This information was then examined and sorted into comprehensive highlights of
the intended areas of interest for the researcher. These units of information served as the
basis for defining categories during the analysis process (Lincoln and Guba, 1987). From
that point, the data was consolidated and reduced into categories or themes. The goal of
data analysis, according to Taylor and Bogdan (1984) is to “come up with reasonable
conclusions and generalizations based on a preponderance of the data” (p. 139). Devising
clusters of related information allows for the researcher to conceptualize the data in order
to achieve insights regarding the case. Miles and Huberman (1984) advocated that
subsuming particular instances within the study into a general context is another tactic of
how to analyze the data. Therefore, descriptive details of student or teacher behavior or
responses were fitted into a more generalized context. For instance, if a student puts his
head down in class, the researcher may deduce that disengagement has occurred during
the instruction. It may be possible for a teacher in the interview to roll their eyes that
would suggest their distaste regarding a particular subject.
Upon transcription of the interviews, data was categorized using open coding.
This means taking all data and developing a smaller number of themes that shed light on
each research question (Creswell, 2007). When the data from interviews, observations
and document reviews were analyzed and coded into categories, patterns and themes