statistics/cf_choose_a_statistical_test (1) (1).pptx Independent Variable [IV] (number of groups)Dependent Variable [DV] (measurement level) Two Groups Three + Groups Independent (“unpaired”)Dependent (“paired”)Independent (“unpaired”) Dependent (“paired”) CategoricalNon-parametric TestsChi-squareMcNemar’sChi-square Cochran’s QOrdinal Mann-Whitney UWilcoxon Signed ranksKruskal Wallis HFriedman’sInterval / Ratio (continuous)Parametric TestsIndependent t-testDependent t-testANOVARM-ANOVA “What is the effect of TREATMENT (IV) on our OUTCOME (DV) of interest?” Example: TREATMENT independent groups (placebo versus drug), OUTCOME interval/ratio (blood pressure) Example: TREATMENT dependent group (pre/post yoga therapy), OUTCOME ordinal (back pain levels) Example: TREATMENT independent 3+ groups (yoga therapy, none, aerobics), OUTCOME categorical (pass/fail of driving test)CorrelationsPhi coefficientSpearman’s rhoPearson’s r Independent Variable (number of groups)Dependent Variable (measurement level) Two Groups Three + Groups Independent (“unpaired”)Dependent (“paired”)Independent (“unpaired”) Dependent (“paired”) CategoricalNon-parametric TestsChi-squareMcNemar’sChi-square Cochran’s QOrdinal Mann-Whitney UWilcoxon Signed ranksKruskal Wallis HFriedman’sInterval / Ratio (continuous)Parametric TestsIndependent t-testDependent t-testANOVARM-ANOVA STEP #1 Check what measurement level your DV is. STEP #2 Choose the column related to the number Groups in your study. STEP #3 Choose the column where intervention groups are either “paired” or “unpaired.” STEP #4 Match your column with the row to find which test to run. STEP #1 Look at your Dependent Variable or outcome. The data that we are looking at here is from the instruments you used to measure the effect of your intervention. Maybe you chose to measure stress with a commonly used psychological questionnaire or maybe you measured cholesterol levels or test scores. What is its measurement level? Categorical (such as yes or no; dead or alive; pass or fail). Ordinal (such as health status – poor, average, excellent). Interval ratio (for instance blood pressure, cholesterol level, rates of infection, or workplace satisfaction scores on a scale of 0-100). STEP #2 Next you will look for the column that corresponds to the number of groups you have for your Independent Variable (also called experimental or predictor variable). Remember, the independent variable is the thing in your study that was controlled by you (such as a medical intervention, or training initiative, or implementation of a modified protocol) for the purpose of making a change on some outcome in the population you are studying. So…how many groups were involved in this intervention? For example, if you were testing the effect of an evidence-based training initiative on employee workplace satisfaction or happiness, you might be interested in comparing the training initiative in one group to no training in another group..

1. statistics/cf_choose_a_statistical_test (1) (1).pptx
Independent Variable [IV]
(number of groups)Dependent Variable [DV]
(measurement level) Two Groups
Three + Groups
Independent
(“unpaired”)Dependent
(“paired”)Independent
(“unpaired”)
Dependent
(“paired”)
CategoricalNon-parametric TestsChi-squareMcNemar’sChi-
square
Cochran’s QOrdinal Mann-Whitney UWilcoxon Signed
ranksKruskal Wallis HFriedman’sInterval / Ratio
(continuous)Parametric TestsIndependent
t-testDependent
t-testANOVARM-ANOVA
“What is the effect of TREATMENT (IV) on our OUTCOME
(DV) of interest?”
Example: TREATMENT independent groups (placebo versus
drug), OUTCOME interval/ratio (blood pressure)
Example: TREATMENT dependent group (pre/post yoga
therapy), OUTCOME ordinal (back pain levels)
Example: TREATMENT independent 3+ groups (yoga therapy,
none, aerobics), OUTCOME categorical (pass/fail of driving
test)CorrelationsPhi coefficientSpearman’s rhoPearson’s r
Independent Variable
(number of groups)Dependent Variable (measurement level)
Two Groups
Three + Groups
Independent

2. (“unpaired”)Dependent
(“paired”)Independent
(“unpaired”)
Dependent
(“paired”)
CategoricalNon-parametric TestsChi-squareMcNemar’sChi-
square
Cochran’s QOrdinal Mann-Whitney UWilcoxon Signed
ranksKruskal Wallis HFriedman’sInterval / Ratio
(continuous)Parametric TestsIndependent
t-testDependent
t-testANOVARM-ANOVA
STEP #1
Check what measurement level your DV is.
STEP #2
Choose the column related to the number Groups in your study.
STEP #3
Choose the column where intervention groups are either
“paired” or “unpaired.”
STEP #4
Match your column with the row to find which test
to run.
STEP #1
Look at your Dependent Variable or outcome.
The data that we are looking at here is from the instruments you
used to measure the effect of your intervention. Maybe you
chose to measure stress with a commonly used psychological
questionnaire or maybe you measured cholesterol levels or test
scores.
What is its measurement level?
Categorical (such as yes or no; dead or alive; pass or fail).
Ordinal (such as health status – poor, average, excellent).
Interval ratio (for instance blood pressure, cholesterol level,

3. rates of infection, or workplace satisfaction scores on a scale of
0-100).
STEP #2
Next you will look for the column that corresponds to the
number of groups you have for your Independent Variable (also
called experimental or predictor variable).
Remember, the independent variable is the thing in your study
that was controlled by you (such as a medical intervention, or
training initiative, or implementation of a modified protocol)
for the purpose of making a change on some outcome in the
population you are studying.
So…how many groups were involved in this intervention?
For example, if you were testing the effect of an evidence-based
training initiative on employee workplace satisfaction or
happiness, you might be interested in comparing the training
initiative in one group to no training in another group. Here,
then, you would have two groups being studied.
But, maybe you wish to modify your intervention so that you
also have training and massage compared to training alone, and
both of these compared to no training or massage. Here you
would have three groups where you would be measuring the DV
(that is, workplace satisfaction).
STEP #3
Before we decide on the statistical test to use, we must examine
another part of the Independent Variable columns that

4. correspond to the number of groups you have in your study. We
are now interested in a characteristic called Independent
(unpaired) and Dependent (paired) groups.
To explain this, we will use the same evidence-based training
initiative example from the previous slide. Let us assume you
have two groups that you want to use in studying employee
workplace satisfaction and happiness.
For the sake of this illustration, we will say that one group,
which comes from a unit X at the hospital you selected, will be
compared to unit Y that will not receive any training. Clearly,
you now have two groups (one with training, and one without
training) that you will be evaluating on your dependent variable
called satisfaction and happiness. What is important here is to
recognize that each group of individuals comes from entirely
distinct units. While they might know each other, there is no
particular way that they might influence each other on the
satisfaction and happiness test. In fact, you have to be sure that
you do not have a husband and wife split between these units
because of their potential to influence each other. In this kind of
study, you create two independent groups.
For a different study design, you may decide to use only one
group, testing them for satisfaction and happiness before the
training, and then right after the training. You would again have
two groups being tested, but this time the groups are composed
of the same people (that is, the same persons tested twice) and
would therefore be considered “dependent.”
This step would appear to be the easiest. You have basically one
test option that shows up at the intersection of the chosen IV
column and DV rows. In reality, however, the selection of the
appropriate inferential test is not quite so simple. But we will
leave most of the exceptions to a statistician to figure out.

