A presentation concerning how to balance Quantitative and Qualitative elements within the scope of your writings

1. Presented by: Nichole Hertel
EDRS 8381 Research Methods in Education II

2. Relying on Data
TOO Much in
Your Research

3.  Laughter is always the best medicine!
 Define the term “the new stupid”
 Answer the question – “What exactly
does it mean to use data or research to
inform decision-making policies?

4.  http://www.youtube.com/watch?v=vkuX0nKiAlU

5.  How much data needs to be included in a
research paper or article?

6.  What are the dangers of the data itself?

7.  But in an age when information is literately
at our fingertips, don’t we have an obligation
to provide as much information as possible
on a specific topic?

8.  Objective – To put together the puzzle found
in the envelopes

9.  “Data-based decision making”
 “Research-based practice”

10.  They can stand-in for the careful cognitive thought
process necessary to improve the system
 They can serve as dressed-up rationales for the same
ole fads, i.e. different words for the same idea
 They can be used to justify incoherent proposals to
decision-making bodies

11. What exactly does it
mean to use data or
research to inform
decisions?

12.  The Old Stupid = resistance to performance
measures
 Today over-reliance on performance measures
has lead to a dependence on a few simple
metrics
 Graduation rate
 Expenditures
 Reading and Math test scores of 3rd through
8th grade students

13.  Element #1 - Using Data in Half-baked Ways
 Element #2 – Translating Research Too
Simplistically
 Element #3 – Giving Short Shrift to
Management Data

14.  Future Superintendent - “Day one, we’re going to start
identifying those higher value-assessed teachers and moving
them to the schools that aren’t making AYP.”
 Can we be confident that teacher who are effective in t heir
current classrooms would be equally effective elsewhere?
 What effect would shifting teachers to different schools have on
the likelihood that teachers would remain in the district?
 Are the measures in question good proxies for teacher quality?
 What steps might either encourage teachers to accept
reassignment or improve recruiting for underserved schools?

15.  Truly embrace the data by asking and
answering the hard questions
 Consider organizational realities
 Contemplating unintended consequences

16.  Even policies or practices informed by rigorous
research can prove ineffective if the translation
is clumsy or ill considered
 Most vexing problem in “research-based
practice” is the failure to recognize the limits of
what even rigorous scientific research can tell us
 Decisions about governance, management and
compensation cannot be examined under
controlled conditions

17.  Data-driven system is the result of leaders
embracing student achievement data instead
of paying attention to collecting/using more
relevant data to improve the performance of
schools and districts
 Current achievement data are of limited use
for management purposes
 Student achievement measures are largely
irrelevant to judging the performance of
many school district employees

18.  Not simply indentify effective teachers or
struggling students, but should also help
render schools and school systems more
supportive of effective teaching and learning
 Require that data assessments need to be
returned quickly, rather than years after it
was first administered

19.  Key #1 – Educators should be wary of allowing
data/research to substitute for good judgment
 Key #2 – Schools must actively seek out the kind of data
they need as well as the achievement data external
stakeholders need
 Key #3 – Educators should have an understanding of the
limitations of research as well as its uses, especially
when crafting policy
 Key #4 – School systems should reward educational
leaders and administrators for pursuing more efficient
ways to deliver services rather than punish them

20.  Research and data are
powerful tools
 In this era, educators stand
to benefit enormously from
advances in research and
data systems
 Be careful of catching “data
enthusiasm” – you might
miss the bigger picture

21.  Hess, F. M. (2009). The new stupid. Educational
Leadership , 66 (4), pp. 12-17.