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Craig K. Enders – Grantee Submission, 2023
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of "Psychological Methods." Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of…
Descriptors: Data, Research, Theories, Regression (Statistics)
Berenson, Mark L. – Decision Sciences Journal of Innovative Education, 2013
There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient…
Descriptors: Spreadsheets, Computer Software, Regression (Statistics), Models
Wagler, Amy E.; Lesser, Lawrence M.; González, Ariel I.; Leal, Luis – Journal of Technical Writing and Communication, 2015
A corpus of current editions of statistics textbooks was assessed to compare aspects and levels of readability for the topics of "measures of center," "line of fit," "regression analysis," and "regression inference." Analysis with lexical software of these text selections revealed that the large corpus can…
Descriptors: Lexicology, Grammar, Higher Education, Statistics
Hayes, Andrew F.; Preacher, Kristopher J. – Multivariate Behavioral Research, 2010
Most treatments of indirect effects and mediation in the statistical methods literature and the corresponding methods used by behavioral scientists have assumed linear relationships between variables in the causal system. Here we describe and extend a method first introduced by Stolzenberg (1980) for estimating indirect effects in models of…
Descriptors: Computation, Methods, Models, Statistical Analysis
LaFleur, Bonnie J.; Greevy, Robert A. – Journal of Clinical Child and Adolescent Psychology, 2009
A resampling-based method of inference--permutation tests--is often used when distributional assumptions are questionable or unmet. Not only are these methods useful for obvious departures from parametric assumptions (e.g., normality) and small sample sizes, but they are also more robust than their parametric counterparts in the presences of…
Descriptors: Sampling, Statistical Inference, Nonparametric Statistics, Hypothesis Testing