Appendix A: Additional Topics A.1 Categorical Platform (Optional)
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Transcript of Appendix A: Additional Topics A.1 Categorical Platform (Optional)
Appendix A: Additional Topics
A.1 Categorical Platform (Optional)A.1 Categorical Platform (Optional)
Objectives Identify specific cases for which the Categorical
platform was designed. Summarize complex categorical data with
the Categorical platform.
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Introduction The purpose of the Categorical platform is to tabulate
and summarize categorical response data.
– Test statistics are available, too. The main advantage of the Categorical platform
is that it recognizes common formats for complex data collection.
– Surveys
– Clinical trials
– Quality assurance This platform reduces or eliminates the need
to reshape the data before analysis.
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Analysis Roles Nine specific roles for the response (Y) are included.
– These roles address the complex data formats (next).
The optional X role provides for grouping variables.– The levels define samples or sample groups.
The optional Sample Size role is used for calculation of the rate of occurrence.
The optional ID role is used to collect multiple responses that appear on separate rows.
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Response Role 1: Separate Responses The responses occur individually, in separate
columns. Each response might have different categories. A separate analysis is performed for each response.
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ID School Grade Subject
GW PS101 6 History
LP PS107 11 Chemistry
WL PS104 2 Knitting
Response Role 2: Aligned Responses All of the responses use the same categories.
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ID Proposal A Proposal B Proposal C
GW Pass Pass Fail
LP Pass Fail Pass
WL Fail Fail Fail
Response Role 3: Repeated Measures All of the responses use the same categories,
but they are measured more than once. This role provides an optional transition report
to determine whether categories change over time.
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ID 2009 2010 2011
GW History English History
LP Physics Chemistry Chemistry
WL Knitting Knitting Knitting
Response Role 4: Rater Agreement All of the responses use the same categories to rate
the same item.
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Item GW LP WL
History Like Like Dislike
Chemistry Dislike Like Dislike
Knitting Like Like Dislike
Response Role 5: Multiple Response All of the responses use the same categories that are
entered into separate columns, but treated as a single grouped response.
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ID Subject 1 Subject 2 Subject 3
GW History Literature
LP Chemistry Physics Biology
WL Knitting
Response Role 6: Multiple Response by ID All of the responses use the same categories that
are entered into one column and one or more rows, but treated as a single grouped response.
Must use ID role to collect responses.
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ID Subject
GW History
GW Literature
LP Chemistry
LP Physics
LP Biology
WL Knitting
Response Role 7: Multiple Delimited All of the responses use the same categories that
are entered into one column and one row, separated by commas.
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ID Subject
GW History, Literature
LP Chemistry, Physics, Biology
WL Knitting
Response Role 8: Indicator Group The responses are binary across multiple columns
in a related group (ID).
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ID Fall Winter Spring
GW Y Y Y
LP Y Y Y
WL N N Y
Response Role 9: Response Frequencies The responses are counts across multiple columns
in a related group (ID).
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ID Fall Winter Spring
GW 3 2 3
LP 4 5 3
WL 0 0 1
Unique Occurrences This option enables you to count a response level just
once when it is duplicated for the same ID.
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Grouping Options There are three options that control the results for your
grouping variables (X):– Combinations: This option results in frequency
reports for combinations of the samples.– Each Individually: This option results in frequency
reports separately for each samples.– Both: This option results in frequency reports both
ways described above.
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Report: Frequency The Frequency report presents a tabulation
of the counts for each category and the total counts (Responses) and total units (Cases).
Grouping variables (X) produce a stratified tabulation.
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Report: Share of Responses The Share of Responses report presents a tabulation
of the proportion of the total counts (Responses) for each category.
Grouping variables (X) produce a stratified tabulation.
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Report: Rate per Case The Rate Per Case report presents a tabulation of the
proportion of the total units (Cases) for each category. Grouping variables (X) produce a stratified tabulation.
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Report Format The reports are formatted as simple or stratified
tables, one each for the frequency, share, and rate. The Crosstab format optionally collects all three values
into one cell. The Table and Crosstab formats can be transposed.
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Chart: Share The Share Chart presents a mosaic plot of the
proportion of the total counts (Responses) for each category.
Grouping variables (X) produce a stratified tabulation.
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Chart: Frequency This chart presents a bar chart of the frequency table. Grouping variables (X) produce a stratified tabulation. This chart is optional. It is not presented by default.
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Available Statistics Test Response Homogeneity Test Each Response
– Relative Risk– Conditional Association
Agreement Statistic Transition Report
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Test Response Homogeneity Determine whether the proportions or probabilities
are the same across all samples. This marginal homogeneity test of independence
is based on the Pearson chi-square test and the likelihood ratio chi-square test.
Typically used when there is one response variable and one explanatory variable.– Multiple explanatory variables are treated
as one variable.
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Test Each Response Determine whether the rates for each category
are the same across all samples. This test is based on Poisson regression.
– Model each response category separately.– The test is a likelihood ratio test.
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Relative Risk (Optional) The risk (probability) of each category is computed
for each sample. The risks are compared across samples. This statistic requires the Unique occurrences within
ID option in the launch dialog.
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Conditional Association (Optional) The probability of each category is computed, given
one of the other categories. This statistic requires the Unique occurrences within
ID option in the launch dialog.
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Agreement Statistic Determine whether the ratings from each rater agree.
– This requires Rater Agreement response role.– The responses must use the same categories.– This is stricter than association.– The agreement test is based on Cohen’s kappa
statistic. Determine whether the lack of agreement
is symmetrical.– The symmetry test is based on Bowker
and McNemar statistic.
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Transition Report Determine whether the frequencies of categories have
changed over time.– This requires the Repeated Measures response
role. This test is based on the counts and the rates
of the transitions.
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Financial Advisor Survey Example A small firm that provides financial management
services sends a survey to customers to assess satisfaction.– Service Quality: rated Low, Medium, or High– Responsiveness: rated Low, Medium, or High– Years as Client– Type of Account: Individual or Business
Analyze Service Quality and Responsiveness over the samples of Years as Client and Type of Account.
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Transform Samples Convert the continuous explanatory variable Years
as Client to a categorical variable, Client Retention.– All survey respondents are current clients.– Client Retention: New (1 to 3), Steady (4 to 8),
or Loyal (>8)
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This demonstration illustrates the concepts discussed previously.
Categorical Platform
Exercise
This exercise reinforces the concepts discussed previously.