Title: Variables
Course: UGQuants (SSPS) > Section: Introduction to Quantitative Analysis

Variables

If we can understand cases as the things that we have data for or about, then variables represent the kind of data we have or want to have for these cases. Just as the nature of our cases tells us something about our research aims and interests, so too does the nature of our variables. Similarly to the case, the concept of the variable is not unique to quantitative research approaches. For example, if we are collecting qualitative data using interviews, then our interview schedule or guide will provide an indication of the kind of data we want to collect from each case. E.g. if we are interviewing the parents of young children then we might be interested in the principles they apply to childcare; how they share and negotiate childcare with their partner; how their children interact with siblings and other children, etc. However, while such dimensions might be understood as variables, in a more qualitative research approach such ‘variables’ need not always be key elements in the analysis of the data and writing up of findings.

Variables in quantitative analysis

- Variables are very specifically defined pieces of information or characteristics of our cases. As the name suggests, these characteristics must have the capacity to ‘vary’ between cases. If this is not so, then we speak of constants: e.g. if we are dealing with data from the Scottish Social Attitudes Survey, then country of residence is a constant: Scotland. The notion of a constant is also important to some types of quantitative analysis.

What would the constant be in the data below?

(Highlight the following text in this line to see the answer. The constant is the gender of the respondents - all of them are male.)

- When we are dealing with the kind of micro data that typically result from individual people’s responses to specific questions, often the variable simply corresponds to the question. E.g. questions such as ‘How old were you on your last birthday?’, ‘Which party did you vote for in the last election?’ or ‘What is your job title?’ would correspond to variables for age, vote and occupation respectively.

- Some variables do not directly correspond to the kind of concepts we introduced in section 1.2. E.g. someone’s occupation gives us useful information about their social class but many such concepts are multidimensional and hence might be operationalised via more than one variable: e.g. for social class as well as job title we might want to know about someone’s employer, whether they manage or supervise other people, their income etc.

- Note that the above examples are ‘objective’ characteristics that we could, at least in theory, check for factual accuracy. But often variables also relate to more ‘subjective’ attitudes or opinions. E.g. ‘Should Scotland be an independent country?’, ‘Are benefits for unemployed people too low or too high?’, ‘Should gay and lesbian couples be able to marry if they want to?’.

- When we are dealing with macro or aggregate data that are typically derived from multiple sources, some examples of variables might be a country’s gross domestic product, the life expectancy of its people or the percentage of adults who are literate. While these types of variables fall into the ‘objective’ category, when dealing with aggregate data of this nature it is wise to give some consideration to the sources and likely reliability of the data.

- While it is possible that we might be interested in just one variable across  a number of cases (e.g. what party do people intend to vote for in a forthcoming election), typically we are interested in multiple variables, and some data sources may include hundreds of variables.