Investigating the structure of the eating inventory (three-factor eating questionnaire): a confirmatory approach

Int J Eat Disord. 2003 Sep;34(2):255-64. doi: 10.1002/eat.10180.

Abstract

Objective: To investigate the internal structure of the Eating Inventory using confirmatory factor analytic (CFA) methods.

Method: Female participants in a population-based twin study (N = 1,510) completed a reduced version of the Eating Inventory as part of a larger study of eating behaviors. Two CFA estimation methods were implemented using LISREL and Mplus to evaluate three different factor structures reported in the literature.

Results: Overall results for Stunkard and Messick's (1985) three-factor model indicated a poor fit to these data. The structure reported by Ganley resulted in an improper solution with factor correlations over 1 and several loadings estimated near 0 or with negative signs. The model proposed by Hyland et al. also fit poorly for all three models, the pattern of loadings on the Restraint factor proved most difficult to interpret.

Discussion: This study represents the first application of these confirmatory methods to the three subscales of the Eating Inventory. The instrument, as administered in this study, did not conform well to the structures proposed in previous studies. Because the Eating Inventory is recommended as a treatment planning tool, poor replication of the constructs purported to be assessed by this instrument underscores the importance of further internal validation research.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Bias
  • Diseases in Twins / diagnosis*
  • Factor Analysis, Statistical
  • Feeding and Eating Disorders / diagnosis*
  • Feeding and Eating Disorders / psychology
  • Feeding and Eating Disorders / therapy
  • Female
  • Humans
  • Mathematical Computing
  • Middle Aged
  • Models, Statistical
  • Personality Inventory / statistics & numerical data*
  • Psychometrics / statistics & numerical data
  • Reproducibility of Results
  • Software