O-K-State PSYC 5314 - An Effect Size Primer: A Guide for Clinicians and Researchers (7 pages)

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An Effect Size Primer: A Guide for Clinicians and Researchers



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An Effect Size Primer: A Guide for Clinicians and Researchers

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Lecture Notes


Pages:
7
School:
Oklahoma State University
Course:
Psyc 5314 - Quant Meth in Psych Ii

Unformatted text preview:

Professional Psychology Research and Practice 2009 Vol 40 No 5 532 538 2009 American Psychological Association 0735 7028 09 12 00 DOI 10 1037 a0015808 An Effect Size Primer A Guide for Clinicians and Researchers Christopher J Ferguson Texas A M International University Increasing emphasis has been placed on the use of effect size reporting in the analysis of social science data Nonetheless the use of effect size reporting remains inconsistent and interpretation of effect size estimates continues to be confused Researchers are presented with numerous effect sizes estimate options not all of which are appropriate for every research question Clinicians also may have little guidance in the interpretation of effect sizes relevant for clinical practice The current article provides a primer of effect size estimates for the social sciences Common effect sizes estimates their use and interpretations are presented as a guide for researchers Keywords effect size statistical null hypothesis testing experimentation statistical analysis statistical significance practical significance Kirk 1996 puts the limitations of NHST succinctly in noting that they fall under three main categories First NHST does not adequately answer research questions Regarding falsify ability scientists need to know the probability that a null hypothesis is true given a data set Unfortunately NHST tells us the opposite namely how likely a data set is to have occurred given that the null hypothesis is true Cohen 1994 Kirk 1996 Second no two sample means are ever identical Tukey 1991 The null hypothesis is on a microscopic level at least always false Kirk 1996 The result is the quixotic quest for power to demonstrate any difference as statistically significant without considering whether small differences are meaningful This is particularly an issue when sample selection is nonrandom as sampling error is underestimated in NHST when sampling is nonrandom NHST risks becoming something of a trivial exercise as



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