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Abstract Interviewers’ postinterview evaluations of respondents’ performance (IEPs) are paradata, used to describe the quality of the data obtained from respondents. IEPs are driven by a combination of factors, including respondents’ and interviewers’ sociodemographic characteristics and what actually transpires during the interview. However, relatively few studies examine how IEPs are associated with features of the response process, including facets of the interviewer-respondent interaction and patterns of responding that index data quality. We examine whether features of the response process—various respondents’ behaviors and response quality indicators—are associated with IEPs in a survey with a diverse set of respondents focused on barriers and facilitators to participating in medical research. We also examine whether there are differences in IEPs across respondents’ and interviewers’ sociodemographic characteristics. Our results show that both respondents’ behaviors and response quality indicators predict IEPs, indicating that IEPs reflect what transpires in the interview. In addition, interviewers appear to approach the task of evaluating respondents with differing frameworks, as evidenced by the variation in IEPs attributable to interviewers and associations between IEPs and interviewers’ gender. Further, IEPs were associated with respondents’ education and ethnoracial identity, net of respondents’ behaviors, response quality indicators, and sociodemographic characteristics of respondents and interviewers. Future research should continue to build on studies that examine the correlates of IEPs to better inform whether, when, and how to use IEPs as paradata about the quality of the data obtained.
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This content will become publicly available on May 9, 2026
Incorporating Process Information Into Cognitive Diagnostic Models: A Four-Component Joint Modeling Approach
Recent studies show increasing interest in using process data (e.g., response time, response actions) to enhance measurement accuracy for respondents’ latent traits. Yet, few have explored the possibility of incorporating process information into cognitive diagnostic models (CDMs). This study proposes a novel CDM approach that utilizes a four-component joint modeling approach with response action sequences (i.e., similarity and efficiency), response time, and item responses. We employed the Markov Chain Monte Carlo method for parameter estimation and evaluated the performance of the proposed model using both an empirical study and two simulation studies. The results suggest that the process data can improve respondents’ classification accuracy under varied conditions and support the interpretation of the association between process and response data.
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- Award ID(s):
- 1749275
- PAR ID:
- 10625418
- Publisher / Repository:
- Sage
- Date Published:
- Journal Name:
- Journal of Educational and Behavioral Statistics
- ISSN:
- 1076-9986
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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