Bates, D., Eddelbuettal, D. (2013). Fast and elegant numerical linear algebra using the RcppEigen Package. Journal of Statistical Software, 52(5), 1–24.
http://www.jstatsoft.org/v52/i05/ Google Scholar
Bollen, K. A., Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimates of variability. Sociological Methodology, 20, 115–140.
https://doi.org/10.2307/271084 Google Scholar
Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical Psychology, 31, 144–152.
https://doi.org/10.1111/j.2044-8317.1978.tb00581.x Google Scholar
Brys, G., Hubert, M., Struyf, A. (2003). A comparison of some new measures of skewness. In Dutter, R., Filzmoser, P., Gather, U., Rousseeuw, P. J. (Eds.), Developments in robust statistics (pp. 98–113). Physica.
https://doi.org/10.1007/978-3-642-57338-5_8 Google Scholar
Brys, G., Hubert, M., Struyf, A. (2004). A robust measure of skewness. Journal of Computational and Graphical Statistics, 13(4), 996–1017.
https://doi.org/10.1198/106186004X12632 Google Scholar
Cheung, M. W. L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 14(2), 227–246.
https://doi.org/10.1080/10705510709336745 Google Scholar
Cohen, J. (1988). Statistical power analyses for the behavioral sciences (2nd ed.). Erlbaum.
Google Scholar
Cox, M. G., Kisbu-Sakarya, Y., Miočević, M., MacKinnon, D. P. (2013). Sensitivity plots for confounder bias in the single mediator model. Evaluation Review, 37(5), 405–431.
https://doi.org/10.1177/0193841X14524576 Google Scholar
Efron, B., Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall.
Google Scholar |
Crossref
Fritz, M. S., Kenny, D. A., MacKinnon, D. P. (2016). The combined effects of measurement error and omitting confounders in the single-mediator model. Multivariate Behavioral Research, 51, 681–697.
https://doi.org/10.1080/00273171.2016.1224154 Google Scholar
Fritz, M. S., MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233–239.
https://doi.org/10.1111/j.1467-9280.2007.01882.x Google Scholar
Fritz, M. S., Taylor, A. B., MacKinnon, D. P. (2012). Explanation of two anomalous results in statistical mediation analysis. Multivariate Behavioral Research, 47(1), 61–87.
https://doi.org/10.1080/00273171.2012.640596 Google Scholar
Fuller-Rowell, T. E., Curtis, D. S., El-Sheikh, M., Duke, A. M., Ryff, C. D., Zgierska, A. E. (2017). Racial discrimination mediates race differences in sleep problems: A longitudinal analysis. Cultural Diversity and Ethnic Minority Psychology, 23(2), 165–173.
https://doi.org/10.1037/cdp0000104 Google Scholar
Goldberg, L., Elliot, D., Clarke, G. N., MacKinnon, D. P., Moe, E., Zoref, L., Green, C., Wolf, S. L., Greffrath, E., Miller, D. J., Lapin, A. (1996). Effects of a multidimensional anabolic steroid prevention intervention: The Adolescents Training and Learning to Avoid Steroids (ATLAS) program. Journal of the American Medical Association, 276(19), 1555–1562.
https://doi.org/10.1001/jama.1996.03540190027025 Google Scholar
Hayes, A. F., Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24(10), 1918–1927.
https://doi.org/10.1177/0956797613480187 Google Scholar
Imai, K., Keele, L., Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309–334.
https://doi.org/10.1037/a0020761 Google Scholar
James, L. R., Brett, J. M. (1984). Mediators, moderators, and tests for mediation. Journal of Applied Psychology, 69(2), 307–321.
https://doi.org/10.1037/0021-9010.69.2.307 Google Scholar
Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., Rosseel, Y. (2019). semTools: Useful tools for structural equation modeling. R package Version 0.5-2.
https://CRAN.R-project.org/package=semTools Google Scholar
Judd, C. M., Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5(5), 602–619.
https://doi.org/10.1177/0193841X8100500502 Google Scholar
Kenny, D. A., Kashy, D., Bolger, N. (1998). Data analysis in social psychology. In Gilbert, D., Fiske, S., Lindzey, G. (Eds.), Handbook of social psychology (4th ed., pp. 233–265). McGraw-Hill.
