Determining which analytical techniques are appropriate for investigating the data is necessary before any analysis takes place. The different analytical tools and techniques available range from simple (e.g. measures of spread) to quite complex (e.g. modelling). Keep in mind that some analytical techniques are not always appropriate for all sets of data. It is important to ensure that appropriate techniques are used in order to avoid misinterpretation or misleading results. Most of these statistical measures can be calculated automatically in spreadsheets.
The different analytical techniques can be broadly broken down into summary statistical measures and graphical analysis, however these are often used in combination.
*Graphical analysis *
Graphical analysis is a useful way to gain an instant picture of the distribution of the data and identifying any relationships in the data that require further investigation. Patterns in data can be more easily discernible when displayed in graphs. A range of graphical techniques can be used to present data in a pictorial format. For example, column graphs, row graphs, dot graphs and line graphs.
One way of summarising data is to produce a frequency distribution table or graph. A frequency table is a grouping of data into categories showing the number of observations in each category. These categories are referred to as classes. Once the class frequencies have been produced, the distribution can be represented graphically by column, row, dot or line graph. It may also be appropriate to plot relative frequencies to show the percentage of the population within each class interval – which enables the different sizes to be directly compared.
*Summary statistical measures *
Calculating summary statistics will assist you to understand the distribution of the data. These summary measures are useful for comparing information and are more precise than graphical analysis. Summary statistics assist you to develop an understanding of:
• the centre of a set of data. This is important as we often want to know what the central value is for the sample or population. The mean, median and mode are useful measures of central location. However, these measures of location can’t tell the whole story about the distribution of the data. It is possible for two data sets to have the same mean but vastly different distributions. Therefore, you should also analyse the amount of variability in the data
• the variability or the spread of the data. The range, inter-quartile range, standard deviation, and variance are useful measures of variability or the spread of the data.
There are also a range of analytical techniques that can enable you to gain a deeper understanding of the data. This can involve analysing the data to determine change over time; comparison between groups; comparing like with like; and relationships between variables. Modelling techniques such as linear regression, logistic regression, and time series analysis are some ways to explore these relationships. Assistance can be sought from experienced analysts when undertaking complex statistical analysis. |