Log-linear modeling: concepts, interpretation, and application
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, New Jersey
Wiley
[2013]
|
| Schlagworte: | |
| Online-Zugang: | FRO01 UBG01 Volltext |
| Beschreibung: | "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"-- Includes bibliographical references and indexes |
| Beschreibung: | 1 Online-Ressource (xv, 450 pages) |
| ISBN: | 9781118391747 1118391748 9781118391778 1118391772 9781283977968 1283977966 9781118146408 1118146409 |
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Datensatz im Suchindex
| _version_ | 1804175975084195840 |
|---|---|
| any_adam_object | |
| author | Eye, Alexander von |
| author_facet | Eye, Alexander von |
| author_role | aut |
| author_sort | Eye, Alexander von |
| author_variant | a v e av ave |
| building | Verbundindex |
| bvnumber | BV043395327 |
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| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | DE-604.BV043395327 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:24:49Z |
| institution | BVB |
| isbn | 9781118391747 1118391748 9781118391778 1118391772 9781283977968 1283977966 9781118146408 1118146409 |
| language | English |
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| physical | 1 Online-Ressource (xv, 450 pages) |
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| spelling | Eye, Alexander von Verfasser aut Log-linear modeling concepts, interpretation, and application Alexander von Eye, Michigan State University, Department of Psychology, East Lansing, MI, Eun-Young Mun, Rutgers, the State University of New Jersey, Center for Alcohol Studies, Piscataway, New Jersey Hoboken, New Jersey Wiley [2013] 1 Online-Ressource (xv, 450 pages) txt rdacontent c rdamedia cr rdacarrier "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"-- Includes bibliographical references and indexes MATHEMATICS / Probability & Statistics / General bisacsh MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh Log-linear models fast Log-linear models Log-lineares Modell (DE-588)4036197-4 gnd rswk-swf Log-lineares Modell (DE-588)4036197-4 s 1\p DE-604 Mun, Eun Young Sonstige oth https://onlinelibrary.wiley.com/doi/book/10.1002/9781118391778 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Eye, Alexander von Log-linear modeling concepts, interpretation, and application MATHEMATICS / Probability & Statistics / General bisacsh MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh Log-linear models fast Log-linear models Log-lineares Modell (DE-588)4036197-4 gnd |
| subject_GND | (DE-588)4036197-4 |
| title | Log-linear modeling concepts, interpretation, and application |
| title_auth | Log-linear modeling concepts, interpretation, and application |
| title_exact_search | Log-linear modeling concepts, interpretation, and application |
| title_full | Log-linear modeling concepts, interpretation, and application Alexander von Eye, Michigan State University, Department of Psychology, East Lansing, MI, Eun-Young Mun, Rutgers, the State University of New Jersey, Center for Alcohol Studies, Piscataway, New Jersey |
| title_fullStr | Log-linear modeling concepts, interpretation, and application Alexander von Eye, Michigan State University, Department of Psychology, East Lansing, MI, Eun-Young Mun, Rutgers, the State University of New Jersey, Center for Alcohol Studies, Piscataway, New Jersey |
| title_full_unstemmed | Log-linear modeling concepts, interpretation, and application Alexander von Eye, Michigan State University, Department of Psychology, East Lansing, MI, Eun-Young Mun, Rutgers, the State University of New Jersey, Center for Alcohol Studies, Piscataway, New Jersey |
| title_short | Log-linear modeling |
| title_sort | log linear modeling concepts interpretation and application |
| title_sub | concepts, interpretation, and application |
| topic | MATHEMATICS / Probability & Statistics / General bisacsh MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh Log-linear models fast Log-linear models Log-lineares Modell (DE-588)4036197-4 gnd |
| topic_facet | MATHEMATICS / Probability & Statistics / General MATHEMATICS / Probability & Statistics / Regression Analysis Log-linear models Log-lineares Modell |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781118391778 |
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