![test the homogeneity of slopes using spss 25 test the homogeneity of slopes using spss 25](https://ezspss.com/wp-content/uploads/2019/09/repeated_anova1.png)
For example, one could assess the speech recognition abilities of younger and older adults under different levels of noise. Psychologists also often favor within-subject designs (repeated measures designs) to explore the effects of fixed values of an independent variable on performance. Once performance measures have been acquired on the participants from these different groups, the basis for their classification into different groups (e.g., gender, hearing status, age) is treated as a between-subjects factor in subsequent statistical analyses. Such designs are often referred to as classification designs because participants are classified into two or more mutually-exclusive groups based on specific criteria (gender, hearing status, age, etc.).
![test the homogeneity of slopes using spss 25 test the homogeneity of slopes using spss 25](https://methods.sagepub.com/images/virtual/ancova-in-rlms-hse-2012/10.4135_9781473942622-fig10.jpg)
For instance, the ability of men to perform a particular task might be compared to that of that of women, or the ability of hearing-impaired individuals to remember details from a lecture they heard might be compared to that of individuals without hearing impairments. It is commonplace in Psychology to compare the performance of participants randomly sampled from two or more mutually-exclusive groups. This paper: (1) alerts potential users of ANCOVA of the need to center the covariate measures when the design contains within-subject factors, and (2) indicates how they can avoid biases when one cannot assume that the expected value of the covariate measure is the same for all of the groups in a classification design. Unless the covariate measures on the participants are centered, estimates of within-subject factors are distorted, and significant increases in Type I error rates, and/or losses in power can occur when evaluating the effects of within-subject factors. A second problem of interpretation will arise if the experimenter fails to center the covariate measures (subtracting the mean covariate score from each covariate score) whenever the design contains within-subject factors. In such cases, estimates of differences among groups can be contaminated by differences in the covariate population means across groups. Designs that include a comparison of younger and older adults, or a comparison of musicians and non-musicians are examples of classification designs. However, it is not generally recognized that serious problems of interpretation can arise when the design contains comparisons of participants sampled from different populations (classification designs). A number of statistical textbooks recommend using an analysis of covariance (ANCOVA) to control for the effects of extraneous factors that might influence the dependent measure of interest.