DMCA
An Empirical Comparison of Regression Analysis Strategies with Discrete Ordinal Variables
BibTeX
@MISC{Kromrey_anempirical,
author = {Jeffrey D. Kromrey and Gianna Rendina-gobioff},
title = {An Empirical Comparison of Regression Analysis Strategies with Discrete Ordinal Variables},
year = {}
}
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Abstract
The Type I error control and statistical power of three tests of regression models incorporating discrete ordinal variables were compared in a Monte Carlo study. Samples were generated from populations measured on discrete ordinal variables representing 5-point and 7-point response scales. Each sample was analyzed using ordinary least squares regression, ordinal multiple regression and cumulative logit models. Factors examined in the Monte Carlo study were the population effect size, number of regressor variables, level of regressor intercorrelation, population distribution shape and sample size. Results suggest that the logistic regression approach evidenced poor Type I error control with small samples or with large numbers of regressors. In contrast, both the ordinary least squares approach and the ordinal multiple regression approach evidenced good Type I error control across the majority of conditions examined. Further, the power differences between these approaches were negligible. common response format for the measurement of many variables in the social sciences is a forced-choice, ordinal scale. For example, data may be collected using a Likert scale in which respondents indicate the extent of their agreement or disagreement with a stimulus. Similarly, ordinal rating scales may be used to obtain ratings by participants regarding their perceptions of the frequency or intensity of a target phenomenon. The level of measurement of this type of variable and the appropriate statistical techniques for the
Keyphrases
discrete ordinal variable regression analysis strategy empirical comparison monte carlo study ordinal rating scale social science sample size logistic regression approach power difference poor type appropriate statistical technique population distribution shape ordinal multiple regression approach ordinal scale square regression many variable target phenomenon statistical power population effect size 7-point response scale ordinary least square approach small sample regressor intercorrelation regression model regressor variable ordinal multiple regression likert scale good type cumulative logit model common response format large number
