Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Using Multiple Imputation To Mitigate The Effects Of Low Examinee Motivation On Estimates Of Student Learning, Kelly J. Foelber
Using Multiple Imputation To Mitigate The Effects Of Low Examinee Motivation On Estimates Of Student Learning, Kelly J. Foelber
Dissertations, 2014-2019
In higher education, we often collect data in order to make inferences about student learning, and ultimately, in order to make evidence-based changes to try to improve student learning. The validity of the inferences we make, however, depends on the quality of the data we collect. Low examinee motivation compromises these inferences; research suggests that low examinee motivation can lead to inaccurate estimates of examinees’ ability (e.g., Wise & DeMars, 2005). To obtain data that better represent what students know, think, and can do, practitioners must consider, and attempt to negate the effects of, low examinee motivation. The primary purpose …
The Effects Of A Planned Missingness Design On Examinee Motivation And Psychometric Quality, Matthew S. Swain
The Effects Of A Planned Missingness Design On Examinee Motivation And Psychometric Quality, Matthew S. Swain
Dissertations, 2014-2019
Assessment practitioners in higher education face increasing demands to collect assessment and accountability data to make important inferences about student learning and institutional quality. The validity of these high-stakes decisions is jeopardized, particularly in low-stakes testing contexts, when examinees do not expend sufficient motivation to perform well on the test. This study introduced planned missingness as a potential solution. In planned missingness designs, data on all items are collected but each examinee only completes a subset of items, thus increasing data collection efficiency, reducing examinee burden, and potentially increasing data quality. The current scientific reasoning test served as the Long …
The Treatment Of Missing Data When Estimating Student Growth With Pre-Post Educational Accountability Data, Jason P. Kopp
The Treatment Of Missing Data When Estimating Student Growth With Pre-Post Educational Accountability Data, Jason P. Kopp
Dissertations, 2014-2019
To ensure program quality and meet accountability mandates, it is becoming increasingly important for educational institutions to show “value-added” for attending students. Value-added is often evidenced by some form of pre-post assessment, where a change in scores on a construct of interest is considered indicative of student growth. Although missing data is a common problem for these pre-post designs, missingness is rarely addressed and cases with missing data are often listwise deleted. The current study examined the mechanism underlying, and bias resulting from, missingness due to posttest nonattendance in a higher-education accountability testing context. Although data were missing for some …