Construct Meaning in Multilevel Settings

We present types of constructs, individual- and cluster-level, and their confirmatory factor analytic validation models when data are from individuals nested within clusters. When a construct is theoretically individual level, spurious construct-irrelevant dependency in the data may appear to signal...

Full description

Saved in:
Bibliographic Details
Published inJournal of educational and behavioral statistics Vol. 41; no. 5; pp. 481 - 520
Main Authors Stapleton, Laura M., Yang, Ji Seung, Hancock, Gregory R.
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.10.2016
American Educational Research Association
Subjects
Children
Correlation
Factor Analysis
Hierarchical Linear Modeling
Kindergarten education
Lactose intolerance
Longitudinal Studies
Modeling
Multilevel models
Multivariate Analysis
Parents
Reliability
Research methods
School violence
Social skills
Statistical variance
Surveys
Validity
confirmatory factor analysis
multilevel
validity
Online AccessGet full text

Cover

More Information
Summary:We present types of constructs, individual- and cluster-level, and their confirmatory factor analytic validation models when data are from individuals nested within clusters. When a construct is theoretically individual level, spurious construct-irrelevant dependency in the data may appear to signal cluster-level dependency; in such cases, however, and consistent with theory, a single-level analysis with a correction for dependency may be appropriate. Regarding cluster-level constructs, we discuss two types—shared and configural—and present appropriate validation models. Illustrative validation analyses with individual, shared, and configurai constructs are provided using empirical data as well as simple simulations demonstrating the spurious effects that can occur with nested data. The article concludes with future directions to be examined in construct validation in multilevel settings.
ISSN:1076-9986
1935-1054
DOI:10.3102/1076998616646200