Chapter 15: Misconceptions

When people read, hear, or prepare research summaries, they sometimes have misconceptions about what is or isn't "sound practice" regarding the collection, analysis, and interpretation of data. Here are some of these common (and dangerous) misconceptions associated with the content of Chapter 15.

  1. The analysis of covariance was designed for situations where the comparison groups are "intact groups."
  2. The analysis of covariance is robust to the assumption of equal regression slopes so long as the sample sizes are equal.
  3. Data on the covariate variable and data on the dependent variable must come from using the same measuring instruments at two different points in time.
  4. The more covariates the better.
  5. Studies involving ANCOVA are inherently better that studies involving ANOVA.
  6. If the pretest means of a study's comparison groups are compared and found not to be significantly different, the analysis of covariance would have no advantage over an analysis of variance in comparing the groups' posttest means.
  7. In an analysis of covariance, the df values are computed in the same manner as they are in an analysis of variance.
  8. Because an analysis of covariance has more power than an analysis of variance (presuming that the ANCOVA's covariates are "good"), there's no need to worry about Type II errors.
  9. When multiple covariates are used within the same ANCOVA, it's good to have high correlations among the covariates and between each of them and the dependent variable.

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Schuyler W. Huck
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