Quality of Life Research

, Volume 23, Issue 2, pp 509–513 | Cite as

Use of factor analysis models to evaluate measurement invariance property of the Asthma Control Questionnaire (ACQ)

  • Yan Yan
  • Wei Wu
  • Robert C. Strunk
  • Jane Garbutt
Brief Communication



To demonstrate the assessment of measurement invariance property in a health status instrument and to increase the awareness of its importance, we evaluate the measurement invariance of the Asthma Control Questionnaire (ACQ) across age and gender subgroups.


Data are obtained from children 7–12 years of age at entry into a randomized trial, which evaluates the effect of a telephone coaching program on improving asthma outcome. Multi-group confirmatory factor analysis is used to assess the comparability of factor loadings and intercepts across age and gender subgroups. Since age is a continuous variable, two different categorizations (7–10 vs 11–12 and 7–9 vs 10–12) are analyzed.


The factor loadings and intercepts of all six items in ACQ are comparable across gender subgroups. Although the factor loadings are comparable across age 7–10 and 11–12 subgroups, one intercept is statistically but not practically different. For age 7–9 versus 10–12 subgroup comparison, the factor loadings are not comparable.


In children, the ACQ can be used to compare asthma control construct between boys and girls and between age 7–10 and 11–12 subgroups. Measurement invariance is an important property that should be examined when the latent construct(s) are compared across different subgroups.


Asthma Control Questionnaire Measurement invariance Factor analysis 



The authors would like to thank two anonymous reviewers for their very thoughtful comments, which help us to improve the paper. Drs Yan, Strunk, and Garbutt are partially funded by a grant from the National Heart, Lung, and Blood Institute (HL072919) at the National Institute of Health.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Yan Yan
    • 1
  • Wei Wu
    • 2
  • Robert C. Strunk
    • 1
  • Jane Garbutt
    • 1
  1. 1.Washington University Medical SchoolSt LouisUSA
  2. 2.University of KansasLawrenceUSA

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