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Luo, Wen; Li, Haoran; Baek, Eunkyeng; Chen, Siqi; Lam, Kwok Hap; Semma, Brandie – Review of Educational Research, 2021
Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results.…
Descriptors: Hierarchical Linear Modeling, Multivariate Analysis, Prediction, Research Problems
Collins, Kathleen M. T.; Onwuegbuzie, Anthony J. – New Directions for Evaluation, 2013
The goal of this chapter is to recommend quality criteria to guide evaluators' selections of sampling designs when mixing approaches. First, we contextualize our discussion of quality criteria and sampling designs by discussing the concept of interpretive consistency and how it impacts sampling decisions. Embedded in this discussion are…
Descriptors: Sampling, Mixed Methods Research, Evaluators, Q Methodology
Iverson, Geoffrey J.; Wagenmakers, Eric-Jan; Lee, Michael D. – Psychological Methods, 2010
The purpose of the recently proposed "p[subscript rep]" statistic is to estimate the probability of concurrence, that is, the probability that a replicate experiment yields an effect of the same sign (Killeen, 2005a). The influential journal "Psychological Science" endorses "p[subscript rep]" and recommends its use…
Descriptors: Effect Size, Evaluation Methods, Probability, Experiments
Capraro, Robert M.; Capraro, Mary Margaret – Middle Grades Research Journal, 2009
This study examines two journals specific to the middles grades where original quantitative empirical articles are published, Research in Middle Level Education and Middle Grades Research Journal to determine what quantitative statistics are used, how they are used, and what study designs are used. Important for those who write for the…
Descriptors: Periodicals, Research Methodology, Social Science Research, Effect Size
Thompson, Bruce – 1987
This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…
Descriptors: Analysis of Variance, Data Interpretation, Editors, Effect Size