3,541
Views
80
CrossRef citations to date
0
Altmetric
Articles

Comparing Models of Change to Estimate the Mediated Effect in the Pretest–Posttest Control Group Design

REFERENCES

  • Bonate, P. L. (2000). Analysis of pretest–posttest designs. Boca Raton, FL: CRC.
  • Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical Psychology, 31, 144–152. doi:10.1111/bmsp.1978.31.issue-2
  • Campbell, D. T., & Kenny, D. A. (1999). A primer on regression artifacts. New York, NY: Guilford.
  • Cheong, J., MacKinnon, D. P., & Khoo, S. T. (2003). Investigation of mediational processes using parallel process latent growth curve modeling. Structural Equation Modeling, 10, 238–262. doi:10.1207/S15328007SEM1002_5
  • Cheung, M. W. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling, 14, 227–246. doi:10.1080/10705510709336745
  • Cheung, M. W. (2009). Comparison of methods for constructing confidence intervals of standardized indirect effects. Behavior Research Methods, 41, 425–438. doi:10.3758/BRM.41.2.425
  • Clark, K. A. (2005). The phantom menace: Omitted variable bias in econometric research. Conflict Management and Peace Science, 22, 341–352. doi:10.1080/07388940500339183
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  • Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577. doi:10.1037/0021-843X.112.4.558
  • 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, 405–431. doi:10.1177/0193841X14524576
  • Cronbach, L. J., & Furby, L. (1970). How we should measure “change”: Or should we? Psychological Bulletin, 74, 68–80. doi:10.1037/h0029382
  • Dwyer, J. H. (1983). Statistical models for the social and behavioral sciences. New York, NY: Oxford University Press.
  • Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466–491. doi:10.1037/1082-989X.9.4.466
  • Fritz, M. S. (2014). An exponential decay model for mediation. Prevention Science, 15, 611–622. doi:10.1007/s11121-013-0390-x
  • Fritz, M. S., Kenny, D. A., & MacKinnon, D. P. (2016). The opposing effects of simultaneously ignoring measurement error and omitting confounders in a single mediator model. Multivariate Behavioral Research, 51, 681–697. doi:10.1080/00273171.2016.1224154
  • Goldberg, L., Elliot, D., Clarke, G. N., MacKinnon, D. P., Moe, E., Zoref, L., … Lapin, A. (1996). Effects of a multidimensional anabolic steroid prevention intervention: The Adolescents Training and Learning to Avoid Steroids (ATLAS) Program. JAMA, 276, 1555–1562. doi:10.1001/jama.1996.03540190027025
  • Gollob, H. F., & Reichardt, C. S. (1991). Interpreting and estimating indirect effects assuming time lags really matter. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, Unanswered questions, Future directions. Washington DC: American Psychological.
  • Hanushek, E. A., & Jackson, J. E. (1977). Statistical methods for the social scientists. New York, NY: Academic Press.
  • Hoyle, R. H., & Kenny, D. A. (1999). Sample size, reliability, and tests of statistical mediation. Statistical Strategies for Small Sample Research, 1, 195–222.
  • Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309–334. doi:10.1037/a0020761
  • Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71. doi:10.1214/10-STS321
  • James, L. R. (1980). The unmeasured variables problem in path analysis. Journal of Applied Psychology, 65, 415–421. doi:10.1037/0021-9010.65.4.415
  • Jamieson, J. (1999). Dealing with baseline differences: Two principles and two dilemmas. International Journal of Psychophysiology, 31, 155–161. doi:10.1016/S0167-8760(98)00048-8
  • Jang, H., Kim, E. J., & Reeve, J. (2012). Longitudinal test of self-determination theory’s motivation mediation model in a naturally occurring classroom context. Journal of Educational Psychology, 104, 1175–1188. doi:10.1037/a0028089
  • Jansen, B. R. J., Louwerse, J., Straatemeier, M., Van, S. H. G., Klinkenberg, S., & Van, H. L. J. (2013). The influence of experiencing success in math on math anxiety, perceived math competence, and math performance. Learning and Individual Differences, 24, 190–197. doi:10.1016/j.lindif.2012.12.014
  • Jo, B. (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods, 13, 314–336. doi:10.1037/a0014207
  • Jo, B., Stuart, E. A., MacKinnon, D. P., & Vinokur, A. D. (2011). The use of propensity scores in mediation analysis. Multivariate Behavioral Research, 46, 425–452. doi:10.1080/00273171.2011.576624
  • Judd, C. M., & Kenny, D. A. (1981). Estimating the effects of social interventions. New York, NY: Cambridge University Press.
