Publication | Closed Access
Using Large Data Sets to Study College Education Trajectories
17
Citations
16
References
2014
Year
Educational OutcomesEngineeringEducationStudent OutcomeProgram EvaluationStudent RetentionData ScienceData MiningLarge Data SetsCollege PipelineVariable OperationalizationUniversity Student RetentionPublic PolicyStudent SuccessEducational Data MiningEducational TestingLearning AnalyticsLow‐income StudentsEducational StatisticsHigher EducationAcademic PreparationEducational AssessmentEducation PolicyStudent Affairs
This chapter presents various considerations researchers undertook to conduct a quantitative study on low‐income students using a national data set. Specifically, it describes how a critical quantitative scholar approaches guiding frameworks, variable operationalization, analytic techniques, and result interpretation. Results inform how policymakers and school administrators can focus their efforts to improve the academic preparation and college enrollment of low‐income students.
| Year | Citations | |
|---|---|---|
Page 1
Page 1