1. #overlyhonestmethods:
Mixed Methods step-by-step
Cat Biddle
EPSSA Methods Workshop
April 11th, 2013

2. Img source: http://runt-of-the-web.com/best-overlyhonest-methods

3. 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?

4. Data and definitions
• Data = school district mission statements
• Place = urbanicity using NCES locale codes

5. 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

6. Coding

7. 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

8. Using Nvivo in MM

9. 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

10. Using Nvivo in MM

11. 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

12. Using Nvivo in MM work

13. 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

14. 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