About CORE: The Culture of Research and Education (C.O.R.E.) webinar series is spearheaded by Dr. Bernice B. Rumala, CORE Chair & Program Director of the Ph.D. in Health Sciences program in collaboration with leaders and faculty across all academic programs. This innovative and wide-ranging series is designed to provide continuing education, skills-building techniques, and tools for academic and professional development. These sessions will provide a unique chance to build your professional development toolkit through presentations, discussions, and workshops with Trident’s world-class faculty. For further information about CORE or to present, you may contact Dr. Bernice B. Rumala at Bernice.rumala@trident.edu

1. May
the
“Power”
(Statistical)
Be
with
You!
Dr.
Mickey
Shachar
C.O.R.E.
Webinar
Series
13
October,
2015

2. Power
Analysis
Topics
Error
Types Effect
Size Power
Analysis
Hypothesis
Testing
2

3. ¡ RQ: Is
there
a
statistical
significant
difference
in
students’
academic
performance
in
Math
between
the
classes
of
Dr.
Adam
and
Dr.
Eve?
¡ Hnull: There
is
no
statistical
significant
difference
in
students’
academic
performance
in
Math
between
the
classes
of
Dr.
Adam.
and
Dr.
Eve.
You
are
the
Dean and
receive
the
following
report:
3

4. ¡ Report:An
Independent
Samples
T
Test
was
run
to
compare
the
means
of
a
Math
test
between
Dr.
Eve:
M
=
90.96
(12.60)
and
Dr.
Adam:
M
=
89.32
(15.38),
yielding
a
statistical
significant
difference
with
t(1358)
=
2.164
p
=
.031
.
Hence
we
reject
Hnull and
conclude
that
the
Dr.
Eve’s
students
outperformed
Dr.
Adam’s
students.
¡ What
should
the
Dean do
based
on
these
accuratetrue
results?
§ A: CritiqueDr.
Adam
on
his
students’
low
performance
and
set
a
deadline
and
minimal
score
for
him
to
meet.
§ B:
Promote
Dr.
Eve
and
let
Dr.
Adam
eat
his
heart
out.
§ C: Results
are
subject
to
chance due
to
small
sample
size,
and
we
need
to
rerun
study
with
a
larger
sample.
§ D: Attend
Dr.
Shachar’s C.O.R.E.
Power Webinar
4

5. 5
q Problems with
Hypothesis
Significant
Testing
-­‐ based
on
p
values
are:
q The
p-­‐value depends
essentially
on
two
things:
the
size
of
the
effect
and the
size
of
the
sample.
One
would
get
a
‘significant’
result
either
if
the
effect
were
very
big
(despite
having
only
a
small
sample)
or
if
the
sample
were
very
big
(even
if
the
actual
effect
size
were
tiny).
q We
are
looking
at
“StatisticalSignificance”
and
not
at
“Practical Significance”.

6. ¡ If
only
the
null
hypothesis
is
available
and
is
rejected,
at
most the
conclusion
is
that
“the
difference
is
not
zero”
¡ When
the
President
asks
the
Five-­‐Star
General
to
estimate
the
war
casualty,
can
he
give
“not
zero”
as
a
satisfactory
answer?!
6

7. ¡ We
should
be
concerned
with
not
only
whether
a
null
hypothesis
is
false
or
not,
but
also
how
false
it
is.
¡ In
other
words,
if
the
difference
is
not
zero,
how
large the
difference
one
should
expect?
¡ The
larger
the
effect
size
(the
difference
between
the
Hnull
and
Halt Means)
is,
the
greater
the power of a test is. 7

