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: • 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 BCD
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.