Data analysis and interpretation is the process of assigning meaning to the collected information and determining the conclusions, significance, and implications of the findings. The steps involved in data analysis are a function of the type of information collected, however, returning to the purpose of the assessment and the assessment questions will provide a structure for the organization of the data and a focus for the analysis.

The analysis of *NUMERICAL (QUANTITATIVE) DATA* is represented in mathematical terms. The most common statistical terms include:

- Mean – The mean score represents a numerical average for a set of responses.
- Standard deviation – The standard deviation represents the distribution of the responses around the mean. It indicates the degree of consistency among the responses. The standard deviation, in conjunction with the mean, provides a better understanding of the data. For example, if the mean is 3.3 with a standard deviation (StD) of 0.4, then two-thirds of the responses lie between 2.9 (3.3 – 0.4) and 3.7 (3.3 + 0.4).
- Frequency distribution – Frequency distribution indicates the frequency of each response. For example, if respondents answer a question using an agree/disagree scale, the percentage of respondents who selected each response on the scale would be indicated. The frequency distribution provides additional information beyond the mean, since it allows for examining the level of consensus among the data.

Higher levels of statistical analysis (e.g., t-test, factor analysis, regression, ANOVA) can be conducted on the data, but these are not frequently used in most program/project assessments.

The analysis of *NARRATIVE (QUALITATIVE) DATA* is conducted by organizing the data into common themes or categories. It is often more difficult to interpret narrative data since it lacks the built-in structure found in numerical data. Initially, the narrative data appears to be a collection of random, unconnected statements. The assessment purpose and questions can help direct the focus of the data organization. The following strategies may also be helpful when analyzing narrative data.

Focus groups and Interviews:

- Read and organize the data from each question separately. This approach permits focusing on one question at a time (e.g., experiences with tutoring services, characteristics of tutor, student responsibility in the tutoring process).
- Group the comments by themes, topics, or categories. This approach allows for focusing on one area at a time (e.g., characteristics of tutor – level of preparation, knowledge of content area, availability).

Documents

- Code content and characteristics of documents into various categories (e.g., training manual – policies and procedures, communication, responsibilities).

Observations

- Code patterns from the focus of the observation (e.g., behavioral patterns – amount of time engaged/not engaged in activity, type of engagement, communication, interpersonal skills).

The analysis of the data via statistical measures and/or narrative themes should provide answers to the assessment questions. Interpreting the analyzed data from the appropriate perspective allows for determination of the significance and implications of the assessment.