Chrome Extension
WeChat Mini Program
Use on ChatGLM

Cross-cultural measurement of social withdrawal motivations across 10 countries using multiple-group factor analysis alignment

Julie C. Bowker, Stefania Sette, Laura L. Ooi, Sevgi Bayram-Ozdemir, Nora Braathu, Evalill Bolstad, Karen Noel Castillo, Aysun Dogan, Carolina Greco, Shanmukh Kamble, Hyoun K. Kim, Yunhee Kim, Junsheng Liu, Wonjung Oh, Ronald M. Rapee, Quincy J. J. Wong, Bowen Xiao, Antonio Zuffiano, Robert J. Coplan

INTERNATIONAL JOURNAL OF BEHAVIORAL DEVELOPMENT(2023)

Cited 5|Views22
No score
Abstract
The goal of this study was to evaluate the measurement invariance of an adapted assessment of motivations for social withdrawal (Social Preference Scale-Revised; SPS-R) across cultural contexts and explore associations with loneliness. Participants were a large sample of university students (N = 4,397; M-age = 20.08 years, SD = 2.96; 66% females) from 10 countries (Argentina, Australia, Canada, China, India, Italy, South Korea, Norway, Turkey, and the United States). With this cross-cultural focus, we illustrate the multiple-group factor analysis alignment method, an approach developed to assess measurement invariance when there are several groups. Results indicated approximate measurement invariance across the 10 country groups. Additional analyses indicated that overall, shyness, avoidance, and unsociability are three related, but distinct factors, with some notable country differences evident (e.g., in China, India, and Turkey). Shyness and avoidance were related positively to loneliness in all countries, but the strength of the association between shyness and loneliness differed in Italy and India relative to the other countries. Results also indicated that unsociability was related positively to loneliness in the United States only. Theoretical and assessment implications are discussed.
More
Translated text
Key words
Social Preference Scale-R,multiple-group factor analysis alignment,social withdrawal motivations,loneliness,culture,university students
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined