Multiple Binomial Regression Models of Learning Style Preferences of Students of Sidhu School, Wilkes University
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Notice bibliographique
Résumé
The interest of this study is to explore the relationship between a dichotomous response, learning style preferences by university students of Sidhu School, Wilkes University, as a function of the following predictors: Gender, Age, employment status, cumulative grade point assessment (GPA) and level of study, as in usual generalized linear model. The response variable is the students’ preference for either Behaviorist or Humanist learning style. Four different binomial regression models were fitted to the data. Model A is a logit regression model that fits all the predictors, Model B is a probit model that fits all the predictors, Model C is a logit model with an effect modifier, while Model D is a probit model also with an effect modifier. Models A and B appeared to have performed poorly in fitting the data. Models C and D fit the data well as confirmed by the non-significant chi-square lack of fit with p-values 0.1409 and 0.1408 respectively. Among the four models considered for fitting the data, Model D, the probit model with effect modifier fit best. There was a marginal difference in the measure of goodness-of-fit for models C and D. Since probit model usually do not lend itself to ease of interpretation, model C was focused on for interpretation of results. The four variables that made significant contributions to model C were gender, age, employment status and the interaction variable. Academic performance of the students measured by their GPA and the level of study of the students were not significant predictors of the learning style preference by the students. The results of Model C revealed that the likelihood that a student prefers Behaviorist learning style is negatively related to his or her gender, age, employment status, GPA and level of study. However, the likelihood is positively related to the interaction term: Gender* Age. The result also showed that every one year increase in age of the students leads to decrease in the log-odds of preference for Behaviorist learning style. Also the odds of an MBA student preference for Behaviorist learning style are 1.1925 times greater than the odds of an undergraduate student. The association between gender and age was significant, so that gender modifies the association between age and preference. The interaction term showed that both the male and female odds ratio indicate an increase of odds of Behaviorist learning style, with increasing age of students, but the rate of increase is greater for male students. Plots of residuals and other diagnostic procedures conducted further confirmed that models A and B did not yield good fit, while both models C and D though identified an outlier which was not influential, but the functional forms of the models appeared suitable and seemed to fit the data well, and were therefore considered adequate. The residual mean deviance of model C was slightly above 1 which an indication of a slight overdispersion problem in the model. Important issues arising from the study were also discussed.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle