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      Seed Sharing in Amazonian Indigenous Rain Forest Communities: a Social Network Analysis in three Achuar Villages, Peru

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          Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

          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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            Local seed systems and their importance for an improved seed supply in developing countries

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              Knowledge networks in the Dutch aviation industry: the proximity paradox

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                Author and article information

                Journal
                Human Ecology
                Hum Ecol
                Springer Science and Business Media LLC
                0300-7839
                1572-9915
                October 2016
                September 27 2016
                October 2016
                : 44
                : 5
                : 577-594
                Article
                10.1007/s10745-016-9852-7
                2a058e25-9068-44cb-8c1e-5305eae7683a
                © 2016

                http://www.springer.com/tdm

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