Chapter 7
Module 2Manipulating data

7.1
Recode

Open the file called Moviezone​.sav. Two students, Femke and Jery were asked to collect information about high school students’ evaluation of a project (“MovieZone”) aimed at the promotion of art house movies. Femke and Jery were also asked to find out whether the project had the desired effect. Hence they also went to high schools that did not have a contract with MovieZone; students from these schools were the control group. Let us now look at ways how to make the data set ready for analysis.

One of the variables measuring respondents’ attitudes toward art house movies (those are questions 25 through 28 in Femke’s and Jery’s questionnaire, which can also be found in the Materials on the website) is coded in a different way than the other three. Check this in the questionnaire. As you will see, high scores on Question 25 indicate a negative attitude while high scores on 26, 27 and 28 reveal a positive attitude. Answer the following question: (a) What would be the problem here? Why not simply leave the data like they are?

Go to TRANSFORM, RECODE, choose RECODE INTO A DIFFERENT VARIABLE. Now answer the following question: (b) What is the advantage of choosing this option over RECODE INTO SAME VARIABLE? Using instructions in Section 7.5.2, recode the variable “arthouse” so that values are comparable to the ones of the other three (filmint, visit, curious). Name this variable “arthous1”, and then save the file. When you are done, go to the second assignment.

7.1
RECODE

This is an exercise in manipulating data. Some of the “tricks” that SPSS can perform will prove to be extremely useful when you prepare your data for analysis. The first that we will practice is the RECODE procedure. Refresh your memory and read about what this procedure does in Chapter 7.

Open the file called moviezone​.sav. One of the variables measuring attitudes toward art house movies (those are questions 25 through 28) is coded in a different way than the other three. Check this in the questionnaire. As you will see, high scores on Question 25 indicate a negative attitude while high scores on 26, 27 and 28 reveal a positive attitude. Answer the following question: (a) What would be the problem here? Why not simply leave the data like they are?

Go to TRANSFORM, RECODE, choose RECODE INTO A DIFFERENT VARIABLE. (Answer the following question: (b) What is the advantage of choosing this option over RECODE INTO SAME VARIABLE?) Using instructions in section 7.5.1, recode the variable “arthouse” so that values are comparable to the ones of the other three (filmint, visit, curious). Name this variable “arthous1”. When you are done, go to the second assignment. (By the way: maybe you don’t see the variable ‘arthouse’ in the list of variables in the left hand column – because they have been entered under the name of the question in the questionnaire. To obtain the variable names, right hand click on a variable in the list and choose DISPLAY VARIABLE NAMES. Now you will see the names of the variables. Scroll down and you will find ‘arthouse’ as the fourth variable from the bottom.)

1.

Solution

If all went well, you had the following dialog box:

fig1.svg

Followed by the one below:

fig2.svg

  1. The reason why you do this is to enable easy comparison of the scores on the four questions with which the students wanted to assess attitude toward art cinema. With high scores on one question indicating a negative attitude, while high scores on the other three indicate positive attitudes, things become a little complicated. What is more, after you used the recode procedure you will be able, for instance, to compute an overall mean for all four variables, while such a mean would be meaningless if you had not first recoded the scores on question 25.

  2. In this case it was not necessary to use recode into a different variable; we might just as well have used “recode into same variable”, because the recode procedure chosen here does not cause you to lose information.