Student instructions bm overview benchmark-your group has been giv

Student Instructions BMOverview - BenchmarkYour group has
been given a dataset containing 240 records, located in the
Student_BM tab of this spreadsheet.. Each student is only
responsible for analyzing 60 of these record records - the
specifics of which will be assigned by the instructor.It is
important that each student has a unique 60 records, as the
results will be an input into the CLC, and duplication ofresults
is not helpful. Note that the data have been randomized, so the
data given to your group are likely different than thedata given
to other groups. The intent of this assignment is for students to
organize their data using a pivot table, get a graphical
understandingof the data through a bar chart, then do hypothesis
testing comparing Bo Diddly Tech results versus the national
average.All of your analysis should be done in the Student_BM
tab of this spreadsheet and submitted as part of the
assignmemt.The location where the pivot table, bar chart, and
relevant information should be placed in the Student_BM tab is
indicated by RED instructions. Once completed, the
Student_BM tab will serve as the basis for writing your
management report. It is expected that anyconclusions you draw
in the management report will be consistent with the data and
analyses contained in the spreadsheet.Instructions Data Analysis
Component:1. Make a pivot table with: Business Student
(Rows), Athlete (Rows), Cheated (Columns), and Cheated
(Summed Value).2. Create a bar chart showing cheating by
athletes and business students.4. Determine if there is a
statistical difference between nonathlete BDT business students
and the national average for businessstudents as reported by the
Chronicle of Higher Education.5. Determine if there is a
statistical difference between athlete BDT business students and
the national average for business students as reported by the
statistical difference between BDT business students and the
national average for business students as reported by the

statistical difference between BDT nonbusiness students and the
national average for nonbusinessstudents as reported by the
Chronicle of Higher Education.Instructions Data Interpretation
Component:Utilizing the data you have analyzed, write a
managerial report of 500-800 words to the dean. The managerial
report needs to include an introduction, analysis, conclusion,
and a minimum of three supporting references.1. Introduction
(Define): Explain in your own words why you are providing this
report and the problem(s) you are trying to solve.2. Collect:
Describe the data set you used.3. Organize: Describe your pivot
table.4. Visualize: Include and describe your bar chart.5.
Analyze: Provide a summary of your conclusions based upon
the four population proportion hypothesis tests.6. The Dean has
expressed a concern related to the amount of cheating currently
taking place at Bo Diddley Tech and has stronglysuggested that
you “tweak” the statistical data such that it favors the image of
the university.Discuss the potential use of unethical
manipulation of statistical data to provide a biased outcome as
well as the ethical counterproposal you would offer the dean in
this scenario.7. Conclusion: What advice would you give to the
dean based on your analysis of the data?
Student_BMCollegeAthleteCheated1. Pivot TableNationwide
Average% CheatedInsert pivot table in this cell -
F2Business56%Nonbusiness47%2. Bar ChartBar chart starts in
this cell - F20Insert the appropriate numbers into the hypothesis
testing calculations below based upon your pivot table results.
Note the results.3-6 Hypothesis TestBusiness Nonathlete vs.
National AverageBusiness Athlete vs. National
AverageBusiness vs. National AverageNonbusiness vs. National
AverageProportionProportionProportionProportionSample Size
(n)
=count(range)Sample Size (n)
=count(range)Response of Interest (ROI)CheatedResponse of

Interest (ROI)CheatedResponse of Interest
(ROI)CheatedResponse of Interest (ROI)CheatedCount for
Response (CFR)
=COUNTIF(range,ROI)Count for Response (CFR)
=COUNTIF(range,ROI)Sample Proportion (pbar)
=CFR/nSample Proportion (pbar)
=CFR/nHighlight your H0 and HaTwo Tail H0: p = po
Ha: p ≠ po
Left Tail H0: p ≥ po
Ha: p < po
Right Tail H0: p ≤ po
Ha: p > poHighlight your H0 and HaTwo Tail H0: p = po
Ha: p ≠ po
Ha: p < po
Ha: p ≠ po
Ha: p < po
Ha: p ≠ po
Ha: p < po
Ha: p >
poHypothesized0.56Hypothesized0.56Hypothesized0.56Hypothe
sized0.47Confidence Coefficient (Coe)0.95Confidence
Coefficient (Coe)0.95Confidence Coefficient
(Coe)0.95Confidence Coefficient (Coe)0.95Level of
Significance (alpha)

=1-Coe0.05Level of Significance (alpha)
=1-Coe0.05Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n)ERROR:#DIV/0!Standard Error
(StdError)
(StdError)
(StdError)
=SQRT(Hypo*(1-Hypo)/n)ERROR:#DIV/0!Test Statistic (Z-
stat)
=(pbar-Hypo)/StdErrorERROR:#DIV/0!Test Statistic (Z-stat)
=(pbar-Hypo)/StdErrorERROR:#DIV/0!Accept or Reject: Left
TailERROR:#DIV/0!Accept or Reject: Left
TailERROR:#DIV/0!Accept or Reject: Right
TailERROR:#DIV/0!Accept or Reject: Two
TailERROR:#DIV/0!p-value (Lower Tail)
=NORM.S.DIST(z,TRUE)ERROR:#DIV/0!p-value (Lower Tail)
=NORM.S.DIST(z,TRUE)ERROR:#DIV/0!p-value (Upper Tail)
=1-LowerTailERROR:#DIV/0!p-value (Upper Tail)
=1-LowerTailERROR:#DIV/0!p-value (Two Tail)

=2*MIN(LowerTail,UpperTail)ERROR:#DIV/0!p-value (Two
Tail)
Tail)
Tail)
=2*MIN(LowerTail,UpperTail)ERROR:#DIV/0!Accept or
Reject p-value: Left TailERROR:#DIV/0!Accept or Reject p-
value: Left TailERROR:#DIV/0!Accept or Reject p-value: Left
TailERROR:#DIV/0!Accept or Reject p-value: Left
TailERROR:#DIV/0!Accept or Reject p-value: Right
TailERROR:#DIV/0!Accept or Reject p-value: Two
TailERROR:#DIV/0!p-Lower Limit
=pbar-
CONFIDENCE.NORM(alpha,StdError,n)ERROR:#DIV/0!p-
Lower Limit
=pbar-
Lower Limit
=pbar-
Lower Limit
=pbar-
Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n)ERROR:#DIV/0
!p-Upper Limit
!p-Upper Limit

!p-Upper Limit
!
8
PRELIMINARY NEEDS ASSESSMENT
Michael Daley
FIU
HAS 4140
Dr. Chanadra Young-Whiting
2/7/2021
PRELIMINARY NEEDS ASSESSMENT
Targeted Objective
Specific HP 2010 objective: Tobacco Use
Targeted behavior: Increase the awareness of the dangers
related to tobacco use and provision of education and facts and
figures to the people to decrease the use of this drug in the
future.
Specific target population: Individuals over 18 years old.
Purpose of Needs Assessment
Tobacco is a plant that is highly valued for its leaves. These

leaves, known as tobacco leaves, are used for making drugs, the
most common of which is the tobacco cigarettes (ODPHP,
2020). These death packs contain small bundles of dried-up
tobacco leaves, which, when burned, are used by the smokers to
satisfy their needs. It includes many harmful chemicals like
nicotine that have a negative effect on the human body and can
result in a lot of fatal diseases like cancer.
The use of tobacco has been a part of almost every society on
earth for more than a century. This nicotine-packed drug is
consumed by millions of people daily, and sadly, with each
passing day, the number of its users is also increasing. It can
arguably be said that this drug has claimed more lives than any
other drug available in the streets, and there is no practical way
found, until now, that can stop the masses from continuing its
use.
This assignment aims to get an inside look as to the causes of
tobacco use, how it affects the human body, and the steps that
can be taken to prevent the users from using it in the future.
There are many ways through which the use of this poison can
be minimized, but because of its excessive use and presumably
minimal harmful effect, combined with the high demand and
cheap pricing, most of these ways never go mainstream
(Antigona C. Trofor, 2018). Those who do make it to the top of
the surface have little to no effect on the masses because of not
being taken severely by those who can make a difference.
In just the last five to six years, approximately 20 million
Americans have died due to reasons which were directly related
to smoking. From this, it is not hard to imagine the sheer size
and number of people worldwide who die from this seemingly
ordinary drug yearly. And millions more fall victim to these
drugs every year, which results in various crimes, among many
other undesirable things. This assessment is critical in order to

raise awareness about the seriousness of this issue and save
lives in the future.
Health Risks of Tobacco Use
There are a lot of risks associated with the use of the potentially
lethal drug. The most common of all is lung cancer. One of the
significant downsides of this drug is that it slowly affects the
consumer's body from the inside. There are no immediate side
effects right away, but after the continuous use of this drug, the
consumer's body is slowly poisoned from inside. In the worst-
case scenario, the subject's lungs or the consumer, when
examined, are jet black. This results from the deposition of tar,
which can be considered a by-product of this drug.
Another downside of this drug is that the soothing effect lasts
for a concise amount of time. After which, the user has to take
another dose, or another cigarette, in this case, to keep the brain
stimulated. Another health risk of tobacco use includes:
· Other types of cancer including kidney, liver, bladder,
stomach, acute myeloid leukemia, etc.
· Stroke as well as heart diseases
· Various lung diseases including chronic airway obstruction,
emphysema, chronic obstructive pulmonary disease, etc.
· Various reproductive effects are incredibly harmful, including
congenital disabilities like cleft-lip and cleft palate, ectopic
pregnancy, low birth weight, reduced fertility in women, etc.
· Other harmful effects like age-related macular degeneration,
impaired immune function, type 2 diabetes, rheumatoid
arthritis, etc.
From all this, it should be pretty clear that this drug is
extremely harmful and fatal for the human body and can result
in some severe diseases in the long and short run.
Incidence and Prevalence Rates of the Health Risks
It is no secret that a lot of people all around the globe use

tobacco in one way or another. Some use it in the cigarettes that
are available almost everywhere at a too cheap rate, while
others consume it in cigars, which are much expensive and out
of the majority's reach. But whatever the case may be, almost
every single person on this planet has seen tobacco use and
smelled this drug, directly or indirectly, at least once in his or
her lifetime. And the eye-opening fact is that almost everyone
also knows how deadly this drug can be to the human body.
According to a survey, the largest cause of death, preventable
within the United States of America, is Tobacco use. 480,000
American lives are lost yearly because of issues related to the
use of tobacco. On top of that, an eye-opening 16 million people
within the US's borders alone suffer from at least one disease
caused by smoking alone. It costs Americans a whopping 300
billion dollars annually for the treatment of smoking-related
illnesses alone. These numbers are the direct proof of how this
poisonous substance is hollowing American lives from inside.
What is even more astonishing is that these are stats of the so-
called reported cases directly related to tobacco use. Millions of
cases, mostly those of homeless people, are never written and
are never treated. These staggering stats will rise even more if
such cases are a lot taken into account.
Minimizing the risk
There are a lot of things that can be done to minimize the risk.
The first thing that can be done, in fact, is crucial, is the
recognition that this drug is no less harmful than other drugs
like cocaine. There are a lot of people all around the globe who
know this fact but still turn a blind eye to it, mainly because of
the reason that smoking does not have any immediate harmful
effects on the human body. Only when this particular fact is
recognized can there be efforts to reduce its consumption.
One of the main things that can be done to stop the rapid

