Establishing Measurement Invariance

BACKGROUND: Registered nurses and nurse researchers often use questionnaires to measure patient outcomes. When questionnaires or other multiple-item instruments have been developed using a relatively homogeneous sample, the suitability of even a psychometrically well-developed instrument for the new population comes into question. Bias or lack of equivalence can be introduced into instruments through differences in perceptions of the meaning of the measured items, constructs, or both in the two groups. OBJECTIVE: To explain measurement invariance and illustrate how it can be tested using the English and Spanish versions of the Paediatric Asthma Quality of Life Questionnaire (PAQLQ). METHODS: A sample of 607 children from the Phoenix Children's Hospital Breathmobile was selected for this analysis. The children were of ages 6-18 years; 61.2% completed the PAQLQ in Spanish. Testing measurement invariance using multiple-group confirmatory factor analysis, a series of hierarchical nested models, is demonstrated. In assessing the adequacy of the fit of each model at each stage, both χ2 tests and goodness-of-fit indexes were used. RESULTS: The test of measurement invariance for the one-factor model showed that the English and Spanish versions of the scale met the criteria for measurement invariance. The level of strict invariance (equal factor loadings, intercepts, and residual variances between groups) was achieved. DISCUSSION: Confirmatory factor analysis is used to evaluate the structural integrity of a measurement instrument; multiple confirmatory factor analyses are used to assess measurement invariance across different groups and to stamp the data as valid or invalid. The PAQLQ, a widely used instrument having evidence to support reliability and validity was used separately in English- and Spanish-speaking groups. Traditional methods for evaluating measurement instruments have been less than thorough, and this article demonstrates a well-developed approach, allowing for confident comparisons between populations.