Manipulating Variables in SPSS
Changing Numerical Variables to
Categorical Variables
• Often want/need to change variables from an
ordinal, interval, or ratio scale to a nominal
(categorical) scale of measurement
• Why?
– To use as an independent variable in an inferential
statistical procedure
– To combine the information from one or more existing
variables
Procedures for Splitting Data
• Determining the cut-offs used to create categories
can be done using a variety of procedures.
– Non-Sample Dependent Procedures:
• Based on researchers previous experiences or hunches
• Based on information from the literature
• Based on categories defined by the scale authors
– Sample Dependent Procedures:
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Median Split
Tertiary Split
Quartile Split
Normal Curve Split
Sample Dependent Procedures
• Median Split:
– Use to split data into two categories (e.g. high and low)
– Determine the median.
• Place individuals who score below the median into one
category.
• Place those that score above the median into the other
category.
• When necessary, use own judgment to decide where to place
those who score at exactly the median.
Sample Dependent Procedures
• Tertiary Split:
– Used to divide numerical data into three categories of
equal number
– Determine the scores at the 33rd and 66th percentiles.
• Place those that score in the bottom third in the first
category.
• Place those that score in the middle third in the
second category.
• Place those that score in the top third in the third
category.
Sample Dependent Procedures
• Quartile Split:
– Used to divide numerical data into three categories
– Determine the scores that correspond to the quartiles.
• Place those that score in the bottom 25 percent in the
low category.
• Place those that score in the top 25 percent in the
high category.
• Place those that score in the middle 50 percent in the
moderate category.
Sample Dependent Procedures
• Normal Curve Split:
– Used to divide numerical data into three categories
– Determine the scores that correspond to z-scores of one
and negative one.
• Place individuals at or below the 16th percentile into
the low category.
• Place individuals above the 84th percentile into the
high category.
• Place individuals who score above the 16th percentile
and below or equal to the 84th percentile into the
moderate category.
Response Styles
• Response Styles:
tendencies to
respond to questions
or test items in a
specific way,
regardless of the
content
Response Styles
• Willingness to Answer: the differences among
people in their style of responding to questions they
are unsure about
• Position Preference: when in doubt about answers
to multiple-choice questions, some people always
select a response in a certain position
A
B
C
D
Response Styles
• Manifest Content: the plain meaning of the words
or questions that actually appear on the page
• Yea-Sayers: people who are apt to agree with a
question regardless of its manifest content
• Nay-Sayers: people who are apt to disagree with a
question regardless of its manifest content
The Social Desirability Response Set
• Latent Content: the “hidden meaning” behind a
question
• Response Set: a tendency to answer questions
based on their latent content with the goal of
creating a certain impression of ourselves
• Some subjects tend to give the socially desirable
answer
Dealing with Response Styles and
Response Sets
• Ask participants to answer all items. Clarify that
there are no right or wrong answers.
• Simple yes/no and agree/disagree questions make
it easy for subjects to respond based on response
style. Build more specific content in the questions.
• Reverse order some of the questions/responses.
• Ask the same question multiple ways.
• Measure social desirability.