increase of tobacco use is to limit its supply and implement
huge taxes on the respective industries. The main reason why
there is so much use of this particular drug is that it is readily
available and cheap. Just a few cents can let anyone have a
cigarette, and before he or she knows it, the respective person
will fall victim to the addiction. Reducing the supply and
increasing the prices will prevent a significant chunk of the
population from getting their hands on this poison.
Another thing that can be done is introducing new substitutes of
the cigarettes to a non-harmful medication in the public so that
the current users can switch to it and eventually be able to get
free from this addiction (CSIMPP, July 20, 2016). There are
items like these available in the market, but they are either so
expensive that it is out of the reach of a poor person or not well
advertised. The government should play its role in ensuring that
such items should reach the masses at a price that can easily be
afforded by anyone who wishes to buy it.
Finally, it is also imperative that the notable personalities in a
given country come forward and raise their voices to stop the
usage of this drug. Nowadays, there is more advertisement for
having a cigarette than commercials or banners criticizing the
use of this lethal drug. If that is turned upside down and the use
of this drug is attacked, this problem can be dealt with in a
relatively easier manner than what it seems like at first glance.
Evidence-Based- Literature Review
According to the center for disease control and prevention,
for every person who has died as a cause of smoking, an
additional thirty people are forced to live with illnesses directly
related to smoking (Centre of disease control and prevention,
2020). If things go on like this, by 2030, almost eight million
people will die from smoking every year. That is more than the
drug trafficking and murder related deaths combined. Apart
from that, those who are passive smokers are also affected by

this poison. Approximately 41,000 people die due to passive
smoking yearly as well.
Other shocking facts include that those who smoke have a life
span of 10 years less than those who do not smoke tobacco. If
everything goes as it is going now, then it is estimated that 5.6
million teens today will die of smoking-related diseases
somewhere down the road. This represents one in every 13
teens that are alive today. Shockingly some of these teens have
already started to smoke this lethal drug even before they have
turned 18.
Conclusion
From the facts and figures mentioned above, it is clear that this
drug is destroying every age group of this country, and it is
happening at a fast pace. The diseases related to smoking are
hazardous and fatal, and the patients do not die a peaceful
death.
From all this, it should be clear that now is the time to take
some serious steps to prevent the further spreading of the use of
this drug. There will be disastrous effects if things get out of
hand. It is the responsibility of every person to stay away from
this poison and try to save the lives of those who are being
rotten alive from inside because of this drug's use.
References
Antigona C. Trofor. (2018). Knowledge of the health risks of
smoking and impact of cigarette warning labels among tobacco
users in six European countries: Findings from the EUREST-
PLUS ITC Europe Surveys. Retrieved from
http://www.tobaccoinduceddiseases.org/Knowledge-of-the-
health-risks-of-smoking-and-impact-of-ncigarette-warning-
labels,99542,0,2.html
Centre of disease control and prevention. (2020). Smoking leads

to disease and disability and harms nearly every organ of the
body. Retrieved from
cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/index.htm
#:~:text=Cigarette%20smoking%20is%20responsible%20for,or
%201%2C300%20deaths%20every%20day.&text=On%20averag
e%2C%20smokers%20die%2010%20years%20earlier%20than%
20nonsmokers.
CSIMPP. (July 20, 2016). Health Effects of Tobacco
Secondhand Smoke: focus on Children Health A Review of the
Evidence and Self-Assessment. Retrieved from
https://www.wma.net/wp-content/uploads/2016/11/SHS-WMA-
rev2.pdf
ODPHP. (2020). Tobacco Use. Retrieved from
https://www.healthypeople.gov/2020/topics-
objectives/topic/tobacco-use
L. Michele Issel, PhD, RN
Professor of PhD Program
University of North Carolina at Charlotte
College of Health and Human Services
Charlotte, North Carolina
Rebecca Wells, PhD, MHSA
Professor
The University of Texas
School of Public Health

Houston, Texas
Health Program
Planning and
Evaluation
A Practical, Systematic Approach
for Community Health
FOURTH EDITION
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Title: Health program planning and evaluation: a practical,
systematic
approach for community health/L. Michele Issel and Rebecca

Wells.
Description: Fourth edition. | Burlington, MA: Jones & Bartlett
Learning,
[2018] | Includes bibliographical references and index.
Identifiers: LCCN 2017010386 | ISBN 9781284112115 (pbk.)
Subjects: | MESH: Community Health Services—organization &
administration |
Program Development—methods | Health Planning—methods |
Program
Evaluation—methods | United States
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iii
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Contents
List of Figures . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . xi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . xiii
List of Exhibits . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . xvii
Preface to the Fourth Edition . . . . . . . . . . . . . . . . .

