Understanding perfectioinsm in academic contexts from a motivational perspective
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Research on perfectionism has developed rapidly since the 1990s (Frost et al., 1990; Hewitt & Flett, 1991). The more adaptive perfectionistic strivings refer to excessively high standards and expectations whereas the more maladaptive perfectionistic concerns tap critical evaluation and dissatisfaction towards one’s performance (Frost et al., 1993; Stoeber & Otto, 2006). Compared to the extensive body of perfectionism research in the mental health and sports fields, the examination of perfectionism within the academic context is relatively rare. However, studies show that the degree of perfectionism among college students has increased over the past three decades (Curran & Hill, 2019) and academics was found to be one of the areas most influenced by perfectionism (Haase et al., 2013; Stoeber & Stoeber, 2009). Thus, a further examination of perfectionism in academic contexts is necessary. The goal of this dissertation is to extend what we know about perfectionism as it functions in academia by conducting two studies connecting perfectionism with achievement motivation (Eccles, 2005; Wigfield & Eccles, 2020). The first study examined whether perfectionism is domain-specific in academic contexts. Specifically, I used mean comparisons and factor analysis to reveal that perfectionism exhibits as domain-specific within academic settings. Moreover, the patterns between perfectionism and several motivational constructs from the expectancy-value theory (Wigfield & Eccles, 2020) were assessed to support its domain-specific nature. The second study adopted a person-centered approach (i.e., latent profile analysis) to examine how individuals’ perfectionistic tendencies and achievement value beliefs cohere simultaneously in generating various profiles. Four profiles were identified in this study. Further, the results showed that academic outcomes differed across the various profiles. Lastly, this study revealed that students with certain demographic backgrounds were over- or under-represented within some profiles.