User:Stargazer.em/Mixed-design analysis of variance

A repeated measures design is used when multiple independent variables or measures exist in a data set, but all participants have been measured on each variable.

An example


Andy Field (2009) provided an example of a mixed-design ANOVA in which he wants to investigate whether personality or attractiveness is the most important quality for individuals seeking a partner. In his example, there is a speed dating event set up in which there are two sets of what he terms "stooge dates": a set of males and a set of females. The experimenter selects 18 individuals, 9 males and 9 females to play stooge dates. Stooge dates are individuals who are chosen by the experimenter and they vary in attractiveness and personality. For males and females, there are three highly attractive individuals, three moderately attractive individuals, and three highly unattractive individuals. Of each set of three, one individual has a highly charismatic personality, one is moderately charismatic and the third is extremely dull.

The participants are the individuals who sign up for the speed dating event and interact with each of the 9 individuals of the opposite sex. There are 10 males and 10 female participants. After each date, they rate on a scale of 0 to 100 how much they would like to have a date with that person, with a zero indicating "not at all" and 100 indicating "very much".

The random factors, or so-called repeated measures, are looks, which consists of three levels (very attractive, moderately attractive, and highly unattractive) and the personality, which again has three levels (highly charismatic, moderately charismatic, and extremely dull). The looks and personality have an overall random character because the precise level of each cannot be controlled by the experimenter (and indeed may be difficult to quantify ); the 'blocking' into discrete categories is for convenience, and does not guarantee precisely the same level of looks or personality within a given block; and the experimenter is interested in making inferences on the general population of daters, not just the 18 'stooges'  The fixed-effect factor, or so-called between-subjects measure, is gender because the participants making the ratings were either female or male, and precisely these statuses were designed by the experimenter.