Transcript Presenting a Sample Using M&Ms

Step Into the Role of
Researcher Without
Leaving the Classroom
or Clinic
Adirondack Assistive Technology Expo
October 27, 2014
Rondalyn Whitney, PhD, OTR/L, FAOTA
Clarkson University OT Department
Acknowledgment for contributions from Sheila Braun and inspiration from Jason M. Molesky
How to think like a
researcher…
What do you want to
know?
Let it Go!
Where do
you get
‘frozen’?
Most of us
get stuck
on the
analysis….
Image from
https://www.google.com/search?rls=com.microsoft%3Aenus&rlz=1I7NDKB_enUS524&tbm=isch&q=frozen%20elsa&revid=1818079338ei
=8ZU6VJ-KFsr8yQT07oKoAQ&ved=0CCMQsyU&biw=1024&bih=640
We use analysis all the
time in practice….
How to think like a
researcher…
• What do you want to know?
• Who else has investigated
the topic?
• How will you measure it?
• How will you analyze what
you measure?
• What does it all mean?
1. Create your research
question
2. Conduct a literature review
3. Establish the methodology
4. Determine the appropriate
statistical measure for your
question and run the
analysis
5. Write the discussion – an
interpretation of what you
learned and how that
compares to the existing
literature.
Basics of Analysis
Method of analysis is predicted by your research
question: Univariate analysis
When you want to know
the number/ count of
data, often shown in the
Mean score…
Nominal data
Frequency
Results will report a count
or a percentage
Example:
Number of boys and girls
in a class.
20 boys, 40 girls
(N = 60; boys n = 20, girls n = 40)
20/60 boys (33%); 40/60 girls
(66%)
Basics of Analysis
Method of analysis is predicted by your research
question: Bivariate analysis
When you want to know if there is
any correlation between two
groups.
The group of females and girl
children have a correlation of 1,
females and boy children have a
correlation of 0. Therefore,
correlation ranges between 0 and 1.
The closer to 1, the higher the
correlation.
Correlation will give you
an r value
Example:
On average scores for difficult
child behaviors are strongly
correlated with maternal stress
(r =.821; p = .001).
Basics of Analysis
Method of analysis is predicted by your research
question: Bivariate analysis
When you want to know if
there is a difference between
two groups.
Did the intervention provided
change the speed of the
child’s performance on written
tasks?
Did the intervention provided
improve the speed of the
child’s performance on written
tasks?
A t test measures the
difference between two
groups.
One-tailed measures direction,
two tailed measures change.
Example1:
Using a one-tailed test, the
control group did not show
lower stress than the
experimental group (p=.045).
No relationship was found between high levels of
socially disruptive child behaviors and mother’s
perceived support in the community (p=.798) but both
family support (r =.267, p=.005) and feelings of
cherishment (r =.216, p=.025) showed a weak
correlation.
p value reports the t-test
r value reports the correlation
Most clinical research questions
can be answered with
univariate or bivariate analysis
Basics of Analysis
Method of analysis is predicted by your research
question: Multivariate analysis
Example:
When you want to know
how one variable changes
as a result of the other.
Negative emotion laden words
accounts for 17.6% of the total
variance in total stress (F(1, 34)
= 7.252, B = -14.894, p = .011).
Question: What is the
strongest predictor of
maternal stress when raising
a child with disruptive
behaviors?
The greater the number of
negative emotion laden
words, the lower the total
stress.
Results 3:
Multivariate
1. Negative emotion laden words
account for 22.8% of the
variance in parental distress
(not controlling for anything
else; F(1, 34) = 10.060, B = 6.948, p = .003).
2. Negative emotion laden words
also account for 17.6% of the
total variance in total stress
(F(1, 34) = 7.252, B = -14.894, p
= .011). The greater the number
of negative emotion laden
words, the lower the total stress.
3. A strong and significant
correlation was found between
Difficult Child behaviors and
maternal stress, both before (r
=.810; p= .001) and after the
intervention (r = .891; p=.001).
4. Perceived support predicts
maternal stress and Quality of
Mother-Child Relationship: as
stress increased , support
increased [community (p =. 005),
family (p = .003), and cherishment (p =
.010)]: As dysfunctional
relationship increased, so did
support[community (p = .003),
cherishment (p = .045), Family not a
significant predictor (p = .516)]
5. ASD droped out as a predictor –
cherishment and SPD held in
almost to the end but family
support highest (B = 1.98, p =
.001), difficult child and dysfunctional
mother-child relationship were
covariates.
*bivariate used in regression model
12
Final Model: Average
Total Stress
1.228
Average
ParentChild
Interaction
1.171
1.987
Average
Difficult
Child Score
100% of the
Variance in
Average
Total Stress
Family
Support
Plus a mix of
things we
don’t know
about (12.9% of
it)
13
All I need to know about
research I learned from a
bag of M&Ms…
What questions can we
ask of a bag of M&Ms
What kind of variable represents
your M&Ms most usefully?
A.Binary?
