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    <title>Krackhardt, D.</title>
    <link>http://repub.eur.nl/res/aut/3/</link>
    <description>List of Publications</description>
    <language>en</language>
    <image>
      <url>http://repub.eur.nl/static-eur/img/logo.png</url>
      <title>RePub, Erasmus University Rotterdam</title>
      <link>http://repub.eur.nl</link>
    </image>
    <item>
      <title>Sensitivity of MRQAP tests to collinearity and autocorrelation conditions (Article)</title>
      <link>http://repub.eur.nl/res/pub/18658/</link>
      <pubDate>2007-12-01T00:00:00Z</pubDate>
      <description>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.</description>
    </item> <item>
      <title>An equilibrium-correction model for dynamic network data (Article)</title>
      <link>http://repub.eur.nl/res/pub/13536/</link>
      <pubDate>2003-01-01T00:00:00Z</pubDate>
      <description>We propose a two-stage MRQAP to analyze dynamic network data, within the framework of an equilibrium-correction (EC) model. Extensive simulation results indicate practical relevance of our method and its improvement over standard OLS. An empirical illustration additionally shows that the EC model yields interpretable parameters, in contrast to an unrestricted dynamic model.</description>
    </item> <item>
      <title>Dynamic Effects of Trust and Cognitive Social Structures on Information Transfer Relationships (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/183/</link>
      <pubDate>2002-03-12T00:00:00Z</pubDate>
      <description>Changes in relationships are due to human actions. We assume that these human actions are functions of perceptions of a focal individual, but also the perceptions of other individuals who are part of the organizational and social environment. We hypothesize that perceptions based trust and perceptions of the structural environment individuals operate in affect relationship change more than the "actual" environment in which individuals operate. An empirically analysis shows the dynamic effects of perceptions on changes in two types of relationships, which are believed to be important in account management. We explore, 1, whether the levels of perceptions, and, 2, whether changes in perceptions affect relationship changes. For example, we consider the effects of the amount of trust as well as the change in the amount of trust one individual puts in another individual. We find that perceptions have more impact on relationship change than "actual" network variables have. Furthermore, the results show that it is useful to distinguish between level and change effects of perceptions.</description>
    </item> <item>
      <title>An Equilibrium-Correction Model for Dynamic Network Data (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/100/</link>
      <pubDate>2001-06-20T00:00:00Z</pubDate>
      <description>We propose a two-stage MRQAP to analyze dynamic network data, within the framework of an equilibrium-correction (EC) model. Extensive simulation results indicate practical relevance of our method and its improvement over standard OLS. An empirical illustration additionally shows that the EC model yields interpretable parameters, in contrast to an unrestricted dynamic model.</description>
    </item>
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