. xix
Acknowledgments . . . . . . . . . . . . . . . . . . . . . .
. . . . . xxv
List of Acronyms . . . . . . . . . . . . . . . . . . . . . .
. . . . . .xxvii
SECTION I The Context of Health
Program Development 1
Chapter 1 Context of Health
Program Development
and Evaluation . . . . . . . . . . . . . . . 3
History and Context . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 4
Concept of Health . . . . . . . . . . . . . . . . . . . . . .
. . . . . 4
Health Programs, Projects, and Services . . . . . . 4
History of Health Program Planning
and Evaluation . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 5
Evaluation as a Profession . . . . . . . . . . . . . . . . . .
. . . . . . . 8
Who Does Planning and Evaluations? . . . . . . .10
Roles of Evaluators . . . . . . . . . . . . . . . . . . . . . .
. . . .10
Planning and Evaluation Cycle . . . . . . . . . . . . . . . .

. . .11
Interdependent and Cyclic Nature
of Planning and Evaluation . . . . . . . . . . . . . . .11
Using Evaluation Results as the
Cyclical Link . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . .13
Program Life Cycle . . . . . . . . . . . . . . . . . . . . . .
. . . .13
The Fuzzy Aspects of Planning . . . . . . . . . . . . . . . .
. . .14
Paradoxes . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .14
Assumptions . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . .16
Uncertainty, Ambiguity, Risk,
and Control . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . .17
Introduction to the Types of Evaluation . . . . . . . . . .19
Mandated and Voluntary Evaluations . . . . . . .20
When Not to Evaluate . . . . . . . . . . . . . . . . . . . .
. .21
The Public Health Pyramid . . . . . . . . . . . . . . . . . .
. . . . .21
Use of the Public Health Pyramid in

Program Planning and Evaluation . . . . . . . .23
The Public Health Pyramid as
an Ecological Model . . . . . . . . . . . . . . . . . . . .
.23
The Town of Layetteville in Bowe County . . . . . . . . .25
Across the Pyramid . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .25
Discussion Questions . . . . . . . . . . . . . . . . . . . . .
. . . . . . .27
Internet Resources . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . .27
References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . .27
Chapter 2 Relevance of Diversity
and Disparities to Health
Programs . . . . . . . . . . . . . . . . . . 29
Health Disparities . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . .30
Diversity and Health Disparities . . . . . . . . . . . . .32
Diversity and Health Programs . . . . . . . . . . . . . .33
Measurement . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . .33
Interventions . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . .38

Influences of Sociocultural Diversity
on Interventions . . . . . . . . . . . . . . . . . . . . . . .
. .38
Influences of Biological Diversity
on Interventions . . . . . . . . . . . . . . . . . . . . . . .
. .39
Approaches to Developing Programs . . . . . . .39
Profession and Provider Diversity . . . . . . . . . . . .40
The Three Health Provider Sectors . . . . . . . . . .43
Diversity Within Healthcare Organizations
and Programs . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .43
Organizational Culture . . . . . . . . . . . . . . . . . . . .
. .44
Cultural Competency Continuum . . . . . . . . . . .44
Enhancing Cultural Competency . . . . . . . . . . .48
iv Contents
Types of Assessments . . . . . . . . . . . . . . . . . . . .
. . . . . . . .75
Organizational Assessment . . . . . . . . . . . . . . . . .75
Marketing Assessment . . . . . . . . . . . . . . . . . . . .

. .76
Needs Assessment . . . . . . . . . . . . . . . . . . . . . .
. . .76
Community Health Assessment . . . . . . . . . . . . .77
Workforce Assessment . . . . . . . . . . . . . . . . . . . .
. .77
Steps in Planning and Conducting
the Assessment . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . .77
Form and Develop the Team . . . . . . . . . . . . . . . .78
Create a Vision . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . .79
Involve Community Members . . . . . . . . . . . . . .79
Define the Population . . . . . . . . . . . . . . . . . . . .
. .80
Define the Problem to Be Assessed . . . . . . . . .81
Investigate . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .81
Prioritize . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . .82
Make a Decision . . . . . . . . . . . . . . . . . . . . . . .
. . . . .82
Implement and Continue . . . . . . . . . . . . . . . . . .

.83
Anticipate Data-Related and Methodological
Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . .83
. . . . . . . .85
. . . . . . .85
. . . . . . . . .86
References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . .87
Chapter 4 Characterizing and
Defining the Health
Problem . . . . . . . . . . . . . . . . . . . 91
Collecting Data From Multiple Sources . . . . . . . . . . .91
Public Data . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .91
Primary Data . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . .92
Observational Data . . . . . . . . . . . . . . . . . . . . . .
. . .92
Archival Data . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . .93

Proprietary Data . . . . . . . . . . . . . . . . . . . . . . .
. . . . .93
Published Literature . . . . . . . . . . . . . . . . . . . . .
. . .93
Data Beyond Street Lamp . . . . . . . . . . . . . . . . . .
.93
Collecting Descriptive Data . . . . . . . . . . . . . . . . . .
. . . .94
Magnitude of the Problem . . . . . . . . . . . . . . . . .
.94
Dynamics Leading to the Problem . . . . . . . . . .94
Population Characteristics . . . . . . . . . . . . . . . . .
.96
Attitudes and Behaviors . . . . . . . . . . . . . . . . . . .
.96
Years of Life and Quality of Life . . . . . . . . . . . . . .96
Stakeholders and Coalitions . . . . . . . . . . . . . . . . .
. . . .50
. . . . . . . .51
. . . . . . .53
. . . . . . . . .53