8. A-­‐Priori-­‐ It
allows
you
to
decide,
in
the
process
of
designingan
experiment/study:
¡ How
large
a
sample
is
needed
to
enable
statistical
judgments
that
are
accurate
and
reliable,
and
¡ How
likely
your
statistical
test
will
be
able
to
detect effects
of
a
given
size
in
a
particular
situation.
¡ Without
these
calculations,
sample
size
may
be
too
high
or
too
low.
§ If
sample
size
is
too
low,
the
experiment
will
lack
the
precision.
§ If
sample
size
is
too
large,
time
and
resources
will
be
wasted.
Post-­‐Hoc
-­‐ It
allows
you
to
decide,
after study
was
executed:
¡ Whether
the
study
attained
an
acceptable
power,
and
¡ Whether
the
results
have
a
practical
significance.
APA
-­‐ Publication
Requirements:
¡ All
study
publications
should
report
in
addition
to
p
values,
the
effect
sizes
(ES) and
their
Confidence
Interval
(CI). 8

9. Power Analysis
Topics
Error
Types
Type
I
=
alpha
Type
II
=
beta
Power
=
1-­‐ beta
Effect
Size Power
Analysis
9

10. ¡ The
null
hypothesis
is
either
true
or
false
¡ The
null
hypothesis
is
either
rejected or
not
rejected.
¡ Only
4
possible
things
can
happen:
State
of
the
World
H0
State
of
the
World
H1
Our
Decision
H0
Correct
Acceptance Type
II
Error
(beta)
Our
Decision
H1
Type
I
Error
(alpha) Correct
Rejection
10

11. Common
acceptance
in
the
social
sciences:
¡ Type
I
error
-­‐ alpha, must
be
kept
at
or
below
.05
¡ Type
II
error
-­‐ beta, must
be
kept
low as
well.
¡ "Statistical
power," which
is
equal
to
1
-­‐ beta,
must
be
kept
correspondingly
high.
¡ Ideally,
power
should
be
at
least
.80 to
detect
a
reasonable
departure
from
the
null
hypothesis. 11

12. Power
Analysis
Topics
Error
Types Effect Size
ES
Indices Cohen’s
Conventions
Power
Analysis
12

13. ¡ Effect
size
(ES)
is
a
name
given
to
a
family
of
indices that
measure
the
magnitude
of
a
treatment
effect
(Becker,
2000).
§ Unlike
significance
tests,
these
indices
are
independent
of
sample
size.
¡ There
is
a
wide
array
of
formulas
used
to
measure
ES:
§ as
the
standardized
difference
between
two
means ‘d’
or
‘g’
§ as
the
correlation between
the
independent
variable
(IV)
classification
and
the
individual
scores
on
the
dependent
variable
(DV)
‘r’.
§ Others:
OR,
HR,
RR,
etc. 13

14. The
simplest
form,
effect
size,
as
denoted
by
the
symbol
‘d’
is
the
mean
difference
between
groups
in
standard
score
form
i.e.,
the
ratio
of
the
difference
between
the
means
to
the
standard
deviation.
14

15. Conventions Standardized
Difference
of
Means
‘d’
Correlation
‘r’
‘Small’ 0.2 0.1
‘Medium’ 0.5 0.3
‘Large’ 0.8 0.5
15

16. PowerAnalysis
Topics
Error Types Effect Size PowerAnalysis
a-priori post-hoc
16

17. The
factors
influencing
power
in
a
statistical
test:
¡ What
kind of
statistical
test
is
being
performed.
§ You
will
need
to
calculate
a
different
effect
size
per
test
type!!!
¡ Sample
size.
In
general,
the
larger
the
sample
size,
the
larger
the
power.
¡ The
size
of
experimental
effects.
If
the
null
hypothesis
is
wrong
by
a
substantial
amount,
power
will
be
higher
than
if
it
is
wrong
by
a
small
amount.
¡ The
level
of
error
in
experimental
measurements.
anything
that
enhances
the
accuracy
and
consistency
of
measurement
can
increase
statistical
power.
17

18. ¡ To
ensure
a
statistical
test
will
have
adequate
power,
one
usually
must
perform
special
analyses
prior
to
running
the
experiment,
to
calculate
how
large
an
N is
required.
¡ The
question
is,
"How
large
an
N is
necessary
to
produce
a
power that
is
reasonably
high"
in
this
situation,
while
maintaining
alpha at
a
reasonably
low
value
. 18

