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Full-Text Articles in Education
Reporting Multiple Imputation: Example Stata Code, Catherine A. Manly, Ryan S. Wells
Reporting Multiple Imputation: Example Stata Code, Catherine A. Manly, Ryan S. Wells
Ryan S. Wells
The miexample.do file includes Stata code illustrating implementation of the recommended multiple imputation (MI) reporting practices from the article "Reporting the use of multiple imputation for missing data in higher education research," Research in Higher Education, doi: 10.1007/s11162-014-9344-9. After a brief description of the example used in the code (which uses a publicly available, downloadable dataset), a sample paragraph offers possible text for writing up results using MI to handle missing data in this example. In both the sample paragraph and the subsequent example Stata code, the recommended MI reporting practices identified in the article (Table 1) are highlighted. These …
Multiple Imputation And Higher Education Research, Catherine A. Manly, Ryan S. Wells
Multiple Imputation And Higher Education Research, Catherine A. Manly, Ryan S. Wells
Ryan S. Wells
Higher education researchers using survey data often face decisions about handling missing data. Multiple imputation (MI) is considered by many statisticians to be the most appropriate technique for addressing missing data in many circumstances. However, our content analysis of a decade of higher education research literature reveals that the field has yet to make substantial use of this technique despite common employment of quantitative analysis, and that many recommended MI reporting practices are not being followed. We conclude that additional information about the technique and recommended reporting practices may help improve the quality of the research involving missing data. In …
The Silent Majority: An Examination Of Nonresponse In College Student Surveys, Ethan A. Kolek
The Silent Majority: An Examination Of Nonresponse In College Student Surveys, Ethan A. Kolek
Open Access Dissertations
Nonresponse is a growing problem in surveys of college students and the general population. At present, we have a limited understanding of survey nonresponse in college student populations and therefore the extent to which survey results may be biased. The purpose of this dissertation is to explore three facets of nonresponse in surveys of college students in order to strengthen our empirical and conceptual understanding of this phenomenon. This dissertation seeks to contribute to our understanding of who participates in surveys and who does not, how students experience the process of being asked to complete surveys, and whether or not …