Extreme Response Style and the Measurement of Intra-Individual Variability in Affect

ABSTRACT

Extreme response style (ERS) has the potential to bias the measurement of intra-individual variability in psychological constructs. This paper explores such bias through a multilevel extension of a latent trait model for modeling response styles applied to repeated measures rating scale data. Modeling responses to multi-item scales of positive and negative affect collected from smokers at clinic visits following a smoking cessation attempt revealed considerable ERS bias in the intra-individual sum score variances. In addition, simulation studies suggest the magnitude and direction of bias due to ERS is heavily dependent on the mean affect level, supporting a model-based approach to the study and control of ERS effects. Application of the proposed model-based adjustment is found to improve intra-individual variability as a predictor of smoking cessation.

KEYWORDS:

• Extreme response style
• intra-individual variability
• item response model

Article Information

Conflict of interest: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.

Funding: This work was supported by Grants P50 DA019706 and P01 CA180945 from NIH.

Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgments: The authors would like to thank two anonymous reviewers and the associate editor and editor for their comments on prior versions of this manuscript. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors' institution or the NIH is not intended and should not be inferred.

Notes

2 We also performed logistic regression analyses in which both PA and NA predictors were entered simultaneously. For the analyses involving sum-scores, latent variable estimates with no ERS correction, and latent variable estimates with ERS correction, the Cox & Snell R² were .068, .067, and .075, and the Nagelkirke R² estimates .096, .095, and .106, respectively. Presumably due to intercorrelations among the mean and IIV predictors across affect types, the only statistically significant effects were for (p = .002) in the latent variable analysis with no ERS control, and for (p = .001) and (p = .030) in the latent variable analysis with ERS control.

1 Data for this study were collected as part of a smoking cessation clinical trial conducted by the University of Wisconsin Center for Tobacco Research and Intervention (UW-CTRI; http://www.ctri.wisc.edu/).