Transcript Slide 1

Quantitative Analysis of Survey Data and
Other Assessments for Non-Experts:
How to do SoTL without a statistician on retainer
Gintaras
Duda
Creighton
University
June 2, 2011
My Background
• I am a theoretical particle physicist
• Came to SoTL (PER) as a junior faculty
member
– New faculty workshop experience
• Huge roadblocks: no experience with
1.How educational research is conducted
2.Quantitative or qualitative analysis
3.Weak background in statistics
Areas of SoTL interest
• Attitude of introductory physics students
– Particularly how it affects learning
• Online discussion behavior
• Realism in physics
• Problem-Based Learning in upper division
courses
• Student note taking
• How students use the internet to learn
physics
Workshop Purpose
• As SoTL matures, publication requires
more and more rigorous measures and
evidence
– Sophisticated statistical tests
– Careful survey design and analysis
– Mixed method research
– Evidence, evidence, evidence!
• But, many of us are untrained in these
things
Who are you?
• Please share with the group:
1.Name, institution, and discipline
2.Why you picked this workshop
3.What you hope to gain
Workshop Purpose continued …
• Leave you with some simple tools to
analyze:
1. Likert scale surveys
2. Effects of instruction
3. Survey reliability and validity
• No stats class or methods courses
required
Part I. What to do with Likert Scales
• Likert scale instruments seem ubiquitous
in SoTL work
• Particularly useful in measuring students’
attitudes, feelings, opinions, dispositions,
etc.
– Can use pre-post scheme to see changes
and growth/deterioration
– Of interest in Jesuit Pedagogy (another
workshop)
Example from physics
• Attitudinal surveys
– Measure students’ changes in attitude
towards physics due to instruction
– Instruments: VASS, MPEX, C-LASS, Attitude
II, and others
• These instruments all show a similar
trend
– Students have more negative attitudes
towards physics after instruction
Example Questions from Attitude II
Instrument
1. Physics is irrelevant to my life
2. I can use physics in my everyday life
3. I will did find it difficult to understand how
physics applies in the real-world
4. I see and understand physics in
technology and the world around me
• 5 point Likert scale: Strongly agree,
weakly agree, neutral, weakly disagree,
strongly disagree
One of my Likert Scale Instruments
What do I do with Likert Scale Data?
• Two camps on analyzing Likert scale data
1.Interval Approach
2.Ordinal Approach
• Methods for data analysis differ between
the two methods
Interval Data
• Basic philosophy: differences between
responses are all equal
– i.e. Difference to a student between strongly
disagree and weakly disagree is the same as
the difference between a neutral response
and weakly agree
• Basic technique: Sum the data and do
some statistics
Ordinal Data
• Basic philosophy: Differences between
responses are not equal
– i.e. Students tend not to distinguish highly
between strongly and weakly statements
– 3 pt Likert scale more appropriate?
• Basic technique: Examine statistically
the number of students who agreed or
disagreed
Controversy over neutral response
• Good debate in the literature about the
neutral/neither agree nor disagree
response
• Some claim it’s crucial
• Some claim you should get rid of it
• Not going to discuss it here
Analyzing Ordinal Data
• One method is to reduce the problem to a
“binomial analysis”
– Lump all disagrees together, all agrees
together, and don’t worry about neutral
responses
• Visual method: Agree-disagree (Redish)
plots
Redish, J. Saul, and R. Steinberg, “Student expectations in introductory
physics,” Am. J. Phys. 66, 212–224 1998.
Agree-Disagree Plots
• Introduced by Redish et al. in their MPEX paper
- called “Redish” plots
New Agree Percentage
New Disagree Percentage
Change from pre to post must be
> 2σ to be considered significant
(at 5% probability level)
Standard Deviation
Redish, J. Saul, and R. Steinberg, Am. J. Phys. 66, 212–224
1998.
Example of an Agree-Disagree Plot
100%
Blog non-participants pre
Disagree (Favorable)
Blog non-participants post
Blog participants pre
80%
Blog participants post
60%
40%
0%
20%
40%
60%
Agree (Unfavorable)
Figure 3: A-D plot for Semester II (spring 2006) for question 7: "I will (or did) find it
difficult to understand how physics applies in the real world."
Duda, G., Garrett, K., Am. J. Phys. 76, 1054 (2008).
100%
Favorable Responses
Blog non-participants pre
Blog non-participants post
80%
Blog participants pre
Blog participants post
60%
40%
0%
20%
40%
60%
Unfavorable Responses
Figure 5a: A-D plot for Semester II (spring 2006) for an average of the "reality link"
questions.
