Title: Choosing a method of analysis
Course: UGQuants (SSPS) > Section: Beyond data collection and analysis

Choosing a method of analysis

Once we have prepared our data we must then select our method of analysis. There are a wide range of statistical methods available to analyse survey data, and the decision as to which to choose is informed by a number of considerations.

Firstly, we need to consider, how many variables we want to analyse. If we are analysing only one variable at a time, for example income level, then we will use a univariate method of analysis. If we are analysing two variables at a time, for example ’Do income levels differ according to gender?’, we will use a bivariate method of analysis. Finally, if we want to analyse three or more variables at a time, for example ’Do income levels differ according to gender and health?’, then we will use a multivariate method of analysis.

Secondly, we need to consider the level of measurement of our variables.  Within the three broad methods of analysis, univariate, bivariate and multivariate, there are a variety of statistical techniques. A key consideration as to which to choose is the level of measurement of the variables we wish to analyse. As previously noted, variables can be measured at interval level, ordinal level or nominal level, with interval level data being the highest level of measurement. The higher the level of measurement, the greater the range of statistical techniques there are available. Not only is there a greater range of statistical techniques available to analyses of interval level data but these techniques are also more complex and powerful.

Thirdly, we need to consider, whether we are looking to use our data in a descriptive manner only or whether we are looking to generalise our findings from our sample to a wider population. Descriptive statistics are used to describe the basic features of our data and to provide summaries of our sample and measures. Inferential statistics, on the other hand, potentially allow us to make inferences about the wider population based on our sample. Very often research uses both descriptive and inferential statistics.