The default report format is the Crosstab format, which gathers all three statistics for each sample level and response together. The Crosstab format displays the responses on the top and the sample levels down the side, with multiple table elements together in each cell of the cross tabulation.
Crosstab Format
The Crosstab format has a transposed version, Crosstab Transposed, which is useful when there are a lot of response categories but not a lot of sample levels. Crosstab Transposed displays the responses down the side and the sample levels across the top, with multiple table elements together in each cell.
Selecting the Legend red triangle menu shows or hides the legend for the response column on the Share Chart.
Calculates the response means, using the numeric categories, or value scores. This is enabled for columns that use numeric codes, or for categories that have a Value Scores property. To make the Mean Score interpretable, you can assign specific value scores in the Column Info window with the Value Scores column property.For more information and an example, refer to Mean Score Example.
Filters data to specific groups or ranges. Opens the Local Data Filter panel allowing you to identify varying subsets of data. The filtered rows do not appear in the reports. Sample levels with 0 values are always hidden. To show the filtered rows in reports, select Include Responses Not in Data in the launch window. You can also select the Set Preferences red triangle menu, and then select Include Responses Not in Data. For more information, refer to Using JMP.
1.
Select Help > Sample Data Library and open Car Poll.jmp.
2.
Select Analyze > Consumer Research > Categorical.
3.
Select country and click Responses on the Simple tab.
4.
Select marital status and click X, Grouping Category.
5.
6.
Select Test Response Homogeneity from the Categorical red triangle menu.
Test Response Homogeneity
1.
Select Help > Sample Data Library and open Consumer Preferences.jmp.
2.
Select Analyze > Consumer Research > Categorical.
3.
Select Brush Delimited and click Multiple Delimited on the Multiple tab.
4.
Select brush and click X, Grouping Category to compare the sample levels across the brush treatment variable.
5.
6.
Select Test Multiple Response and then Count Test, Poisson from the Categorical red triangle menu.
Test Multiple Response, Poisson
The p-values show that After Meal and Before Sleep are the most significantly different. Wake is not significantly different with this amount of data.
7.
Select Test Multiple Response and then Homogeneity Test, Binomial from the Categorical red triangle menu.
Test Multiple Response, Binomial
The Homogeneity Test, Binomial option always produces a larger test statistic (and therefore a smaller p-value) than the Count Test, Poisson option. The binomial distribution compares not only the rate at which the response occurred (the number of people who reported that they brush upon waking) but also the rate at which the response did not occur (the number of people who did not report that they brush upon waking).
1.
Select Help > Sample Data Library and open Consumer Preferences.jmp.
2.
Select Analyze > Consumer Research > Categorical.
3.
Select I am working on my career and click Responses on the Simple tab.
4.
Select Age Group and click X, Grouping Category.
5.
6.
Select Crosstab Transposed from the red triangle menu.
7.
Select Cell Chisq from the red triangle menu.
Cell Chisq
The response variable is a Multiple Response and the Unique occurrences within ID box is checked on the Categorical launch window.
The Conditional Association option is used to compute the conditional probability of one response given a different response. A table and color map of the conditional probabilities are given. This option is available only when the Unique occurrences within ID box is checked on the Categorical launch window. A common application of this analysis is when the responses represent adverse events (side effects) from a drug. The computations represent the conditional probability of one side effect given the presence of another side effect. For AdverseR.jmp, given the response in each row, Conditional Association shows the rate of also having the response in a column. Conditional Association only displays a few variables in the table due to size constraints.
Conditional Association
The Rater Agreement analysis answers the questions of how closely raters agree with one another and if the lack of agreement is symmetrical. For example, open Attribute Gauge.jmp. The Attribute Chart script runs the Variability Chart platform, which has a test for agreement among raters.
Agreement Comparisons
Launch the Categorical platform and designate the three raters (A, B, and C) as Rater Agreement responses on the Related tab on the launch window. In the resulting report, you have a similar test for agreement that is augmented by a symmetry test that the lack of agreement is symmetric.
Agreement Statistics
1.
Select Help > Sample Data Library and open Presidential Elections.jmp.
2.
Select Analyze > Consumer Research > Categorical.
3.
Select 1980 Winner through 2012 Winner and click Repeated Measures on the Related tab.
4.
Select State and click X, Grouping Category.
5.
Repeated Measures
Compare Each Sample
Compare Each Cell (cut off after column AE)
Lowercase letters are also used for comparisons that are slightly less significant, according to Letter Comparisons. These comparisons suffer when the count for that sample level group (the Base Count) is small, and asterisks start to appear in the comparison cells to warn you.
The options are initialized to the current state. Select the appropriate options and select either Submit Platform Preferences or Create Platform Preference Script to submit the options to your preferences as the new default. When the Categorical platform is launched, the preferences associated with the current preference set are enacted.
Set Preferences Window
Free Text is used for comment fields where the analysis counts the frequency of each word used. Free Text gives word counts in both word order and frequency order, and the rate of non-empty text. The following example uses the Consumer Preferences.jmp sample data table, which contains survey data relating to oral hygiene preferences. A comment field was included in the survey asking for reasons why the participant did not floss.
1.
Select Help > Sample Data Library and open Consumer Preferences.jmp.
2.
Select Analyze > Consumer Research > Categorical.
3.
Select Reasons Not to Floss and click Free Text on the Multiple tab.
4.
5.
Select Score Words by Column from the first Free Text Word Counts for Reasons Not to Floss red triangle menu and then select Floss. Click OK.
Free Text Report Example
Free Text Report Example details the free text word counts the respondents included as reasons why they do not floss. From the analysis, you can determine the number of words, cases, non-empty cases, and portions of non-empty cases. You can also view the word counts alphabetically, in terms of frequency, or by the mean scores. There are more commands to further customize the analysis on the Free Text red triangle menu on the report:
The Structured tab enables you to construct complex tables of descriptive statistics by dragging column names into green icon drop zones to create side-by-side and nested results. The following example uses the Consumer Preferences.jmp sample data table. From this data, suppose that you wanted to compare job satisfaction and salary against gender by age group and position tenure.
1.
Select Help > Sample Data Library and open Consumer Preferences.jmp.
2.
Select Analyze > Consumer Research > Categorical.
4.
Drag Gender to the green drop zone at the Top of the table on the Structured tab.
5.
Drag Age Group to the green drop zone just below Gender.
6.
Drag Position Tenure to the green drop zone at the Top of the table next to Gender.
7.
Drag Job Satisfaction to the green drop zone at the Side of the table.
8.
Drag Salary Group to the green drop zone at the Side of the table under Job Satisfaction.
Structured Tab Report Setup
9.
Click Add=>.
10.
Structured Tab Report Example
Structured Tab Report Example shows that the majority of both the male and female respondents were somewhat satisfied with their jobs, with the highest percentage of males being in the 25-29 age group, while the females were in the 30-34 age group. Most of those who were somewhat satisfied had been in their current position for less than 5 years.