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Jiesi Guo

Researcher at Australian Catholic University

Publications -  65
Citations -  3000

Jiesi Guo is an academic researcher from Australian Catholic University. The author has contributed to research in topics: Academic achievement & Medicine. The author has an hindex of 21, co-authored 47 publications receiving 1810 citations. Previous affiliations of Jiesi Guo include The Catholic University of America.

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Validating the Copenhagen Psychosocial Questionnaire (COPSOQ-II) Using Set-ESEM: Identifying Psychosocial Risk Factors in a Sample of School Principals.

TL;DR: The research presented here closes the theory application gap of a strong multi-dimensional measure of psychosocial risk-factors by using the Copenhagen Psychosocial Questionnaire (COPSOQ-II) to evaluate factor structure and longitudinal, discriminant, and convergent validity.
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The murky distinction between self-concept and self-efficacy. Beware of lurking jingle-jangle fallacies

TL;DR: In this paper, the authors extended the classic constructive dialogue/debate between self-concept and self-efficacy researchers regarding the distinctions between these two constructs, and demonstrated negative frame-of-reference effects in social (bigfish-little-pond effect) and dimensional (internal/external frame of reference effect) comparisons for three selfconcept-like constructs in each of the first four years of secondary school.
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Achievement, motivation, and educational choices: A longitudinal study of expectancy and value using a multiplicative perspective

TL;DR: The results suggest that both math self-concept and intrinsic value interact in predicting advanced math course selection, matriculation results, entrance into university, and STEM fields of study andGender differences in educational outcomes are mediated by gender differences in motivational beliefs and prior academic achievement.
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What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups.

TL;DR: Alignment augmented by AwC provides applied researchers from diverse disciplines considerable flexibility to address substantively important issues when the traditional CFA-MI scalar model does not fit the data.