Analyzing and interpreting NSSE data

Chen, Pu-Shih Daniel; Gonyea, Robert M.; Sarraf, Shimon; BrckaLorenz, Allison; Korkmaz, Ali; Lambert, Amber D.; Shoup, Rick; Williams, Julie M.

doi:10.1002/ir.285

Cited by 13 publications

(12 citation statements)

References 5 publications

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“…The large-scale American and Australasian surveys do not just report multi-and single-institutional results. They are able to drill down into the results of disaggregated populations such as women, international students and students participating in certain programmes or fields of study (Chen et al, 2009). Several articles have appeared using national survey data to investigate special populations within and across institutions.…”

Section: Literature Foundationssupporting

confidence: 45%

Institutional research and improving the quality of student engagement

Zepke

Butler

Leach

2012

Quality in Higher Education

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This article asks whether institutional research could help improve the quality of student engagement by researching at sub-institutional levels such as in courses bridging students from school into higher education. In answer it argues two interacting propositions: one, that student engagement provides useful indicators of quality in higher education; and, two, that research into student engagement is best conducted at a sub-institutional level. The authors use evidence obtained from a funded Teaching and Learning Research Initiative project in New Zealand to support these propositions. The project gauged the perceptions of firsttime students in four different bridging (access) courses in one higher education institution. Results showed significant differences in the way they perceived the efforts of teachers and the institution to support their engagement. Not only did course results differ from institutional scores, courses also differed significantly from each other. These results suggest that the quality of student engagement could be enhanced if institutional research focused closely on engagement at sub-institutional levels such as courses.

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Section: Literature Foundationssupporting

confidence: 45%

Institutional research and improving the quality of student engagement

Zepke

Butler

Leach

2012

Quality in Higher Education

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“…32 It is understood as the extent to which actions and thought processes of test takers or survey responders demonstrated that they understood the construct in the same way the researchers intended. 33 The HDCC survey performed by Haggstrom and colleagues generated data used in this simulation study. 3 Therefore, SME assessment was employed to validate the following aspects: (1) the original researcher’s interpretation of summary measure data as agent, task, belief, or knowledge measure, (2) the assignment of specific summary measures to describe the behavior of each of the five agents used in the simulation, and (3) the assignment of each of the 44 CHCs to one of five performance levels used in the simulation.…”

Section: Discussionmentioning

confidence: 99%

Hypothesis generation using network structures on community health center cancer-screening performance

Carney

Morgan

Jones

et al. 2015

Journal of Biomedical Informatics

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Research Objectives Nationally sponsored cancer care quality improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer screening rates among vulnerable populations. Despite some immediate and short-term gains screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or diffuse to others as repeatable best practices. One reason is that facility-level changes typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to better understand the factors that help shape community health center facility-level cancer screening performance over time. This study applies a computational modeling approach that combines principles of health services research, health informatics, network theory, and systems science. Methods In order to investigate the role of knowledge acquisition, retention, and sharing within the setting of the community health center and the effect of this role on the relationship between clinical decision support capabilities and improvement in cancer screening rate improvement, we employed Construct TM to create simulated community health centers using previous collected point-in-time survey data. Construct TM is a multi-agent model of network evolution. Social, knowledge, and belief networks co-evolve. Groups and organizations are treated as complex systems, thus capturing the variability in human and organizational factors. In Construct TM, individuals and groups interact communicate, learn, and make decisions in a continuous cycle. Data from the survey was used to create high-performing simulated community health centers and low-performing ones based on extent of both computer decision support use and cancer-screening rates. Results Our virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge absorption rates, and fewer unconnected agents to key knowledge resources. Conclusions and Research Implications Using the point-in-time survey data outlining community health center cancer screening practices our computational model successfully distinguished between high and low performers. Our study showed that high performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time and thereby enhance sustainability of long-term...

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“…Each survey year implemented oversampling to increase the response rate. A response rate is the percentage of a sample that completes a questionnaire (Chen et al, 2009). The final response rate for each of the cohort years was 33% in 2005, 37% in 2006, 23% in 2007, and 28% in 2008.…”

Section: Methodsmentioning

confidence: 99%

“…The description for this new variable was "The year the data was created" with a response variable equaling "0" for 2005 to signify it being the base year. In addition, dummy-coding the year variable allows a researcher to test the base year against subsequent years for significant differences (Chen et al, 2009).…”

Section: File Merge/population Accumulationmentioning

confidence: 99%

“…Factor analysis consists of a set of procedure to establish construct validity, which involves using the correlations among a set of variables to determine the number of composite variables or factors that underlay a large number of continuous variables (Schuh, 2009). In addition, EFA allows researchers to reduce the number of variables into workable scales that may have better reliability and convey more meaningful information than individual survey questions (Chen et al, 2009). Therefore, researcher used factor analysis to reduce the 60 NSSE variables into seven factors or composite variables, which was composed of 38 variables.…”

Section: Exploratory Factor Analysis (Efa)mentioning

confidence: 99%

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Factors that predict students' educational experience at a Midwestern research university: A comparative study of STEM and non-STEM majors

Wilson

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The National Survey of Student Engagement (NSSE) is an annual, nationwide survey used to assess the level of engagement of four-year college and university students. Only randomly sampled freshman and seniors participate in the self-reporting of their college experience. Annually, the survey results are organized into five benchmarks or clusters of survey questions as a common language/framework for institutions to compare themselves to similar institutions. However, the NSSE benchmarks do not account individual institutional characteristics. Therefore, the purpose of this quantitative study was to use secondary NSSE

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Analyzing and interpreting NSSE data

Cited by 13 publications

References 5 publications

Institutional research and improving the quality of student engagement

Institutional research and improving the quality of student engagement

Hypothesis generation using network structures on community health center cancer-screening performance

Factors that predict students' educational experience at a Midwestern research university: A comparative study of STEM and non-STEM majors

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