Cross-Cultural Validation of the Modified Practice Attitudes Scale: Initial Factor Analysis and a New Factor Model. (January 2018)

• Record Type: Journal Article
• Authors: Park, Heehoon; Ebesutani, Chad K.; Chung, Kyong-Mee; Stanick, Cameo
• Is Part Of: Assessment. Volume 25:Number 1(2018)
• Page Start: 126
• Page End: 138
• Publication Date: 2018-01
• Subjects: evidence-based treatments -- cross-cultural assessment -- attitudes -- South Korea -- dissemination -- factor analysis; Psychological tests -- Periodicals; Psychodiagnostics -- Periodicals; Personality tests -- Periodicals; 150.287
• DOI: 10.1177/1073191116634202
• Languages: English
• ISSNs: 1073-1911

Abstract

Objective: The objective of this study was to create the Korean version of the Modified Practice Attitudes Scale (K-MPAS) to measure clinicians' attitudes toward evidence-based treatments (EBTs) in the Korean mental health system.Method: Using 189 U.S. therapists and 283 members from the Korean mental health system, we examined the reliability and validity of the MPAS scores. We also conducted the first exploratory and confirmatory factor analysis on the MPAS and compared EBT attitudes across U.S. and Korean therapists.Results: Results revealed that the inclusion of both "reversed-worded" and "non–reversed-worded" items introduced significant method effects that compromised the integrity of the one-factor MPAS model. Problems with the one-factor structure were resolved by eliminating the "non–reversed-worded" items. Reliability and validity were adequate among both Korean and U.S. therapists. Korean therapists also reported significantly more negative attitudes toward EBTs on the MPAS than U.S. therapists.Conclusions: The K-MPAS is the first questionnaire designed to measure Korean service providers' attitudes toward EBTs to help advance the dissemination of EBTs in Korea. The current study also demonstrated the negative impacts that can be introduced by incorporating oppositely worded items into a scale, particularly with respect to factor structure and detecting significant group differences.