NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 6 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
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
Peer reviewed Peer reviewed
Wood, Phillip Karl; Games, Paul – Multivariate Behavioral Research, 1990
Conceptual rationales from five research contexts are presented, which all posit unmeasured variables that interact with observed independent variables to produce a complete model of the dependent variable. Strategies for overcoming related difficulties are outlined, including increased longitudinal assessment, oversampling of levels of…
Descriptors: Estimation (Mathematics), Longitudinal Studies, Mathematical Models, Multivariate Analysis
Peer reviewed Peer reviewed
Wessels, Holger; Lamb, Michael E.; Hwang, Carl-Philip – European Journal of Psychology of Education, 1996
Illustrates problems facing researchers trying to demonstrate causal relationships between types of nonparental care and differences between groups of Swedish children. Argues that efforts must be made to validate and interpret differences that are found. Indicates ways to avoid misinterpretation of differences that are attributable to…
Descriptors: Causal Models, Child Development, Day Care, Educational Assessment