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The Blackwell Encyclopedic Dictionary of Organizational Behavior

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Document<br />

Bibliography<br />

Page 597<br />

Cook, T. D., Campbell, D. T. & Peracchio, L. (1990). Quasi experimentation. In M. D. Dunnette & L.<br />

M. Hough (Eds), Handbook <strong>of</strong> industrial & organizational psychology (pp. 491–576). Palo Alto, CA:<br />

Consulting Psychologists Press.<br />

Ghiselli, E. E., Campbell, J. P. & Zedeck, S. (1981). Measurement theory for the behavioral sciences.<br />

San Francisco: W. H. Freeman.<br />

Landy, F. J. & Schmitt, N. (1993). <strong>The</strong> concept <strong>of</strong> validity. In N. Schmitt and W. C. Borman (Eds)<br />

Personnel selection in organizations (pp. 275–309). San Francisco: Jossey-Bass.<br />

Runkel, P. J. & McGrath, J. E. (1972). Research on human behavior. A systematic guide for method.<br />

New York: Holt, Rinehart, & Winston.<br />

Schmitt, N. W. & Klimoski, R. J. (1991). Research methods in human resources management.<br />

Cincinnati, OH: South-Western.<br />

RICHARD KLIMOSKI<br />

Validity Generalization<br />

This is an approach to summarizing what is known about a key characteristic (usually predictive<br />

validity) <strong>of</strong> a test or test type (see STATISTICAL METHODS). It is one <strong>of</strong> a class <strong>of</strong> meta-analytic<br />

techniques which treats the validation study as the unit <strong>of</strong> analysis. It is based on the assumption that the<br />

results from any one study might be misleading, given the potential impact <strong>of</strong> one or more factors in a<br />

piece <strong>of</strong> research which are known to artificially raise or lower computed correlations.<br />

After a frequency distribution <strong>of</strong> the results (e.g., VALIDITY coefficients) found in published and<br />

unpublished studies is developed, various statistical procedures are applied. Thus the steps in a validity<br />

GENERALIZATION study are as follows:<br />

(1) Identify a set <strong>of</strong> studies from the research domain <strong>of</strong> interest. Investigators usually attempt to be as<br />

complete as possible in this step.<br />

(2) Code key information from each study in a way that would allow one to compute an estimate <strong>of</strong><br />

effect size.<br />

(3) Record any additional information that could plausibly be a factor in affecting the results (e.g.,<br />

whether it was a study in one type <strong>of</strong> industry or another, one type <strong>of</strong> employee, etc).<br />

(4) Correct the frequency distribution and/or individual effect size estimates for sources <strong>of</strong> artifactual<br />

variance. Such sources have traditionally included such things as lack <strong>of</strong> predictor (test) RELIABILITY<br />

and size <strong>of</strong> the sample respondents in the study.<br />

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