1. REF 762: Individual Project Jennifer Styron November 13, 2008

5. Frequency Analysis- Sample Size 1500

6. Descriptive Analysis **Scale: 1= Not at all 2= Minor extent 3= Moderate extent 4= Major extent **Interesting points noted: Means of Program development costs, Concerns about faculty workload, and Lack of faculty rewards/incentives **Average number of courses around 71

10. Regression- Coefficient Table **Since the unstandardized scores are not meaningful in this model (do not reflect accurate unit measurements) I have noted the strong standardaized values found at lack of faculty interest and access to library or other resources for instructional support. Also these relationships negatively impact the D.V. =Statistically Significant

11. Linearity Graph Total number of courses offered for all levels and audiences by your institution in 2000-2001 (Centered) Access to library or other resources for instructional support It appears visually that the independent variable has a linear relationship with the dependent variable thus we have not violated the assumption of linearity.

12. Assumptions- Linearity Regression We need to determine whether or not our squared value, after being centered, is significant to determine whether or not a curvilinear relationship exists. As indicated, our squared variable is .182 and is not significant thus we have not violated the assumption of linearity. =Statistical Significance level

13. Multicollinearity Tolerance levels will allow us to determine whether or not our model will have issues with multicollinearity. The results show no issues with multicollinearity. It is also important to note that the model shows that only three of our variables, lack of faculty interest, access to library or other resources for instructional support, and Interinstitutional issues, are statistically significant effects on total number of distance education courses offered.

14. Assumptions- Homoscedasticity Homoscedasticity ensures equality of variance across our independent variables. Judging from the uneven pattern and variance between our points on the scatterplot this model violates the assumption of homoscedasticity.

15. Assumptions- Normality of Residuals Here we test the normality of residuals to determine whether or not there is an issue with either skewness or kurtosis, or both. Judging from the bars that exceed the normal curve we probably have major skewness within this model. Also there is a fair amount of white space underneath the curve which could mean the model also have issues of kurtosis. Judging from this graph, it would be determined that the assumption of normality of residuals has been violated. Skewness Kurtosis

16. Assumptions- Normality of Residuals Here we test the normality of residuals to determine whether or not there is an issue with either skewness or kurtosis, or both. Judging from the bars that exceed the normal curve we probably have major skewness within this model. Also there is a fair amount of white space underneath the curve which could mean the model also have issues of kurtosis. Judging from this graph, it would be determined that the assumption of normality of residuals has been violated. Skewness: 3.084/.074= 41.675 Much greater than 1 so we do have problems with skewness. Kurtosis: 14.440/.148=97.567 Much greater than 1 so we do have problems with kurtosis.

20. Interactions Tested the interaction between access to library or other resource for instruction support and institutional mission Found the change in r squared not significant. Dichotomized institutional mission as extent_nom 0= Institutional mission had minor extent or below on total number of courses offered 1= Institutional mission had moderate extent or above on total number of courses offered.

21. Interactions Ran sequential regression D.V. Total number of courses I.V. Access to library or other resources instructional support Added Interaction term (extent_nom) to determine whether or not the relationship is significant Not accounting for interactions it looks as if I would have predicted accurately for below the mean scores but my above the mean scores I would have underpredicted and as the extent to which access to library or other resources gets higher I would have overpredicted these scores.