Iowa Support System for Schools and Districts in Need of Assistance
Data Analysis Methods
Assessing Impact: Evaluating Staff Development, pp 104-105
Data can be analyzed in several ways. Weiss (1998) provides a comprehensive list of these
analysis techniques and they are adapted below.
Describing: Narrative description of the program; descriptive statistics such as a mean,
median, mode, and range, e.g., description of the types of staff development provided.
Counting: The numerical description of the data; allows the evaluator to compare the
program to some standard or other programs, e.g., the number of students who achieved
proficiency on the state test.
Factoring: In the algebraic sense, breaking down aggregates into their parts; e.g., the factors
contributing to student academic success such as attendance, previous academic work; parents’
involvement in school activities.
Clustering: Putting things together by forming classes, categories, or groups based on some
common feature; e.g., students whose reading level has increase more than or less than one grade
level.
Comparing: Examining the similarities and differences in the features of the participants
before, during, and/or after the program; e.g., students’ ability to complete a classroom
performance task in geography before their teachers participated in the staff development
program measured against their ability after their teachers participated in staff development.
Seeking Trends/Patterns: Identifying recurring patterns, trends, or commonalities; e.g.,
students’ use of the language of science to describe their actions in the lab activity.
Examining Outliers: Looking at the situations at the extreme ends of the data set to
determine what, if any, information can be learned that does not appear in the data tending
toward the mean.
Finding Co-variation: Noting the pattern where changes in one feature occur in tandem
with changes in another feature; e.g., teachers’ increase use of journal writing in social studies
and math and students’ increased proficiency on the school-wide writing sample in language arts.
Eliminating Rival Explanations: Using the data to rule out other plausible explanations
for the changes observed; e.g., students’ improved performance in history based increased
attendance rather than new instructional strategies used by teachers.
Modeling: Depicting how a program works with a graphic display, including relationship,
sequence, and importance of the program’s components or features.
Evaluation Phase: Data Analysis Methods
©2007
Evaluation - 166