Abstract
When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http:// brm.psychonomic-journals.org/content/supplemental.
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The research reported in this article was partially supported by Grant 2009SGR1549 from the Catalan Ministry of Universities, Research and the Information Society, and by Grant PSI2008-00236/PSIC from the Spanish Ministry of Education and Science.
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Lorenzo-Seva, U., Ferrando, P.J. & Chico, E. Two SPSS programs for interpreting multiple regression results. Behavior Research Methods 42, 29–35 (2010). https://doi.org/10.3758/BRM.42.1.29
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DOI: https://doi.org/10.3758/BRM.42.1.29