To set variables in SPSS, as of questions in a survey, it is not always easy. Although some are more difficult than others, if we are beginners in SPSS, Â we tend to complicate or to devalue the correct completion of SPSS sheet.
Take for example the following cover page of a survey:
1. Age: _________2. Gender:
 Male
 Female
3. Marriage status:
 Single
 Married or living together
 Divorced or separated
 Widower
4. Educational background:
 Elementary school
 Middle School
 High school
 College
5. Family income (month):
 Up tp 500â‚¬
 501â‚¬ e 1000â‚¬
 1001â‚¬ e 2000â‚¬
 2001â‚¬ e 3000
 3001â‚¬ e 4000â‚¬
 4001â‚¬ e 4500â‚¬
 More than 4500â‚¬
The previous questions in a survey, which are used to describe the sample of a study, are called sociodemographic variables. This variables will assume the following types and measures in SPSS.
Question 
Type 
SPSS 

Â 
Â 
Type 
Measure 
Age 
Â Quantitative 
Numeric 
Scale 
Gender 
Qualitative 
Numeric 
Nominal 
Marital status 
Qualitative 
Numeric 
Nominal 
Educational background 
Qualitative 
Numeric 
Ordinal 
Family income (month) 
Qualitative 
Numeric 
Ordinal 
Variables can be qualitative or quantitative:
 Qualitative variables show a quality, present or absent, and each category is mutually exclusive and exhaustive. That is, if an individual belongs to a category he can’t belong to any other. The reason is thatÂ one of the categories comprehensively qualifies that individual. This type of variable can be in a nominal or ordinal scale.
An example of a nominal variable is gender: either you are male or female (the categories are mutually exclusive) and one of the categories comprehensively qualifies the individual. Numbers may be used to identify the categories of a measure. For this reason the variable takes a numeric type (Type = numeric): “0” for male and “1” for female.Â As for marital status, categories can have the following values: “1” = single; “2” = married; “3” = divorced; “4” = widower. The use of numbers is an easier way to enter data in SPSS, saving time and effort.
For ordinal variables, besides being mutually exclusive and exhaustive , they showÂ an order of magnitude. The variable educational background is a good example, with 4 categories. It can assume the following values: “1” = Elementary school; “2” = Middle School; “3” = High school; “4” = professional course; “5” = College. We know that an individual on category 2 presents a higher order than other category 1. ThisÂ does not mean, however, that the value 2 is twice the value of 1; nor does it mean that the difference between categories 2 and 3 is the same as the difference between categories 1 and 2.
 Quantitative variables have the the same properties as qualitative variables do and additionally allow to measure the difference between values. That is, the difference between 8 and 10 is the same as the difference between values 100 and102. This difference is equal at any point of the scale. These variables can be presented in an interval or ratio scale. In either case, they are considered a scale measure and values are allways numeric.
An example of an interval variable is temperature in Celsius. A difference of five values is equal at any point of the scale, either between 16 and 21, or between 36 and 41. Although a zero value can exist , this does not mean the absence of heat and, as such, is not an absolute zero. In temperature, zero is an arbitrary value corresponding to the freezing point of water.
Ratio variables have an absolute zero, in addition to all the properties of an interval variable. This is true for income, where a zero corresponds to an absence of income.