1. Exploiting Rapid Change in Technology Enhanced Learning
… for Post Graduate Education
Designing Methodology 2:
First Considering Data

2. A point of view can be a dangerous luxury when substituted for insight and understanding. Marshall McLuhan, Canadian Communications Professor
He uses statistics as a drunken man uses lamp posts – for support rather than for illumination. Andrew Lang, Scottish Writer
Not everything that can be counted counts, and not everything that counts can be counted. Albert Einstein, Physicist

3. Agenda Moving past our point of view – following the trail of lit
Setting up your data quest – what to consider?
Putting pieces in place
Clearing bias?
Will it work?
Remember it is iterative!

4. Getting Started

5. Moving past our point of view
– following the trail of lit
First step read and notate the methodology of others studying similar things
Spreadsheet idea

6. Setting up our own tests
– what to consider?
comments

7. Putting Pieces Together

10. Student Example of Their Research Framework

11. Overview of Design, Collection and Analysis Process
Qualitative
Research Question
List each research question (RQ) in a separate row below.
Data Collection Tools
List which instrument(s) are used to collect the data that will address each RQ.
Datapoints Yielded
List which specific questions/varia bles/scales of the instrument will address each RQ.
Data Source
List which persons/artifact s/records will provide the data.
Data Analysis
Briefly describe the specific statistical or qualitative analyses that will address each RQ.

12. Espino, M. M. (2008). Master narratives and counter narrative: An analysis of Mexican American life stories of oppression and resistance along the journeys to the doctorate. (PhD), The University of Arizona, Arizona.
Research Question
List each research question (RQ) in a separate row below.
Data Collection Tools
List which instrument(s) are used to collect the data that will address each RQ.
Datapoints Yielded
List which specific questions/variables/scale s of the instrument will address each RQ.
Data Source
List which persons/artifacts/records will provide the data.
Data Analysis
Briefly describe the specific statistical or qualitative analyses that will address each RQ.
To what extent has racism, sexism, and or classism surfaced in the Mex/Am PhD journey?
Narrative inquiry- testimonials from people about their lives and experiences/ taken during interviews
What master narratives reproduce racism, sexism and classism?
What MA counter narratives are crafted?
Everyone contributed to both questions
Critical Race framework – actively looking for the story that was not true from a European dominant view
To what extent do the way MA’s share narratives reflect issues?
Structures or mechanisms activated differently by gender?
Structures or mechanisms activated differently by socio-economic status?
Preliminary codes: social class, racism, gender, perceptions of struggle/obstacles, social networks, external perceptions, posturing/performance, perceptions of self, resistance, parents (parental education, parental activism), siblings/extended family, catalysts, regional/geographic affiliation, college/graduate school aspirations, secondary school/college/graduate school experiences (academic tracking, public/private school, co-curricular involvement, financial aid, research opportunities, advisor relationships), peer groups, language, phenotype, and terminology.

13. Overview of Design, Collection and Analysis Process
Quantitative
Research Question
Data Collection Tools
Datapoints Yielded
Data Source
Data Analysis
List each hypothesis you have developed
List all the possible tools or describe the simulation to test it
What data will develop? Be specific as to amount /power
What groups are involved if surveying people, what tests are needed? What makes gathering data difficulty
What analysis will be needed? What makes this difficult?
Note – SEE HANDOUTS FOR MORE QUANT

14. Clearing Bias

15. Martin Rees In other words…
The most important advances, the qualitative leaps, are the least predictable. Not even the best scientists predicted the impact of nuclear physics, and everyday consumer items such as the iPhone would have seemed magic back in the 1950s.
Martin Rees
In other words…
The fun in research is the unexpected – what would you rather have – an adventure or a repetitive experience – make sure your design allows for the unexpected

16. Clearing Bias Design Bias. ... Selection/Sampling Bias. ...
It is human to want things to work out … It is deadly in research
Design Bias. ...
Selection/Sampling Bias. ...
Procedural Bias. ...
Measurement Bias. ...
Interviewer Bias. ...
Response Bias. ...
Reporting Bias.

17. Will it work?

18. Will it Fail? Does it Matter?
No harm in failure if research is well planned!
The power of negative results
Hermann Boerhaave Visiting Professor of Medicine at Leiden University, said he was surprised to see how significant his negative results had been:
Many of the biggest discoveries of my career were the results of failure of another research project. ... Failure strikes a negative tone, but it appeared in my personal history that it was an essential experience on the path to important discoveries.

19. Tools to Help
Left hand column every DN or MN site

20. Remember it is iterative!

21. Vote – 1-5 Moving past our point of view – following the trail of lit
Setting up your data quest – what to consider?
Putting pieces in place
Clearing bias?
Will it work?
Remember it is iterative!

22. What’s Up at DoctoralNet?
Phone apps for both IOS and Android available May 1st from the resources link on the mastersnet or doctoralnet page that you sign onto.
Upcoming webinars…
Critical Writing Part Two
Designing Your Methodology Series: Feedback Loops to Speed the Design Process
Transferable skills.2 Leadership in Complex Environments
Transferable skills 3: Creativity and Innovation
The Master Key for Learning: Growth Assessment
We are not alone : Self and Mentors as Growth Assessors
Writing Effective Paragraphs
For full list through end of year go to
8 week groups starting soon…
Academic Writing
Lingerers… For All But Dissertation/All But Thesis or Those who have been at this too long

23. Looking for comments –
What do our services mean to your
Graduate advancement?
1-5 a little ? A lot?
What makes that
True?
To new to tell?