“More than Meets the Eye”: A Guide to Interpreting the Descriptive Statistics and Correlation Matrices Reported in Management Research

Arthur G. Bedeian

Abstract


The author of this essay wonders whether in teaching our students the latest analytic techniques we have neglected to emphasize the importance of understanding the most basic aspects of a study’s primary data. In response, he provides a 12-part answer to a fundamental question: “What information can be derived from reviewing the descriptive statistics and correlation matrix that appear in virtually every empirically based, nonexperimental paper published in the management discipline?” The seeming ubiquity of strained responses, to what many at first consider to be a vexed question about a mundane topic, leads the author to suggest that students at all levels, seasoned scholars, manuscript referees, and general consumers of management research may be unaware that the standard Table 1 in a traditional Results section reveals “more than meets the eye!”


References


Almehrizi, R. S. 2013. Coefficient alpha and reliability of scale scores. Applied Psychological Measurement, 37: 438 – 459.

Altman, D. G., & Bland, J. M. 1996. Detecting skewness from summary information. BMJ, 313: 1200.

Baraldi, A. N., & Enders, C. K. 2010. An introduction to modern missing data analyses. Journal of School Psychology, 48: 5–37.

Bedeian, A. G. 1996. Lessons learned along the way: Twelve suggestions for optimizing career success. In P. J. Frost & M. S. Taylor (Eds.), Rhythms of academic life: Personal ac- counts of careers in academia: 3–9. Thousand Oaks, CA: Sage.

Bedeian, A. G., Day, D. V., & Kelloway, E. K. 1997. Correcting for measurement error attenuation in structural equation models: Some important reminders. Educational Psychological Measurement, 57: 785–799.

Bedeian, A. G., Sturman, M. C., & Streiner, D. L. 2009. Decimal dust, significant digits, and the search for stars. Organizational Research Methods, 12: 687– 694.

Bliese, P. D. 2000. Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In K. J. Klein and S. W. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions: 349 –381. San Fran- cisco: Jossey-Bass.

Bliese, P. D., & Hanges, P. J. 2004. Being both too liberal and too conservative: The perils of treating grouped data as though they were independent. Organizational Research Methods, 7: 400 – 417.

Bobko, P. 2001. Correlation and regression: Applications for industrial organizational psychology and management, (2nd ed). Thousand Oaks, CA: Sage.

Chan, D. 2009. So why ask me? Are self-report data really bad? In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: 309 –336. New York: Routledge.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. 2003. Applied multiple regression/correlation analysis for the behavioral sciences, (3rd ed). Mahwah, NJ: Erlbaum.

Cortina, J. M. 1993. What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78: 98 –104.

DeVellis, R. F. 2006. Classical test theory. Medical Care, 44: S50 –S59.

Eng, J. 2003. Sample size estimation: How many individuals should be studied? Radiology, 227: 309 –313.

Evans, A. N., & Rooney, B. J. 2011. Methods in psychological research, (2nd ed). Thousand Oaks, CA: Sage.

Fisher, R. A. 1935. The design of experiments. Edinburgh, Scot- land: Oliver and Boyd.

Good, I. J. 1962. A classification of fallacious arguments and interpretations. Technometrics, 4: 125–132.

Goodwin, L. D., & Goodwin, W. L. 1999. Measurement myths and misconceptions. School Psychology Quarterly, 14: 408 – 427.

Gu, F., Little, T. D., & Kingston, N. M. 2013. Misestimation of reliability using coefficient alpha and structural equation modeling when assumptions of tau-equivalence and uncorrelated errors are violated. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9: 30 – 40.

Havlicek, L. L., & Peterson, N. L. 1977. Effect of the violation of assumptions upon significance levels of the Pearson r. Psychological Bulletin, 84: 373–377.

Heck, A. K. 2000. Workplace whining: Antecedents and process of noninstrumental complaining. Unpublished doctoral dissertation, Louisiana State University, Baton Rouge.

Helms, J. E., Henze, K. T., Sass, T. L., & Mifsud, V. A. 2006. Treating Cronbach’s alpha reliability coefficients as data in counseling research. Counseling Psychologist, 34: 630 – 660.

Hutchinson, J. W., Kamakura, W. A., & Lynch, J. G., Jr. 2000. Unobserved heterogeneity as an alternative explanation for “reversal” effects in behavioral research. Journal of Consumer Research, 27: 324 –344.

Huysamen, G. K. 2006. Coefficient alpha: Unnecessarily ambiguous, unduly ubiquitous. South Africa Journal of Industrial Psychology, 32: 34 – 40.