5. All dependent variables that are either ordinal or categorical in
nature must have nonparametric testing. Interval or ratio data,
on the other hand, may require a bit more evaluation since it can
be tested with either parametric tests or nonparametric tests.
Here is the most likely scenario you may encounter:
If you have determined that your data is at the interval or ratio
type of measurement level (that is, continuous), you still must
determine if it is distributed normally before you select the
statistical test. In this course, you will only be responsible for
testing to see if your interval or ratio data is normally
distributed. Your readings and resources have more on this
topic.
If your distribution test comes back saying that your outcome
data is not normally distributed, then you will likely need to use
a nonparametric test. To find the appropriate nonparametric
equivalent tests for non-normal interval or ratio data, you
simply move to the row just above the parametric test you
selected. In other words, if you had selected an independent t-
test for your intervalor ratio data, you would use the Mann-
Whitney U test if the data turns out to fail the assumption of
normal distribution.
STEP #4
statistics/Instructions.docx
Analyzing a Health Care Dataset
Overview
Public health researchers are often involved in collaborating in
the design, development, and analysis of community initiatives
of varying complexity. While this course alone will not provide
sufficient training for you to act as a statistical consultant, it
does offer a broad and practice-based analytic foundation that

6. can position you to better understand and more fully contribute
to real-world project teams. Building on the basic statistical
concepts and analytical techniques of the previous units, this
assignment is an opportunity to use your cumulative
quantitative-analysis skills to address a broad set of real-world
research questions.
Instructions
Complete the following for this two-part assignment:
Part 1: Yoga and Stress Study Statistical Tests
1. Using the dataset linked in Resources, determine the
measurement level of data of the dependent or outcome variable
(Psychological Stress Score) you are analyzing.
. Is the data categorical, ordinal, or interval or ratio?
· Before performing any statistical tests, you must determine
which tests would be most appropriate for your data type.
. First, perform a pre-evaluation of the data for outliers (all
variables) and normal distribution (only dependent variables) as
you have done previously.
. Then, use How to Choose a Statistical Test (linked in
Resources) as general guidance in helping you to decide which
test to use.
. Use the readings, media, resources, and textbook as guides to
perform an analysis of the selected variables.
· Perform and interpret an appropriate series of statistical tests
(including pre-analytical testing for outliers and normal
distribution of data) that answer the following research
questions:
. How would you quantitatively describe the study population?
. Summarize the primary demographic data using descriptive
statistics.
· Is there any association between gender and race in this
military study?
. Perform an appropriate chi-square analysis.
· Perform preliminary assessment of the data, then compare
pretest to post-test scores.
· In total population being studied, what was the effect of the

7. yoga intervention on stress?
· Provide the Excel output file that shows your programming
and results for this assignment.
Part 2: Interpretive Report
1. Summarize the clinical implications related to the statistical
outcomes for each of the questions above.
2. Describe potential limitations of the study (Part 1, number 3).
Additional Requirements
· Length: Your paper will be 3–4 typed, double-spaced pages of
content plus title and reference pages.
· Font: Times New Roman, 12 points.
· APA Format: Your title and reference pages must conform to
APA format and style guidelines. The body of your paper does
not need to conform to APA guidelines. Do make sure that it is
clear, persuasive, organized, and well written, without
grammatical, punctuation, or spelling errors. You also must cite
your sources according to APA guidelines.
Refer to the helpful links in Resources as you prepare your
assignment.
Please review the assignment scoring guide before completing
your submission. The requirements outlined above correspond
to the grading criteria in the scoring guide, so be sure to address
each point. In addition, you may want to review the
performance-level descriptions for each criterion to see how
your work will be assessed.
Resources
· Analyzing a Health Care Dataset Scoring Guide.
· How to Choose a Statistical Test [PPTX].
· Yoga Stress (PSS) Study Dataset [XLSX].
· APA Module.
statistics/U8A1 - Analyzing a Health Care Dataset Scoring
Guide (1) (1).pdf

8. 3/1/2019 Analyzing a Health Care Dataset Scoring Guide
https://courserooma.capella.edu/bbcswebdav/institution/NHS/N
HS8070/190100/Scoring_Guides/u08a1_scoring_guide.html 1/1
Analyzing a Health Care Dataset Scoring Guide
Due Date: End of Unit 8
Percentage of Course Grade: 22%.
CRITERIA NON-PERFORMANCE BASIC PROFICIENT
DISTINGUISHED
Assess the
assumption of normal
distribution prior to
analysis.
10%
Does not assess the
assumption of
normal distribution
prior to analysis.
Assesses the assumption
of normal distribution prior
to analysis but the
assumption test is
incomplete or performed
inaccurately.
Assesses the
assumption of
normal distribution
prior to analysis.

