Log-Linear Models / Edition 1

by David Knoke, Peter J. Burke

Overview

Discusses 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.

Product Details

ISBN-13:	9780803914926
Publisher:	SAGE Publications
Publication date:	08/01/1980
Series:	Quantitative Applications in the Social Sciences , #20
Edition description:	New Edition
Pages:	80
Product dimensions:	5.50(w) x 8.50(h) x 0.20(d)

About the Author

David Knoke (Ph.D., University of Michigan, 1972) is a professor of sociology at the University of Minnesota, where he teaches and does research on diverse social networks, including political, economic, healthcare, intra- and interorganizational, and terrorist & counterterror networks. In addition to many articles and chapters, he has written seven books about networks: Network Analysis (1982, with James Kuklinski), The Organizational State (1985, with Edward Laumann), Political Networks (1990), Comparing Policy Networks (1996, with Franz Pappi, Jeffrey Broadbent, and Yutaka Tsujinaka), Changing Organizations (2001), Social Network Analysis (2008, with Song Yang), and Economic Networks (2012).

Table of Contents

	Editor's Introduction	5
1.	Relationships in Crosstabulations	8
2.	The Log-Linear Model	11
A.	Specifying Models	11
	Saturated Models	12
	Nonsaturated Models	17
B.	Fitting Marginals	19
	Generating Expected Frequencies	22
C.	Analyzing Odds	24
3.	Testing for Fit	30
A.	How To Evaluate Models Fitted to Data	30
B.	Comparisons of Different Models of the Same Data	31
	Independence Hypothesis	32
	Equal Marginal Distributions Hypothesis	33
C.	More Complex Models: Polytomous Variables	33
D.	More Complex Hypotheses	37
E.	An Analog to Multiple R[superscript 2] for Large Samples	40
4.	Applications to Substantive Problems	42
A.	Causal Models for Log-Linear Models	42
B.	Analyzing Change Over Time	47
	Comparative Cross-Sections	47
	Two-Wave Panels	48
	Markov Chain Models	54
	Age, Period, and Cohort Models	57
5.	Special Techniques with Log-Linear Models	63
A.	What To Do About Zero Cells	63
B.	Fixing Start Values	65
C.	Analyzing Ordered Data	67
D.	Collapsing Polytomous Variables	70
E.	Nonhierarchical Models	72
6.	Conclusions	76
	References	77