Full-Information Item Bi-Factor Analysis

Abstract

A plausible s-factor solution for many types of psychological and educational tests is one in which there is one general factor and s-1 group or method related factors. The bi-factor solution results from the constraint that each item has a non-zero loading on the primary dimension alpha j 1 and at most one of the s-1 group factors. This structure has been termed the bi-factor solution by Holzinger & Swineford but it also appears in the work of Tucker and Joreskog. All attempts at estimating the parameters of this model have been restricted to continuously measured variables; it has not been previously considered in the context of item-response theory (IRT). It is conceivable, however, that the bi-factor structure might arise in IRT related problems. The purpose of this paper is to derive a bi-factor item-response model for binary response data, and to develop a corresponding method of parameter estimation. This restriction leads to a major simplification of the likelihood equations that (1) permits the statistical evaluation of problems of unlimited dimensionality; (2) permits conditional dependence among discrete and previously identified subsets of items, and (3) in some cases provides more parsimonious factor solutions than an unrestricted full-information item factor analysis might provide (e.g. Bock and Aitkin, 1981). Keywords: Factor analysis, Psychometrics, Biometry.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1990
Accession Number
ADA229346

Entities

People

  • Donald R. Hedeker
  • R. D. Bock
  • Robert D. Gibbons

Tags

Communities of Interest

  • Air Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Cognitive Science
  • Data Science
  • Education
  • Factor Analysis
  • Illinois
  • Information Science
  • Maximum Likelihood Estimation
  • Measurement
  • Military Research
  • Probability
  • Psychology
  • Security
  • South Carolina
  • Two Dimensional
  • United States

Readers

  • Medical Imaging.
  • Regression Analysis.
  • Statistical inference.