B. Continuous?
C. Categorical? (a.k.a.
“nominal”)
Huh?
Are the M&Ms binary?
•
You have a binary sample if you have things that can
be in one state or another. Binary means 2, like yes or no
o
Example: M&Ms might be eaten or not eaten.
o
We can call “eaten” a 0 and “not eaten” a 1 (which gives us a value) or leave as
a nominal variable (i.e. a name).
•
You should have a sample that are all “not eaten”
(presumably).
•
This is a CONSTANT. All the values are exactly the same
for all the M&Ms.
Continuous?
• A continuous variable can hold any value
• You have a certain number of M&Ms, so you might say
that “M&M” is a continuous variable.
• You have only 1 bag of M&Ms
• If you had 100 bags of M&Ms of different sizes you might
say the number of M&Ms into your data set for each bag
“One bag of multicolored M&Ms (N = 42)”
• Using a continuous variable, you could come up with
the “mean value” or “average” number of M&Ms found
in each bag
Is the variable “M&M”
categorical?
• You have a categorical variable if you have a
bunch of states (in the case of M&Ms the states
are colors, such as red, yellow, green brown, &
blue).
• Then you can set each CATEGORY to a number
that represents how many you have.
This is the
most useful
unit of analysis
for this study!
Break into small groups
1. separate your M&Ms into colors
2. Create an analysis of your data
3. Report on your findings when we return
as a large group
results
The sample of M&Ms, (N = 57) included a six
colors: orange, brown, green, yellow, blue and red.
Orange and brown appeared with greatest
frequency in the sample (n = 12). The sample
distribution is shown in Table 1.
M&M distribution
(example)
Blue
Orange
Brown
Red
Green
Yellow
8
12
12
6
10
9
Results
Make sure your answers (results) answer your
question.
• Did you ask how many? Results should give a count
of the total sample
• Did you ask how many of each? Results should give
a count of the sub-variables
• Did you ask about the mean differences between
two things? Then you should be comparing the
means (averages)
……Etcetera Etcetera Etcetera ….
What’s the best way to tell people
about it VISUALLY?
OR
Bar chart?
Pie chart?
Either way. BUT…
• APA 6th recommends using a pie chart to show how a
TOTAL POPULATION is broken down into categories. Here
our total population is the population of M&Ms in your bag
compared with those in the bags of your classmates.
M&Ms per Bag
150
100
50
0
Bag 1
Bag 2
Bag 3
• Use a bar chart when you are showing just a portion of the
population or a series of scalar variables.
So which is better?.
A.Pie chart
B.Bar chart
Table 1: Color distribution in Sample of M&Ms
Yellow
Green
Blue
Red
Orange
Brown
Red
Green
Brown
Yellow
Orange
Blue
0
2
4
6
8
10
12
14
12
10
Blue
8
Orange
Brown
Red
6
Green
Yellow
4
2
0
Blue
Orange
Brown
Red
Green
Yellow
T1: COLOR DISTRIBUTION
(3 GROUPS)
Grand Total
Yellow
Brown
Grand Total
G3
Blue
G2
G1
Green
Red
Orange
0
20
40
60
80
100
120
140
160
Figure 2. Trial 1 bar graph of color distribution for all groups (N = 3)
180
DISTRIBUTION (ALL
GROUPS)
Grand Total
Yellow
Brown
Grand Total
G3
Blue
G2
G1
Green
Red
Orange
0
20
40
60
80
100
120
140
Figure 6. Trial 2 bar graph of color distribution for all groups
160
180
Who cares about M&M
studies?
• Connecting the
dots to
practice….
• Nominal is
nominal is
nominal
• Categorical is
categorical is
categorical…
Image from http://giantgag.net/chocolate-is-the-answer-who-careswhat-the-question-is/
Important Academic
Verbiage
• Show and report categorical variables or binary
variables using “frequencies” of each category
(“How frequently does it occur?”).
• Show scalar variables using their “mean” and
“standard deviation.”
Example of Academic
Verbiage
• “The sample of M&Ms (N = 22) consisted of six colors: red (n =
1), brown (n = 2), green (n = 3), orange (n = 4), yellow (n = 5),
and blue (n = 7).”
• Note the use of past tense. This is correct APA language.
• Note the use of a capital N to represent the entire sample and
the use of lowercase ns to represent the individual groups.
• Note the use of italics to show that you are giving “a statistic,”
or a number that describes your sample.
Another APA Rule
• Never, ever, EVER use
BOTH words AND
pictures to discuss your
sample. Use either
words or an image.
• For instance, to use
only the image, write,
“See Figure 1 for the
breakdown of
frequencies in the
sample.”
• Do not repeat yourself.
• Do not repeat yourself!
Figure 1. Pie chart of
M&Ms.
References
Molesky, J.M. (n.d.) Everything I Ever Needed to Learn
about AP Statistics I Learned From a Bag of m&m’s:
Class Activities for Advanced Placement Statistics
http://web.mac.com/statsmonkey