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S. Mabungane; S. Ramroop; H. Mwambi – African Journal of Research in Mathematics, Science and Technology Education, 2023
The issue of missing data raises concerns in all statistical and educational research. In this study, we focus on missing data in school-based assessment data generated by progressed high school learners (those who did not meet the promotional requirements for their current grades but were allowed to move to the next grade because of policy…
Descriptors: Data Analysis, Research Problems, High School Students, Student Promotion
Wu, Wei; Jia, Fan – New Directions for Child and Adolescent Development, 2021
Longitudinal panel studies are widely used in developmental science to address important research questions on human development across the lifespan. These studies, however, are often challenging to implement. They can be costly, time-consuming, and vulnerable to test--retest effects or high attrition over time. Planned missingness designs (PMDs),…
Descriptors: Longitudinal Studies, Research Design, Data Analysis, Developmental Psychology
Klumpp, Matthias – Education Sciences, 2018
An increasing effort has been put into dealing with the question of time-series analysis regarding institutional efficiency, including in the area of higher education. Universities are important institutions for economies and societies and are expected to provide excellence as well as efficiency in their processes and outputs. This is reflected in…
Descriptors: Efficiency, Universities, Data Analysis, Achievement Rating
Lee, Daniel Y.; Harring, Jeffrey R.; Stapleton, Laura M. – Journal of Experimental Education, 2019
Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from…
Descriptors: Longitudinal Studies, Research Methodology, Research Problems, Data Analysis
Garvey, Jason C.; Hart, Jeni; Metcalfe, Amy Scott; Fellabaum-Toston, Jennifer – Review of Higher Education, 2019
We examine the American landscape of higher education quantitative research concerning how gender and sex demographic information is collected. We use a directed content analysis to examine the prevalence and operationalization of gender and sex among widely used higher education survey instruments. Our findings illuminate a seemingly haphazard…
Descriptors: Research Problems, Educational Research, Higher Education, Data Collection
Lee, Katherine J.; Roberts, Gehan; Doyle, Lex W.; Anderson, Peter J.; Carlin, John B. – International Journal of Social Research Methodology, 2016
Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple times and the resulting estimates of the parameter(s) of interest are combined across the completed datasets, is becoming increasingly popular for handling missing data. However, MI can result in biased inference if not carried out appropriately or if the…
Descriptors: Data Analysis, Statistical Inference, Computation, Research Problems
Newman, David; Newman, Isadore; Hitchcock, John H. – International Journal of Adult Vocational Education and Technology, 2016
The purpose of this article is to inform researchers about and encourage the use of longitudinal designs to further understanding of human resource development and organizational theory. This article presents information about a variety of longitudinal research designs, related statistical procedures, and an overview of general data collecting…
Descriptors: Longitudinal Studies, Organizational Theories, Labor Force Development, Research Design
Driscoll, Dana Lynn; Gorzelsky, Gwen; Wells, Jennifer; Hayes, Carol; Jones, Ed; Salchak, Steve – Composition Forum, 2017
Researching writing-related dispositions is of critical concern for understanding writing transfer and writing development. However, as a field we need better tools and methods for identifying, tracking, and analyzing dispositions. This article describes a failed attempt to code for five key dispositions (attribution, self-efficacy, persistence,…
Descriptors: Longitudinal Studies, Writing Attitudes, Student Attitudes, Attitude Measures
Rhemtulla, Mijke; Jia, Fan; Wu, Wei; Little, Todd D. – International Journal of Behavioral Development, 2014
We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such…
Descriptors: Data Analysis, Error of Measurement, Models, Longitudinal Studies
Pampaka, Maria; Hutcheson, Graeme; Williams, Julian – International Journal of Research & Method in Education, 2016
Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
Garnier-Villarreal, Mauricio; Rhemtulla, Mijke; Little, Todd D. – International Journal of Behavioral Development, 2014
We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a "gold standard" that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based)…
Descriptors: Longitudinal Studies, Data Analysis, Error of Measurement, Research Problems
Lin, Johnny Cheng-Han – ProQuest LLC, 2013
Many methods exist for imputing missing data but fewer methods have been proposed to test the missing data mechanism. Little (1988) introduced a multivariate chi-square test for the missing completely at random data mechanism (MCAR) that compares observed means for each pattern with expectation-maximization (EM) estimated means. As an alternative,…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Probability
Martin, Andrew J.; Wilson, Rachel; Liem, Gregory Arief D.; Ginns, Paul – Journal of Higher Education, 2014
In the context of "academic momentum," a longitudinal study of university students (N = 904) showed high school achievement and ongoing university achievement predicted subsequent achievement through university. However, the impact of high school achievement diminished, while additive effects of ongoing university achievement continued.…
Descriptors: Foreign Countries, College Students, Longitudinal Studies, Academic Achievement
Timmons, Vianne; Ostridge, Randy – McGill Journal of Education, 2009
Data analysis is critical to educational planning. Determining the number of school leavers is crucial for a school board when planning for interventions and supports. In researching the number of early school leavers in the province of Prince Edward Island, the method in which the data were reported affected the rates. Two critical considerations…
Descriptors: Educational Planning, Dropouts, Data Analysis, Boards of Education
Bueno de Mesquita, Paul; Dean, Ross F.; Young, Betty J. – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2010
Advances in digital video technology create opportunities for more detailed qualitative analyses of actual teaching practice in science and other subject areas. User-friendly digital cameras and highly developed, flexible video-analysis software programs have made the tasks of video capture, editing, transcription, and subsequent data analysis…
Descriptors: Elementary School Science, Guidelines, Educational Technology, Data Analysis

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