Johnson, D. R., & Young, R. 2011. Toward best practices in analyzing datasets with missing data: Comparisons and recommendations. Journal of Marriage and the Family, 73: 920 –945.

Johnson, R. B., & Christensen, L. B. 2012. Educational research: Quantitative, qualitative, and mixed approaches, (4th ed). Thousand Oaks, CA: Sage.

Karabinus, R. A. 1975. The r-point biserial limitation. Educational Psychological Measurement, 34: 277–282.

Kenny, D. A., & Judd, C. M. 1996. A general procedure for the estimation of interdependence. Psychological Bulletin, 119: 138 –148.

Kipling, R. 1911. The glory of the garden. In C. L. R. Fletcher and R. Kipling (Eds.) A school history of England: 249 –250. Ox- ford, England: Clarendon Press.

Kozak, M. 2009. How to show that sample size matters. Teaching Statistics, 31: 52–54.

Lee, B., & Cassell, C. M. 2013. Research methods and research practice: History, themes and topics. International Journal of Management Reviews, 15: 123–131.

Lenth, R. V. 2001. Some practical guidelines for effective sample size determination. American Statistician, 55: 187–193.

McGrath, R. E., & Meyer, G. J. 2006. When effect sizes disagree: The case of r and d. Psychological Methods, 11: 386 – 401.

Miller, A. N., Taylor, S. G., & Bedeian, A. G. 2011. Publish or perish: Academic life as management faculty live it. Career Development International, 16: 422– 445.

Miller, M. B. 1995. Coefficient alpha: A basic introduction from the perspectives of classical test theory and structural equation modeling. Structural Equation Modeling, 2: 255– 273.

Muthén, B. O. 1989. Teaching students of educational psychology new sophisticated statistical techniques. In M. C. Wittrock & F. Farley (Eds.), The future of educational psychology: 181–189. Hillsdale, NJ: Erlbaum.

Nimon, K., Zientak, L. R., & Henson, R. K. 2012. The assumption of a reliable instrument and other pitfalls to avoid when considering the reliability of data. Frontiers in Quantitative Psychology and Measurement, 3: 1–13. Available from http://www.frontiersin.org/Quantitative_Psychology_and_Measurement/10.3389/fpsyg.2012.00102/abstract. Accessed on January 6, 2013.

Pandy, S., & Elliott, W. 2010. Suppressor variables in social work research: Ways to identify in multiple regression models. Journal of the Society for Social Work Research, 1: 28 – 40.

Peterson, R. A., & Kim, Y. 2013. On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98: 194 –198.

Raykov, T., & Marcoulides, G. A. 2013. Meta-analysis of scale reliability using latent variable modeling. Structural Equation Modeling, 20: 338 –353.

Rodriguez, M. C., & Maeda, Y. 2006. Meta-analysis of coefficient alpha. Psychological Methods, 11: 306 –322.

Sherman, S. J. 1990. Commentary on “graduate training in statistics, methodology, and measurement in psychology: A survey of PhD programs in North America,” American Psychologist, 45: 729.

Sheskin, D. J. 2011. Handbook of parametric and nonparametric statistical procedures, (5th ed). Boca Raton, FL: Chapman & Hall/CRC.

Streiner, D. L., & Norman, G. R. 2011. Correction for multiple testing: Is there a resolution? Chest, 140: 16 –18.

Tu, Y. K., Kellett, M., Clerehugh, V., & Gilthorpe, M. S. 2005. Problems of correlations between explanatory variables in multiple regression analyses in the dental literature. British Dental Journal, 199: 457– 461.

Venter, A., & Maxwell, S. E. 2000. Issues in the use and application of multiple regression analysis. In H. E. A. Tinsley & S. D. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling: 152–182. San Diego, CA: Academic Press.

Wainer, H. 1999. The most dangerous profession: A note on nonsampling error. Psychological Methods, 4: 250 –256.

Weber, D. A. 2001. Restriction of range: The truth about consequences and corrections. (ERIC Document Reproduction No. Service ED449213).

Wiberg, M., & Sundström, A. 2009. A comparison of two approaches to correction of restriction of range in correlation analysis. Practical Assessment Research and Evaluation, 14: 1–9.

Zientek, L. R., & Thompson, B. 2009. Matrix summaries improve research reports: Secondary analyses using published literature. Educational Researcher, 38: 343–352.


Full Text: PDF/ENGLISH (Português (Brasil))

Refbacks

  • There are currently no refbacks.




Iberoamerican Journal of Strategic Management  e-ISSN: 2176-0756

Licença Creative Commons
Este obra está licenciado com uma Licença
Creative Commons Atribuição-NãoComercial-CompartilhaIgual 4.0 Internacional