Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions
نویسندگان
چکیده
Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called "double semi-partialing", or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman-Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions.
منابع مشابه
Multicollinearity Robust QAP for Multiple-Regression
The quadratic assignment procedures for inference on multiple-regression coefficients(MRQAP) has become popular in social network analysis. These tests have been developed to assess the sizes of a set of multiple-regression coefficients. However, research practitioners often use these tests to assess the size of individual multiple-regression coefficients. Although this might be a harmless exte...
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