#overlyhonestmethods:Mixed Methods step-by-step
Cat BiddleEPSSA Methods Workshop
April 11th, 2013
Img source: http://runt-of-the-web.com/best-overly-honest-methods
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?
Data and definitions
• Data = school district mission statements
• Place = urbanicity using NCES locale codes
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
Coding
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
Using Nvivo in MM
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
Using Nvivo in MM
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
Using Nvivo in MM work
Scooped!
• Stemler, Bebell and Sonnabend (2011) EAQ piece is a MM piece looking at high school mission statements (including looking at them by urbanicity, amongst some other factors).
• BUT, their findings were really different from our initial findings.
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
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