Jason Rights

Assistant Professor

Research Interests

R-squared measures and methods for multilevel models
unappreciated consequences of conflating level-specific effects in analysis of multilevel data
delineating relationships between multilevel models and other commonly used models
advancing model selection and comparison methods for latent variable models

Relevant Thesis-Based Degree Programs


Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Distinguish the bifactor and higher-order factor model : a comparison of three RMSEA-related approaches under model misspecification (2023)

The bifactor model (BFM) is widely used in psychology, often compared with its nested models like the higher-order factor model (HFM) using fit indices. Previous simulation studies have shown that the BFM tends to outperform the HFM via fit indices, even when the HFM is the data-generating model (Greene et al., 2019; Morgan et al., 2015; Murray & Johnson, 2013). The superior model fit of the BFM has been described as a fit index “bias” rather than an indication of model correctness. Focusing on the root mean square error of approximation (RMSEA), the dominant approaches in the nested model comparison are the simple RMSEA approach (i.e., compare RMSEA of both models) and the ∆RMSEA approach (i.e., calculate the difference between two RMSEA values and use a non-zero cut-off to evaluate). An alternative approach, which uses an RMSEA associated with the chi-square difference test (i.e., RMSEA_D) has also been re-discovered and advocated (Brace, 2020; Savalei, et al., 2023). In the study, I evaluated the performance of three approaches when the true model is the HFM, containing varying degrees of misspecification. The results showed that the simple RMSEA approach was biased in favour of the BFM under minor misspecification, while the ∆RMSEA approach leaned towards HFM, even with severe misspecification. The RMSEA_D approach quantifies the misfit introduced by the HFM for each model misspecification condition without favouring either model. Additionally, I investigated how sample size and model size affect the equivalence test results of RMSEA_D. Recommendations are also made for future nested model comparison.

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Finding your place at school : momentary fit, state authenticity and academic experiences (2023)

Individuals have the tendency to approach or avoid a given situation, yet this choice may be implicitly influenced by one’s experience of fit to the environment. As proposed by the State Authenticity as Fit to Environment (SAFE) model, individuals tend to gravitate towards situations where they experience self-concept fit, goal fit, and/or social fit, composing a gestalt sense of state authenticity. With two experience sampling studies, this work aims to validate this key assertion of the SAFE model and examine its implications in academic settings. Study 1 validated that when people experienced self-concept fit, goal fit, and social fit in a given situation, they felt authentic to themselves. Notably, each type of fit was cued by distinctive contextual features, and uniquely predicted the gestalt sense of state authenticity. All three types of fit positively predicted individuals’ willingness to return to the current situation and state attachment to university. Self-concept fit and low social fit specifically predicted individual’s working memory capacity. In a conceptual replication of Study 1, Study 2 additionally demonstrated that in classes where students felt three types of fit, they reported higher class/self overlap and state attachment to their major. Study 2 also showed cumulative effects of fit and state authenticity on longer-term academic outcomes, such as major/university commitment and course grades. Furthermore, Study 2 yielded preliminary evidence indicating that experiences of marginalization had a dampening effect on individuals' overall sense of fit and authenticity. This work establishes a foundation for understanding students’ academic outcomes through the lens of the SAFE model, paving the way for examining the detrimental impacts of marginalization on self-segregation.

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Membership Status

Member of G+PS
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Program Affiliations

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