Data Analysis Overview
Once the data are collected, consolidated, and tallied, the next step is to make sense of it all. Generally both quantitative and qualitative analyses should be performed.
Quantitative analysis uses computational procedures and statistical tests to examine the data that is quantifiable and can be expressed in numerical terms. Therefore a quantitative analysis might include, for example, comparing frequencies of occurrences.
Qualitative analysis reviews the non-quantified, narrative information and analyzes the data using content, thematic, or cognitive analysis approaches.
Both of these types of analyses help you reach conclusions about the whether the effort has achieved its aim or goal.
The analysis of data will include interpreting and contextualizing the data, and suggesting conclusions. The analysis may be a simple mathematical comparison or a more sophisticated statistical approach using complex specialized computer software.
The evaluation plan will have already specified the methods of analysis to be used for the outcome evaluation. Generally both quantitative and qualitative analyses are used.
Various methods can make qualitative analysis more systematic, including:
- Categorization and coding – developing categories of themes and coding responses accordingly.
- Cross-validation – analyzing collected responses by more than one analyst to determine whether they perceive the information similarly and draw similar conclusions.
The result of data analysis is a set of interpretations of the data. From these interpretations, conclusions can be drawn about the effectiveness of the strategic approach. This provides the foundation for making decisions about what processes to continue, change, and eliminate, and what direction to take in the future.
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