@article{ref1,
title="Methods for addressing publication bias in school psychology journals: a descriptive review of meta-analyses from 1980 to 2019",
journal="Journal of school psychology",
year="2021",
author="McClain, Maryellen Brunson and Callan, Gregory L. and Harris, Bryn and Floyd, Randy G. and Haverkamp, Cassity R. and Golson, Megan E. and Longhurst, David N. and Benallie, Kandice J.",
volume="84",
number="",
pages="74-94",
abstract="Although meta-analyses are often used to inform practitioners and researchers, the resulting effect sizes can be artificially inflated due to publication bias. There are a number of methods to protect against, detect, and correct for publication bias. Currently, it is unknown to what extent scholars publishing meta-analyses within school psychology journals use these methods to address publication bias and whether more recently published meta-analyses more frequently utilize these methods. A historical review of every meta-analysis published to date within the most prominent school psychology journals (N = 10) revealed that 88 meta-analyses were published from 1980 to early 2019. Exactly half of them included grey literature, and 60% utilized methods to detect and correct for publication bias. The most common methods were visual analysis of a funnel plot, Orwin's failsafe N, Egger's regression, and the trim and fill procedure. None of these methods were used in more than 20% of the studies. About half of the studies incorporated one method, 20% incorporated two methods, 7% incorporated three methods, and none incorporated all four methods. These methods were most evident in studies published recently. Similar to other fields, the true estimates of effects from meta-analyses published in school psychology journals may not be available, and practitioners may be utilizing interventions that are, in fact, not as strong as believed. Practitioners, researchers employing meta-analysis techniques, education programs, and editors and peer reviewers in school psychology should continue to guard against publication bias using these methods.<p /> <p>Language: en</p>",
language="en",
issn="0022-4405",
doi="10.1016/j.jsp.2020.11.002",
url="http://dx.doi.org/10.1016/j.jsp.2020.11.002"
}