For data that is either paired or has overlap, one of the Dependent Paired/Overlap T-Tests is the appropriate test.
A table is Multi if its banner is a set of attributes which qualify the respondent for the respective banner points and whose rows are some other data about the respondents. For example, you want to compare the mean age of those who consumed Coke with the mean age of those who consumed Pepsi and the mean age of those who consumed Sprite. The rows of the table are the age breaks and the banner points are the following:
Have consumed in the last 30 days:
Coke Pepsi Sprite
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In this example a respondent may have consumed none, all three or any combination. Since respondents can drink Coke, Pepsi and Sprite, the same respondent can appear in all three columns of the table which makes the means dependent. In this example, Dependent Paired/Overlap (Multi) is the appropriate test.
A table is LOC+/VAR+ if its banner is a set of attributes which qualify the respondent for the respective banner points and whose rows are separate measurements relating to the banner points. For example, you want to compare the mean rating of Coke for those who rated Coke with the mean rating of Pepsi for those who rated Pepsi. The rows of the table are the rating scale and the banner points are the following:
Rated Coke Rated Pepsi
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Since respondents can rate both Coke and Pepsi, the same respondent can appear in both columns of the table which makes the means dependent. In this example, Dependent Paired/Overlap (LOC+/VAR+) is the appropriate test.
IMPORTANT: The Dependent Paired/Overlap T-Test (LOC+/VAR+) is only appropriate when the LOC instruction or VAR instruction is used in the banner. The LOC instruction lets you show data from the same respondent in multiple data locations (can be used with ASCII or Variable type data). The VAR instruction lets you show data from the same respondent in multiple variables (can only be used with Variable type data). |
The Dependent Paired/Overlap tests provide for the possibility that there are both independent and dependent data being analyzed. This could happen, if for example, you had data from a sample of those who rated Coke only, those who rated Pepsi only and those who rated both. When there are no dependent data in the samples, the T-Test becomes an Independent (assume unequal variances) T-Test for those banner points with independent data and a Dependent Paired/Overlap (either Multi or LOC+/VAR+) T-Test for those banner points with dependent data.
T-Test Options
Confidence levels
Show significance indicators in banner
Displays the significance annotation displayed for each banner column.
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