Exploring the item-sets of the Recovering Quality of Life (ReQoL) measures using factor analysis

Anju Devianee Keetharuth (Lead / Corresponding author), Jakob Bue Bjorner, Michael Barkham, John Browne, Timothy Croudace, John E. Brazier

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Abstract

Purpose: This paper presents two studies exploring the latent structure of item sets used in the development of the Recovering Quality of Life mental health outcome measures: ReQoL-10 and ReQoL-20.

Method: In study 1, 2262 participants completed an initial set of 61 items. In study 2, 4266 participants completed a reduced set of 40 items. Study 2 evaluated two formats of the questionnaires: one version where the items were intermingled and one where the positively worded and negatively worded items were presented as two separate blocks. Exploratory and confirmatory factor analyses were conducted on both datasets where models were specified using ordinal treatment of the item responses. Dimensionality based on the conceptual framework and methods effects reflecting the mixture of positively worded and negatively worded items were explored. Factor invariance was tested across the intermingled and block formats.

Results: In both studies, a bi-factor model (study 1: RMSEA = 0.061; CFI = 0.954; study 2: RMSEA = 0.066; CFI = 0.971) with one general factor and two local factors (positively worded questions and negatively worded questions) was preferred. The loadings on the general factor were higher than on the two local factors suggesting that the ReQoL scale scores can be understood in terms of a general factor. Insignificant differences were found between the intermingled and block formats.

Conclusions: The analyses confirmed that the ReQoL item sets are sufficiently unidimensional to proceed to item response theory analysis. The model was robust across different ordering of positive and negative items.

Original languageEnglish
Pages (from-to)1005-1015
Number of pages11
JournalQuality of Life Research
Volume28
Issue number4
Early online date21 Dec 2018
DOIs
Publication statusPublished - Apr 2019

Keywords

  • Bi-factor model
  • Dimensionality
  • Factor analysis
  • Latent structure
  • Recovering Quality of Life

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