The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The Problem of Statistical Power in MIS Research
Abstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Belief in the law of small numbers.
Tversky, A., & Kahneman, D. (1971) · Psychological Bulletin · doi:10.1037/h0031322 · Cited by 3,246
The statistical power of abnormal-social psychological research: A review.
Cohen, J. (1962) · The Journal of Abnormal and Social Psychology · doi:10.1037/h0045186 · Cited by 1,824
User Involvement and MIS Success: A Review of Research
Ives, B., & Olson, M. H. (1984) · Management Science · doi:10.1287/mnsc.30.5.586 · Cited by 1,318
Consequences of prejudice against the null hypothesis.
Greenwald, A. G. (1975) · Psychological Bulletin · doi:10.1037/h0076157 · Cited by 1,022
Cited by
Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009) · MIS Quarterly · doi:10.2307/20650284 · Cited by 4,944
Ellis, P. D. (2010) · doi:10.1017/cbo9780511761676 · Cited by 2,461
Zhu, K., & Kraemer, K. L. (2005) · Information Systems Research · doi:10.1287/isre.1050.0045 · Cited by 1,723
Survey research methodology in management information systems: an assessment
Pinsonneault, A., & Kraemer, K. (1993) · Journal of Management Information Systems · doi:10.1080/07421222.1993.11518001 · Cited by 1,288
Factors Affecting the Adoption of Open Systems: An Exploratory Study1
Chau, P. Y. K., & Tam, K. Y. (1997) · MIS Quarterly · doi:10.2307/249740 · Cited by 1,067
ZHU, K., KRAEMER, K. L., & DEDRICK, J. (2004) · Journal of Management Information Systems · doi:10.1080/07421222.2004.11045797 · Cited by 775
Robey, D., & Boudreau, M.-C. (1999) · Information Systems Research · doi:10.1287/isre.10.2.167 · Cited by 765
In Pursuit of Moderation: Nine Common Errors and Their Solutions1
Carte, T. A., & Russell, C. J. (2003) · MIS Quarterly · doi:10.2307/30036541 · Cited by 565
A Critical Perspective on Action Research as a Method for Information Systems Research
Baskerville, R. L., & Wood-Harper, A. T. (1996) · Journal of Information Technology · doi:10.1177/026839629601100305 · Cited by 390
An Assessment of the Contingency Theory of Management Information Systems
Weill, P., & Olson, M. H. (1989) · Journal of Management Information Systems · doi:10.1080/07421222.1989.11517849 · Cited by 351
Factors affecting adoption of online banking: A meta-analytic structural equation modeling study
Montazemi, A. R., & Qahri-Saremi, H. (2015) · Information & Management · doi:10.1016/j.im.2014.11.002 · Cited by 293
Xu, S., Zhu, K., & Gibbs, J. (2004) · Electronic Markets · doi:10.1080/1019678042000175261 · Cited by 253
The role of readiness factors in E-learning outcomes: An empirical study
Keramati, A., Afshari-Mofrad, M., & Kamrani, A. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.04.005 · Cited by 208
E-business value creation in Small and Medium Enterprises: a US study using the TOE framework
Wen, K. W., & Chen, Y. (2010) · International Journal of Electronic Business · doi:10.1504/ijeb.2010.030717 · Cited by 88
The use of meta-analysis in MIS research: promises and problems
Hwang, M. I. (1996) · ACM SIGMIS Database: the DATABASE for Advances in Information Systems · doi:10.1145/264417.264433 · Cited by 56
Sikhondze, B., Ye, H. J., Wang, W., Zhan, X., & Santhanam, R. (2025) · Journal of Management Information Systems · doi:10.1080/07421222.2025.2487314 · Cited by 18
Ang, S., & Slaughter, S. A. (2007) · Information Systems Outsourcing · doi:10.1007/978-3-540-34877-1_15 · Cited by 11
Exemplary Works on Information Systems Research
Whitman, M. E., & Woszczynski, A. B. (2011) · The Handbook of Information Systems Research · doi:10.4018/978-1-59140-144-5.ch001 · Cited by 1
Metadata
- Authors
- Baroudi, J. J., & Orlikowski, W. J.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 1989-03-01
- Volume
- 13
- Issue
- 1
- Pages
- 87-106
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W1603898944
- Cited by
- 302
- References
- 4
- FWCI
- 10.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.