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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

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changing numerical variables to categorical variables
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
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:
      • Median Split
      • Tertiary Split
      • Quartile Split
      • Normal Curve Split
sample dependent procedures
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 procedures1
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 procedures2
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 procedures3
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
  • Response Styles: tendencies to respond to questions or test items in a specific way, regardless of the content
response styles1
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 BCD

response styles2
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
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
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.