

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 twothirds 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., ttest, 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 builtin 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.