  • Kisbu-Sakarya, Y., MacKinnon, D. P., & Aiken, L. S. (2013). A Monte Carlo comparison study of the power of the analysis of covariance, simple difference, and residual change scores in testing two-wave data. Educational and Psychological Measurement, 73, 47–62. doi:10.1177/0013164412450574
  • Kraemer, H. C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. (2001). How do risk factors work together? mediators, moderators, and independent, overlapping, and proxy risk factors. The American Journal of Psychiatry, 158, 848–856. doi:10.1176/appi.ajp.158.6.848
  • Laird, N. (1983). Further comparative analyses of pretest–posttest research designs. The American Statistician, 37, 329–330.
  • Lazarsfeld, P. F. (1955). Interpretation of statistical relations as a research operation. In P. F. Lazarsfeld & M. Rosenburg (Eds.), The language of social research: A reader in the methodology of social research (pp. 115–125). Glencoe, IL: The Free Press.
  • Lord, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304–305. doi:10.1037/h0025105
  • MacKinnon, D. P. (1994). Analysis of mediating variables in prevention and intervention research. In A. Cazares & L. A. Beatty (Eds.), Scientific methods for prevention/intervention research (NIDA Research Monograph Series 139, DHHS Pub. No. 94-3631, pp. 127–153). Washington, DC: U.S. Department of Health and Human Services.
  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Mahwah, NJ: Erlbaum.
  • MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144–158. doi:10.1177/0193841X9301700202
  • MacKinnon, D. P., Fritz, M. S., Williams, J., & Lockwood, C. M. (2007). Distribution of the product confidence limits for the indirect effect: Program PRODLIN. Behavior Research Methods, 39, 384–389. doi:10.3758/BF03193007
  • MacKinnon, D. P., Goldberg, L., Clarke, G. N., Elliot, D. L., Cheong, J., Lapin, A. … 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. doi:10.1023/A:1010082828000
  • MacKinnon, D. P., Johnson, C. A., Pentz, M. A., Dwyer, J. H., Hansen, W. B., Flay, B. R., & Wang, E. Y. (1991). Mediating mechanisms in a school-based drug prevention program: First-year effects of the Midwestern Prevention Project. Health Psychology, 10, 164–172. doi:10.1037/0278-6133.10.3.164
  • MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1(4), 173–181.
  • 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, 83–104. doi:10.1037/1082-989X.7.1.83
  • 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, 99–128. doi:10.1207/s15327906mbr3901_4
  • MacKinnon, D. P., & Pirlott, A. G. (2015). Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review, 19(1), 30–43. doi:10.1177/1088868314542878
  • MacKinnon, D. P., & Valente, M. J., (2015, May). Causal mediation approaches for the pretest–posttest control group design in prevention research. Presentation at the 23rd annual meeting of the Society for Prevention Research, Washington, DC.
  • MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30, 41–62. doi:10.1207/s15327906mbr3001_3
  • Mauro, R. (1990). Understanding L. O. V. E. (left out variables error): A method for estimating the effects of omitted variables. Psychological Bulletin, 108, 314–329. doi:10.1037/0033-2909.108.2.314
  • Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12, 23–44. doi:10.1037/1082-989X.12.1.23
  • Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46, 816–841. doi:10.1080/00273171.2011.606716
  • Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd ed.). Mahwah, NJ: Erlbaum.
