Log-Linear Causal Analysis of Cross-Classified Categorical Data

2016 ◽  
pp. 311-331 ◽  
Author(s):  
Kazuo Yamaguchi
Keyword(s):  
Categorical Data ◽  
Causal Analysis ◽  
Log Linear

Some Common Pitfalls in Causal Analysis of Categorical Data

1982 ◽  
Vol 19 (4) ◽  
pp. 461-471 ◽  
Author(s):  
Jay Magidson
Keyword(s):  
Categorical Data ◽  
Causal Analysis ◽  
Statistical Technique ◽  
Omitted Variables ◽  
Causal Interpretation ◽  
Linear Modeling ◽  
Chi Square ◽  
Interaction Detection ◽  
Biasing Effects ◽  
Log Linear

Examples of some common pitfalls in the analysis of categorical data are discussed in the context of causal interpretation of the results. Though no statistical technique can replace theory, the author shows that log-linear modeling and chi square automatic interaction detection can provide researchers with powerful tools for gaining valuable causal insights into their data. Examples include the biasing effects of omitted variables, omitted interactions, improper contrast coding, and misspecification of the structure of an hypothesized interaction.

Ethnicity and Voting Patterns in South Africa

1979 ◽  
Vol 27 (3) ◽  
pp. 458-468 ◽  
Author(s):  
Henry Lever
Keyword(s):  
South Africa ◽  
South African ◽  
Categorical Data ◽  
Economic Status ◽  
Party Choice ◽  
Political Choices ◽  
Electoral Behaviour ◽  
Log Linear ◽  
Published Research

There is some controversy concerning the role of ethnicity in South African electoral behaviour. Since the society is segmented on ethnic lines it is to be expected that ethnicity would play a crucial role in affecting political choices. Some writers have gone so far as to suggest that ethnicity is the only significant factor affecting voting preferences. The controversy arose at a time when Goodman's method of log-linear analysis for hierarchical models had not yet been developed. This method provides the most powerful tool available for the multivariate analysis of categorical data. A re-analysis of previously published research using Goodman's method shows that ethnicity is not the only significant factor having a bearing on voting preferences. The first four-way table of voting preferences in South Africa is presented. The order of importance of the variables affecting party choice is: (1) ethnicity (2) socio-economic status (3) age of the voter. The recursive model suggested by the analysis explains approximately 98 per cent of the data.

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