Forfatter av avsnitt: Danielle J. Navarro and David R. Foxcroft

Analyse av kategoriale data

Now that we have covered the basic theory behind hypothesis testing, it is time to start looking at specific tests that are commonly used in psychology. So where should we start? Not every textbook agrees on where to start, but I am going to start with “χ² tests” (“Categorical data analysis”, this chapter) and “t-tests” (chapter Sammenligning av to gjennomsnitt). Both of these tools are very frequently used in scientific practice, and whilst they are not as powerful as “regression” (chapter Korrelasjon og lineær regresjon) and “Analysis of Variance” (chapters Sammenligning av flere gjennomsnitt (enveis ANOVA) and Faktoriell ANOVA) they are much easier to understand. Finally, there is Faktoranalyse that aims to describe the variability among observed, correlated variables in terms of a lower number of unobserved variables called factors or latent Variables.

The term “categorical data” in the title of this chapter is just another name for “nominal scale data” . It is nothing that we have not already discussed, it is just that in the context of data analysis people tend to use the term “categorical data” rather than “nominal scale data”. I do not know why. In any case, categorical data analysis refers to a collection of tools that you can use when your data are nominal scale . Those tools are often called “χ² tests” (pronounced “chi-square”, sometimes “chi-squared”). They determine whether there is a statistically significant difference between expected and observed frequencies and whether the observations follows a χ² frequency distribution. However, there are a lot of different tools that can be used for categorical data analysis, and this chapter covers only a few of the more common ones.