Author List: Baroudi, Jack J.; Orlikowski, Wanda J.;
MIS Quarterly, 1989, Volume 13, Issue 1, Page 87-106.
Statistical power is a topic of importance to any researcher using statistical inference testing. Studies with low levels of statistical power usually result in inconclusive findings, even though the researcher may have expended much time and effort gathering the data for analysis. A survey of the statistical power of articles employing statistical inference testing published in leading MIS journals shows that their statistical power is, on average, substantially below accepted norms. The consequence of this low power is that MIS researchers typically have a 40 percent chance of not detecting the phenomenon under study, even though it, in fact, may exist. Fortunately, there are several techniques, beyond expanding the sample size (which often may be impossible), that researchers can use to improve the power of their studies. Some are as easy as using a different but more powerful statistical test, while others require developing more elaborate sampling plans or a more careful construction of the research design. Attention to the statistical power of a study is one key ingredient in assuring the success of the study. This article should serve as a useful guide for MIS researchers in the planning, execution, and interpretation of inferential statistical analyses.
Keywords: empirical research; research methods; statistical inference testing; Statistical power
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#215 0.178 data classification statistical regression mining models neural methods using analysis techniques performance predictive networks accuracy method variables prediction problem measure
#282 0.136 power perspective process study rational political perspectives politics theoretical longitudinal case social rationality formation construction shows multiple instead understanding fact
#32 0.134 research studies issues researchers scientific methodological article conducting conduct advanced rigor researcher methodology practitioner issue relevance findings validation papers published
#133 0.105 data predictive analytics sharing big using modeling set power inference behavior explanatory related prediction statistical generated substantially novel building million
#255 0.078 mis management article resources sciences developing organization future recommendations procedures informing organizational assessment professional groups area improving conference evaluate activity
#193 0.064 time use size second appears form larger benefits combined studies reasons selected underlying appear various significantly result include make attention
#106 0.062 integration present offer processes integrating current discuss perspectives related quality literature integrated benefits measures potential regarding issues finally taken propose