Chapter9 Flashcards
Dependent t-test
see paired-samples t-test
Dummy variables
a way of recoding a categorical variable with more than two categories into a series of variables all of which are dichotomous and can take on values of only 0 or 1. There are seven basic steps to create such variables: (1) count the number of groups you want to recode and subtract 1; (2) create as many new variables as the value you calculated in step 1 (these are your dummy variables); (3) choose one of your groups as a baseline (i.e., a group against which all other groups should be compared, such as a control group); (4) assign that baseline group values of 0 for all of your dummy variables; (5) for your first dummy variable, assign the value 1 to the first group that you want to compare against the baseline group (assign all other groups 0 for this variable); (6) for the second dummy variable assign the value 1 to the second group that you want to compare against the baseline group (assign all other groups 0 for this variable); (7) repeat this process until you run out of dummy variables.
Grand mean
the mean of an entire set of observations.
Independent t-test
a test using the t-statistic that establishes whether two means collected from independent samples differ significantly.
Paired-samples t-test
a test using the t-statistic that establishes whether two means collected from the same sample (or related observations) differ significantly.
Standard error of differences
if we were to take several pairs of samples from a population and calculate their means, then we could also calculate the difference between their means. If we plotted these differences between sample means as a frequency distribution, we would have the sampling distribution of differences. The standard deviation of this sampling distribution is the standard error of differences. As such it is a measure of the variability of differences between sample means.
Variance sum law
states that the variance of a difference between two independent variables is equal to the sum of their variances.