Kristopher J. Preacher: Publications

Liang, A., Preacher, K. J., Williams, N. J., Allison, P. D., Marcus, S. C., & Sterba, S. K. (in press). Determining the power of a 1-sided z-test given only the power of the corresponding 2-sided test. Journal of Behavioral Medicine.
Bonifay, W., Cai, L., Falk, C. F., & Preacher, K. J. (in press). Reassessing the fitting propensity of factor models. Psychological Methods. [Supplemental materials]
Cole, D. A., Abitante, G., Kan, H., Liu, Q., Preacher, K. J., & Maxwell, S. E. (in press). Practical problems estimating and reporting power when hypotheses are embedded in complex statistical models. Advances in Methods & Practices in Psychological Science. [Supplemental materials]
Yaremych, H. E., & Preacher, K. J. (2024). Understanding the consequences of collinearity for multilevel models: The importance of disaggregation across levels. Multivariate Behavioral Research, 59, 693-715. [Online appendices]
Cho, S.-J., Preacher, K. J., Yaremych, H. E., Naveiras, M., Fuchs, D., & Fuchs, L. (2024). Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model. Behavior Research Methods, 56, 2094-2113. [Online supplement]
Yaremych, H. E., Preacher, K. J., & Hedeker, D. (2023). Centering categorical predictors in multilevel models: Best practices and interpretation. Psychological Methods, 28, 613-630. [Online appendices]
Williams, N. J., Preacher, K. J., Allison, P. D., Mandell, D., & Marcus, S. C. (2022). Required sample size to detect mediation in 3-level implementation studies. Implementation Science, 17, 66. [Online supplement]
Ozkok, O., Vaulont, M. J., Zyphur, M. J., Zhang, Z., Preacher, K. J., Koval, P., & Zheng, Y. (2022). Interaction effects in cross-lagged panel models: SEM with latent interactions applied to work-family conflict, job satisfaction, and gender. Organizational Research Methods, 25, 673-715. [Online appendices]
Cho, S.-J., Preacher, K. J., Yaremych, H. E., Naveiras, M., Fuchs, D., & Fuchs, L. (2022). Modeling multilevel nonlinear treatment-by-covariate interactions in cluster randomized controlled trials using a generalized additive mixed model. British Journal of Mathematical & Statistical Psychology, 75, 493-521. [Online supplement]
Zyphur, M. J., Hamaker, E. L., Tay, L., Voelkle, M., Preacher, K. J., Zhang, Z., Allison, P. D., Pierides, D. C., Koval, P., & Diener, E. F. (2021). From data to causes III: Bayesian priors for general cross-lagged panel models (GCLM). Frontiers in Psychology, 12, 112. [Online supplement]
Deboeck, P. R., Cole, D. A., Preacher, K. J., Forehand, R., & Compas, B. E. (2021). Modeling dynamic processes with panel data: An application of continuous time models to prevention research. International Journal of Behavioral Development, 45, 28-39. [Online supplement]
Rights, J. D., Preacher, K. J., & Cole, D. A. (2020). The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects. British Journal of Mathematical & Statistical Psychology, 73, 194-211. [Online supplement]
Zyphur, M. J., Allison, P. D., Tay, L., Voelkle, M. C., Preacher, K. J., Zhang, Z., Hamaker, E. L., Shamsollahi, A., Pierides, D. C., Koval, P., & Diener, E. (2020). From data to causes I: Building a general cross-lagged panel model (GCLM). Organizational Research Methods, 23, 651-687. [Supplemental files]
Zyphur, M. J., Voelkle, M. C., Tay, L., Allison, P. D., Preacher, K. J., Zhang, Z., Hamaker, E. L., Shamsollahi, A., Pierides, D. C., Koval, P., & Diener, E. (2020). From data to causes II: Comparing approaches to panel data analysis. Organizational Research Methods, 23, 688-716. [Supplemental files]
Zhang, G., Preacher, K. J., Hattori, M., Jiang, G., & Trichtinger, L. A. (2019). A sandwich standard error estimator for exploratory factor analysis with nonnormal data and imperfect models. Applied Psychological Measurement, 43, 360-373.
