On the benefits of latent variable modeling for norming scales: The case of the Supports Intensity Scale - Children's Version

Hyojeong Seo, Todd D. Little, Karrie A. Shogren, Kyle M. Lang

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Structural equation modeling (SEM) is a powerful and flexible analytic tool to model latent constructs and their relations with observed variables and other constructs. SEM applications offer advantages over classical models in dealing with statistical assumptions and in adjusting for measurement error. So far, however, SEM has not been fully used to develop norms of assessments in educational or psychological fields. In this article, we highlighted the norming process of the Supports Intensity Scale - Children's Version (SIS-C) within the SEM framework, using a recently developed method of identification (i.e., effects-coding method) that estimates latent means and variances in the metric of the observed indicators. The SIS-C norming process involved (a) creating parcels, (b) estimating latent means and standard deviations, (c) computing T scores using obtained latent means and standard deviations, and (d) reporting percentile ranks.

Original languageEnglish
Pages (from-to)373-384
Number of pages12
JournalInternational Journal of Behavioral Development
Volume40
Issue number4
DOIs
StatePublished - Jul 1 2016

Keywords

  • effects-coding method of identification
  • norming
  • structural equation modeling
  • supports intensity scale - children's version

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