Google Scholar
Kisbu-Sakarya, Y., MacKinnon, D. P, Miočević, M. (2014). The distribution of the product explains normal theory mediation confidence interval estimation. Multivariate Behavioral Research, 49(3), 261–268.
https://doi.org/10.1080/00273171.2014.903162 Google Scholar
Lix, L. M., Keselman, H. J. (1998). To trim or not to trim: Tests of location equality under heteroscedasticity and nonnormality. Educational and Psychological Measurement, 58(3), 409–429.
https://doi.org/10.1177/0013164498058003004 Google Scholar
Lomnicki, Z. A. (1967). On the distribution of products of random variables. Journal of the Royal Statistical Society: Series B, 29(3), 513–524.
https://doi.org/10.1111/j.2517-6161.1967.tb00713.x Google Scholar
Lundgren, T., Dahl, J., Hayes, S. C. (2008). Evaluation of mediators of change in the treatment of epilepsy with acceptance and commitment therapy. Journal of Behavioral Medicine, 31, 225–235.
http://doi.org/10.1007/s10865-008-9151-x Google Scholar
MacKinnon, D. P., Dwyer, J. H. (1993). Estimating mediating effects in prevention studies. Evaluation Review, 17, 144–158.
https://doi.org/10.1177/0193841X9301700202 Google Scholar
MacKinnon, D. P., Fairchild, A. J., Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614.
https://doi.org/10.1146/annurev.psych.58.110405.085542 Google Scholar
MacKinnon, D. P., Fritz, M. S., Williams, J., Lockwood, C. M. (2007). Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods, 39(3), 384–389.
https://doi.org/10.3758/BF03193007 Google Scholar
MacKinnon, D. P., Goldberg, L., Clarke, G. N., Elliot, D. L., Cheong, J., Lapin, A., Moe, E. L., Krull, J. L. (2001). Mediating mechanisms in a program to reduce intentions to use anabolic steroids and improve exercise self-efficacy and dietary behavior. Prevention Science, 2(1), 15–28.
https://doi.org/10.1023/A:1010082828000 Google Scholar
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83–104.
https://doi.org/10.1037//1082-989X.7.1.83 Google Scholar
MacKinnon, D. P., Lockwood, C. M., Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99–128.
https://doi.org/10.1207/s15327906mbr3901_4 Google Scholar
MacKinnon, D. P., Valente, M. J., Gonzalez, O. (2020). The correspondence between causal and traditional mediation analysis: The link is the mediator by treatment interaction. Prevention Science, 21, 147–157.
https://doi.org/10.1007/s11121-019-01076-4 Google Scholar
MacKinnon, D. P., Warsi, G., Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30(1), 41–62.
https://doi.org/10.1207/s15327906mbr3001_3 Google Scholar
Mallinckrodt, B., Abraham, W. T., Wei, M., Russell, D. W. (2006). Advances in testing the statistical significance of mediation effects. Journal of Counseling Psychology, 53(3), 372–378.
https://doi.org/10.1037/0022-0167.53.3.372 Google Scholar
Maruska, K., Hansen, J., Hanewinkel, R., Isensee, B. (2016). The role of substance-specific skills and cognitions in the effectiveness of a school-based prevention program on smoking incidence. Evaluation & the Health Professions, 39(3), 336–355.
https://doi.org/10.1177/0163278715588825 Google Scholar
McManus, F., Surawy, C., Muse, K., Vazquez-Montes, M., Williams, J. M. G. (2012). A randomized clinical trial of mindfulness-based cognitive therapy versus unrestricted services for health anxiety (hypochondriasis). Journal of Consulting and Clinical Psychology, 80(5), 817–828.
https://doi.org/10.1037/a0028782 Google Scholar
Meeker, W. Q., Cornwell, L. W., Aroian, L. A. (1981). Selected tables in mathematical statistics, volume VII: The product of two normally distributed random variables. American Mathematical Society.
