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
Estimating sample size through simulationsArthur8898
Determining sample size is one critical and important procedure for designing an experiment. The sample size for most statistical models can be easily calculated by using the POWER procedure. However, the PROC POWER cannot be used for a complicated statistical model. This paper reviews a more generalized method to estimate the sample size through a simulation approach by using SAS® software. The simulation approach not only applies to the simple but also to a more complex statistical design.
Hypothesis Testing: Central Tendency – Normal (Compare 1:1)Matt Hansen
An extension on a series about hypothesis testing, this lesson reviews the 2 Sample T & Paired T tests as central tendency measurements for normal distributions.
Hypothesis Testing: Central Tendency – Normal (Compare 2+ Factors)Matt Hansen
An extension on a series about hypothesis testing, this lesson reviews the ANOVA test as a central tendency measurement for normal distributions. It also explains what residuals and boxplots are and how to use them with the ANOVA test.
Estimating sample size through simulationsArthur8898
Determining sample size is one critical and important procedure for designing an experiment. The sample size for most statistical models can be easily calculated by using the POWER procedure. However, the PROC POWER cannot be used for a complicated statistical model. This paper reviews a more generalized method to estimate the sample size through a simulation approach by using SAS® software. The simulation approach not only applies to the simple but also to a more complex statistical design.
Hypothesis Testing: Central Tendency – Normal (Compare 1:1)Matt Hansen
An extension on a series about hypothesis testing, this lesson reviews the 2 Sample T & Paired T tests as central tendency measurements for normal distributions.
Hypothesis Testing: Central Tendency – Normal (Compare 2+ Factors)Matt Hansen
An extension on a series about hypothesis testing, this lesson reviews the ANOVA test as a central tendency measurement for normal distributions. It also explains what residuals and boxplots are and how to use them with the ANOVA test.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 1:Standard)Matt Hansen
An extension on hypothesis testing, this lesson reviews the 1 Sample Sign & Wilcoxon tests as central tendency measurements for non-normal distributions.
Alleviating Privacy Attacks Using Causal ModelsAmit Sharma
Machine learning models, especially deep neural networks have been shown to reveal membership information of inputs in the training data. Such membership inference attacks are a serious privacy concern, for example, patients providing medical records to build a model that detects HIV would not want their identity to be leaked. Further, we show that the attack accuracy amplifies when the model is used to predict samples that come from a different distribution than the training set, which is often the case in real world applications. Therefore, we propose the use of causal learning approaches where a model learns the causal relationship between the input features and the outcome. An ideal causal model is known to be invariant to the training distribution and hence generalizes well to shifts between samples from the same distribution and across different distributions. First, we prove that models learned using causal structure provide stronger differential privacy guarantees than associational models under reasonable assumptions. Next, we show that causal models trained on sufficiently large samples are robust to membership inference attacks across different distributions of datasets and those trained on smaller sample sizes always have lower attack accuracy than corresponding associational models. Finally, we confirm our theoretical claims with experimental evaluation on 4 moderately complex Bayesian network datasets and a colored MNIST image dataset. Associational models exhibit upto 80\% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess. Our results confirm the value of the generalizability of causal models in reducing susceptibility to privacy attacks. Paper available at https://arxiv.org/abs/1909.12732
Hypothesis Testing: Central Tendency – Non-Normal (Compare 1:Standard)Matt Hansen
An extension on hypothesis testing, this lesson reviews the 1 Sample Sign & Wilcoxon tests as central tendency measurements for non-normal distributions.
Alleviating Privacy Attacks Using Causal ModelsAmit Sharma
Machine learning models, especially deep neural networks have been shown to reveal membership information of inputs in the training data. Such membership inference attacks are a serious privacy concern, for example, patients providing medical records to build a model that detects HIV would not want their identity to be leaked. Further, we show that the attack accuracy amplifies when the model is used to predict samples that come from a different distribution than the training set, which is often the case in real world applications. Therefore, we propose the use of causal learning approaches where a model learns the causal relationship between the input features and the outcome. An ideal causal model is known to be invariant to the training distribution and hence generalizes well to shifts between samples from the same distribution and across different distributions. First, we prove that models learned using causal structure provide stronger differential privacy guarantees than associational models under reasonable assumptions. Next, we show that causal models trained on sufficiently large samples are robust to membership inference attacks across different distributions of datasets and those trained on smaller sample sizes always have lower attack accuracy than corresponding associational models. Finally, we confirm our theoretical claims with experimental evaluation on 4 moderately complex Bayesian network datasets and a colored MNIST image dataset. Associational models exhibit upto 80\% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess. Our results confirm the value of the generalizability of causal models in reducing susceptibility to privacy attacks. Paper available at https://arxiv.org/abs/1909.12732
At the end of this lecture, the students should be able to
1.Understand structure of research study appropriate for ANOVA test
2.Understand how to evaluate the assumptions underlying this test
3. interpret SPSS outputs and report the results
Sampling and Inference: Learn about the importance of random sampling in political research; learn why samples that seem small can yield accurate information about larger groups; learn how to figure out the margin of error of a sample; learn how to make inferences about the information in a sample.
