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Data analysis refers to the use of descriptive and inferential statistical techniques to transform raw data into information that is easier to manipulate, understand, and report. Ultimately, the purpose of analysis is to understand what is happening with your program or what is changing as a result of those efforts.

To ensure that the conclusions of your study are sound, the analytical approach you choose must be carefully matched to the evaluation questions, the study design, and the data collected. In addition, the analysis should identify and address any potentially confounding variables that could affect the interpretation of the study findings. These two aspects of research quality—appropriateness of analysis and potentially confounding variables—are both among the six Quality of Research criteria used by NREPP.

The care and skill with which data analysis is conducted has a major impact on the quality of your overall evaluation effort. For this reason, if you (or others in your agency) do not possess strong expertise in data analysis, it is often worth hiring experts who can provide a high level of technical competence for this part of the evaluation.

Next, we consider some of the common analytical techniques used in data analysis.

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