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“Significance” in Strategic Management Research. Steve Werner Department of Management Bauer College of Business University of Houston. Overview. The problems of statistical significance. The solutions. Conclusions. The Problems of Statistical Significance.
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“Significance” in Strategic Management Research Steve Werner Department of Management Bauer College of Business University of Houston
Overview • The problems of statistical significance. • The solutions. • Conclusions.
The Problems of Statistical Significance • Statistical significance is not practical significance. • The conventional cut-offs are arbitrary and misleading. • Frequently occurring Type II errors.
Statistical Significance is not Practical Significance • Practical significance is effect size and importance. • Related but not the same. • Statistical significance largely affected by n.
Conventional Cut-offs are Arbitrary and Misleading • Convention is: • p<.10 = marginally significant; • p<.05 = significant; • p<.01 = highly significant; • p<.001=very highly significant. • p<.05 from Fisher (1925). • Counters classical view of predetermining alpha. • Simplifies hypothesis testing.
Frequently OccurringType II Errors • Studies conclude no relationship because findings not significant. • Power issues. • This occurs in 60% of all studies (Hunter, 1997).
The Solutions • Solutions to the practical significance problem. • Solutions to the conventional cut-offs problem. • Solutions to the frequently occurring Type II errors problem.
Solutions to the Practical Significance Problem • Report confidence intervals. • Report effect sizes. • Avoiding using “significance”. • Many journals still do not report effect sizes. • SMJ: 98.5% of studies report effect sizes.
Solutions to theArbitrary Cut-off Problem • Go back to classical Neyman-Pearson hypothesis testing with researcher pre-determined alphas. • Report actual p values (Fisher, 1956). • Easier to accomplish now. • SMJ: 13.4% (35/262) of studies report some actual p values.
Solutions to theType II Error Problem • Increase Power: • Conduct power analyses; • Increase sample size; • Increase effect size. • Relax alpha. • SMJ: 65.6% (172/262) of studies use p<.10 as cut-off. • Report actual p values.
Conclusion • The problems of statistical significance. • The solutions. • Report actual p values. • Consider p, power, effect size, sample size, and importance when evaluating research findings.