Zyphur, M. J., Zhang, Z., Preacher, K. J., & Bird, L. J. (2019). Moderated mediation in multilevel structural equation models: Decomposing effects of race on math achievement within versus between high schools in the United States. In S. E. Humphrey & J. M. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 473-494). Washington, DC: American Psychological Association. [Online supplement]
Preacher, K. J., & Sterba, S. K. (2019). Aptitude-by-treatment interactions in research on educational interventions. Exceptional Children, 85, 248-264.
Rucker, D. D., & Preacher, K. J. (2019). Mediation analysis in consumer psychology: Models, methods, and considerations. In F. R. Kardes, P. M. Herr, & N. Schwarz (Eds.), Handbook of research methods in consumer psychology (pp. 373-384). New York: Routledge.
Rights, J. D., Sterba, S. K., Cho, S.-J., & Preacher, K. J. (2018). Addressing model uncertainty in item response theory person scores through model averaging. Behaviormetrika, 45, 495-503.
Lachowicz, M. J., Preacher, K. J., & Kelley, K. (2018). A novel measure of effect size for mediation analysis. Psychological Methods, 23, 244-261.
Hattori, M., Zhang, G., & Preacher, K. J. (2017). Multiple local solutions and geomin rotation. Multivariate Behavioral Research, 52, 720-731. [Online appendices]
Cho, S.-J., & Preacher, K. J. (2016). Measurement error correction formula for cluster-level group differences in cluster randomized and observational studies. Educational & Psychological Measurement, 76, 771-786.
Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2016). Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychological Methods, 21, 189-205. [Simulation appendix and Supplementary code] [e-mail me]
Merkle, E. C., You, D., & Preacher, K. J. (2016). Testing non-nested structural equation models. Psychological Methods, 21, 151-163. [R code]
Deboeck, P. R., & Preacher, K. J. (2016). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling, 23, 61-75. [Online appendices A and B]
Zhang, G., & Preacher, K. J. (2015). Factor rotation and standard errors in exploratory factor analysis. Journal of Educational & Behavioral Statistics, 40, 579-603. [Supporting materials]
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.
Cho, S.-J., Preacher, K. J., & Bottge, B. A. (2015). Detecting intervention effects in a cluster randomized design using multilevel structural equation modeling for binary responses. Applied Psychological Measurement, 39, 627-642. [Supplementary material, Mplus code, and simulation results]
Lachowicz, M. J., Sterba, S. K., & Preacher, K. J. (2015). Investigating multilevel mediation with fully or partially nested data. Group Processes & Intergroup Relations, 18, 274-289. [Online appendix and R and Mplus syntax, output, and data files]
Preacher, K. J., & Hancock, G. R. (2015). Meaningful aspects of change as novel random coefficients: A general method for reparameterizing longitudinal models. Psychological Methods, 20, 84-101. [Online appendix]
Timmons, A. C., & Preacher, K. J. (2015). The importance of temporal design: How do measurement intervals affect the accuracy and efficiency of parameter estimates in longitudinal research? Multivariate Behavioral Research, 50, 41-55. [Additional simulation results, comparisons of temporal designs, simulation R code, and customizable R code]
Wang, L., & Preacher, K. J. (2015). Moderated mediation analysis using Bayesian methods. Structural Equation Modeling, 22, 249-263. [Appendix A and Appendix B]
Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66, 825-852. [Bibliography]
Preacher, K. J. (2015). Extreme groups designs. In R. L. Cautin & S. O. Lilienfeld (Eds.), The encyclopedia of clinical psychology (Vol. 2, pp. 1189-1192). Hoboken, NJ: John Wiley & Sons, Inc.
Pornprasertmanit, S., Lee, J., & Preacher, K. J. (2014). Ignoring clustering in confirmatory factor analysis: Some consequences for model fit and standardized parameter estimates. Multivariate Behavioral Research, 49, 518-543. [Online appendices A-D]
Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical & Statistical Psychology, 67, 451-470. [Supplement]
Cole, D. A., & Preacher, K. J. (2014). Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychological Methods, 19, 300-315.
