![]() you can’t compare personality in one group to reaction time in another group since those values would not be the same anyway).įigure 1. you can only be part of one group at any given time) and the groups have to be measured on the same variable (i.e. In any time of research design looking at group mean differences, there are some key criteria we must consider: the groups must be mutually exclusive (i.e. That is, on average, do we expect a person from Group A to be higher or lower on some variable that a person from Group B. Logically, we can then say that these research questions are concerned with group mean differences. Many research ideas in the behavioral sciences and other areas of research are concerned with whether or not two means are the same or different. If we want to know if two populations differ and we do not know the mean of either population, we take a sample from each and then conduct an independent sample t-test. Research Questions about Independent MeansĪn independent samples t-test is also designed to compare populations. However, if there’s no logical or meaningful way to link individuals across groups, or if there is no overlap between the groups, then we say the groups are independent and use the independent samples t-test, the subject of this chapter. Can individuals in one group being meaningfully matched up with one and only one individual from the other group? For example, are they a romantic couple? If so, we call those data matched and we use a matched pairs/dependent samples t-test. If it came from a single time point that used separate groups, you need to look at the nature of those groups and if they are related. If they came from two time points with the same people (sometimes referred to as “longitudinal” data), you know you are working with repeated measures data (the measurement literally was repeated) and will use a paired/dependent samples t-test. When in doubt, think about how the data were collected and where they came from. It is very important to keep these two tests separate and understand the distinctions between them because they assess very different questions and require different approaches to the data. As with all of our other tests as well, both of these analyses are concerned with a single variable. This analysis involves TWO groups and ONE time point. Now, we will deal with the difference of the means, that is, the average values of separate groups that are represented by separate descriptive statistics. Those difference scores came from ONE group and TWO time points (or two perspectives). Last chapter, we learned about mean differences, that is, the average value of difference scores. However, we will be adding a few extra steps this time to account for the fact that our data are coming from different sources. The process of testing hypotheses about two means is exactly the same as it is for testing hypotheses about a single mean, and the logical structure of the formulae is the same as well. ![]() Now, we will learn how to compare two separate means from separate groups that do not overlap to see if there is a difference between them. ![]() ![]() We have seen how to compare a single mean against a given value and how to utilize difference scores to look for meaningful, consistent change via a single mean difference using a repeated measures design.
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