Duda, G., Garrett, K., Am. J. Phys. 76, 1054 (2008).
Analyzing Interval Data
• Basic idea here is assign a numerical
value to each response
•
•
•
•
•
Strong Disagree = -2 (or 0)
Weakly Disagree = -1 (or 1)
Neither Agree/Nor Disagree = 0 (or 2)
Weakly Agree = 1 (or 3)
Strong Agree = 2 (or 4)
• Sum the responses then analyze using
standard statistical techniques
Simple (student) t-test
• The t-test is a simple (but robust)
statistical test
• Tests a hypothesis: Is there a difference
between two sets of data?
– Are differences statistically significant?
– 95% confidence level, i.e. only a 5%
probability the difference is due to statistical
fluctuations
Example: The “Gender Gap” in Intro
Physics
Students
Normalized Gain
Statistically
Significant?
Control Group
Males (n=82)
0.67
Females (n=63)
0.50
Experimental
Group
Males (n=50)
0.63
Females (n=96)
0.56
Is there a difference between male and female
students?
Which image is random?
Sometimes our eyes can deceive us!
And sometimes we think things are true because we’d
like them to be true …
The “Gender Gap”: FMCE Gains
Students
Normalized Gain
P-value
Control Group
Males (n=82)
0.67
< 0.05
Females (n=63)
0.50
(significant)
Males (n=50)
0.63
0.165
Females (n=96)
0.56
(not significant)
Experimental
Group
In the experimental group, there is no statistically
significant difference between the two genders.
Student’s t-test
• Assumptions:
– Each data set follows a normal distribution
• Parameters:
– One-tailed vs. two-tailed
– Types: paired, two-sample equal variance,
and a two-sample unequal variance test
• Can have different # of data points if
conducting an unpaired test
Demo
Two Sample t-test
Group A Group B
1.02
0.65
1.01
1.27
0.59
0.75
0.62
0.65
0.66
0.56
0.15
0.32
0.94
0.35
0.53
0.36
0.31
0.35
t-test output
Mean
Variance
Observations
Group A
0.801
0.057
9.000
Pooled Variance
0.054
Group B
0.428
0.051
9.000
Hypothesized Mean
Difference
0.000
df
16.000
t Stat
3.404
P(T<=t) one-tail
0.002
t Critical one-tail
2.583
P(T<=t) two-tail
0.004
t Critical two-tail
2.921
Here p < 0.05, so the null hypothesis is falsified – statistical difference
between Group A and Group B
Measuring Effects of Instruction
• Suppose you apply some educational
innovation
– Control group and experimental group
– Or pre-test and post-test
• How do you know if it’s effective?
• Say you give some sort of standard
assessment
– How big do the changes need to be to be
statistically significant?
Method #1: Use a t-test
• You can always use a t-test
• Compare scores of control vs.
experimental group
or
• Compare pre vs. post tests
– More difficult due to other variables
Method #2: Effect Size
• Effect Size (ES) is a method to quantify
how effective an educational intervention
has been relative to a control group
• Extremely useful when there is no familiar
scale to judge outcomes
A thought experiment
• Suppose we do a study to see if children
learn better in the morning or afternoon
• Morning trial: 15.2 average on assessment
• Afternoon trial: 17.9 average on assessment
• Is this a big difference? It depends on overlap!
Robert Coe: “What is an Effect Size: A guide for users”
Two distributions
If the distributions of scores looked like this, you
would think the result is quite significant
Robert Coe: “What is an Effect Size: A guide for users”
Two distributions
But if the distributions of scores looked like this
you wouldn’t be so impressed
Robert Coe: “What is an Effect Size: A guide for users”
Effect Size Continued
• The Effect Size
– Compares the difference between groups in
light of the variance of scores within each
group
ES= (mean of experimental group) – (mean of control group)
Standard Deviation
Actually quite simple to calculate
Robert Coe has great information online about ES
Robert Coe: “What is an Effect Size: A guide for users”
How to Interpret Effect Size
Effect Size
% of control
group below
mean of
experimental
group
Probability you
could guess
which group an
individual
belongs to
based on their
score
0.0
50%
0.50
0.2
58%
0.54
0.4
66%
0.58
0.6
73%
0.62
0.8
79%
0.66
1.0
84%
0.69
2.0
98%
0.84
Robert Coe: “What is an Effect Size: A guide for users”
How to Interpret Effect Size
Effect Size
How Large?