Sterba, S. K., Preacher, K. J., Forehand, R., Hardcastle, E., Cole, D. A., & Compas, B. E. (2014). Structural equation modeling approaches for analyzing partially nested data. Multivariate Behavioral Research, 49, 93-118. [Online appendix and Mplus and LISREL syntax]
Gu, F., Preacher, K. J., Wu, W., & Yung, Y.-F. (2014). A computationally efficient state space approach to estimating multilevel regression models and multilevel confirmatory factor models. Multivariate Behavioral Research, 49, 119-129.
Gu, F., Preacher, K. J., & Ferrer, E. (2014). A state space modeling approach to mediation analysis. Journal of Educational and Behavioral Statistics, 39, 117-143.
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19, 72-91. [Online appendices A-C]
Cho, S.-J., Athay, M., & Preacher, K. J. (2013). Measuring change for a multidimensional test using a generalized explanatory longitudinal item response model. British Journal of Mathematical & Statistical Psychology, 66, 353-381. [Supplementary results, lmer script, and data]
Hayes, A. F., & Preacher, K. J. (2013). Conditional process modeling: Using structural equation modeling to examine contingent causal processes. In G. R. Hancock & R. O. Mueller (Eds.). Structural equation modeling: A second course (2nd ed.), pp. 219-266. Charlotte, NC: Information Age Publishing.
Preacher, K. J., Zhang, G., Kim, C., & Mels, G. (2013). Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48, 28-56. [Supplementary Figures, Mplus output, and data]
Deboeck, P. R., Nicholson, J. S., Bergeman, C. S., & Preacher, K. J. (2013). From modeling long-term growth to short-term fluctuations: Differential equation modeling is the language of change. In R. E. Millsap, L. A. van der Ark, D. M. Bolt, & C. M. Woods (Eds.), Springer proceedings in mathematics & statistics: Vol. 66. New developments in quantitative psychology (pp. 427-447). New York: Springer.
Selig, J. P., Preacher, K. J., & Little, T. D. (2012). Modeling time-dependent association in longitudinal data: A lag as moderator approach. Multivariate Behavioral Research, 47, 697-716.
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17, 137-152.
Zhang, G., Preacher, K. J., & Jennrich, R. I. (2012). The infinitesimal jackknife with exploratory factor analysis. Psychometrika, 77, 634-648. [R code]
Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6, 77-98.
Preacher, K. J., & Merkle, E. C. (2012). The problem of model selection uncertainty in structural equation modeling. Psychological Methods, 17, 1-14.
Preacher, K. J., & Hancock, G. R. (2012). On interpretable reparameterizations of linear and nonlinear latent growth curve models. In J. R. Harring & G. R. Hancock (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 25-58). Charlotte, NC: Information Age Publishing. [Supplemental LISREL and Mplus code]
Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2011). Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM. Structural Equation Modeling, 18, 161-182. [Supplementary code and simulation results]
Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93-115. [Supplementary material on graphical presentation]
Hayes, A. F., Preacher, K. J., & Myers, T. A. (2011). Mediation and the estimation of indirect effects in political communication research. In E. P. Bucy & R. L. Holbert (Eds.), The sourcebook for political communication research: Methods, measures, and analytical techniques (pp. 434-465). New York: Routledge.
Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E. (2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5/6, 359-371.
Rigdon, E. E., Preacher, K. J., Lee, N., Howell, R. D., Franke, G. R., & Borsboom, D. (2011). Avoiding measurement dogma: A response to Rossiter. European Journal of Marketing, 45, 1589-1600.
Preacher, K. J. (2011). Multilevel SEM strategies for evaluating mediation in three-level data. Multivariate Behavioral Research, 46, 691-731. [Appendices and Mplus syntax and simulated data for example 1, example 2, and example 3]
Lee, J., Little, T. D., & Preacher, K. J. (2011). Methodological issues in using structural equation models for testing differential item functioning. In Davidov, E., Schmidt, P., & Billiet, J. (Eds.), Cross-cultural analysis: Methods and applications (pp. 55-84). New York: Routledge.
