S2E27: (re)Connecting With Discrete Data

e27s2

In this episode Patrick and Greg discuss the challenges of having ordered categorical data, as well as the seemingly magical limited information and full information analytical options to deal with such data. Along the way they also discuss sky cranes, the Mars Climate Orbiter, metric vs. imperial units of measurement, Lockheed-Martin, left hands and right hands, the A380, 6-inch extension cords, Home Depot, billion dollar shooting stars, being unidextrous, playing the recorder, star wipe, Jell-O molds, throbbing and pulsating distributions, Fast Pass walk of shame, Monarch notes, Mahnamahna, and 1.7.

Additional Show Notes

Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement7, 249-253.

Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods9, 466-491.

Krieg, E. F., Jr. (1999). Biases induced by coarse measurement scales. Educational and Psychological Measurement59, 749-766.

MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods7, 19-40.

Maxwell, S. E., & Delaney, H. D. (1993). Bivariate median splits and spurious statistical signficance. Psychological Bulletin113, 181-190.

Muthén, B. & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology38, 171-189.

Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika44, 443-460.

Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods17, 354-373.

Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology66, 201-223.

Takane, Y. & de Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika52, 393-408.

Taylor, A. B., West, S. G., & Aiken, L. S. (2006). Loss of power in logistic, ordinal logistic, and probit regression when an outcome variable is coarsely categorized. Educational and Psychological Measurement66, 228-239.

Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods12, 58-79.

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