9. Assesses the assumption
of normal distribution prior
to analysis and outlines
the steps taken to perform
the test, including
determination of the
measurement level of data
for each variable.
Perform the most
appropriate
parametric or
nonparametric test to
answer each
question.
35%
Does not perform the
most appropriate
parametric or
nonparametric test to
answer each
research question.
Performs the most
appropriate parametric or
nonparametric test to
answer each research
question, but the test is
performed incorrectly.
Performs the most
appropriate
parametric or

10. nonparametric test
to answer each
research question.
Performs an appropriate
alternative nonparametric
test (Mann-Whitney U or
Wilcoxon) and outlines the
steps taken to perform the
test.
Appropriately
interpret the
statistical output
(such as estimate, p-
value, confidence
interval, and effect
size) resulting from
each statistical test.
25%
Does not
appropriately
interpret the
statistical output
(such as estimate, p-
value, confidence
interval, and effect
size) resulting from
each statistical test.
Appropriately interprets the
statistical output (such as
estimate, p-value,
confidence interval, and

11. effect size) resulting from
each statistical test. but the
interpretation is incomplete
or inaccurate.
Appropriately
interprets the
statistical output
(such as estimate,
p-value, confidence
interval, and effect
size) resulting from
each statistical test.
Appropriately the statistical
output (such as estimate,
p-value, confidence
interval, and effect size)
resulting from each
statistical test. Justifies or
explains the basis for the
interpretation.
Describe the practical
significance of the
results of statistical
tests.
10%
Does not describe
the practical
significance of the
results of statistical
tests.

12. Describes the practical
significance of the results
of statistical tests, but the
description is inappropriate,
incomplete, or otherwise
flawed.
Describes the
practical
significance of the
results of statistical
tests.
Describes the practical
significance of the
statistical result for each
research question.
Provides at least one
citation of research that
supports the explanation.
Write clearly,
accurately, and
professionally.
15%
Does not write
clearly, accurately,
and professionally.
Writes clearly, accurately,
and professionally, but with
frequent errors or lapses.
Writes clearly,

13. accurately, and
professionally.
Writes clearly, accurately,
and professionally, and the
information is logically and
appropriately organized.
Cite sources
appropriately, using
APA formatting.
5%
Does not cite
sources
appropriately, using
APA formatting.
Cites sources using APA
formatting, but with some
errors.
Cite sources
appropriately, using
APA formatting.
Cites sources
appropriately, using APA
formatting, and
paraphrases accurately
when appropriate.
statistics/Yoga_Stress (PSS) study dataset (1) (1).xlsx
HDAP 8070-02 DATAPatient

14. IDAGEGENDERRACEEDUCATIONMIL_STATUSPRE_PSSPO
ST_PSS300123MaleAfrican AmericanGraduate education or
aboveactive duty2520300226MaleAsianCollege graduateactive
duty2215300333MaleCaucasianSome collegeactive
duty1716300435MaleHispanicSome collegeactive
duty3225300548MaleAfrican AmericanGraduate education or
aboveactive duty2214300651FemaleAfrican AmericanCollege
graduateactive duty1816300722FemaleAfrican AmericanSome
collegeactive duty1412300818FemaleAsianSome collegeactive
duty2216300944FemaleCaucasianCollege graduateactive
duty2320301040FemaleNative AmericanSome collegeactive
duty3336400130MaleNative AmericanCollege graduateUS
Civilian2221400255MaleTwo or more racesLess than HSUS
Civilian2515400357FemaleAfrican AmericanCollege
graduateUS Civilian1310400447FemaleAfrican AmericanLess
than HSUS Civilian1212400539MaleAsianHS graduateUS
Civilian1712400629MaleCaucasianHS graduateUS
Civilian1010400733MaleCaucasianGraduate education or
aboveUS Civilian3422400844FemaleHispanicCollege
graduateUS Civilian1812400955FemaleHispanicCollege
graduateUS Civilian1210401060FemaleNative AmericanCollege
graduateUS Civilian169