Widaman, K. F., Little, T. D., Preacher, K. J., & Sawalani, G. M. (2011). On creating and using short forms of scales in secondary research. In K. H. Trzesniewski, M. B. Donnellan, & R. E. Lucas (Eds.), Secondary data analysis: An introduction for psychologists (pp. 39-61). Washington, DC: American Psychological Association.
Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209-233. [Supplementary code]
Hayes, A. F., & Preacher, K. J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45, 627-660.
Zhang, G., Preacher, K. J., & Luo, S. (2010). Bootstrap confidence intervals for ordinary least squares factor loadings and correlations in exploratory factor analysis. Multivariate Behavioral Research, 45, 104-134. [Supporting materials]
Preacher, K. J. (2010). Latent growth curve models. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer's guide to quantitative methods in the social sciences (pp. 185-198). London: Routledge.
Zhang, Z., Zyphur, M. J., & Preacher, K. J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12, 695-719. [Supplementary code]
Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6, 144-164. [Supplementary code]
Little, T. D., Card, N. A., Preacher, K. J., & McConnell, E. (2009). Modeling longitudinal data from research on adolescence. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed.) (pp. 15-54). Hoboken, NJ: Wiley.
Franke, G. R., Preacher, K. J., & Rigdon, E. E. (2008). Proportional structural effects of formative indicators. Journal of Business Research, 61, 1229-1237.
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, 879-891. [Supplementary material]
Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communication research. In A. F. Hayes, M. D. Slater, & L. B. Snyder (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 13-54). Thousand Oaks, CA: Sage.
Preacher, K. J. (2008). Chi-square. In International Encyclopedia of the Social Sciences (2nd ed.) (Vol. 1, pp. 523-524). Detroit, MI: Macmillan Reference USA.
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227. [Supplementary SPSS macro and Mathematica code]
Little, T. D., Card, N. A., Bovaird, J. A., Preacher, K. J., & Crandall, C. S. (2007). Structural equation modeling of mediation and moderation with contextual factors. In T. D. Little, J. A. Bovaird, & N. A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 207-230). Mahwah, NJ: Lawrence Erlbaum Associates.
Little, T. D., Preacher, K. J., Selig, J. P., & Card, N. A. (2007). New developments in latent variable panel analyses of longitudinal data. International Journal of Behavioral Development, 31, 357-365.
Preacher, K. J., Cai, L., & MacCallum, R. C. (2007). Alternatives to traditional model comparison strategies for covariance structure models. In T. D. Little, J. A. Bovaird, & N. A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 33-62). Mahwah, NJ: Lawrence Erlbaum Associates. [Appendix]
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437-448.
Preacher, K. J. (2006). Quantifying parsimony in structural equation modeling. Multivariate Behavioral Research, 41, 227-259.
Preacher, K. J. (2006). Testing complex correlational hypotheses using structural equation modeling. Structural Equation Modeling, 13, 520-543. [LISREL syntax]
Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142-163. [Supplemental material for publications, also in SPSS and HLM]
Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations. Psychological Methods, 10, 178-192.
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717-731. [SPSS data for Example 1]
Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift's electric factor analysis machine. Understanding Statistics, 2, 13-32.
Preacher, K. J., & MacCallum, R. C. (2002). Exploratory factor analysis in behavior genetics research: Factor recovery with small sample sizes. Behavior Genetics, 32, 153-161.
Preacher, K. J., & Rucker, D. D. (2002). Dumbing it down: The dangers of appealing to the lowest common denominator. Dialogue, 17(1), 26-27, 31.
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.
MacCallum, R. C., Browne, M. W., & Preacher, K. J. (2002). Comments on the Meehl-Waller procedure for appraisal of path analysis models. Psychological Methods, 7, 301-306.
MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-637.