References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . .54
SECTION II Defining the Health
Problem 57
Chapter 3 Community Health Assessment
for Program Planning . . . . . . . . 59
Defining Community . . . . . . . . . . . . . . . . . . . . .
. . . . . . .59
Community as Context and Intended
Recipient . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . .60
Defining Terms: Based, Focused,
and Driven . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . .61
Types of Needs . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . .62
Types of Strengths . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . .63
Approaches to Planning . . . . . . . . . . . . . . . . . . . .
. . . . .64
Incremental Approach . . . . . . . . . . . . . . . . . . . .
. .64
Apolitical Approach . . . . . . . . . . . . . . . . . . . . .
. . .66

Advocacy Approach . . . . . . . . . . . . . . . . . . . . .
. . .66
Communication Action Approach . . . . . . . . . .67
Comprehensive Rational Approach . . . . . . . . .67
Strategic Planning Approach . . . . . . . . . . . . . . .68
Summary of Approaches . . . . . . . . . . . . . . . . . .
.69
Models for Planning Public Health Programs . . . . .69
Mobilizing for Action through Planning
and Partnership (MAPP) . . . . . . . . . . . . . . . . . .70
Community Health Improvement
Process (CHIP) . . . . . . . . . . . . . . . . . . . . . . . .
. . . .70
Protocol for Assessing Community
Excellence in Environmental Health
(PACE-EH) . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . .70
In Summary . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . .70
Perspectives on Assessment . . . . . . . . . . . . . . . . . .
. . .71
Epidemiological Perspective . . . . . . . . . . . . . . . .72
Public Health Perspective . . . . . . . . . . . . . . . . . .
.74

Social Perspective . . . . . . . . . . . . . . . . . . . . . .
. . . .74
Asset Perspective . . . . . . . . . . . . . . . . . . . . . . .
. . . .74
Rapid Perspective . . . . . . . . . . . . . . . . . . . . . .
. . . .75
v Contents
Path to Program Outcomes
and Impacts . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 134
Components of the Effect Theory . . . . . . . . . 135
Matching Levels: Audience, Cause,
Intervention, and Effects . . . . . . . . . . . . . . . 137
Generating the Effect Theory . . . . . . . . . . . . . . . . .
. 138
Involve Key Stakeholders . . . . . . . . . . . . . . . . .
138
Draw Upon the Scientific Literature . . . . . . . 138
Diagram the Causal Chain
of Events . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 140
Check Against Assumptions . . . . . . . . . . . . . . 141

Functions of Program Theory . . . . . . . . . . . . . . . .
. . 141
Provide Guidance . . . . . . . . . . . . . . . . . . . . . .
. . . 141
Enable Explanations . . . . . . . . . . . . . . . . . . . . .
. 142
Form a Basis for Communication . . . . . . . . . . 142
Make a Scientific Contribution . . . . . . . . . . . . 143
. . . . . . 143
Discussion Questions and Activities . . . . . . . . . . . .
144
. . . . . . . 144
References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 145
Chapter 6 Program Objectives
and Setting Targets . . . . . . . . 147
Program Goals and Objectives . . . . . . . . . . . . . . . .
. 147
Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 147
Foci of Objectives . . . . . . . . . . . . . . . . . . . . . .

. . . 148
Objectives and Indicators . . . . . . . . . . . . . . . . . 151
Good Goals and Objectives . . . . . . . . . . . . . . . 154
Using Data to Set Target Values . . . . . . . . . . . . . . .
. 156
Decisional Framework for Setting
Target Values . . . . . . . . . . . . . . . . . . . . . . . .
. . . 156
Stratification and Object Target
Values . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 159
Use of Logic Statements to Develop
Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 160
Options for Calculating Target
Values . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 160
Caveats to the Goal-Oriented Approach . . . . . . . 170
. . . . . . 171
171
. . . . . . . 172

References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 172
Statistics for Describing Health
Problems . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . .99
Descriptive Statistics . . . . . . . . . . . . . . . . . . . . .
. 100
Geographic Information Systems:
Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 101
Small Numbers and Small Areas . . . . . . . . . . 101
Epidemiology Rates . . . . . . . . . . . . . . . . . . . . .
. 102
Stating the Health Problem . . . . . . . . . . . . . . . . . .
. . 102
Diagramming the Health Problem . . . . . . . . 102
Writing a Causal Theory of the Health
Problem . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 108
Prioritizing Health Problems . . . . . . . . . . . . . . . . .
. . 110
Nominal Group Technique . . . . . . . . . . . . . . . . 111
Basic Priority Rating System . . . . . . . . . . . . . . . 111
Propriety, Economics, Acceptability,

Resources, and Legality (PEARL)
Component . . . . . . . . . . . . . . . . . . . . . . . . . .
. . 113
Prioritizing Based on Importance
and Changeability . . . . . . . . . . . . . . . . . . . . .
114
. . . . . . 115
117
. . . . . . . 118
References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 118
SECTION III Health Program
Development
and Planning 121
Chapter 5 Program Theory
and Interventions
Revealed . . . . . . . . . . . . . . . . . . 123
Program Theory . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 124
Process Theory . . . . . . . . . . . . . . . . . . . . . . .
. . . . 125
Effect Theory . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 125