19. To
determine
the
sample
size
needed,
we
play
with
four factors
(in
red
below):
1. Obtain
“ES”
-­‐ where
do
we
find
it?
1. Lit
review
2. Pilot
3. An
“educated
conjecture”
2. Define
alpha <=.05
3. Define
power (1-­‐beta)
.80
4. Calculate
sample
size
(by
stat
calculator)
see example
19

20. To
determine
the
sample
size
needed,
we
play
with
four factors
(in
red
below):
1. Obtain
“ES”
-­‐ where
do
we
find
it?
1. Lit
review
2. Pilot
3. An
“educated
conjecture”
2. Define
alpha <=.05
3. Define
power (1-­‐beta)
.80
4. Calculate
sample
size
(can
use
Gpower)
see example
20

21. 21
For
a
t
test
with:
ES=
.02,
Alpha=.05,
Power =
.8,
We
will
need
N=788 subjects
for
our
sample

22. Now
that
we
are
done
with
our study,
we
need
to
check
how
well
did
the
actual
results
we
found
do
in
terms
of
power:
Again,
we
play
with
four factors:
1. Input
“ES”
– from
our study
2. Define
alpha <=.05
3. Input
sample
size
-­‐ from
our study
4. Calculate
power
– can
use
G-­‐Power
22

23. 23
For
our
t
test
with:
ES=
.091,
Alpha=.05,
Sample
size
N=1360,
We
have
obtained
a
dismal.388
power
!!!

24. 24
¡ Hypothesis
Testing
based
on
p
value
–
provides
only
statisticalsignificance.
¡ Power
analysis
is
crucial for
your
study:
¡ A-­‐priori:
to
determine
required
sample
size
¡ Post-­‐hoc:
§ To
calculate
and
examine
power from
actual
research
study
§ To
examine
the
practical significance
of
the
research
findings.
¡ If
you
fired Dr.
Adam
– Reinstatehim!!!

25. 25
¡ “G
Power”
v.
3.1.9.2.
(2015).
Buchner,
Erdfelder,
Faul,
&
Lang.
§ To
download
software
for
free:
http://www.psycho.uni-­‐
duesseldorf.de/abteilungen/aap/gpower3
¡ Using
“G
Power”
for
Statistical
Power
and
Sample
Size
Analysis
(2008).
Eveland,
J.D.
§ Download
instructions
to
follow
for
PPT.
¡ Becker,
L.
A.
(2000).
Effect
Sizes.
Retrieved:
http://www.uccs.edu/lbecker/effect-­‐size.html

26. Visit
https://www.trident.edu/webinars/core/
26

27. 27

28. 28

29. Attention
Faculty,
Students,
Alumni
and
Guest
Speakers
in
Business,
Health
Sciences,
and
Education:
¡ Have
you
wanted
to
present
your
ongoing
scholarly
and
professional
work
to
a
general
audience?
¡ CORE Grand Rounds provides
a
platform
for
professional
development
and
increased
engagement
to
receive
constructive
feedback
from
peers
and
scholars-­‐in-­‐training.
¡ Email
Dr.
Bernice
B.
Rumala at
Bernice.Rumala@Trident.edu
to
sign
up

30. 30
Thank You
May the “power” be with you
Dr. Mickey Shachar
Mickey.Shachar@Trident.edu

31. ¡ To
receive
more
information
about
C.O.R.E.
please
visit
the
C.O.R.E.
webpage
at:
www.trident.edu/webinars/core
¡ For
further
information
about
Trident’s
doctoral
programs
in
educational
leadership,
business
and
health
sciences
please
visit
:
https://www.trident.edu/degrees/doctoral/
¡ Do
you
have
any
comments
for
C.O.R.E.,
you
may
email
Dr.
Bernice
B.
Rumala,
C.O.R.E.
Chair,
at:
bernice.rumala@trident.edu
31