Analyse Data

Analysing data to summarise it and look for patterns is an important part of every evaluation. The options for doing this have been grouped into two categories - quantitative data (number) and qualitative data (text, images).

Options

Numeric analysis

Analysing numeric data such as cost, frequency, physical characteristics.

  • Correlation: a statistical measure ranging from +1.0 to -1.0 that indicates how strongly two or more variables are related. A positive correlation (+1.0 to 0) indicates that two variables will either increase or decrease together, while a negative correlation (0 to -1.0) indicates that as one variable increases, the other will decrease.
  • Crosstabulations: using contingency tables of two or more dimensions to indicate the relationship between nominal (categorical) variables. In a simple crosstabulation, one variable occupies the horizontal axis and another the vertical. The frequencies of each are added in the intersecting squares and displayed as percentages of the whole, illustrating relationships in the data. 
  • Data mining: computer-driven automated techniques that run through large amounts of text or data to find new patterns and information.
  • Exploratory Techniques: taking a ‘first look’ at a dataset by summarising its main characteristics, often by using visual methods.
  • Frequency tables: a visual way of summarizing nominal and ordinal data by displaying the count of observations (times a value of a variable occurred) in a table.
  • Measures of central tendency: a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution. The mean (the average value), median (the middle value) and mode (the most frequent value) are all measures of central tendency. Each measure is useful for different conditions.
  • Measures of dispersion: a summary measure that provides information about how much variation there is in the data, including the range, inter-quartile range and the standard deviation.
  • Multivariate descriptive: providing simple summaries of (large amounts of) information (or data) with two or more related variables.
    • Multiple regression
    • Factor analysis
    • Cluster analysis
    • Structural equation modelling
  • Non-Parametric inferential statistics: methods for inferring conclusions about a population from a sample's data that are flexible and do not follow a normal distribution (ie, the distribution does not parallel a bell curve), including ranking: the chi-square test, binomial test and Spearman's rank correlation coefficient.  
  • Parametric inferential statistics: methods for inferring conclusions about a population from a sample's data that follows certain parameters: the data will be normal (ie, the distribution parallels the bell curve); numbers can be added, subtracted, multiplied and divided; variances are equal when comparing two or more groups; and the sample should be large and randomly selected. 
  • Summary statistics: providing a quick summary of data which is particularly useful for comparing one project to another, before and after.
  • Time series analysis: observing well-defined data items obtained through repeated measurements over time.

Textual analysis

Analysing words, either spoken or written, including questionnaire responses, interviews, and documents. 

  • Content analysis: reducing large amounts of unstructured textual content into manageable data relevant to the (evaluation) research questions.
  • Thematic coding: recording or identifying passages of text or images that are linked by a common theme or idea allowing the indexation of text into categories.
  • Framework matrices: a method for summarising and analysing qualitative data in a two-by-two matrix table. It allows for sorting data across case and by theme. 
  • Timelines and time-ordered matrices: aids analysis by allowing for visualisation of key events, sequences and results.

Resources

Websites

WISE: Web Interface for Statistics Education: This website organises a large amount of statistics resources into one central place. It is also home to a series of interactive, sequenced tutorials on key statistical concepts. On WISE, you can find WISE tutorials, WISE applets, excel downloads, teaching papers, quick guides, and publications.

Tools

For an overview of specialist tools for qualitative data analysis, see the CAQDAS site at the University of Surrey which compares ten packages including Atlas.Ti, HyperResearch and NVivo.

 

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