Understanding Statistical Concepts

Running head: UNDERSTANDING STATISTICAL
CONCEPTS1
UNDERSTANDING STATISTICAL CONCEPTS22
Final Exam
Focus of the Final Exam
The purpose of the Final Exam is to assess your understanding
of the main statistical concepts covered in this course and to
evaluate your ability to critically review a quantitative research
article. The exam will consist of two parts: Part I includes
three essay questions and Part II includes a research critique.
All of your responses should be included in a single Word
document for submission.
Please include the following general headings for each section
of the written exam within your Word document:
Part I: Essay Questions
1. Essay 1
2. Essay 2
3. Essay 3
Part II: Research Study Critique
1. Introduction
2. Methods
3. Results
4. Discussion
Your complete Word document must include a title page with
the following:
1. Student’s name
2. Course name and number
3. Instructor’s name
4. Date submitted

There are three essay questions in this section. You must
answer all three questions. The length of each essay should be
one to two double-spaced pages (excluding title and reference
pages). Use 12-point font and format your paper with regular 1-
inch margins. Do not include the essay prompt in your
document. It will not count toward the length requirement for
your essays.
Essay 1
A group of researchers conducted an experiment to determine
which vaccine is more effective for preventing getting the flu.
They tested two different types of vaccines: a shot and a nasal
spray. To test the effectiveness, 1000 participants were
randomly selected with 500 people getting the shot and 500 the
nasal spray. Of the 500 people were treated with the shot, 80
developed the flu and 420 did not. Of the people who were
treated with the nasal spray, 120 people developed the flu and
380 did not. The level of significance was set at .05. The
proportion of people who were treated with the shot who
developed the flu = .16, and the proportion of the people who
were treated with the nasal spray was .24. The
calculated p value = .0008.
For this essay, describe the statistical approaches (e.g., identify
the hypotheses and research methods) used in this excerpt from
a research study. Interpret the statistical results and examine
the limitations of the statistical methods. Finally, evaluate the
research study as a whole and apply what you have learned
about hypothesis testing and inferential statistics by discussing
how you might conduct a follow-up study.
Your essay must address the following points:
· Describe the research question for this experiment.
· What were the null and alternative hypotheses?
· Were the results of this test statistically significant?
· If so, why were they significant?
· Would the researchers reject or fail to reject the null
hypothesis?

· Do the results provide sufficient evidence to support the
alternative hypothesis?
· Was the sample appropriate for this study? Explain your
answer.
· What are some possible limitations to this study?
· Discuss how you would conduct a follow up study to this one.
Explain your answer.
· Describe the difference between practical and statistical
significance.
Essay 2
A researcher has investigated the relationship between IQ and
grade point average (GPA) and found the correlation to be .75.
For this essay, critique the results and interpretation of a
correlational study.
· Evaluate the correlational result and identify the strength of
the correlation.
· Examine the assumptions and limitations of the possible
connection between the researcher’s chosen variables.
· Identify and describe other statistical tests that could be used
to study this relationship.
Your essay response must address the following questions:
· How strong is this correlation?
· Is this a positive or negative correlation?
· What does this correlation mean?
· Does this correlation imply that individuals with high
Intelligence Quotients (IQ) have high Grade Point Averages
(GPA)?
· Does this correlation provide evidence that high IQ causes
GPA to go higher?
· What other variables might be influencing this relationship?
· What is the connection between correlation and causation?
· What are some of the factors that affect the size of this
correlation?
· Is correlation a good test for predicting GPA?
· If not, what statistical tests should a researcher use, and why?
Essay 3

A researcher has recorded the reaction times of 20 individuals
on a memory assessment. The following table indicates the
individual times:
2.2
4.7
7.3
4.1
9.5
15.2
4.3
9.5
2.7
3.1
9.2
2.9
8.2
7.6
3.5
2.5
9.3
4.8
8.5
8.1
In this essay, demonstrate your ability to organize data into
meaningful sets, calculate basic descriptive statistics, interpret
the results, and evaluate the effects of outliers and changes in
the variables. You may use Excel, one of the many free online
descriptive statistics calculators, or calculate the values by hand
and/or with a calculator.
Next, separate the data into two groups of 10; one group will be
the lower reaction times, and the second group will be the
higher reaction times. Then, address the following points in
your essay response:
· Calculate the sum, mean, mode, median, standard deviation,

range, skew, and kurtosis for each group.
· How do the two groups differ?
· Are there any outliers in either data group?
· What effect does an outlier have on a sample?
Lastly, double each sample by repeating the same 10 data points
in each group. You will have a total of 20 data points for each
group. After completing this, address the following in your
essay response:
· Calculate the following for the new data groups: sum, mean,
mode, median, standard deviation, range, skew, and kurtosis.
· Did any of the values change?
· How does sample size affect those values?
Part B: Research Study Critique
In this second portion of the Final Exam, you will identify and
critically evaluate a quantitative research article based on a
social science topic. Your selected article must include a
research question(s) and/or hypothesis(es) and utilize statistical
analyses covered in the course. The article must be peer-
reviewed and published within the last 10 years.
In the body of your critique, describe the statistical approaches
used, the variables included, the hypothesis(es) proposed, and
the interpretation of the results. In your conclusion, suggest
other statistical approaches that could have been used and, if
appropriate, suggest alternative interpretations of the results.
This process will allow you to apply the concepts learned
throughout the course in the interpretation of actual scientific
research. Your critique must include the following sections:
1. Introduction: This section will include a general introduction
of the quantitative study from a peer-reviewed source published
within the last 10 years. The research questions and/or
hypothesis(es) as well as the purpose of the study should be
clearly defined.
2. Methods: Describe and evaluate the procedures and methods
of data collection, measures/instruments used, the participants
and how they were selected, and the statistical techniques used.
3. Results: In this section describe and critique the results

