Variable Transformations
Examine the basic statistics and distribution of the variable Year of birth
(yrbrn). Does the distribution make sense? In case you notice
any clear mistakes or irregularities, code them into missing values
(Transform/Recode/Into Different Variables). Save the command
into the Syntax Window using the “Paste” button. Inspect the basic
statistics of the new variable and check that the distribution looks
ok. Write a short interpretation on the basic statistics.
It’s useful to transform the variable “Year of Birth” into an
age variable. This is done by substracting the variable in question
from the year when the material was collected (in this case, 2008)
(Transform/Compute).
Categorizing Continuous Variables
Examine the frequency distribution of the new age variable. To what
kind of age categories should the variable be divided? Make a
categorized age variable (Transform/Recode/Into Different
Variables) and paste the command into the Syntax Window. Remember
to enter value labels for the new variable.
Reverse Coding a Likert Scale Variable
A common reason for recoding a variable is to reverse code a 5-point
Likert scale. Often some questions in a survey are reverse worded, so
the scale needs to be reversed before the point values can be compared
or used in a “summated” scale.
Pick one variable, for example “Nowadays customers and consumers are in a
better position to protect their interests” (question E22, variable name
‘cmprcti’), and reverse code it so that 5 becomes “agree strongly” and 1
becomes “disagrees strongly”. After reverse coding it, you might compare it
to questions E20 and E21.
Remember to paste the command into the Syntax Window. By modifying the
syntax, you can easily reverse code multiple variables. Remember to
rename the value lables of the resulting variable.
Alternatively, you can paste this into your Syntax Window:
RECODE oldvar (1=5) (2=4) (3=3) (4=2) (5=1) (MISSING=SYSMIS) INTO reversed_var . EXECUTE .
- Recoding Variables in SPSS
- Entering value labels for a variable: Help/Tutorial/Using the Data
Editor/Defining Data/Adding Value Labels for Numeric Variables
Creating a Summated Scale
A summated scale is built from individual items that are supposed
to describe the same phenomenon. Check if any of the variables chosen
for the summated scale need to be reversed (see previous exercise).
Before building the summated scale, one should check that the items
chosen correlate positively
(Analyze/Correlate/Bivariate). What do the correlations look
like? (We return to the question of interpreting correlations on Day 4.)
What is the Cronbach’s Alpha for the planned summated
scale (Analyze/Scale/Reliability analysis)? Should one of the
items be removed (Alpha if item deleted) so that the alpha
would be higher? Would the removal make sense substance-wise, or
weaken the validity of the scale? Write down your interpretations.
Compute the summated scale (Transform/Compute) with the
following formula: (x1+x2+x3)/3, where x1, x2 and x3 stand for
variable names, and the sum is divided by the number of variables. You
may also enter the equation directly into the Syntax Window if you
wish:
COMPUTE newvar = ( var1 + var2 + var3 ) / 3 . EXECUTE .
- General Issues in Scaling
- Likert Scaling
- Reliability and Validity (Oxford Dictionary of Sociology)
- Cronbach’s Alpha (Oxford Dictionary of Psychology)
- Interpreting Cronbach’s Alpha
Variable Transformation II
Conduct a variable transformation, where you’ll compute the amount of time
people spend reading newspapers on issues other than politics and current
affairs. Use variables “Newspaper reading, total time on average weekday”
(nwsptot) and “Newspaper reading, politics/current affairs on average weekday”
(nwsppol). Remember to enter value labels for the new variable.
Comparing Different Formulas for Building a Summated Scale
We can apply different ways to build a summated scale depending on
the situation and the amount of missing values. Compare scales built
using the following formulas, when some of the variables contain
missing values: (x1+x2+x3)/3, sum(x1,x2,x3)/3, mean(x1,x2,x3). Write
down your interpretations.