© 2004, 205 pages, hardcover
This page includes resources for the book Regression Analysis for Categorical Moderators (2004, Guilford Press). In case of queries, feel free to email me at haguinis@gwu.edu
Click here to download the programs and data files to your computer.
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This program was used to create the data set for Chapter 3. The program is a multivariate random normal scores generator that uses the Box and Muller and Choleski Factorization algorithms. Click here to download the source code and executable versions of the program.
Source: Aguinis, H. (1994). A QuickBasic program for generating correlated multivariate random normal scores. Educational and Psychological Measurement, 54, 687-689. [pdf]
Click here to download the data file described in Chapter 3. The data file is included in SPSS and excel formats.
This program is described in Chapter 4. It checks whether the homogeneity of error variance assumption has been violated and conducts a test for moderation using analyses other than MMR when there is a violation of the assumption.
Source: Aguinis, H., Petersen, S. A., & Pierce, C. A. (1999). Appraisal of the homogeneity of error variance assumption and alternatives to multiple regression for estimating moderating effects of categorical variables. Organizational Research Methods, 2, 315-339. [pdf]
This program is described in Chapter 6. It administers questionnaires by prompting respondents to indicate their answer by clicking on a graphic line segment displayed on the screen.
Click here to download the source code and executable versions of CAQ.
Source: Aguinis, H., Bommer, W. H., & Pierce, C. A. (1996). Improving the estimation of moderating effects by using computer-administered questionnaires. Educational and Psychological Measurement, 56, 1043-1047. [pdf]
This program is described in Chapter 7. It estimates the statistical power to detect a dichotomous moderator variable using moderated multiple regression (MMR). It computes power for specified conditions of effect size (i.e., absolute difference between subgroup correlation coefficients), total sample size, and sample sizes in moderator-based subgroups.
Click here to download the source code and executable versions of POWER.
Source: Aguinis, H., Pierce, C. A., & Stone-Romero, E. F. (1994). Estimating the power to detect dichotomous moderators with moderated multiple regression. Educational and Psychological Measurement, 54, 690-692. [pdf]
This program is described in Chapter 7. It estimates the statistical power to detect a dichotomous moderator variable with MMR specified conditions of effect size (i.e., absolute difference between subgroup correlation coefficients), total sample size, sample sizes in moderator-based subgroups, predictor-moderator intercorrelation, and predictor variable range restriction.
Click here to download the source code and executable versions of MMRPWR.
Source: Aguinis, H., & Pierce, C. A. (1998). Statistical power computations for detecting dichotomous moderator variables with moderated multiple regression. Educational and Psychological Measurement, 58, 668-676. [pdf]
This program is described in Chapter 7 and Chapter 8. It estimates statistical power to detect a dichotomous moderator variable with MMR using a theory-based approximation. It can be used for moderators with more than 2 levels and it also allows you to specify variable reliabilities as well as range restriction, making it the most comprehensive, and therefore recommended, power estimator.
Source: Aguinis, H., Boik, R.J., & Pierce, C.A. (2001). A generalized solution for approximating the power to detect effects of categorical moderator variables using multiple regression. Organizational Research Methods, 4, 291-323. [pdf]
f 2
This program computes the modified f 2 moderating effect size index described in Chapter 9.
Source for Algorithm: Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90, 94-107. [pdf]
Source for Program: Aguinis, H., & Pierce, C. A. (2006). Computation of effect size for moderating effects of categorical variables in multiple regression. Applied Psychological Measurement, 30, 440-442. [pdf]
