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  <titleInfo>
    <title>Log-linear modeling</title>
    <subTitle>concepts, interpretation, and application</subTitle>
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  <titleInfo type="alternative">
    <title>Log linear modeling</title>
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  <name type="personal">
    <namePart>Eye, Alexander von.</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
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  <name type="personal">
    <namePart>Mun, Eun Young.</namePart>
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  <name type="corporate">
    <namePart>ebrary, Inc</namePart>
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  <typeOfResource>text</typeOfResource>
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  <genre authority="local">Electronic books.</genre>
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    <place>
      <placeTerm type="text">Hoboken, N.J</placeTerm>
    </place>
    <publisher>Wiley</publisher>
    <dateIssued>2013</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">electronic</form>
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    <extent>xv, 450 p. : ill.</extent>
  </physicalDescription>
  <abstract>"Over the past ten years, there have been many important advances in log-linear modeling, including the specification of new models, in particular non-standard models, and their relationships to methods such as Rasch modeling. While most literature on the topic is contained in volumes aimed at advanced statisticians, Applied Log-Linear Modeling presents the topic in an accessible style that is customized for applied researchers who utilize log-linear modeling in the social sciences. The book begins by providing readers with a foundation on the basics of log-linear modeling, introducing decomposing effects in cross-tabulations and goodness-of-fit tests. Popular hierarchical log-linear models are illustrated using empirical data examples, and odds ratio analysis is discussed as an interesting method of analysis of cross-tabulations. Next, readers are introduced to the design matrix approach to log-linear modeling, presenting various forms of coding (effects coding, dummy coding, Helmert contrasts etc.) and the characteristics of design matrices. The book goes on to explore non-hierarchical and nonstandard log-linear models, outlining ten nonstandard log-linear models (including nonstandard nested models, models with quantitative factors, logit models, and log-linear Rasch models) as well as special topics and applications. A brief discussion of sampling schemes is also provided along with a selection of useful methods of chi-square decomposition. Additional topics of coverage include models of marginal homogeneity, rater agreement, methods to test hypotheses about differences in associations across subgroup, the relationship between log-linear modeling to logistic regression, and reduced designs. Throughout the book, Computer Applications chapters feature SYSTAT, Lem, and R illustrations of the previous chapter's material, utilizing empirical data examples to demonstrate the relevance of the topics in modern research"--</abstract>
  <note type="statement of responsibility">Alexander von Eye, Eun-Young Mun.</note>
  <note>Includes bibliographical references and indexes.</note>
  <note>Electronic reproduction. Palo Alto, Calif. : ebrary, 2015. Available via World Wide Web. Access may be limited to ebrary affiliated libraries.</note>
  <subject authority="lcsh">
    <topic>Log-linear models</topic>
  </subject>
  <classification authority="lcc">QA278 .E95 2013eb</classification>
  <classification authority="ddc" edition="23">519.5/36</classification>
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