Abstract
Self-determination theory proposes that the extent to which students’ motivation is self-determined is critical to learning outcomes. Based on occasional research evidence and our perceptions, we hypothesize that college students in certain majors have profiles that are higher in self-determined motivation than students in other majors. Specifically, our primary hypothesis is that students in the social sciences and humanities tend to be more self-determined, whereas students in business-related majors tend to be less self-determined. The results from two studies using large samples and advanced analytical methods support the primary hypotheses. Comparison results were also obtained for other majors (e.g., engineering and natural sciences), and supplemental analyses supported the critical role of self-determined motivation in learning outcomes among students in all majors. Study 2 also found support for two mechanisms for such differences, i.e., the majors’ learning climates and students’ individual differences in autonomous functioning. The current evidence suggests the importance of promoting more humanistic learning environments in certain academic disciplines.
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Notes
It should be noted that it is possible to combine the data from the two studies and run the comparison analyses on the entire sample. However, we perceive this approach as less convincing than the 2-study layout because it is possible for us to overfit this one dataset with an arbitrary model. The two studies, which occurred naturally, can help cross-validate each other and support the generalizability of the findings to other samples.
We adopted this coding approach because from the raw major data we obtained, there is no existing labeling to determine which major falls under which category for our analysis purposes.
Z-tests, rather than t-tests, were used because according to the central limit theorem, when the sample size is large enough (as is the case of the current research), the distribution of sample means follow a normal distribution.
Levene’s test for homogeneity was significant for all dimensions except integrated extrinsic motivation and the overall SDI. However, an inspection of the standard deviations (SD) revealed that the largest (SD = 1.74) was within 1.5 times of the smallest (SD = 1.25), and none of the Welch or Brown-Forsythe robustness tests produced a significant result that differed from the ordinary ANOVA. Hence, the ordinary ANOVA results are reported here.
Although latent mean comparison methods are superior to the traditional mean comparison methods used in Study 1, we nonetheless decided to retain the traditional analyses to improve the comparability between the two studies and also as a triangulation for the bifactor ESEM results, which were a recent development.
According to Little et al. (2002), the benefits of using parcels include the following: they yield more continuous observed variables that are less likely to violate normal distribution assumption; they reduce the chance of type-I error and subsequent model misfit or artificial overfitting, as caused by random spurious correlations (in other words, the parceling approach is more robust against sample characteristics that lead to violations of the local independence assumption); they reduce the unwanted contamination of item relationships by constructs that are irrelevant to the researchers’ interest; and they increase the stability of solutions (especially when using just-identified latent constructs, which consists of three parcels). The only drawback that is relevant to the current model is that when the parcels themselves are multidimensional (which is the case for self-determined motivation but not for learning climate), it is difficult to interpret the variance of the latent construct and the structural relations, because parceling obscures the contribution of items. However, Little et al. (2002) also suggested that this limitation on multidimensional parceling is only a problem when the researcher is interested in the items themselves. As they put it, “if the relations among constructs are of focal interest, parceling is more strongly warranted.” In our path analysis, we are not interested in the item relations within the academic motivation scale or the Learning Climate Questionnaire. Our focus is to examine how motivation and learning climate play mediating roles between major and learning outcomes. In such cases, the pros of parceling clearly outweigh the cons. This is especially true considering the large number of items in the measurement of academic motivation (18 items), which can cause great potential problems of model misfit and instability if all of them are included under the latent academic motivation construct.
Grades are used as an outcome variable in the current research. However, it is possible that grades may function as another socialization mechanism. Some majors may be harsher in grading, hence undermining students’ needs satisfaction and motivation. In other words, the predictive effect between grades and motivation may be reciprocal. However, there is no way to test the causal direction between grades and motivation in the current data. Therefore, we simply note this possibility and test only the model in which learning climate is the socialization mechanism and grades are the learning outcome because it is more conceptually established and empirically tested in existing literature (e.g., Guay and Vallerand 1997).
Similar to Study 1, Levene’s tests are significant for all dimensions of motivation, except for integration. However, an inspection of standard deviations revealed that the largest (SD = 1.82) is within 1.5 times of the smallest (SD = 1.31), and none of the robust test results made a difference. Therefore, the standard ANOVA results are reported here.
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Acknowledgements
We thank Professor Alexandre Morin for his generous assistance with data analysis in this study. We thank Dr. Jennifer D Moss for double coding of the majors. We thank Professor Richard Koestner, Professor Rong Su, and members of the Levesque-Bristol lab for providing valuable suggestions for the manuscript.
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Yu, S., Levesque-Bristol, C. Are students in some college majors more self-determined in their studies than others?. Motiv Emot 42, 831–851 (2018). https://doi.org/10.1007/s11031-018-9711-5
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DOI: https://doi.org/10.1007/s11031-018-9711-5