0.2
Small
0.5
Medium
0.8
Large
Equivalent to the
height
differences
between
15 and 16 yr old
girls
14 and 18 yr old
girls
13 and 18 yr old
girls
IQ differences between typical freshmen and Ph.D.s
corresponds to an effect size of 0.8
Effect Size Example
Duda, G., Garrett, K., Am. J. Phys. 76, 1054 (2008).
Making a better survey
• In my experience surveys and
assessment instruments are difficult to
write
• How do you know your instrument is
1. Reliable
2. Valid
• Are there alternatives to writing your own
instruments?
Reliability: Cronbach Alpha
• Cronbach Alpha: measure of how closely
items in a group are related
• Cronbach Alpha is often used for
instruments which are not marked right or
wrong
– Think Likert Scale
• Measures if students responses are the
same for similar types of questions
How to Cronbach Alpha
• You could calculate it by hand
• or you buy SPSS and figure out how to
use it
• or you could download an excel
spreadsheet which is programmed to do
this:
http://www.gifted.uconn.edu/siegle/research/Instrument
Reliability and validity/reliabilitycalculator2.xls
Cronbach Alpha Values
• Typically a Cronbach Alpha (α) > 0.8 is
considered good
– At this level survey is “reliable”
• However, there are exceptions:
– Different types of surveys/instruments may
have different natural levels of reliability
– Experimental instruments may be still useful
even if α~0.6
Warning! Common Mistakes with
Cronbach Alpha
• Paper: “Calculating, Interpreting, and
Reporting Cronbach’s Alpha Reliability
Coefficient for Likert-Type Scales” by
Joseph A. Gliem and Rosemary R. Gliem
• Lesson
– Use Cronbach Alpha for Likert scale surveys
– Draw conclusions based on clusters of items
– Single item reliability is generally very low
Instrument Validity
• Validity is never universal
– “Valid” for a certain population and for a
specific purpose
• Three general categories of validity:
1.Content validity
2.Predictive validity
3.Concurrent validity
Ideas for Establishing Validity
1. Establish content or face validity
– Correlate with other independent measures
such as exam scores, course grades, other
assessment instruments
2. Predictive validity
– Longitudinal studies and student tracking
are needed here
3. Concurrent validity
– Compare with other assessment
instruments or calibrate with the proper
groups
Survey/Assessment Creation Tips
• Build in measures to show reliability
– e.g. multiple questions within a survey on the
same topic (both positive and negative)
– Questions that establish that students are
taking the survey seriously
• For content driven assessments,
research student difficulties
• Beta-version: open ended questions
• Correlations can help show validity
An Example of evidence for Validity
Duda, G., Garrett, K., Am. J. Phys. 76, 1054 (2008).
Buros Institute of Mental Measurement
• “By providing professional assistance,
expertise, and information to users of
commercially published tests, the Institute
promotes meaningful and appropriate test
selection, utilization, and practice.”
http://www.unl.edu/buros/bimm
Conclusion
• Some simple statistical tests can provide
rigorous evidence of
– Student learning
– Instructional effectiveness
– Improvements in attitude
• All of these methods are extremely
effective when coupled with qualitative
methods
• Stats involved can be done with little or
no training
My SoTL advice
• Plan a throw-away semester in any SoTL
study
– “trial” period to tinker with your study design
– Flexibility to alter your study design when
you find it doesn’t work
• Involving students in SoTL work can be
very effective
• Try to publish in discipline specific
journals
• When in doubt, ask your students!
Good References
• Analysis of Likert Scales (and attitudinal
data in general) – CLASS survey
– http://www.colorado.edu/sei/class/
• Effect Size:
– “What is an Effect Size: A guide for users” by
Robert Coe (easily found by google)
– Coe also has an excel spreadsheet online to
calculate effect size
Good references
• Reliability and Validity:
– http://www.gifted.uconn.edu/siegle/research/I
nstrument%20Reliability%20and%20Validity/
Reliability.htm
– http://www.gifted.uconn.edu/siegle/research/I
nstrument%20Reliability%20and%20Validity/
validity.htm
• T-test
– Step by step video on excel:
http://www.youtube.com/watch?v=JlfLnx8sho
Good References
• The FLAG: Field-Tested Learning
Assessment Guide
– www.flaguide.org
– Contains “broadly applicable, self-contained
modula classroom assessment
techniques (CATs) and discipline-specific
tools for STEM instructors”
Good References
John Creswell’s books (and courses) have been highly recommended to
me