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Measurement Invariance and Response Bias: A Stochastic Frontier Approach


  • Robert Rosenman
  • Vidhura Tennekoon
  • Laura G. Hill

    () (School of Economic Sciences, Washington State University)


The goals of the present paper were to assess measurement invariance using a common econometric method and to illustrate the approach with self-reported measures of parenting behaviors before and after a family intervention. Most recent literature on measurement invariance (MI) in psychological research 1) explores the use of structural equation modeling (SEM) and confirmatory factor analysis to identify measurement invariance, and 2) tests for measurement invariance across groups rather than across time. We use method, Stochastic Frontier Estimation, or SFE, to identify response bias and covariates of response bias both across individuals at a single point in time and across two measurement occasions (before and after participation in a family intervention). We examined the effects of participant demographics (N = 1437) on response bias; gender and race/ethnicity were related to magnitude of bias and to changes in bias across time, and bias was lower at posttest than at pretest. We discuss analytic advantages and disadvantages of SFE relative to SEM approaches and note that the technique may be particularly useful in addressing the problem of “response shift bias” or “recalibration” in program evaluation -- that is, a shift in metric from before to after an intervention which is caused by the intervention itself and may lead to underestimates of program effects.

Suggested Citation

  • Robert Rosenman & Vidhura Tennekoon & Laura G. Hill, 2010. "Measurement Invariance and Response Bias: A Stochastic Frontier Approach," Working Papers 2010-10, School of Economic Sciences, Washington State University.
  • Handle: RePEc:wsu:wpaper:rosenman-10

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    More about this item


    Measurement invariance; measurement equivalence; response bias; response-shift bias; stochastic frontier analysis;

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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