This study examines 16 factors that may influence institutions' decisions to offer distance education programs. The researcher analyzed data from 1,500 institutions regarding their distance education offerings in 2000-2001. The regression model found that lack of faculty interest, limited access to instructional resources, and interinstitutional issues significantly impacted the number of distance education courses offered. However, the model violated assumptions of homoscedasticity and normality of residuals, indicating the relationships may not be reliable. Further analysis of outliers and interactions showed some cases had disproportionate influence on the model.

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

2. Selection of Archival Data To identify key factors that determine whether or not an institution will start and/or expand its distance education offerings. This study will explore sixteen predictors that may have an impact on distance education offerings and will allow the researcher to determine what institutional factors are found in both successful and unsuccessful distance education programs. The public data file made available from the Postsecondary Education Quick Information System (PEQIS) through the National Center for Education Statistics (NCES), U.S. Department of Education website will be used for this study. This file contains data collected from a 2000-2001 study that surveyed administrators at higher education institutions who were knowledgeable in their respective distance education offerings. By identifying characteristics of successful and unsuccessful distance education programs we can provide the tools needed for administrators to make decisions regarding their respective institution’s distance education programs. Predictors identified that can influence the creation and/or expansion of programs will assist institutions in supporting and building sustainable distance education programs. Furthermore information obtained from this study will provide characteristics of distance education programs as well as present future implications of which future research can draw from.

3. Sixteen Predictors For my project I utilized 16 interval predictors. These predictors included: Institution mission Perceived need Support from institution administrators Program development costs Equipment failures/ cost of maintaining equipment Limited technological infrastructure Faculty workload concerns Faculty interest Faculty rewards or incentives Legal concerns Course quality concerns Accessibility to library or other resources for instructional support Interinstitutional issues Restrictive federal, state or local policies, and state authorization Inability to obtain state authorization Additional challenges not listed within our other predictors.

4. Variable Selection My variables, predictors were all interval thus, I created a dichotomous variable to run my interaction graph. I utilized Institutional Mission after the variable had been centered and separated its values one of two ways: below the mean (coded as zero) as well as above the mean (coded as one). For my assumptions I used the the variable, “extent of lack of access to library or other resources for instructional support keeping institution from starting or expanding distance education offerings.” I utilized this variable for these tests because its standardized coefficient showed it had the greatest impact on the dependent variable.

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

7. Correlation Analysis These predictors did not have statistically significant correlations with the D.V. Support from institution administrators Faculty workload concerns Faculty rewards or incentives Legal concerns Interinstitutional issues Restrictive federal, state or local policies, and state authorization Inability to obtain state authorization Additional challenges not listed within our other predictors. Some predictors were correlated with other predictors at the .4/.5 level. This could be high and could be an area of concern for our model.

8. Regression-Model Summary Table a. Predictors: (Constant), (Centered) Extent of other (specific) factors keeping institution from starting or expanding distance education offerings, (Centered) Extent of program deveopment costs keeping institution from starting or expanding distance education offerings, (Centered) Extent of inability to obtain state authorization keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of perceived need keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of access to library or other resources for instructional support keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of faculty interest keeping institution from starting or expanding distance education offerings, (Centered) Extent of restrictive federal, state, or local parties keeping institution from starting or expanding distance education offerings, (Centered) Extent of limited technological infrastructure to support distance education keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of support from insitution administrators keeping institution from starting or expanding distance education offerings, (Centered) Extent of legal concerns keeping institution from starting or expanding distance eduation offerings, (Centered) Extent of interinstitutional issues keeping our institution from starting or expanding distance education offerings, (Centered) Extent of concerns about course quality keeping institution from starting or expanding distance eduation offerings, (Centered) Extent of lack of fit with institution's mission keeping institution from starting or expanding distance education offerings, (Centered) Extent of concerns about faculty workload keeping institution from starting or expanding distance education offerings, (Centered) Extent of equipment failures/costs of maintaining equipment keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of faculty rewards or incentives keeping institution from starting or expanding distance education offerings Model explains 6.1% of the variability within the model. Based on prior literature and research the explained variability could be high or low for the model.

9. Regression- ANOVA Table Predictors: (Constant), (Centered) Extent of other (specific) factors keeping institution from starting or expanding distance education offerings, (Centered) Extent of program deveopment costs keeping institution from starting or expanding distance education offerings, (Centered) Extent of inability to obtain state authorization keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of perceived need keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of access to library or other resources for instructional support keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of faculty interest keeping institution from starting or expanding distance education offerings, (Centered) Extent of restrictive federal, state, or local parties keeping institution from starting or expanding distance education offerings, (Centered) Extent of limited technological infrastructure to support distance education keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of support from insitution administrators keeping institution from starting or expanding distance education offerings, (Centered) Extent of legal concerns keeping institution from starting or expanding distance eduation offerings, (Centered) Extent of interinstitutional issues keeping our institution from starting or expanding distance education offerings, (Centered) Extent of concerns about course quality keeping institution from starting or expanding distance eduation offerings, (Centered) Extent of lack of fit with institution's mission keeping institution from starting or expanding distance education offerings, (Centered) Extent of concerns about faculty workload keeping institution from starting or expanding distance education offerings, (Centered) Extent of equipment failures/costs of maintaining equipment keeping institution from starting or expanding distance education offerings, (Centered) Extent of lack of faculty rewards or incentives keeping institution from starting or expanding distance education offerings Dependent Variable: Total number of courses offered for all levels and audiences by your insitution in 2000-2001 My ANOVA table allows me to determine whether or not my model is significant. My F statistic is in fact significant at the .01 level thus, F (16, 1065) = 4.343, p<.01.

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.

17. Diagnostics- Studentized Residuals Looked for anything over +/- 3 SD and for significant jumps in the data. Starting at ID Number 10742 and proceeding down these cases are above +3 SD. Cases 10854, 11485, and 11014 also jump significantly.

18. Diagnostics- Leverage Here I was looking for cases that doubled or halfed. The two cases that had a noticeably bigger jump were ID Number 11485 and 11014.

19. Diagnostics- DFFits Here I was looking for cases that doubled or halfed. The two cases that had a noticeably bigger jump were ID Number 11485 and 11014.

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.