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      Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

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          Abstract

          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.

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          Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp

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            NEW SPECIFICATIONS FOR EXPONENTIAL RANDOM GRAPH MODELS

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              Syndication Networks and the Spatial Distribution of Venture Capital Investments

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                Author and article information

                Contributors
                ddekker@few.eur.nl
                Journal
                Psychometrika
                Psychometrika
                Springer-Verlag (New York )
                0033-3123
                1860-0980
                7 August 2007
                7 August 2007
                December 2007
                : 72
                : 4
                : 563-581
                Affiliations
                [1 ]Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000DR Rotterdam, The Netherlands
                [2 ]The H. John Heinz III School for Public Policy and Management, Carnegie Mellon University, Pittsburgh, PA 15213 USA
                [3 ]Nuffield College, Oxford University, New Road, Oxford, OX1 1NF UK
                Article
                9016
                10.1007/s11336-007-9016-1
                2798974
                20084106
                © The Psychometric Society 2007
                Categories
                Theory and Methods
                Custom metadata
                © The Psychometric Society 2007

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