A Two-Level Confirmatory Factor Analysis of a Modified Rosenberg Self-Esteem Scale

Classical factor analysis assumes independent and identically distributed observations. Educational data, however, are often hierarchically structured, with, for example, students being nested within classes. In this study, data on self-esteem gathered in a sample of 1,107 students within 72 school...

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Bibliographic Details
Published in:Educational and psychological measurement 2005-06, Vol.65 (3), p.465-481
Main Authors: Zimprich, Daniel, Perren, Sonja, Hornung, Rainer
Format: Article
Language:English
Subjects:
Adolescent
Biological and medical sciences
Data analysis
Developmental psychology
Factor Analysis
Factor Structure
Foreign Countries
Fundamental and applied biological sciences. Psychology
Individual Differences
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics
Samples
Self Esteem
Self image
Social Environment
Students
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Summary:Classical factor analysis assumes independent and identically distributed observations. Educational data, however, are often hierarchically structured, with, for example, students being nested within classes. In this study, data on self-esteem gathered in a sample of 1,107 students within 72 school classes in Switzerland were analyzed using two-level confirmatory factor analysis. Considering a sequence of two-level confirmatory factor models, the results indicate that a one-factor model of self-esteem with an additional orthogonal method or response-style factor of negatively worded items adequately described within-class (individual) differences in self-esteem. By contrast, at the between-class level, a general factor of self-esteem was sufficient to capture school class differences in self-esteem. Thus, apart from other influences, for students, the social context (school class) seems to matter in forming their self-esteem. At the same time, the findings imply that studies examining self-esteem using samples of clustered observations should account for multiple levels of analysis (i.e., separate within- and between-class variances).
ISSN:0013-1644
1552-3888
DOI:10.1177/0013164404272487