Using network analysis to conduct a system-wide program evaluation within a university |
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| Affiliation: | 1. Texas A&M University, College of Education and Human Development, Department of Health and Kinesiology, MS 4243, College Station, TX 77843-4243, United States;2. Baylor University, College of Health and Human Sciences, Department of Health, Human Performance, and Recreation, One Bear Place #97343, Waco, TX 76798, United States;3. Baylor University, Division of Student Life, One Bear Place #97016, Waco, TX 76798, United States;1. Department of Agricultural Education and Communication at the University of Florida, 213 Rolfs Hall, PO Box 110540, 32611, Gainesville, FL, United States;2. University of Florida. 218 Rolfs Hall, PO Box 110540, 32611 Gainesville, FL, United States;3. University of Florida. 3028B McCarty Hall D, University of Florida, Gainesville, FL 32611, United States;4. University of Florida. 113C Bryant Hall, PO Box 112060, Gainesville, FL 32611, United States;1. School of Politics and Public Administration, Guangdong University of Foreign Studies, No. 2, Guangzhou Baiyun Avenue North, China;2. School of Social and Political Science, The University of Edinburgh, Old College South Bridge, Edinburgh, Post Code: EH8 9YL, United Kingdom;1. School of Economics and Management, Xidian University, No. 2 South Taibai Street, Xi’an, Shaanxi Province, PR China;2. Shaanxi Xi ''an Yanta District, Shida Road, Shaanxi Normal University, Xi’an, Shaanxi Province, PR China;1. School of Social Sciences, Western Sydney University, Locked Bag 1797, Penrith NSW, 2751, Australia;2. School of Business, Western Sydney University, Locked Bag 1797, Penrith NSW, 2751, Australia;3. School of Allied Health, Australian Catholic University, 25A Barker Rd., Strathfield, NSW, Australia |
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| Abstract: | ObjectiveTo conduct a system-wide assessment using social network analysis (SNA) to examine how 14 important issues (e.g., consent; racism) are addressed through education, training, and programming at a university.MethodsEvaluators conducted interviews with campus departments responsible for educating/training on the 14 issues. Interviews revealed which programs (n = 52) were offered that addressed the 14 issues, and data on audience characteristics, date of delivery, and which issues were covered in each program were collected. SNA was used to calculate degree and create visualization graphs illustrating patterns of content-coverage across all 52 programs.ResultsThe average degree was 19.38 (SD = 9.70), meaning programs overlapped in topic area with nearly 20 other programs, on average. Most programs (n = 36; 69.2 %) were attended by audiences of 500 people or less. “Diversity and inclusion” represented the topic area with the most programs (n = 23), whereas “suicide” and “bullying/hazing” had the least number of programs (n = 3). Degree was negatively correlated with attendance numbers (r=-.310, p < .001), indicating the more a program overlapped in content with other programs, the smaller the audience.ConclusionsThis study supports the use of network analysis in conducting systemic evaluations of programs offered at a university, complementing the work of ongoing, local-level program evaluations. |
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| Keywords: | Assessment Social network analysis Student affairs Student life |
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