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Journal of Educational and Behavioral Statistics is a peer-reviewed journal published by SAGE, covering Psychometric Methodologies and Testing, Statistical Methods and Bayesian Inference, Advanced Causal Inference Techniques.
| Publisher | SAGE |
|---|---|
| Reference style | Author–year (Harvard) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Journal of Educational and Behavioral Statistics, 12(3), pp. 45–58.
Formats any DOI in Journal of Educational and Behavioral Statistics style. No sign-up. |
| Publishes research in | Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Advanced Causal Inference Techniques Advanced Statistical Modeling Techniques Advanced Statistical Methods and Models |
| ISSN | 1076-9986 |
| Citation impact (2-yr) | 1.26 |
| h-index | 99 |
| i10-index | 579 |
| Total citations | 55,621 |
| Top institutions publishing here | Educational Testing Service |
| Journal website | www.sagepub.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the conditional relations is often a tedious…
SAS PROC MIXED is a flexible program suitable for fitting multilevel models, hierarchical linear models, and individual growth models. Its position as an integrated program within the SAS statistical package makes it an ideal choice for empirical researchers and applied statisticians seeking to do data reduction, management, and analysis within a single statistical package. Because…
A new approach to problems of multiple significance testing was presented in Benjamini and Hochberg (1995), which calls for controlling the expected ratio of the number of erroneous rejections to the number of rejections–the False Discovery Rate (FDR). The procedure given there was shown to control the FDR for independent test statistics. When some of…
The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population effects and sampling variance. In this context, the usual goal is to estimate the central tendency and the amount of heterogeneity in the population effect…
In this article, the authors outline methods for using fixed and random effects power analysis in the context of meta-analysis. Like statistical power analysis for primary studies, power analysis for meta-analysis can be done either prospectively or retrospectively and requires assumptions about parameters that are unknown. The authors provide some suggestions for thinking about these…
