this is an example of one the tables of what my data should look like. im sending 3 SPSS data analysis that need to be turned into tables following the example given.
A Pearson product moment correlation coefficient (r) was calculated for the relationship between student-professor interaction, as an independent variable, and ASC, as a dependent variable. There was a weak positive correlation between student-professor interaction and ASC (r (98) = .199, p < .05). This finding seems to suggest that as positive student-professor interaction increases, there is a tendency for ASC to increase also. If your results do not have correlations, then disregard the first paragraph about pearson correlations.
To examine the relationship between family structure and academic performance a 3 x 2 x 7 Between-Subjects Factorial ANOVA was conducted with gender and age as included independent variables. There was a significant main effect difference for family structure with regards to grade point average (F (2, 98) = 3.074, p = .053), which seems to suggest that students from nuclear family structures obtained higher grade point averages (Mgpa = 2.865) than students from single-parent (Mgpa = 2.710) or other non-traditional family structures (Mgpa = 2.532, see Table 1 below). A significant interaction between family structure and age with regard to grade point average also surfaced, F (11, 98) = 2.099, p = .033. This finding seems to suggest that students who are 18 and under and from a non-traditional family structure (Mgpa = 5.00) and students who are 19 and from a nuclear family structure (Mgpa = 4.500) obtained higher grade point averages than other age groups and family structures.
Table 1: Tests of Between-Subjects Effects
Dependent Variable: GPA
Type III Sum of Squares
Family Structure * Gender
Family Structure * Age
Gender * Age
Family Structure * Gender * Age
a R Squared = .381 (Adjusted R Squared = .067)
To examine the relationship between family structure, gender, and ASC a 2 x 3 x 2 Between-Subjects Factorial ANOVA was conducted with gender and type of schooling (public or private) included as independent variables, but there was no significant effect. Another factorial ANOVA was conducted with ASC as the dependent variable, but with gender, type of schooling, and household emphasis of obtaining a college degree as independent variables producing a 2 x 2 x 2 Between-Subjects ANOVA. There was a significant interaction between gender and type of schooling with regard to ASC, F (1, 98) = 3.908, p = .051(see Table 2). This finding seems to suggest that males that received private schooling or a combination of both private and public schooling had higher ASC (M = 110.200) than males that only received public schooling (M = 105.875) and that females that received public schooling had higher ASC (M = 109.973) than females who received private or a combination of both private and public schooling (M = 103.000).
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