Multidimensional self-concept structure for preadolescents with mild intellectual disabilities : a hybrid multigroup-MIMC approach to factorial invariance and latent mean differences

Herbert W. Marsh, Danielle K. Tracey, Rhonda Craven

Research output: Contribution to journalArticle

104 Citations (Scopus)

Abstract

Confirmatory factor analysis of responses by 211 preadolescents (M age = 10.25 years,SD = 1.48) with mild intellectual disabilities (MIDs) to the individually administered Self Description Questionnaire I-Individual Administration (SDQI-IA) counters widely cited claims that these children cannot differentiate multiple self-concept factors. Results provide clear support for the a priori eight-factor solution, modest correlations between the factors (Mdn r = .38), substantial reliabilities (Mdn = .90), and invariance of the factor solution over gender, age, and educational placement (regular vs. special, segregated classes). Also introduced is a new hybrid compromise between multigroup and multipleindicator-multiple-cause (MIMIC) approaches to latent mean differences. Consistent with a priori predictions, preadolescents with MIDs have lower self-concepts in segregated classes than in regular classes for three academic self-concept scales (reading, math, and general-school) and, to a lesser extent, peer relationships and global selfesteem, but not for the other three nonacademic components of self-concept (physical, appearance, and parent relationships).
Original languageEnglish
Pages (from-to)795-818
Number of pages24
JournalEducational and Psychological Measurement
Volume66
Issue number5
DOIs
Publication statusPublished - Oct 2006

Keywords

  • MIMIC models
  • big-fish-little-pond effect
  • confirmatory factor analysis with mean structures
  • preadolescents with mild intellectual disabilities
  • self description questionnaire I – individual administration
  • Big-fish-little-pond effect
  • Preadolescents with mild intellectual disabilities
  • Self Description Questionnaire I-Individual Administration
  • Confirmatory factor analysis with mean structures

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