Some SPSS and SAS Macros you may find useful.
Computes a bootstrap-based estimate of a population mean and a bias corrected and accelerated 95% confidence interval for the mean.
Permutation test of null hypothesis of random association between X and Y using Pearson’s r or the simple regression weight as the test statistic.
Used to create a multivariate random normal sample of cases from a population with a specific variance-covariance matrix (or correlation matrix).
Used to compute Z’ for pooling the significance of doubly nonindependent correlations. See Hayes, A. F. (1998). Within-study meta-analysis: Pooling the significance of ‘doubly nonindependent’ correlations. Psychological Methods, 3, 32-45.
Estimates the size of an indirect effect of X on Y through a mediator Z, using both a normal theory (Sobel’s test) and bootstrap approaches. A SAS version of the macro is also available on this page. For a copy of the paper describing this macro, see Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers, 36, 717-731. For a PDF, click here.
This macro for SPSS and SAS estimates the path coefficients in a multiple mediator model and generates bootstrap confidence intervals (percentile, bias-corrected, and bias-corrected and accelerated) for total and specific indirect effects of X on Y through the mediator(s). This is macro is far superior to SOBEL.SPS, as it allows for more than one mediator and adjusts all paths for the potential influence of covariates not proposed to be mediators in the model. For a copy of the paper describing the methods this macro implements, see 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. For a PDF, click here.
This SPSS macro conducts tests of conditional indirect
effects, also called moderated mediation,
as described in Preacher, K. J., Rucker,
This is a paper that describes SPSS and SAS macros for generating heteroscedasticity-consistent standard errors in the linear regression model using the HC0, HC1, HC2, HC3, and HC4 procedures described by MacKinnon and White (1985), Long and Ervin (2000), and Cribari-Neto (2004). An electronic copy of the macros can be obtained here.
This is a paper that describes a new test for the regression coefficients in OLS regression that does not assume homoscedasticity. The paper includes some simulation results showing its superiority over the heteroscedasticity-consistent standard error estimators summarized by Long & Ervin (2000). An electronic copy of the SAS macro can be obtained here.
This macro will compute a significance test for the difference between a set of k independent Cronbach’s alpha coefficients.
This paper describes an SPSS and SAS macro that generates all possible subscales of at least two items from an additive scale containing k items. For each possible subscale, it generates Cronbach’s alpha and the subscale-full scale correlation and displays information about each subscale in a data spread sheet. It also generates summary statistics making it easy to find the most psychometrically appealing subscale in the set as well as some item analysis statistics useful for scale construction. To download the SPSS macro, click here. For the SAS version, click here. See the paper for instructions on the use of the macro.
This macro computes Krippendorff's alpha reliability estimate for judgments made at any level of measurement, any number of judges, with or without missing data.
This macro conducts an approximate randomization test equivalent of a oneway ANOVA using F as the test statistic.
This macro conducts the Meng, Rosenthal, & Rubin test for nonindependent correlations. It is used to compare the correlation between Y and X1 and Y and X2.
This is a SPSS macro that generates a 95% confidence interval for a population correlation, quantified using Pearson’s coefficient.
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