Google Scholar
Pearl, J. (2001). Direct and indirect effects. In Breese, J., Koller, D. (Eds.), Proceedings of the 17th conference on uncertainty in artificial intelligence (pp. 411–420). Morgan Kaufmann.
Google Scholar
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(4), 717–731.
https://doi.org/10.3758/BF03206553 Google Scholar
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 Google Scholar
Preacher, K. J., Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77–89.
https://doi.org/10.1080/19312458.2012.679848 Google Scholar
R Core Team . (2019). R: A language and environment for statistical computing (Version 3.6.2) [Computer Software]. R Foundation for Statistical Computing.
https://www.R- project.org/ Google Scholar
Revelle, W. (2018). psych: Procedures for personality and psychological research. Northwestern University.
https://CRAN.R-project.org/package=psychVersion=1.8.12 Google Scholar
Segaert, P., Hubert, M., Rousseeuw, P., Raymaekers, J. (2019). mrfDepth: Depth measures in multivariate, regression and functional settings. R package Version 1.0.11.
https://CRAN.R-project.org/package=mrfDepth Google Scholar
Sella, F., Sader, E., Lolliot, S., Kadosh, R. C. (2016). Basic and advanced numerical performances relate to mathematical expertise but are fully mediated by visuospatial skills. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(9), 1458–1472.
http://dx.doi.org/10.1037/xlm0000249 Google Scholar
Shrout, P. E., Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445.
https://doi.org/10.1037/1082-989X.7.4.422 Google Scholar
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.
http://dx.doi.org/10.2307/270723 Google Scholar
Springer, M. D., Thompson, W. E. (1966). The distribution of products of independent random variables. SIAM Journal on Applied Mathematics, 14(3), 511–526.
https://doi.org/10.1137/0114046 Google Scholar
Stone, C. A., Sobel, M. E. (1990). The robustness of estimates of total indirect effects in covariance structure models estimated by maximum likelihood. Psychometrika, 55, 337–352.
https://doi.org/10.1007/BF02295291 Google Scholar
Tallman, B. A., Altmaier, E., Garcia, C. (2007). Finding benefit from cancer. Journal of Counseling Psychology, 54(4), 481–487.
https://doi.org/10.1037/0022-0167.54.4.481 Google Scholar
Tingley, D., Yamamoto, T., Hirose, K., Keele, L., Imai, K. (2014). Mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 1–38.
https://doi.org/10.18637/jss.v059.i05 Google Scholar
VanderWeele, T. J. (2010). Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology, 21, 540–551.
https://doi.org/10.1097/EDE.0b013e3181df191c Google Scholar
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag.
Google Scholar |
Crossref
Wickham, H. (2017). tidyverse: Easily install and load the ‘Tidyverse’. R package version 1.2.1.
https://CRAN.R-project.org/package=tidyverse Google Scholar
Wilcox, R. R. (1995). ANOVA: The practical importance of heterscedastic methods, using trimmed means versus means, and designing simulation studies. British Journal of Mathematical and Statistical Psychology, 48, 99–114.
https://doi.org/10.1111/j.2044-8317.1995.tb01052.x Google Scholar
Wilcox, R. R., Keselman, H. J., Kowalchuk, R. K. (1998). Can tests for treatment group equality be improved?: The bootstrap and trimmed means conjecture. British Journal of Mathematical and Statistical Psychology, 51, 123–134.
https://doi.org/10.1111/j.2044-8317.1998.tb00670.x Google Scholar
Williams, J., MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 23–51.
https://doi.org/10.1080/10705510701758166 Google Scholar
留言 (0)