The Missouri Reader Vol. 35, Issue 1 | Page 35

Data collection
The initial assignment was a blend of Professor A and Professor B’s protocols from Study 1. Students were
asked to generate three metaphors for teaching literacy or a related area (see appendix A) on the first day of class.
Students were also expected to explain how the metaphors related to teaching and/or literacy. After submitting
electronic versions to “Blackboard ©,” students shared their metaphors and explanations in class. Then during an
end of the semester meeting, students were given the same set of instructions and grading rubric from the beginning
of the semester. Students were thus given the opportunity to compose/select three new metaphors, or stick with
their original metaphors. They were also encouraged to explain how the metaphors related to teaching. The
reasoning for this was to anecdotally determine if students would spontaneously generate similar or different
metaphors and explanations at the beginning and end of the semester.
Data analysis
To analyze the data, we selected the methodology of “metaphor analysis” (Moser, 2000). This approach
involves both qualitative and quantitative methods. There are five steps: naming/labeling stage, sorting (clarification
and elimination) stage, brainstorming (deciding the unit of analysis) stage, sample metaphor compilation and
categorization stage and analyzing data stage. In the first stage, we used Microsoft Word © and created a preliminary
alphabetic list of all the metaphors supplied. In the second stage we went through the data and analyzed each
metaphor for the topic (subject such as “teaching literacy”) and vehicle (comparison term such as sunrise, crayons).
This allowed us to reduce the metaphors into analyzable parts and look for salient features and common elements.
For the third stage we used the categories developed by Professor B during study one and grouped the metaphors by
similarity. In the fourth stage, we rechecked our data and created a title for each category to validate our analysis
and interpretations. In the final stage we entered the data into the SPSS © program and calculated frequencies for
each university and compared them.
Results and Discussion Study 2
Each preservice teacher generated three metaphors at the commencement and again at the conclusion of
the semester. Therefore, we analyzed 90 metaphors from University A (45 pre, 45 post) and 60 metaphors from
University B (30 pre, 30 post). Table 1 documents the frequency counts for each university. As can be seen, the
diversity of responses at both universities reflects all 25 possible categories. For example, University A had preservice
teachers liken teaching literacy to a portal, flower, gardener, builder of a home, and a journey. Nonetheless, we can
see some influences of professor/programs by the differences in percentages preservice teachers chose.
Students attending University A chose far more metaphors focusing on “literacy” (N=55) than those
attending University B (N=19). For example, when talking about the act of reading, University A’s class mentioned
“literacy” in their metaphors 33 times and “reading” 32 times, while University B’s class never mentioned “literacy”
as an act, and only mentioned “reading” 19 times. Only a small number of students in either class chose metaphors
focused on learning to read. Nonetheless, University A’s class chose 14 metaphors focusing on learning to read while
University B’s class chose 5.
University A students chose the same metaphor across the semester with minimal change. University B
students showed more movement between their topics (change of reading/literacy decreased and teacher selfhood
increased).
From such analysis, we can conclude that program differences occur. Yet, despite the rankings of dominant
metaphors at each university, the results of our investigation documents that metaphorical themes are universal and
go beyond one university’s philosophy and structured learning for preservice teachers.

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