Statistics is the science of dealing with numbers.
It is used for collection, summarization, presentation and analysis of data.
Statistics provides a way of organizing data to get information on a wider and more formal (objective) basis than relying on personal experience (subjective).
TEST #1Perform the following two-tailed hypothesis test, using a.docxmattinsonjanel
TEST #1
Perform the following two-tailed hypothesis test, using a .05 significance level:
· Intrinsic by Gender
· State the null and an alternate statement for the test
· Use Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test. Copy and paste the results of the output to your report in Microsoft Word.
· Identify the significance level, the test statistic, and the critical value.
· State whether you are rejecting or failing to reject the null hypothesis statement.
· Explain how the results could be used by the manager of the company.
TEST #2
Perform the following two-tailed hypothesis test, using a .05 significance level:
· Extrinsic variable by Position Type
· State the null and an alternate statement for the test
· Use Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test.
· Copy and paste the results of the output to your report in Microsoft Word.
· Identify the significance level, the test statistic, and the critical value.
· State whether you are rejecting or failing to reject the null hypothesis statement.
· Explain how the results could be used by the manager of the company.
GENERAL ANALYSIS (Research Required)
Using your textbook or other appropriate college-level resources:
· Explain when to use a t-test and when to use a z-test. Explore the differences.
· Discuss why samples are used instead of populations.
The report should be well written and should flow well with no grammatical errors. It should include proper citation in APA formatting in both the in-text and reference pages and include a title page, be double-spaced, and in Times New Roman, 12-point font. APA formatting is necessary to ensure academic honesty.
Be sure to provide references in APA format for any resource you may use to support your answers.
Making Inferences
When data are collected, various summary statistics and graphs can be used for describing data; however, learning about what the data mean is where the power of statistics starts. For example, is there really a difference between two leading cola products? Hypothesis testing is an example of making these types of inferences on data sets.
Hypothesis Tests
Claims are made all the time, such as a particular light bulb will last a certain number of hours.
Claims like this are tested with hypothesis testing. It is a straight forward procedure that consists of the following steps:
1. A claim is made.
2. A value for probability of significance is chosen.
3. Data are collected.
4. The test is performed.
5. The results are analyzed.
Hypothesis tests are performed on the mean of the population. µ
It is not possible to test the full population. For example, it would be impossible to test every light bulb. Instead, the hypothesis test is performed on a sample of the population.
Setting up a Hypothesis Test
When performing hypothesis testing, the test is setup with a null hypothesis (or claim) and the alternative hypothesis. ...
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, ...Musfera Nara Vadia
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, confidence interval, two-tailed and one tailed test, and other misunderstood issues.
P-values the gold measure of statistical validity are not as reliable as many...David Pratap
This is an article that appeared in the NATURE as News Feature dated 12-February-2014. This article was presented in the journal club at Oman Medical College , Bowshar Campus on December, 17, 2015. This article was presented by Pratap David , Biostatistics Lecturer.
What is the significance of p value while reporting statistical analysis. Is there an alternate approach for Fisher, if so what is that approach. These are some of the issues addressed here.
Hypothesis Testing Definitions A statistical hypothesi.docxwilcockiris
Hypothesis Testing
Definitions:
A statistical hypothesis is a guess about a population parameter. The guess may or not be
true.
The null hypothesis, written H0, is a statistical hypothesis that states that there is no
difference between a parameter and a specific value, or that there is no difference between
two parameters.
The alternative hypothesis, written H1 or HA, is a statistical hypothesis that specifies a
specific difference between a parameter and a specific value, or that there is a difference
between two parameters.
