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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
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Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…
Descriptors: Error of Measurement, Correlation, Simulation, Bayesian Statistics
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McNeish, Daniel – Review of Educational Research, 2017
In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…
Descriptors: Models, Statistical Analysis, Sampling, Sample Size
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Jia, Fan; Moore, E. Whitney G.; Kinai, Richard; Crowe, Kelly S.; Schoemann, Alexander M.; Little, Todd D. – International Journal of Behavioral Development, 2014
Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This article explores this question by using…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
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Gemici, Sinan; Bednarz, Alice; Lim, Patrick – International Journal of Training Research, 2012
Quantitative research in vocational education and training (VET) is routinely affected by missing or incomplete information. However, the handling of missing data in published VET research is often sub-optimal, leading to a real risk of generating results that can range from being slightly biased to being plain wrong. Given that the growing…
Descriptors: Vocational Education, Educational Research, Data, Statistical Analysis
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Muthen, Bengt; Joreskog, Karl G. – Evaluation Review, 1983
Selectivity problems are discussed in terms of a general model that is estimated by the maximum likelihood method. Both single-group and multiple-group analyses are considered. An extension of the general model to latent variable models is discussed. (Author/PN)
Descriptors: Mathematical Models, Maximum Likelihood Statistics, Quasiexperimental Design, Research Methodology