@article{ref1,
title="Meta-analysis and the myth of generalizability",
journal="Industrial and organizational psychology",
year="2017",
author="Tett, Robert P. and Hundley, Nathan A. and Christiansen, Neil D.",
volume="10",
number="3",
pages="421-456",
abstract="Rejecting situational specificity (SS) in meta-analysis requires assuming that residual variance in observed correlations is due to uncorrected artifacts (e.g., calculation errors). To test that assumption, 741 aggregations from 24 meta-analytic articles representing seven industrial and organizational (I-O) psychology domains (e.g., cognitive ability, job interviews) were coded for moderator subgroup specificity. In support of SS, increasing subgroup specificity yields lower mean residual variance per domain, averaging a 73.1% drop. Precision in mean rho (i.e., low SD(rho)) adequate to permit generalizability is typically reached at SS levels high enough to challenge generalizability inferences (hence, the &quot;myth of generalizability&quot;). Further, and somewhat paradoxically, decreasing K with increasing precision undermines certainty in mean r and Var(r) as meta-analytic starting points. In support of the noted concerns, only 4.6% of the 741 aggregations met defensibly rigorous generalizability standards. Four key questions guiding generalizability inferences are identified in advancing meta-analysis as a knowledge source.<p /> <p>Language: en</p>",
language="en",
issn="1754-9426",
doi="10.1017/iop.2017.26",
url="http://dx.doi.org/10.1017/iop.2017.26"
}