Interventions . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 126
Finding and Identifying Interventions . . . . . 126
Types of Interventions . . . . . . . . . . . . . . . . . . . .
127
Specifying Intervention Administration
and Dosage . . . . . . . . . . . . . . . . . . . . . . . . . .
. . 128
Interventions and Program Components . . . . 130
Characteristics of Good Interventions . . . . . 131
vi Contents
Budgeting as Part of Planning . . . . . . . . . . . . . . . . .
. 204
Monetize and Compute Program Costs . . . . . 204
Budget for Start-Up and Evaluation Costs . . . 205
Break-Even Analysis . . . . . . . . . . . . . . . . . . . . .
. . 205
Budget Justification . . . . . . . . . . . . . . . . . . . . .
. 207
Budget as a Monitoring Tool . . . . . . . . . . . . . . . . .
. . 209

Budget Variance . . . . . . . . . . . . . . . . . . . . . . .
. . . 209
Types of Cost Analyses . . . . . . . . . . . . . . . . . . . .
209
Information Systems . . . . . . . . . . . . . . . . . . . . .
. . . . . . 213
Health Informatics Terminology . . . . . . . . . . . 214
Information Systems Considerations . . . . . . 214
. . . . . . 216
217
. . . . . . . 217
References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 218
Chapter 9 Implementation Evaluation:
Measuring Inputs
and Outputs . . . . . . . . . . . . . . . 219
Assessing the Implementation . . . . . . . . . . . . . . . . .
219
Implementation Documentation . . . . . . . . . 220
Implementation Assessment . . . . . . . . . . . . . 221

Implementation Evaluation . . . . . . . . . . . . . . . 221
Efficacy, Effectiveness, and Efficiency . . . . . . . . . . .
222
Data Collection Methods . . . . . . . . . . . . . . . . . . .
. . . 223
Quantifying Inputs
to the Organizational Plan . . . . . . . . . . . . . . . . . .
. 223
Human Resources . . . . . . . . . . . . . . . . . . . . . .
. . 228
Physical Resources . . . . . . . . . . . . . . . . . . . . . .
. . 229
Quantifying Outputs
of the Organizational Plan . . . . . . . . . . . . . . . . . .
. 230
Information Systems . . . . . . . . . . . . . . . . . . . . .
. 230
Monetary Resources . . . . . . . . . . . . . . . . . . . . .
. 230
Quantifying Inputs to the Services
Utilization Plan . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 230
Participants and Recipients . . . . . . . . . . . . . . . 230
Intervention Delivery and Fidelity . . . . . . . . . 231

Quantifying Outputs of the Services
Utilization Plan . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 234
Coverage as Program Reach . . . . . . . . . . . . . . 234
Participant-Related Issues . . . . . . . . . . . . . . . . .
238
Program Logistics . . . . . . . . . . . . . . . . . . . . . .
. . . 240
SECTION IV Implementing and
Monitoring the Health
Program 173
Chapter 7 Process Theory for Program
Implementation . . . . . . . . . . . 175
Organizational Plan Inputs . . . . . . . . . . . . . . . . . .
. . . 175
Human Resources . . . . . . . . . . . . . . . . . . . . . .
. . 177
Physical Resources . . . . . . . . . . . . . . . . . . . . . .
. . 179
Transportation . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 180
Informational Resources . . . . . . . . . . . . . . . . . .
180
Time . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . 180
Managerial Resources . . . . . . . . . . . . . . . . . . . .
180
Fiscal Resources . . . . . . . . . . . . . . . . . . . . . . .
. . . 182
Organizational Plan Outputs . . . . . . . . . . . . . . . . .
. . 182
Time Line . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 182
Operations Manual . . . . . . . . . . . . . . . . . . . . . .
. 182
Organizational Chart . . . . . . . . . . . . . . . . . . . . .
. 184
Information System . . . . . . . . . . . . . . . . . . . . . .
. 185
Inputs to Service Utilization Plan . . . . . . . . . . . . . . .
185
Social Marketing . . . . . . . . . . . . . . . . . . . . . . .
. . . 185
Eligibility Screening . . . . . . . . . . . . . . . . . . . . .
. . 185
Queuing . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 189
Intervention Delivery . . . . . . . . . . . . . . . . . . . . .

189
Services Utilization Plan Outputs . . . . . . . . . . . . . . .
191
Summary: Elements of Organizational
and Services Utilization Plans . . . . . . . . . . . 192
Alternative Plan Formats . . . . . . . . . . . . . . . . . . .
. . . . 192
Logic Models . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 193
Business Plans . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 195
. . . . . . 195
197
. . . . . . . 197
References . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 198
Chapter 8 Monitoring Implementation
Through Budgets and
Information Systems . . . . . . . 201
Budgets and Budgeting . . . . . . . . . . . . . . . . . . .
. . . . 201

Budgeting Terminology . . . . . . . . . . . . . . . . . . .
202
vii Contents
Evaluation and Research . . . . . . . . . . . . . . . . . .
268
Rigor in Evaluation . . . . . . . . . . . . . . . . . . . . . .
. . 270
Variables from the Program Effect Theory . . . . . . 271
Outcome and Impact Dependent
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 271
Causal Factors as Independent
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 273
Antecedent, Moderating, and
Mediating Factors as Variables . . . . . . . . . . 273
Measurement Considerations . . . . . . . . . . . . . . . . .
. 275
Units of Observation . . . . . . . . . . . . . . . . . . . . .
. 275
Types of Variables (Levels
of Measurement) . . . . . . . . . . . . . . . . . . . . . . .
275

