossRefGoogle Scholar
Friendly, M. (1994). Mosaic displays for multi-way contingency tables. Journal of the American Statistical Association, 89, 190–200. doi:10.1080/01621459.1994.10476460CrossRefGoogle Scholar
Friendly, M. (1995). Conceptual and visual models for categorical data. The American Statistician, 49, 153. doi:10.2307/2684630Google Scholar
George, C., Herman, K. C., & Ostrander, R. (2006). The family environment and developmental psychopathology: The unique and interactive effects of depression, attention, and conduct problems. Child Psychiatry and Human Development, 37, 163–177. doi:10.1007/s10578-006-0026-5CrossRefGoogle ScholarPubMed
Greenberg, M. T., Speltz, M. L., Deklyen, M., & Jones, K. (2001). Correlates of clinic referral for early conduct problems: Variable- and person-oriented approaches. Development and Psychopathology, 13, 255–276. doi:10.1017/S0954579401002048CrossRefGoogle ScholarPubMed
Hartigan, J. A., & Kleiner, B. (1984). A mosaic of television ratings. The American Statistician, 38, 32. doi:10.2307/2683556Google Scholar
Hatzinger, R., & Dittrich, R. (2012). Prefmod: An R package for modeling preferences based on paired comparisons, rankings, or ratings. Journal of Statistical Software, 48. doi:10.18637/jss.v048.i10CrossRefGoogle Scholar
Herman, K. C., Dong, N., Reinke, W. M., & Bradshaw, C. P. (2020). Accounting for traumatic historical events in social, behavioral, and educational randomized control trials. (Under Review).Google Scholar
Herman, K. C., Ostrander, R., Walkup, J. T., Silva, S. G., & March, J. S. (2007). Empirically derived subtypes of adolescent depression: Latent profile analysis of co-occurring symptoms in the Treatment for Adolescents with Depression Study (TADS). Journal of Consulting and Clinical Psychology, 75, 716–728. doi:10.1037/0022-006X.75.5.716CrossRefGoogle Scholar
Hildebrand Karlén, M., Nilsson, T., Wallinius, M., Billstedt, E., & Hofvander, B. (2020). A bad start: The combined effects of early onset substance use and ADHD and CD on criminality patterns, substance abuse and psychiatric comorbidity among young violent offenders. Journal for Person-Oriented Research, 6, 39–55. doi:10.17505/jpor.2020.22045CrossRefGoogle ScholarPubMed
Hjort, N. L., & Koning, A. (2002). Tests for constancy of model parameters over time. Journal of Nonparametric Statistics, 14, 113–132. doi:10.1080/10485250211394CrossRefGoogle Scholar
Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75, 800–802. doi:10.1093/biomet/75.4.800CrossRefGoogle Scholar
Holland, B. S., & Copenhaver, M. D. (1987). An improved sequentially rejective Bonferroni test procedure. Biometrics, 43, 417. doi:10.2307/2531823CrossRefGoogle Scholar
Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15, 651–674. doi:10.1198/106186006X133933CrossRefGoogle Scholar
Hothorn, T., & Zeileis, A. (2015). Partykit: A modular toolkit for recursive partytioning in R. Journal of Machine Learning Research, 16, 3905–3909.Google Scholar
Huang, F. L. (2019). Alternatives to logistic regression models in experimental studies. The Journal of Experimental Education, 1–16. doi:10.1080/00220973.2019.1699769Google Scholar
Hunt, P. R., Friesen, M. C., Sama, S., Ryan, L., & Milton, D. (2015). Log-linear modeling of agreement among expert exposure assessors. Annals of Occupational Hygiene, 59, 764–774. doi:10.1093/annhyg/mev011CrossRefGoogle ScholarPubMed
Iacobucci, D., Posavac, S. S., Kardes, F. R., Schneider, M. J., & Popovich, D. L. (2015). Toward a more nuanced understanding of the statistical properties of a median split. Journal of Consumer Psychology, 25, 652–665. doi:10.1016/j.jcps.2014.12.002CrossRefGoogle Scholar
Kagan, J. (1994). Galen's prophecy: Temperament in human nature. New York: Basic Books.Google Scholar
Kaplan, A., Cromley, J., Perez, T., Dai, T., Mara, K., & Balsai, M. (2020). The role of context in educational RCT findings: A call to redefine “evidence-based practice.”. Educational Researcher, 49, 285–288. doi:10.3102/0013189X20921862CrossRefGoogle Scholar
Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29, 119. doi:10.2307/2986296CrossRefGoogle Scholar
Keller, T. E., Spieker, S. J., & Gilchrist, L. (2005). Patterns of risk and trajectories of preschool problem behaviors: A person-oriented analysis of attachment in context. Development and Psychopathology, 17. doi:10.1017/S0954579405050170CrossRefGoogle ScholarPubMed
