Grit is defined as passion and perseverance for achieving long-term goals and consists of two proposed subcomponents: consistency of interests and perseverance of effort. It has become a much-discussed construct even though research on its underlying factor structure has produced inconclusive results. Furthermore, grit as measured by its most frequently used measure, the Grit-S, does not clearly define "long term" or include the word passion. In addition, only a few studies have looked at factor structure variation and predictive validity of grit in different age and cultural groups. We examined, using state of the art multidimensional item response models and structural equation models, the factor structure of both the Grit-S and a newly developed scale (LT-Grit scale) that specifies what is meant by long term. Participants included 1,250 U.S. high school students, 600 U.S. college students, and 500 Korean college students. We found varying factor structures for the Grit-S and a stable one-factor structure for LT-Grit, across age and culture. Perseverance of effort as measured by the Grit-S was the strongest predictor of grades in the three samples. LT-Grit predicted grades only in the U.S. high school and Korean college samples. Thus, there is no evidence that a consistent grit factor or factors exist across age and culture.
Educational Impact and Implications StatementGrit is defined as individuals' passion and perseverance for achieving long-term goals. Over the last 10ϩ years it has received much attention in the popular press and from education policymakers despite the lack of consensus on its exact meaning or how strongly it relates to academic achievement. We investigated (using state of the art data analytic techniques) how similar or different grit was in high school and college students and in two different cultures (Korea and the U.S) and how strongly its two proposed components, perseverance of effort (PE) and consistency of interests (CI), relate to students' grades. We found differences in the nature of grit in the different groups, and also that PE related more strongly to achievement than did CI. A main implication of our results is that calls to intervene to improve students' overall grit as a way to enhance their achievement are at best premature and at worst a mistake given our findings and those of others.
While hierarchical linear modeling is often used in social science research, the assumption of normally distributed residuals at the individual and cluster levels can be violated in empirical data. Previous studies have focused on the effects of nonnormality at either lower or higher level(s) separately. However, the violation of the normality assumption simultaneously across all levels could bias parameter estimates in unforeseen ways. This article aims to raise awareness of the drawbacks associated with compounded nonnormality residuals across levels when the number of clusters range from small to large. The effects of the breach of the normality assumption at both individual and cluster levels were explored. A simulation study was conducted to evaluate the relative bias and the root mean square of the model parameter estimates by manipulating the normality of the data. The results indicate that nonnormal residuals have a larger impact on the random effects than fixed effects, especially when the number of clusters and cluster size are small. In addition, for a simple random-effects structure, the use of restricted maximum likelihood estimation is recommended to improve parameter estimates when compounded residuals across levels show moderate nonnormality, with a combination of small number of clusters and a large cluster size.
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