Timing . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 278
Sensitivity of Measures . . . . . . . . . . . . . . . . . . . .
278
Threats to Data …
Student Instructions BMOverview - BenchmarkYour group has
been given a dataset containing 240 records, located in the
Student_BM tab of this spreadsheet.. Each student is only
responsible for analyzing 60 of these record records - the
specifics of which will be assigned by the instructor.It is
important that each student has a unique 60 records, as the
results will be an input into the CLC, and duplication ofresults
is not helpful. Note that the data have been randomized, so the
data given to your group are likely different than thedata given
to other groups. The intent of this assignment is for students to
organize their data using a pivot table, get a graphical
understandingof the data through a bar chart, then do hypothesis
testing comparing Bo Diddly Tech results versus the national
average.All of your analysis should be done in the Student_BM
tab of this spreadsheet and submitted as part of the
assignmemt.The location where the pivot table, bar chart, and
relevant information should be placed in the Student_BM tab is
indicated by RED instructions. Once completed, the
Student_BM tab will serve as the basis for writing your
management report. It is expected that anyconclusions you draw
in the management report will be consistent with the data and
analyses contained in the spreadsheet.Instructions Data Analysis
Component:1. Make a pivot table with: Business Student
(Rows), Athlete (Rows), Cheated (Columns), and Cheated
(Summed Value).2. Create a bar chart showing cheating by
athletes and business students.4. Determine if there is a
statistical difference between nonathlete BDT business students
and the national average for businessstudents as reported by the

statistical difference between athlete BDT business students and
the national average for business students as reported by the
statistical difference between BDT business students and the
national average for business students as reported by the
statistical difference between BDT nonbusiness students and the
national average for nonbusinessstudents as reported by the
Chronicle of Higher Education.Instructions Data Interpretation
Component:Utilizing the data you have analyzed, write a
managerial report of 500-800 words to the dean. The managerial
report needs to include an introduction, analysis, conclusion,
and a minimum of three supporting references.1. Introduction
(Define): Explain in your own words why you are providing this
report and the problem(s) you are trying to solve.2. Collect:
Describe the data set you used.3. Organize: Describe your pivot
table.4. Visualize: Include and describe your bar chart.5.
Analyze: Provide a summary of your conclusions based upon
the four population proportion hypothesis tests.6. The Dean has
expressed a concern related to the amount of cheating currently
taking place at Bo Diddley Tech and has stronglysuggested that
you “tweak” the statistical data such that it favors the image of
the university.Discuss the potential use of unethical
manipulation of statistical data to provide a biased outcome as
well as the ethical counterproposal you would offer the dean in
this scenario.7. Conclusion: What advice would you give to the
dean based on your analysis of the data?
Student_BMCollegeAthleteCheated1. Pivot TableNationwide
Average% CheatedInsert pivot table in this cell -
F2Business56%Nonbusiness47%Insert pivot table in this cell -
F2Bar chart starts in this cell - F20Bar chart starts in this cell -
F20Insert the appropriate numbers into the hypothesis testing
calculations below based upon your pivot table results. Note
the results.3-6 Hypothesis TestBusiness Nonathlete vs. National
AverageBusiness Athlete vs. National AverageBusiness vs.

National AverageNonbusiness vs. National AverageBusiness
Athlete vs. Business
NonathleteProportionProportionProportionProportionp1 and p2
ProportionSample Size (n)
=count(range)ERROR:#REF!Sample Size (n)
=count(range)ERROR:#REF!AthleteNonathleteResponse of
Interest (ROI)CheatedResponse of Interest
(ROI)CheatedResponse of Interest (ROI)CheatedResponse of
Interest (ROI)CheatedSample Size (n1 or n2)
=COUNT(range)ERROR:#REF!ERROR:#REF!ERROR:#REF!C
ount for Response (CFR)
=COUNTIF(range,ROI)ERROR:#REF!Count for Response
(CFR)
(CFR)
(CFR)
=COUNTIF(range,ROI)ERROR:#REF!Response of Interest
(ROI)ERROR:#REF!ERROR:#REF!ERROR:#REF!Sample
Proportion (pbar)
=CFR/nERROR:#REF!Sample Proportion (pbar)
=CFR/nERROR:#REF!Count for Response (CFR)
=COUNTIF(n1or2,ROI)Did Not CheatDid Not CheatSample
Proportion (p1 or p2)
=CFR1or2/n1or2ERROR:#REF!ERROR:#REF!Highlight your
H0 and HaTwo Tail H0: p = po
Ha: p ≠ po
Ha: p < po
Ha: p ≠ po

Ha: p < po
Ha: p ≠ po
Ha: p < po
Ha: p ≠ po
Ha: p < po
Ha: p >
poHypothesized0.56Hypothesized0.56Hypothesized0.56Hypothe
sized0.47Highlight your H0 and HaTwo Tail H0: p1-p2=0
Left Tail H0: p1-p2≥0
Right Tail H0: p1-p2≤0Ha: p1-p2≠0
Ha: p1-p2<0
Ha: p1-p2>0Confidence Coefficient (Coe)0.95Confidence
Coefficient (Coe)0.95Confidence Coefficient
(Coe)0.95Confidence Coefficient (Coe)0.95Hypothesized
Value0Level of Significance (alpha)
=1-Coe0.05Level of Sig. α0.05Point Estimation of Difference
(Point)
=p1-p2ERROR:#REF!Standard Error (StdError)
=SQRT(Hypo*(1-Hypo)/n)ERROR:#REF!Standard Error
(StdError)
(StdError)
(StdError)
=SQRT(Hypo*(1-Hypo)/n)ERROR:#REF!Test Statistic (Z-stat)