Khoury, M. J., Iademarco, M. F., & Riley, W. T. (2016). Precision public health for the era of precision medicine. American Journal of Preventive Medicine, 50, 398–401. doi:10.1016/j.amepre.2015.08.031CrossRefGoogle ScholarPubMed
Koth, C. W., Bradshaw, C. P., & Leaf, P. J. (2009). Teacher observation of classroom adaptation—checklist: Development and factor structure. Measurement and Evaluation in Counseling and Development, 42, 15–30. doi:10.1177/0748175609333560CrossRefGoogle Scholar
Lautsch, E., & Ninke, L. (2000). Kombinierter Einsatz von CHAID und KFA bei der soziodemographischen Beschreibung von Kriminalitätsfurcht. Psychologische Beiträge, 42, 347–361.Google Scholar
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.Google Scholar
Lienert, G. A. (1968). Die Konfigurationsfrequenzanalyse als Klassifikationsmethode in der Klinischen Psychologie. [Configural Frequency Analysis as Classification Method in Clinical Psychology.]. Paper Presented at the 26. Kongress der Deutschen Gesellschaft für Psychologie in Tübingen.Google Scholar
Lienert, G. A., & Krauth, J. (1973). Die Konfigurationsfrequenzanalyse als Prädiktionsmodell in der angewandten Psychologie. In Eckensberger, H. (Ed.), Bericht über den 28. Kongreß der deutschen gesellschaft für psychologie in saarbrücken 1972 (pp. 219–228). Göttingen: Hogrefe.Google Scholar
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19–40. doi:10.1037//1082-989X.7.1.19CrossRefGoogle ScholarPubMed
Magnusson, D. (1985). Implications of an interactional paradigm for research on human development. International Journal of Behavioral Development, 8, 115–137. doi:10.1177/016502548500800201CrossRefGoogle Scholar
Magnusson, D. (2001). The holistic-interactionistic paradigm: Some directions for empirical developmental research. European Psychologist, 6, 153–162. doi:10.1027//1016-9040.6.3.153CrossRefGoogle Scholar
Magnusson, D., & Allen, V. L. (1983). Implications and applications of an interactional perspective for human development. In Magnusson, D., & Allen, V. L. (Eds.), Human development: An interactional perspective (pp. 369–387). New York: Academic Press.Google Scholar
Magnusson, D., & Stattin, H. (1998). Person-context interaction theories. In Damon, W., & Lerner, R. M. (Eds.), Handbook of child psychology: Theoretical models of human development (pp. 685–759). New York: Wiley & Sons.Google Scholar
Maxwell, S. E., & Delaney, H. D. (1993). Bivariate median splits and spurious statistical significance. Psychological Bulletin, 113, 181–190. doi:10.1037/0033-2909.113.1.181CrossRefGoogle Scholar
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). London: Chapman & Hall.CrossRefGoogle Scholar
Melcher, A. H., Lautsch, E., & Schmutz, S. (2012). Non-parametric methods – tree and P-CFA – for ecological evaluation and assessment of suitable aquatic habitats: A contribution to fish psychology. Psychological Test and Assessment Modeling, 54, 293–306.Google Scholar
Molenaar, P. C. M. (2010). Testing all six person-oriented principles in dynamic factor analysis. Development and Psychopathology, 22, 255–259. doi:10.1017/S0954579410000027Google ScholarPubMed
Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18, 112–117. doi:10.1111/j.1467-8721.2009.01619.xCrossRefGoogle Scholar
Morgan, J. N., & Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association, 58, 415–434. doi:10.1080/01621459.1963.10500855CrossRefGoogle Scholar
Müller, M. J., Netter, P., & von Eye, A. (1997). Catecholamine response curves of male hypertensives identified by Lehmacher's two sample configural frequency analysis. Biometrical Journal, 39, 29–38. doi:10.1002/bimj.4710390104CrossRefGoogle Scholar
Mun, E. Y., Bates, M. E., & Vaschillo, E. (2010). Closing the gap between person-oriented theory and methods. Development and Psychopathology, 22, 261–271. doi:10.1017/S0954579410000039CrossRefGoogle ScholarPubMed
Mun, E. Y., von Eye, A., Fitzgerald, H. A., & Zucker, R. A. (2001). Using mosaic displays in configural frequency analysis. Methods of Psychological Research-Online, 6, 3.Google Scholar
Olejnik, S., Li, J., Supattathum, S., & Huberty, C. J. (1997). Multiple testing and statistical power with modified Bonferroni procedures. Journal of Educational and Behavioral Statistics, 22, 389. doi:10.2307/1165229CrossRefGoogle Scholar
Philipp, M., Rusch, T., Hornik, K., & Strobl, C. (2018). Measuring the stability of results from supervised statistical learning. Journal of Computational and Graphical Statistics, 27, 685–700. doi:10.1080/10618600.2018.1473779CrossRefGoogle Scholar
Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo: Morgan Kaufmann.Google Scholar
R Core Team. (2020). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org/.Google Scholar
Reinke, W. M., & Herman, K. C. (2002). Creating school environments that deter antisocial behaviors in youth. Psychology in the Schools, 39, 549–559. doi:10.1002/pits.10048CrossRefGoogle Scholar
Reinke, W. M., Herman, K. C., & Dong, N. (2018). The incredible years teacher classroom management program: Outcomes from a group randomized trial. Prevention Science, 19, 1043–1054. doi:10.1007/s11121-018-0932-3CrossRefGoogle ScholarPubMed
Rose, T. (2015). The end of average: How we succeed in a world that values sameness. San Francisco: HarperOne/HarperCollins.Google Scholar
Rothenberg, W. A., Hussong, A. M., & Chassin, L. (2016). Intergenerational continuity in high-conflict family environments. Development and Psychopathology, 28, 293–308. doi:10.1017/S0954579415000450CrossRefGoogle ScholarPubMed
Rucker, D. D., McShane, B. B., & Preacher, K. J. (2015). A researcher's guide to regression, discretization, and median splits of continuous variables. Journal of Consumer Psychology, 25, 666–678. doi:10.1016/j.jcps.2015.04.004CrossRefGoogle Scholar
Rusch, T., & Zeileis, A. (2013). Gaining insight with recursive partitioning of generalized linear models. Journal of Statistical Computation and Simulation, 83, 1301–1315. doi:10.1080/00949655.2012.658804CrossRefGoogle Scholar
Schrepp, M. (2006). The use of configural frequency analysis for explorative data analysis. British Journal of Mathematical and Statistical Psychology, 59, 59–73. doi:10.1348/000711005X66761CrossRefGoogle ScholarPubMed
Stattin, H., & Magnusson, D. (1996). Antisocial development: A holistic approach. Development and Psychopathology, 8, 617–645. doi:10.1017/S0954579400007331CrossRefGoogle Scholar
Stemmler, M., & Heine, J.-H. (2017). Using configural frequency analysis as a person-centered analytic approach with categorical data. International Journal of Behavioral Development, 41, 632–646. doi:10.1177/0165025416647524CrossRefGoogle Scholar
Stemmler, M., Heine, J. H., & Wallner, S. (2019). Analyzing tree structures with configural frequency analysis and the R-package confreq. Psychological Test and Assessment Modeling, 61, 419–433.Google Scholar
Sterba, S. K., & Bauer, D. J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22, 239–254. doi:10.1017/S0954579410000015CrossRefGoogle ScholarPubMed
Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14, 323–348. doi:10.1037/a0016973Google ScholarPubMed
Supplee, L. H., Parekh, J., & Johnson, M. (2018). Principles of precision prevention science for improving recruitment and retention of participants. Prevention Science, 19, 689–694. doi:10.1007/s11121-018-0884-7CrossRefGoogle ScholarPubMed
Turney, P. (1995). Technical note: Bias and the quantification of stability. Machine Learning, 20, 23–33. doi:10.1007/BF00993473CrossRefGoogle Scholar
van Wie, M., Li, X., & Wiedermann, W. (2019). Identification of confounded subgroups using linear model based recursive partitioning. Psychological Test and Assessment Modeling, 61, 365–387.Google Scholar
von Eye, A. (1990). Introduction to configural frequency analysis. Cambridge: Cambridge University Press.Google Scholar
von Eye, A. (2002). Configural frequency analysis: Methods, models, and applications. Mahwah: Lawrence Erlbaum Associates.Google Scholar
von Eye, A. (2004). Base models for configural frequency analysis. Psychology Science, 46, 150–170.Google Scholar
von Eye, A. (2010). Developing the person-oriented approach: Theory and methods of analysis. Development and Psychopathology, 22, 277–285. doi:10.1017/S0954579410000052CrossRefGoogle ScholarPubMed
von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology, 15, 553–580. doi:10.1017/S0954579403000294CrossRefGoogle ScholarPubMed
von Eye, A., Bergman, L. R., & Hsieh, C. H. (2015). Person oriented methodological approaches. In Overton, W. F., & Molenaar, P. C. M. (Eds.), Handbook of child psychology and developmental science: Theory and methods (pp. 789–841). Hoboken: Wiley & Sons.Google Scholar
von Eye, A., & Bogat, G. A. (2005). Logistic regression and prediction configural frequency analysis – a comparison. Psychology Science, 47, 326–341.Google Scholar
von Eye, A., & Gutiérrez Peña, E. (2004). Configural frequency analysis: The search for extreme cells. Journal of Applied Statistics, 31, 981–997. doi:10.1080/0266476042000270545CrossRefGoogle Scholar
