Consumer Research • Categorical Response Analysis • Additional Examples of the Categorical Platform • Example of Mean Score with Comparison Letters
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Example of Mean Score with Comparison Letters
This example uses the Consumer Preferences.jmp sample data table to explore the relationship between employee tenure and having school age children. The Employee Tenure column is a numeric column with values 1, 2, 3, and 4. These values have been assigned Value Labels using the Value Labels column property. To evaluate a mean employee tenure using the mean score option in the categorical platform, you need to assign Value Scores to the column values. For more information about column properties, refer to The Column Info Window in the Using JMP book.
1.
Select Help > Sample Data Library and open Consumer Preferences.jmp.
2.
In the data table, right-click the Employee Tenure column heading and select Column Properties > Value Scores.
3.
Enter 1 for Value and 3 for Score and click Add.
4.
Enter 2 for Value and 7.5 for Score and click Add.
5.
Enter 3 for Value and 15 for Score and click Add.
6.
Enter 4 for Value and 25 for Score and click Add.
7.
Click OK.
8.
Select Analyze > Consumer Research > Categorical.
9.
Select Employee Tenure and click Responses on the Simple tab.
10.
Select School Age Children and click X, Grouping Category.
11.
Click OK.
12.
Click the Categorical red triangle and select Mean Score.
13.
Click the Categorical red triangle and select Mean Score Comparison.
Categorical Report with Mean Scores
The mean employee tenure for those with school age children is 10.17 and 9.53 for those without school age children. The means are not statistically different as the Compare Means column in the Crosstab table is empty. If there was a difference, a letter would indicate the difference.
Tip: Be aware of how your data is recorded when using the mean score option. If your data is recorded as coded numeric data with value labels, the mean value calculations are based on the numeric data. If the numeric data does not have meaning, use Value Scores to assign meaningful data values to the response levels.

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Help created on 9/19/2017