  • Maxwell, S. E., Delaney, H. D., & Manheimer, J. M. (1985). ANOVA of residuals and ANCOVA: Correcting an illusion by using model comparisons and graphs. Journal of Educational and Behavioral Statistics, 10, 197–209. doi:10.3102/10769986010003197
  • McArdle, J. J. (2001). A latent difference score approach to longitudinal dynamic structure analysis. In R. Cudeck, S. Du Toit, & D. Sorbom (Eds.), Structural equation modeling: Present and future. A Festschrift in honor of Karl Jöreskog (pp. 341–380). Washington, DC: American Psychological Association.
  • McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577–605. doi:10.1146/annurev.psych.60.110707.163612
  • Miller, Y. D., Trost, S. G., & Brown, W. J. (2002). Mediators of physical activity behavior change among women with young children. American Journal of Preventive Medicine, 23, 98–103. doi:10.1016/S0749-3797(02)00484-1
  • Miočević, M., MacKinnon, D. P., & Levy, R. (2015, June). Bayesian longitudinal mediation for stable processes. Poster presented at O-Bayes workshop, Valencia, Spain.
  • Morgan, S. L., & Winship, C. (2014). Counterfactuals and causal inference. New York, NY: Cambridge University Press.
  • Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
  • Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods, 19, 459–481. doi:10.1037/a0036434
  • Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93–115. doi:10.1037/a0022658
  • Reid, A. E., & Aiken, L. S. (2013). Correcting injunctive norm misperceptions motivates behavior change: A randomized controlled sun protection intervention. Health Psychology, 32, 551–560. doi:10.1037/a0028140
  • Rogosa, D. (1988). Myths about longitudinal research. In K. W. Schaie, R. T. Campbell, & W. Meredith (Eds.), Methodological issues in aging research (pp. 171–209). New York, NY: Springer.
  • Schmiege, S. J., Broaddus, M. R., Levin, M., & Bryan, A. D. (2009). Randomized trial of group interventions to reduce HIV/STD risk and change theoretical mediators among detained adolescents. Journal of Consulting and Clinical Psychology, 77, 38–50. doi:10.1037/a0014513
  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.
  • Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422–445. doi:10.1037/1082-989X.7.4.422
  • Sobel, M. E. (1990). Effect analysis and causation in linear structural equation models. Psychometrika, 55, 495–515. doi:10.1007/BF02294763
  • Tein, J., Sandler, I. N., MacKinnon, D. P., & Wolchik, S. A. (2004). How did it work? Who did it work for? Mediation in the context of a moderated prevention effect for children of divorce. Journal of Consulting and Clinical Psychology, 72, 617–624. doi:10.1037/0022-006X.72.4.617
  • Valente, M. J., Gonzalez, O., Miočević, M., & MacKinnon, D. P. (2016). A note on testing mediated effects in structural equation models: Reconciliing past and current research of the test of joint significance. Educational and Psychological Measurement, 76, 889–911. doi:10.1177/0013164415618992
  • Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18, 137–150. doi:10.1037/a0031034
  • Van Breukelen, G. J. P. (2006). ANCOVA versus change from baseline: More power in randomized studies, more bias in nonrandomized studies. Journal of Clinical Epidemiology, 59, 920–925. doi:10.1016/j.jclinepi.2006.02.007
  • Van Breukelen, G. J. P. (2013). ANCOVA versus CHANGE from baseline in nonrandomized studies: The difference. Multivariate Behavioral Research, 48, 895–922. doi:10.1080/00273171.2013.831743
  • VanderWeele, T. J. (2010). Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology, 21, 540–551. doi:10.1097/EDE.0b013e3181df191c
  • VanderWeele, T., & Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface, 2, 457–468. doi:10.4310/SII.2009.v2.n4.a7
  • Wen, Z., & Fan, X. (2015). Monotonicity of effect sizes: Questioning kappa-squared as mediation effect size measure. Psychological Methods, 20, 193–203. doi:10.1037/met0000029
  • Wright, D. B. (2006). Comparing groups in a before-after design: When t test and ANCOVA produce different results. British Journal of Educational Psychology, 76, 663–675. doi:10.1348/000709905X52210

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.