Analysis of a Two-Level Structural Equation Model With Missing Data

August 12, 2010

Wai-Yin Poon and Hai-Bin Wang, Analysis of a Two-Level Structural Equation Model With Missing Data, Sociological Methods & Research 2010 39: 25-55.

Structural equation models are widely used to model relationships among latent unobservable constructs and observable variables. In some studies, the data set used for analysis is comprised of observations that are drawn Read the rest of this entry »

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Michael Greenacre and Rafael Pardo, Subset Correspondence Analysis: Visualizing Relationships Among a Selected Set of Response Categories From a Questionnaire Survey, Sociological Methods & Research 2006 35: 193-218.

February 7, 2010
This study shows how correspondence analysis may be applied to a subset of response categories from a questionnaire survey (e.g., the subset of undecided responses or the subset of responses for a particular category across several questions). The idea is to maintain the original relative frequencies of the categories and not reexpress them relative to totals within the subset, as would normally be done in a regular correspondence analysis of the subset. Furthermore, the masses and chi-square distances assigned to the subset of categories are the same as those in the correspondence analysis of the whole data set, which leads to a decomposition of total variance into parts if the whole data set is subdivided into disjoint subsets. This variant of the method, called subset correspondence analysis, is illustrated on data from the International Social Survey Programme’s Family and Changing Gender Roles survey.

Key Words: categorical data • correspondence analysis • missing data • principal component analysis • questionnaire survey • singular value decomposition


Ivy Jansen, Ann Van den Troost, Geert Molenberghs, Ad A. Vermulst, and Jan R. M. Gerris, Modeling Partially Incomplete Marital Satisfaction Data, Sociological Methods & Research 2006 35: 113-136.

February 7, 2010
The authors analyze data on marital satisfaction, obtained from couples at two distinct moments in time (1990, 1995). The data are of a bivariate longitudinal type. Moreover, some couples provide incomplete records only, usually because the 1995 follow-up interview has not taken place. The authors propose a hierarchical modeling strategy that takes all these features into account and is more generally valid than a classical complete case or single imputation-based strategy.

Key Words: complete case analysis • direct likelihood • dropout • imputation • missing at random • missing completely at random • missing data


David B. Grusky and Asaf Levanon, Describing Occupational Segregation in Sparse and Incomplete Arrays, Sociological Methods & Research 2006 34: 554-572.

February 7, 2010

The study of sex segregation is increasingly based on log-multiplicative and related models that allow analysts to characterize the amount and structure of segregation independently of (a) the mix of occupations in the economy and (b) the relative size of the male and female labor forces. Although these models are elegant and powerful, methodological complications can arise when the segregation arrays are sparse and small occupations frequently show up as perfectly segregated (i.e., all male or all female). The authors develop a general approach that makes it possible to analyze such sparse arrays with log-multiplicative and related models.

Key Words: sex segregation • log-multiplicative model • sampling zeros • missing data


Multiple Imputation for Missing Data: A Cautionary Tale

February 7, 2010

Sociological Methods & Research 2000 28: 301-309.

Two algorithms for producing multiple imputations for missing data are evaluated with simulated data. Read the rest of this entry »