von Eye, A., & Mair, P. (2008). A functional approach to configural frequency analysis. Austrian Journal of Statistics, 37, 161–173.Google Scholar
von Eye, A., & Mair, P. (2012). On the effects of dichotomizing information. In Barragan, A., Martínez, F. M., Barajas, L. E. N., & Covarrubias, C. C. (Eds.), Memorias del XXIV Foro National de Estadistica (pp. 11–19). Aguascalientes: Instituto Nacional de Estadistica y Geografia.Google Scholar
von Eye, A., Mair, P., & Bogat, G. A. (2005). Prediction models for configural frequency analysis. Psychology Science, 47, 342–355.Google Scholar
von Eye, A., Mair, P., & Mun, E. Y. (2010). Advances in configural frequency analysis. New York: Guilford Press.Google Scholar
von Eye, A., Mun, E. Y., & Bogat, G. A. (2008). Temporal patterns of variable relationships in person-oriented research: Longitudinal models of configural frequency analysis. Developmental Psychology, 44, 437–445. doi:10.1037/0012-1649.44.2.437CrossRefGoogle ScholarPubMed
von Eye, A., Mun, E. Y., & Mair, P. (2009). What carries a mediation process? Configural analysis of mediation. Integrative Psychological and Behavioral Science, 43, 228–247. doi:10.1007/s12124-009-9088-9CrossRefGoogle ScholarPubMed
von Eye, A., & Schuster, C. (1998). On the specification of models for configural frequency analysis—sampling schemes in prediction CFA. Methods of Psychological Research Online, 3.Google Scholar
von Eye, A., Schuster, C., & Rogers, W. M. (1998). Modelling synergy using manifest categorical variables. International Journal of Behavioral Development, 22, 537–557. doi:10.1080/016502598384261CrossRefGoogle Scholar
von Eye, A., & Wiedermann, W. (2021). CFA. Configural frequency analysis. New York: Springer (in prep.).CrossRefGoogle Scholar
von Eye, A., Wiedermann, W., & von Weber, S. (2019). Log-linear and configural analysis of tree structures. Psychological Test and Assessment Modeling, 61, 435–451.Google Scholar
von Weber, S., Lautsch, E., & von Eye, A. (2003). On the limits of configural frequency analysis: Analyzing small tables. Psychology Science, 45, 339–354.Google Scholar
Webster-Stratton, C., Reid, M. J., & Hammond, M. (2004). Treating children with early-onset conduct problems: Intervention outcomes for parent, child, and teacher training. Journal of Clinical Child & Adolescent Psychology, 33, 105–124. doi:10.1207/S15374424JCCP3301_11CrossRefGoogle ScholarPubMed
Westfall, P. H. (2011). Improving power by dichotomizing (even under normality). Statistics in Biopharmaceutical Research, 3, 353–362. doi:10.1198/sbr.2010.09055CrossRefGoogle Scholar
Wiedermann, W., Bergman, L. R., & von Eye, A. (2016). Developments in methods for person-oriented research. Journal for Person-Oriented Research, 2, 1–4. doi:10.17505/jpor.2016.01CrossRefGoogle Scholar
Wiedermann, W., Reinke, W., & Herman, K. (2020). Prosocial skills causally mediate the relation between effective classroom management and academic competence: An application of direction dependence analysis. Developmental Psychology, 56, 1723–1735.CrossRefGoogle ScholarPubMed
Wiedermann, W., & von Eye, A. (2016). Local associations in latent class analysis: Using configural frequency analysis for model evaluation. Journal for Person-Oriented Research, 2. doi:10.17505/jpor.2016.15CrossRefGoogle Scholar
Wiedermann, W., & von Eye, A. (2020a). Log-linear models to evaluate direction of effect in binary variables. Statistical Papers, 61, 317–346. doi:10.1007/s00362-017-0936-2CrossRefGoogle Scholar
Wiedermann, W., & von Eye, A. (2020b). Reciprocal relations in categorical variables. Psychological Methods, 25, 708–725. doi:10.1037/met0000257CrossRefGoogle Scholar
Zeileis, A., & Hornik, K. (2007). Generalized M-fluctuation tests for parameter instability. Statistica Neerlandica, 61, 488–508. doi:10.1111/j.1467-9574.2007.00371.xCrossRefGoogle Scholar
Zeileis, A., Hothorn, T., & Hornik, K. (2008). Model-based recursive partitioning. Journal of Computational and Graphical Statistics, 17, 492–514. doi:10.1198/106186008X319331CrossRefGoogle Scholar
Zhang, H., & Singer, B. (2010). Recursive partitioning and applications (2nd ed.). New York: Springer.CrossRefGoogle Scholar
Supplementary material: File

Wiedermann et al. supplementary material

Wiedermann et al. supplementary material

Download Wiedermann et al. supplementary material(File)
File 100.6 KB