Example 1:
A medical researcher is interested in finding out whether a new medication will have
undesirable side effects. She is particularly concerned with the pulse rate of patients who
take the medication. The research question is, will the pulse rate increase, decrease, or
remain the same after a patient takes the medication?
Since the researcher knows that the mean pulse rate for the population under study is 82
beats per minute, the hypotheses for this study are:
H0: µ = 82
HA: µ ≠ 82
The null hypothesis specifies that the mean will remain unchanged and the alternative
hypothesis states that it will be different. This test is called a two-tailed test since the
possible side effects could be to raise or lower the pulse rate. Notice that this is a non
directional hypothesis. The rejection region lies in both tails. We divide the alpha in two
and place half in each tail.
Example 2:
An entrepreneur invents an additive to increase the life of an automobile battery. If the
mean lifetime of the automobile battery is 36 months, then his hypotheses are:
H0: µ ≤ 36
HA: µ > 36
Here, the entrepreneur is only interested in increasing the lifetime of the batteries, so his
alternative hypothesis is that the mean is greater than 36 months. The null hypothesis is
that the mean is less than or equal to 36 months. This test is one-tailed since the interest
is only in an increased lifetime. Notice that the direction of the inequality in the alternate
hypothesis points to the right, same as the area of the curve that forms the rejection
region.
Example 3:
A landlord who wants to lower heating bills in a large apartment complex is considering
using a new type of insulation. If the current average of the monthly heating bills is $78,
his hypotheses about heating costs with the new insulation are:
H0: µ ≥ 78
HA: µ < 78
This test is also a one-tailed test since the landlord is interested only in lowering heating
costs. Notice that the direction of the inequality in the alternate hypothesis points to the
left, same as the area of the curve that forms the rejection region.
Study Design:
After stating the hypotheses, the researcher’s next step is to design the study. In designing
the study, the researcher selects an appropriate statistical test, chooses a level of
significance, and formulates a plan for conducting the study..
Hypothesis Testing: Central Tendency – Non-Normal (Compare 2+ Factors)Matt Hansen
An extension on hypothesis testing, this lesson reviews the Mood’s Median & Kruskal-Wallis tests as central tendency measurements for non-normal distributions.
This presentation discusses the following topics:
Hypothesis Test
Potential Outcomes in Hypothesis Testing
Significance level
P-value
Sampling Errors
Type I Error
What causes Type I errors?
What causes Type II errors?
4 possible outcomes
Directors and Professors Roundtable/Q & A: Strategically Approaching the Doct...Trident University
This roundtable will highlight strategies for successfully navigating the Doctoral Programs and participants will receive insight into the various fields that one can pursue.
With Dr. Frank Gomez, Dr. Heidi Gilligan, Dr. Wenling Li, and Dr. Indira Guzman.
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
How to Write Your Teaching Philosophy for Jobs in Academia (Part 1)Trident University
If you are interested in teaching at an institution of higher learning, this webinar is for you. This workshop will provide essentials of what to include in your teaching philosophy for careers in academia.
With Dr. Stephen Fitzgerald (Moderator); Dr. Tanya Murray (Presenter)
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
CORE: Communicating Your Professional and Scholarly Work with the MediaTrident University
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
Research methods can generally be divided into two main categories: Quantitative and Qualitative. This webinar will provide an overview of quantitative methods with a brief distinction between quantitative and qualitative methods. We will focus on when and how to use quantitative research and discuss type of variables and statistical analysis.
Presentation will be led by Dr. Carlos Cardillo.
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
Dissertation Pitfalls: Navigating Common Issues In Your DissertationTrident University
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.
Today's webinar, Dissertation Pitfalls: Navigating Common Issues In Your Dissertation, led by Dr. Frank Gomez and Dr. Carlos Cardillo, will highlight issues encountered throughout the dissertation process and solutions for preventing and overcoming these challenges.
For further information about CORE or to present, you may contact Dr. Bernice B. Rumala at Bernice.rumala@trident.edu
Research Ethics from an IRB Perspective - Presented by Dr. Heidi Sato & Dr. Stefan Hanson
Understanding the IRB requirements before initiating a research study may be a challenging task. Learn about research ethics from an IRB perspective. In this webinar you will learn:
· What is an IRB and its purpose?
· What requires IRB review and approval?
· What are the different types of IRB review?
· What is the process for submitting an IRB application?
· What criteria must be met for IRB approval?
· What are possible outcomes of an IRB review?
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
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
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
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
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
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
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