epssa methods workshop overly honest mixed methods - 4-11-13
TRANSCRIPT
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#overlyhonestmethods:Mixed Methods step-by-step
Cat BiddleEPSSA Methods Workshop
April 11th, 2013
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Img source: http://runt-of-the-web.com/best-overly-honest-methods
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The project
What role does place play in defining school purpose?
• Tradition of local control of schooling in America • Neo-institutional theory suggests normative,
coercive and mimetic forces may play a role in shaping school purpose
• Schools are local/national institutions – so, who/what determines school purpose?
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Data and definitions
• Data = school district mission statements
• Place = urbanicity using NCES locale codes
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Method selection
• Content analysis of district mission statements-Data must be durable in nature-used to understand specified characteristic of messages
• Emergent coding structure: – Randomly selected 25 mission statements and coded
them by hand, compared our coding and developed initial coding structure
– Coded 50 mission statements by hand using initial coding schematic, reconciled any differences through more discussion of what codes signified
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Coding
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Using Nvivo in MM
Step 1: Import spreadsheet
-External Data Dataset
-Differentiate between codable and classifying fields in your spreadsheet (this creates attributes)
-Create nodes for each “case” in your spreadsheet (this is just a good idea in any project) through
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Using Nvivo in MM
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Using Nvivo in MM
Step 2: Code the data and reconcile coding divergence-Merge Kai’s coding file with my version
-Compare Kai’s and my coding by using the “Show coding stripes” function (and selecting “users”)
-View Coding Stripes “Selected Items” Users select desired codes
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Using Nvivo in MM
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Using Nvivo in MM
• Matrix queries allowed us to see preliminary results that led us to pursue more analysis
• Exported a spreadsheet of a Matrix Query of all districts for all codes (with presence of code = 1 and absence of code = 0)
• Save query right click on it and “Export list” to Excel
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Using Nvivo in MM work
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Method Selection
• Chi-square tests of bivariate association – By urbanicity
• Binary logistic regression using CCD– Coded themes as dichotomous outcome variables– Variables included students eligible for
free/reduced price lunch, PSSA score of advanced on reading and math, % non-white students*, district drop out rate*, and student-teacher ratio
*log-transformed to correct for non-normal distribution
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Responding to reviewers
• Questions about the unit of analysis led us to collect additional data
• Randomly selected – 75 elementary schools– 75 middle schools – 75 high schools
Compared these mission statements (if they existed) to the district mission statement
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