Title: Deriving analysis variables
Course: UGQuants (SSPS) > Section: Beyond data collection and analysis

Deriving analysis variables

Before beginning analysis, we must first prepare our variables. There are four key processes which we may wish to or are indeed required to undertake in deriving appropriate variables for analysis.

Firstly, it may be necessary to change, collapse or reorder the response categories for particular variables. Whilst decisions regarding response categories will have been taken at the coding stage, we may wish to refine these categories before undertaking analysis. For example, we may have collected age as a continuous variable but for the purposes of analysis wish to collapse this variable into a number of categories indicative of age groups.

Secondly, we may wish to derive new variables from existing variables. For example, we may wish to combine information from a number of variables to create a scale. Say, for instance, we are exploring happiness. We may have a number of variables measuring aspects of happiness and want to combine these variables to produce an overall measure of happiness.

Thirdly, often in survey research we are looking to compare things which are not directly comparable, and we may therefore wish to standardise particular variables to assist with comparisons. For example, we may want to compare and combine scores for variables with markedly different distributions. Alternatively we may want to compare our survey data with data from another survey but the units of measurement differ. Standardisation can be used in these circumstances to allow comparisons.

Finally, it may be necessary to address missing data. In almost all circumstances we will have some level of missing data. Before beginning analysis, we need to check if this missing data introduces bias into our sample. For example, it may be that a particular question has a high level of missing data due to refusal to answer the question amongst members of particular age group, ethnic group or social class. If this is this the case, analysis of that variable may be biased as those particular groups of people will be under-represented in our analysis. There are a range of methods which can be used to address the problem of missing data. Detailed discussion of these methods is beyond the scope of this module but such methods include the deletion of cases, deletion of variables or substituting the sample mean of a variable for cases with missing values.