title: Examining Measure Correlations with Incomplete Data Sets.
abstract: A two-stage procedure for estimation and testing of observed measure correlations in the presence of missing data is discussed. The approach uses maximum likelihood for estimation and the false discovery rate concept for correlation testing. The method can be utilized in initial exploration oriented empirical studies with missing data, where it is of interest to estimate manifest variable interrelationship indexes and test hypotheses about their population values. The procedure is applicable also with violations of the underlying missing at random assumption, via inclusion of auxiliary variables. The outlined approach is illustrated with data from an aging research study.
authors.affiliation: Michigan State University.
authors.forename: Tenko
authors.initials: T
authors.lastname: Raykov
authors.affiliation: VA Health Care System, West Los Angeles.
authors.forename: Brooke C
authors.initials: BC
authors.lastname: Schneider
authors.affiliation: University of California at Riverside.
authors.forename: George A
authors.initials: GA
authors.lastname: Marcoulides
authors.affiliation: Wayne State University.
authors.forename: Peter A
authors.initials: PA
authors.lastname: Lichtenberg
keywords: auxiliary variable; correlation; false discovery rate; incomplete data; missing at random