By Patrick E. McKnight PhD
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Additional info for Missing Data: A Gentle Introduction
The two groups may react differently throughout the study. Disappointed individuals may drop out of the study (Bradley, 1993; McPherson & Consequences of Missing Data 31 Britton, 2001). If groups are nonequivalent with respect to satisfaction with group assignment, that would result in differential attrition and might influence study outcomes. Differential attrition also results in unequal group size. As noted previously, missing data reduce the sample size and thus the power to detect significant treatment effects.
The goal is for these measures to converge (or triangulate) on the construct, thereby increasing the likelihood that it has been adequately captured by the measurement process. In many ways, the use of multiple measures protects researchers from problems stemming from missing data that occur when only a single measure is used. With multiple measures of the same construct, if data are missing for one or two measures, the others can still provide information about that construct, and therefore all is not lost.
Unbalanced cell sizes may limit the use of some statistical tests (if deletion methods are used; see Chapter 7) and may reduce statistical power to detect significant treatment effects. Unequal cell sizes may result in the violation of two assumptions, normality and homogeneity of variance, that are the basis of the t-test and the F-test. Under these conditions, alternative formulas for these tests should be used to minimize the error of inference (Keppel & Wickens, 2004). A mismatch between participants’ preferences for treatment and their assignment to study groups could contribute to differential attrition bias as well.