Related Experiment Video
Updated: May 23, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
Assessment of response shift using two structural equation modeling techniques
Pranav K Gandhi1, L Douglas Ried, I-Chan Huang
1South College, School of Pharmacy, 400 Goodys Lane, Knoxville, TN 37922, USA. pgandhi@southcollegetn.edu
Objective:
To identify response shift using two structural equation modeling (SEM) techniques.
Study Design And Setting:
Hypertensive patients (n = 909) with coronary artery disease (CAD) completed SF-36 surveys at both baseline and 1-year follow-up. Response shift was identified using Oort and Schmitt SEM techniques. The type of response shift linked to changes in various parameters of the SEM measurement model is defined differently for both SEM approaches. Effect sizes were calculated for the impact of response shift on the change of SF-36 domain scores when using the Oort approach.
Results:
Both Oort and Schmitt SEM approaches identified response shift only in the SF-36 physical functioning (PF) scale. The effect size of recalibration on the change of PF domain scores when using the Oort approach was -0.12.
Conclusion:
This study showed that hypertensive patients with CAD experienced a response shift over a 1-year period. Both the SEM approaches identified response shift (uniform recalibration using the Oort approach and recalibration using the Schmitt approach); however, both approaches use different parameters to define and test response shift. We found that either the variation in analytic methods or the sample used may influence the identification and type of response shift.
Related Concept Videos
Response Surface Methodology
The process of RSM involves several key steps:
Group Design
Friedman Two-way Analysis of Variance by Ranks
Theory of Attribution II: Kelley's Covariation Theory
Mechanistic Models: Compartment Models in Individual and Population Analysis
Two-Way ANOVA
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...