Deddy A Suhardi, Isfarudi Isfarudi


Structurally relationship of variables is important in deeply analysis of path models, but the process of effect distribution must be concerned. In this situation, one or more variable would be a mediator variable which assessing effect of an independent to a dependent variable. We studied the simple mediation model that is one of path analytical models which contain of one independent variable, dependent variable and mediator variable. A necessary component of mediation is effectiveness that is a statistically significant indirect effect, formal significance tests of indirect effects are early conducted by Sobel (1982). According to sequential regression analysis on a simple mediation model, a mediator variable come after an independent variable exist in the model, the contribution of upcoming variable to the model could be obtained. We argue the importance of investigating empirical relationship between the significance of indirect effects and sequential contribution of mediator variable with a normal theory approach using Microsoft Excel simulation tools developed by Myerson (2000). We find that the higher contribution of mediator variable to the model, the more effectiveness is. This result comes up with three level correlation of independent and dependent variable which each 1000 times iteration that gives relatively immediate information about the recent empirical relationship between the significance of indirect effects and sequential contribution of mediator in the simple mediation models.


sequential regression, simple mediation models, sobel test

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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, 1173-1182.

James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation. Journal of Applied Psychology, 69, 307-321.

MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144-158.

MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30(1), 41-62.

Morrison, D. F. (1990). Multivariate statistical methods (3rd ed). Singapore: McGraw-Hill.

Myers, R. H., & Milton, J.S. (1991). A first course in the theory of linear statistical models. Boston: PWS-Kent.

Myerson, R. B. (2000). Simtools (3.31a). Northwestern University : Kellog Scholl of Management. Diambil 4 Agustus 2000, dari http://www.kellogg.nwu.edu/faculty/myerson/ftp/addins.htm.

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.

Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equations models. Dalam S. Leinhart (Editor), Sociological Methodology 1982, pp.290-312. San Francisco: Jossey-Bass.

Preacher, K. J., & Leonardelli, G. J. (2006). Calculation for the sobel test: An interactive calculation tool for mediation tests. Diambil 22 Januari 2008, dari http://people.ku.edu/~preacher/sobel/sobel.htm.


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