RECODING INPUT VARIABLES USING SPSS ASSIGNMENT INSTRUCTIONS OVERVIEW This assignment concerns an important step in data analysis: recoding. Recoding input variables (questionnaire results) is a routine task in SPSS. Recoding input variables is often used to change the codes for categories of a particular input variable or when there are too many variable input options for one question within a questionnaire. The original research design of the questionnaire may have warranted the inclusion of too many options. Often, the researcher is curious or interested in stating all of the variable input options for a given question or interested in past research endeavors that used the same scale as input variables. When certain statistical calculations are run, the format of the input variable is non-negotiable. Most of the time, variables need to be combined when the number of particular responses for one category is too small to analyze. For example, a questionnaire could have asked respondents to give a specific age, which could be anywhere from 1100 (100 choices). The original responses (continuous data) could be recoded into a new variable (ordinal data) that reflect 35 categories (30 and younger, 3150, 5170, and 71 and older). Below is an example of recoding: Original Code New Codes 0 = never 1 = never to infrequently 1 = less than once a year 1 = never to infrequently 2 = about twice a year 1 = never to infrequently 3 = several times a year 1 = never to infrequently 4 = about once a month 2 = relatively frequently 5 = several times a month 2 = relatively frequently 6 = once day every week 2 = relatively frequently 7 = weekly 2 = relatively frequently 8 = several times a week 2 = relatively frequently 9 = no answer 9 = no answer INSTRUCTIONS To answer these questions, open up the Dell SPSS data set. If you have any questions about recoding, check out the websites below and the SPSS tutorials. 1. Recode the respondents based on total hours per week spent online into 2 groups: 5 hours or less (light users) and 610 hours (medium users). Calculate a frequency distribution. 2. Recode the respondents based on total hours per week spent online into 3 groups: 5 hours or less (light users), 610 hours (medium users), and 11 hours or more (heavy users). Calculate a frequency distribution. 3. Form a new variable that denotes the total number of things that people have ever done online based on q2_1 to q2_7. Run a frequency distribution of the new variable and interpret the results. Note that the missing values for q2_1 to q2_7 are coded as 0. 4. Recode q4 (overall satisfaction) into 2 groups: very satisfied (rating of 1) and somewhat satisfied or dissatisfied (ratings of 24). Calculate a frequency distribution of the new variable and interpret the results. To interpret the results, run a frequency distribution of the original variable and compare and contrast it with the new frequency distribution of the new variable. Analyze the difference between these 2 frequency distributions along the dimensions of mean, standard deviation, range, kurtosis, and skewness. What do these differences mean? 5. Recode q5 (would recommend) into 2 groups: definitely would recommend (rating of 1) and probably would or less likely to recommend (ratings of 25). Calculate a frequency distribution of the new variable and interpret the results. To interpret the results, run a frequency distribution of the original variable and compare and contrast it with the new frequency distribution of the new variable. Analyze the difference between these 2 frequency distributions along the dimensions of mean, standard deviation, range, kurtosis, and skewness. What do these differences mean? 6. Recode q6 (likelihood of choosing Dell) into 2 groups: definitely would choose (rating of 1) and probably would or less likely to choose (ratings of 25). Calculate a frequency distribution of the new variable and interpret the results. To interpret the results, run a frequency distribution of the original variable and compare and contrast it with the new frequency distribution of the new variable. Analyze the difference between these 2 frequency distributions along the dimensions of mean, standard deviation, range, kurtosis, and skewness. What do these differences mean? 7. Recode q9_5 per into 3 groups: definitely or probably would have purchased (ratings of 12), might or might not have purchased (rating of 3), and probably or definitely would not have purchased (ratings of 45). Calculate a frequency distribution of the new variable and interpret the results. To interpret the results, run a frequency distribution of the original variable and compare and contrast it with the new frequency distribution of the new variable. Analyze the difference between these 2 frequency distributions along the dimensions of mean, standard deviation, range, kurtosis, and skewness. What do these differences mean? 8. Recode q9_10 per into 3 groups: definitely or probably have purchased and might or might not have purchased (ratings 1 3), probably would not have purchased (rating of 4), and definitely would not have purchased (rating of 5). Calculate a frequency distribution of the new variable and interpret the results. To interpret the results, run a frequency distribution of the original variable and compare and contrast it with the new frequency distribution of the new variable. Analyze the difference between these 2 frequency distributions along the dimensions of mean, standard deviation, range, kurtosis, and skewness. What do these differences mean? 9. Recode the demographics as follows: a. Combine the 2 lowest education (q11) categories into a single category. Thus, some high school or less and high school graduate will be combined into a single category labeled high school graduate or less. b. Recode age (q12) into 4 new categories: 1829, 3039, 4049, and 50 or older. c. Combine the 2 lowest income (q13) categories into a single category labeled Under $30,000. d. Calculate frequency distributions of the new variables and interpret the results. To interpret the results, run a frequency distribution of the original variable and compare and contrast it with the new frequency distribution of the new variable. Analyze the difference between these 2 frequency distributions along the dimensions of mean, standard deviation, range, kurtosis, and skewness. What do these differences mean?