=(pbar-Hypo)/StdErrorERROR:#REF!Test Statistic (Z-stat)
=(pbar-Hypo)/StdErrorERROR:#REF!Pooled Estimation of p
(PE)
=(n1*p1+n2*p2)/(n1+n2)ERROR:#REF!Accept or Reject: Left
TailERROR:#REF!Accept or Reject: Left
TailERROR:#REF!Standard Error (StdError)
=SQRT(PE*(1-PE)*(1/n1+1/n2))ERROR:#REF!Accept or
Reject: Right TailERROR:#REF!Accept or Reject: Right
TailERROR:#REF!Accept or Reject: Right
TailERROR:#REF!Accept or Reject: Right
TailERROR:#REF!Test Statistic Z-stat
=(Point-Hypo)/StdErrorERROR:#REF!Accept or Reject: Two
TailERROR:#REF!Accept or Reject: Two
TailERROR:#REF!Accept or Reject: Right TailERROR:#REF!p-
value (Lower Tail)
=NORM.S.DIST(z,TRUE)ERROR:#REF!p-value (Lower Tail)
=NORM.S.DIST(z,TRUE)ERROR:#REF!Accept or Reject: Two
TailERROR:#REF!p-value (Upper Tail)
=1-LowerTailERROR:#REF!p-value (Upper Tail)
=1-LowerTailERROR:#REF!p-value (Two Tail)
=2*MIN(LowerTail,UpperTail)ERROR:#REF!p-value (Two
Tail)
Tail)

Tail)
=2*MIN(LowerTail,UpperTail)ERROR:#REF!p-value (Lower
Tail)
=NORM.S.DIST(Zstat,TRUE)ERROR:#REF!Accept or Reject p-
value: Left TailERROR:#REF!Accept or Reject p-value: Left
TailERROR:#REF!Accept or Reject p-value: Left
TailERROR:#REF!p-value (Upper Tail)
=1-LowerTailERROR:#REF!Accept or Reject p-value: Right
TailERROR:#REF!Accept or Reject p-value: Right
TailERROR:#REF!p-value (Two Tail)
=2*MIN(LowerTail,UpperTail)ERROR:#REF!Accept or Reject
p-value: Two TailERROR:#REF!Accept or Reject p-value: Two
TailERROR:#REF!Accept or Reject p-value: Two
TailERROR:#REF!Accept or Reject p-value: Two
TailERROR:#REF!p-Lower Limit
=pbar-
CONFIDENCE.NORM(alpha,StdError,n)ERROR:#REF!p-Lower
Limit
=pbar-
Limit
=pbar-
Limit
=pbar-
CONFIDENCE.NORM(alpha,StdError,n)ERROR:#REF!Accept
or Reject p-value: Two TailERROR:#REF!p-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n)ERROR:#REF!p
-Upper Limit
-Upper Limit

-Upper Limit
=pbar+CONFIDENCE.NORM(alpha,StdError,n)ERROR:#REF!
Based upon the count of cheaters, categorized into business and
nonbusiness students, place the relevant numbers in the purple
area of the table below and note the conclusion. 8. Test of
Independence - Is cheating independent of college and athletic
participation?Comparing business and nonbusiness students
number of cheaters for athletes and nonathletesHo: all groups
cheat at the same rateHa: there is a difference in cheating based
upon college or athletic participationIndependent
VariableBusinessNonbusinessCalculationsDependent
variableObsExpObsExpTotalERROR:#REF!ERROR:#REF!Athle
teERROR:#REF!ERROR:#REF!ERROR:#REF!ERROR:#REF!E
RROR:#REF!ERROR:#REF!ERROR:#REF!NonathleteERROR:#
REF!ERROR:#REF!ERROR:#REF!ERROR:#REF!ERROR:#REF
!TotalERROR:#REF!ERROR:#REF!ERROR:#REF!Chi square
test statistic =ERROR:#REF!Level of signicance0.05# of
rows2# of columns2df = 1df = (rows - 1)(columns - 1)p-factor =
ERROR:#REF!Chi square
critical3.8415Conclusion:ERROR:#REF!
DataCollegeAthleteCheatedBusinessAthleteCheatedBusinessAth
leteCheatedBusinessAthleteCheatedBusinessAthleteCheatedBusi
nessAthleteCheatedBusinessAthleteCheatedBusinessAthleteChe
atedBusinessAthleteCheatedBusinessAthleteCheatedBusinessAt
hleteCheatedBusinessAthleteCheatedBusinessAthleteCheatedBu
sinessAthleteCheatedBusinessAthleteCheatedBusinessAthleteCh
eatedBusinessAthleteCheatedBusinessAthleteCheatedBusinessA
thleteCheatedBusinessAthleteCheatedBusinessAthleteCheatedB
usinessAthleteCheatedBusinessAthleteCheatedBusinessAthleteC
heatedBusinessNonathleteCheatedBusinessNonathleteCheatedBu
sinessNonathleteCheatedBusinessNonathleteCheatedBusinessNo
nathleteCheatedBusinessNonathleteCheatedBusinessNonathlete
CheatedBusinessNonathleteCheatedBusinessNonathleteCheated
BusinessNonathleteCheatedBusinessAthleteDid Not
CheatBusinessAthleteDid Not CheatBusinessAthleteDid Not

CheatBusinessAthleteDid Not CheatBusinessAthle teDid Not
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