The similarities between the students' background variables reinforce this view.
Cross-product variables between ComputersC and each of the three student variables were therefore computed and entered into the model.
Finally, multiple regression analysis was done between student variables and design elements with learning outcomes to determine the significant predictors for blended learning effectiveness.
Overall, this study represents a multilevel analysis in examining the relationships between the student and school level affective variables and mathematics performance in PISA 2012 across Indonesia, Malaysia, and Thailand.
We also tested interactions between each attribute and a binary variable indicating concordance between the student's preferred and favourite specialty.
We omit the results of other models where we found insignificant interactions between each attribute and sex, age, university, expected graduation date, the presence of physicians in the family, as well as a binary variable indicating concordance between the student's preferred and favourite specialty.
One-way ANOVA were performed to evaluate the relationship between the mean difference in number of students' pre- and post-instruction misconceptions and various student variables.
Figure 6 shows the evolutionary analysis of the concordances between the students and echocardiographers for these variables during the training.
Finally, a paired samples t-test was conducted to compare the SEEQ variable scores between the students in this sample (n = 60) and the scores of students enrolled in the year prior to the implementation of the Flipped Classroom model (n = 52).
A "Decision Tree" was used to identify the variables that discriminate between the students engaged in "Extra-curricular" undergraduate scientific research activities (ECA) (n = 74) and those engaged in "Curricular" undergraduate scientific research activities (CA) (n = 96).
Multiple-choice and true/false questions were examined both separately and combined, using chi-square tests in SAS 9.1 (SAS Institute, Cary, NC) to test the null hypothesis of no association (i.e., no effect) between the row variable (student response to question) and the column variable (question type, bar graph displayed, or student grade).
