Statistical conclusion validity: Difference between revisions

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Statistical conclusion validity refers to the appropriate use of statistics to infer whether the presumed independent and dependent variables covary (Cook & Campbell, 1979). It concerns two related statistical inferences: (1) whether the presumed cause and effect covary and (2) how strongly they covary.

The most common threats to statistical conclusion validity are:

Violated assumptions of the test statistics
Fishing and the error rate problem
Unreliability of measures
Restriction of range
Unreliability of treatment implementation
Extraneous variance in the experimental setting
Heterogeneity of the units under study
Inaccurate effect size estimation

References

Cohen, R. J., & Swerdlik, M. E. (2004). Psychological testing and assessment (6th edition). Sydney: McGraw-Hill, pg. 161.
Cook, T. D., Campbell, D. T., & Day, A. (1979). Quasi-experimentation: Design & analysis issues for field settings. Houghton Mifflin Boston.
Shadish, W., Cook, T. D.,& Campbell, D. T. (2006). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

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 {{Cleanup|date=July 2007}}
-'''Statistical conclusion validity''' establishes the existence and strength of the co-variation between the cause and effect variables. This type of validity involves ensuring adequate sampling procedures, appropriate statistical tests, and reliable measurement procedures.
+'''Statistical conclusion validity''' refers to the appropriate use of [[statistics]] to infer whether the presumed independent and dependent variables [[covary]] (Cook & Campbell, 1979).  It concerns two related statistical inferences: (1) whether the presumed cause and effect covary and (2) how strongly they covary.
+The most common threats to statistical conclusion validity are:
+* Violated assumptions of the test statistics
+* Fishing and the error rate problem
+* Unreliability of measures
+* Restriction of range
+* Unreliability of treatment implementation
+* Extraneous variance in the experimental setting
+* Heterogeneity of the units under study
+* Inaccurate effect size estimation
-The validity coefficient is (always) less than or equal to the geometric mean of the test's reliability coefficient and the criterion's reliability coefficient.  (Geometric mean is the square root of the product of the two).
 == References ==
 * Cohen, R. J., & Swerdlik, M. E. (2004). Psychological testing and assessment (6th edition). Sydney: McGraw-Hill, pg. 161.
+* Cook, T. D., Campbell, D. T., & Day, A. (1979). Quasi-experimentation: Design & analysis issues for field settings. Houghton Mifflin Boston.
+* Shadish, W., Cook, T. D.,& Campbell, D. T. (2006). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
 ==See also==