# Log-Linear Modeling: Concepts, Interpretation, and by Alexander von Eye PDF

By Alexander von Eye

ISBN-10: 1118146409

ISBN-13: 9781118146408

ISBN-10: 1118391772

ISBN-13: 9781118391778

Content material:

Chapter 1 fundamentals of Hierarchical Log?Linear types (pages 1–11):

Chapter 2 results in a desk (pages 13–22):

Chapter three Goodness?of?Fit (pages 23–54):

Chapter four Hierarchical Log?Linear types and Odds Ratio research (pages 55–97):

Chapter five Computations I: uncomplicated Log?Linear Modeling (pages 99–113):

Chapter 6 The layout Matrix method (pages 115–132):

Chapter 7 Parameter Interpretation and importance exams (pages 133–160):

Chapter eight Computations II: layout Matrices and Poisson GLM (pages 161–183):

Chapter nine Nonhierarchical and Nonstandard Log?Linear versions (pages 185–253):

Chapter 10 Computations III: Nonstandard versions (pages 255–275):

Chapter eleven Sampling Schemes and Chi?Square Decomposition (pages 277–292):

Chapter 12 Symmetry versions (pages 293–311):

Chapter thirteen Log?Linear versions of Rater contract (pages 313–330):

Chapter 14 evaluating institutions in Subtables: Homogeneity of institutions (pages 331–343):

Chapter 15 Logistic Regression and different Logit types (pages 345–369):

Chapter sixteen lowered Designs (pages 371–385):

Chapter 17 Computations IV: extra versions (pages 387–424):

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Content material: bankruptcy 1 fundamentals of Hierarchical Log? Linear versions (pages 1–11): bankruptcy 2 results in a desk (pages 13–22): bankruptcy three Goodness? of? healthy (pages 23–54): bankruptcy four Hierarchical Log? Linear versions and Odds Ratio research (pages 55–97): bankruptcy five Computations I: easy Log? Linear Modeling (pages 99–113): bankruptcy 6 The layout Matrix procedure (pages 115–132): bankruptcy 7 Parameter Interpretation and value exams (pages 133–160): bankruptcy eight Computations II: layout Matrices and Poisson GLM (pages 161–183): bankruptcy nine Nonhierarchical and Nonstandard Log?

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**Additional resources for Log-Linear Modeling: Concepts, Interpretation, and Application**

**Example text**

Many measures have been proposed for diagnostic purposes or to identify extreme cells. , [242]). Here, we present a selection of the more frequently used of these measures. Let us start with the binomial probability given by Um1) = YJ[M{l-pi)N-k, * k=nij ' where pi — rhi/N and i goes over all cells of the table. The advantage of the binomial test is that it is exact. However, it can suggest conservative decisions if p is estimated from the data. If Np > 10 (Osterkorn [176]), the binomial distribution is so close to the normal distribution that the normal distribution can be used as an approximation.

The asymptotic variance of the estimated parameter 36 GOODNESS-OF-FIT can be estimated by van Using this variance estimate, the asymptotic standard error, ase, of the parameter estimate can be estimated using ase(Aij) = ΣΣ \ a. nijj A z-score for the parameter estimate can be calculated using this standard error. One obtains z(A, -) = A» ' ~7hase(Aij) The z-scores of parameter estimates for saturated models are, under the null hypothesis, standard normally distributed (mean(z) = 0; sd(z) = 1). 96 or, equivalently, when the 95% confidence interval about the parameter, Aij - zaase(Xij) < A,·; < λη + zaase(Xjj) does not contain the parameter value λη = 0.

The first is a model that does not fit. We anticipate that the approximations of the normal distribution are poor. The second condition involves a model that describes the data well. For this condition, we anticipate closer approximations of the normal distribution. We use the data again that were collected in von Eye et al. [237] study on the interpretation of proverb-like statements. For the present illustration, we use the first three proverbs, and we ask, whether the written interpretation that each of the 178 respondents (48 males) provided was correct or incorrect.

### Log-Linear Modeling: Concepts, Interpretation, and Application by Alexander von Eye

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