## 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 .

## 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.