Log-Linear ModelsDiscusses the innovative log-linear model of statistical analysis. This model makes no distinction between independent and dependent variables, but is used to examine relationships among categoric variables by analyzing expected cell frequencies. |
Contents
Editors Introduction | 11 |
The LogLinear Model | 11 |
Testing for Fit | 30 |
Copyright | |
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Common terms and phrases
algorithm analysis analyze baseline model calculated categoric variables Catholic causal model chi-square cohort effects College comparing conditional odds contingency table data in Table degrees of freedom dependent variable df's difference effect parameters equal Equation example expected cell frequencies expected frequencies expected odds ratio Fij's fit the data Fitted Marginals four-variable four-way table Goodman hierarchical models High School Graduate homicide independent iterative proportional fitting Knoke Less than High linear log-linear models logit model marginal distributions marginal homogeneity marginal table Markov chain membership mobility model 28 Models Fitted Non-Catholic nonsaturated models notation observed frequencies odds on voting panel data party identification period polytomous variables procedure quasi-symmetry race relationship row and column sample saturated model significant starting values statistical structural zeros substantive subtable symmetry tau parameters techniques three-way two-variable two-way table Type I error V M VM voluntary association voting turnout YPCJO