presented in the study.
4. Discussion: Discuss and evaluate the efficacy of the results
presented in the study. Address, the strengths, weaknesses, and
limitations of the study, and suggest future research directions.
Include additional forms of statistical analyses as part of the
suggestions for future research.
The Research Study Critique:
1. Must be three to four double-spaced pages in length
(excluding title and reference pages) and formatted according to
APA style as outlined in the Ashford Writing Center. Use 12-
point font, with 1-inch margins.
2. Must use the sections and headings described above.
3. Must address the article with critical thought by examining,
reflecting, and evaluating the article from an objective
viewpoint and by using facts to support your argument. Refer to
the Critical Thinking Community (Links to an external
site.)Links to an external site. website for further assistance.
4. Must end with a conclusion that summarizes your critical
evaluation.
5. Must use one quantitative research study from a peer-
reviewed source that was published within the last 10 years.
6. Must properly cite the source article in APA style as outlined
in the Ashford Writing Center.
7. Must include a separate reference page, formatted according
to APA style as outlined in the Ashford Writing Center.
Week Five Final Project: Understanding Statistical Concepts
and Applications
Table of Contents
Introduction
...............................................................................................
......................................3

Essay One
...............................................................................................
.........................3, 4
Essay Two
...............................................................................................
........................5, 6
Essay Three
...............................................................................................
..................6, 7, 8
Introduction
...............................................................................................
.........................8
Methods
...............................................................................................
...........................8, 9
Results
...............................................................................................
............................9, 10
Discussion
...............................................................................................
..........................11
Conclusion
...............................................................................................
....................................11
References
...............................................................................................
.....................................12
Appendices
Appendix A Essay Three Data Set
..................................................................................13
Appendix B Essay Three Data Set Group A
...................................................................14
Appendix C Essay Three Data Set Group B
...................................................................15

Appendix D Essay Three Data Set Group A Doubled
....................................................16
Appendix E Essay Three Data Set Group B Doubled
.....................................................17
Week Five Final Project: Understanding Statistical Concepts
and Applications
Many psychology students are surprised to find that
mathematical and statistical courses are required to obtain the
degrees needed to become a helping professional. They often
question the necessity or relevance of such courses and pose the
argument that understanding human behavior and treating
mental illness have little or nothing to do with statistics save
for work in research. Students holding this position are
frequently surprised to learn that all areas of psychology from
industrial to practical relate to statistics in significant ways. In
fact, statistics have been instrumental in unlocking many of the
mysteries of human psychology. Through the important area of
research, statistics help mental health care providers understand
the causes, treatments, and prevalence of various illnesses both
common and rare. Within this paper, the integral part statistical
analysis plays in the field of psychology will be explored and
the different methods of statistical analysis will be identified
and discussed.
Essay One
Researchers conducted an experimental study to determine if a
particular flu prevention vaccine was more effective when
administered in nasal spray form or injection form. The
question to be answered was whether the method of delivery of
the flu vaccination had an impact on the efficacy of the drug.
The null hypothesis (H0: n1 = n2) is that no differences exist
between the two methods of delivery and the alternative
hypothesis (HA: n1 ≠ n2) would be that a difference exists. The
study involved a sample of 1000 randomly chosen individuals
(N = 1000) sorted into two groups; group one received the drug

via nasal spray (n1 = 500) and the second group received the
drug by way of injection (n2 = 500). The level of significance
was established to be .05 and the calculated p-value was .0008.
The results of the study showed that out of the 500 participants
who received the nasal spray 120 contracted the flu while 420
did not (n1 = .24). In the group of individuals who received the
drug via injection, 80 participants developed the flu while 420
did not (n2 = .16). In this example the calculated p-value of
.0008 is less than the established level of confidence which is
.05 which suggests that the results are statistically significant
resulting in the rejection of the null hypothesis. In order for the
null hypothesis to be correct, these results would occur in only
8 out of every 10,000 trials which is very unlikely.
The sample size of 1000 participants is adequate and will likely
produce results that are a reliable representation of the entire
population. It is important to note that a very limited amount of
information was provided in the assignment prompt which
makes assessing the validity of the study and the findings
produced very challenging. I assume that the sample was
selected randomly from a pool of participants with a high level
of homogeneity (similar ages, comparable health, etc.) and feel
confident that this study and its findings were valid with
relatively few limitations. One limitation was that no control
group was included within the study. The inclusion of a
placebo-control group helps researchers avoid common errors
such as placebo-effect or observer error from skewing the
findings (Tanner, 2011). Additionally, the study did not specify
how participants were exposed to the flu virus, whether
naturally by way of their regular routines or through intentional
lab-controlled exposure. This information could be important as
strains vary in aggressiveness, severity of symptoms, and ease
of transmission (Center for Disease Control, 2015). Finally, this
study was limited in that it could not easily be conducted as a
double-blind or even blind study. To render this study blind or
double-blind in follow-up studies, the participants would have
to receive both nasal spray and a shot; one containing the drug

and the other being a placebo.
Essay Two
In this scenario a researcher might be interested in establishing
a definitive link between high intelligence quotient (I.Q.) and a
high grade point average (GPA). To accomplish this, the
researcher would collect interval data such as scores from I.Q
tests and G.P.A records and implement a method such as
Pearson’s Correlation Coefficient to produce a value that can be
used to identify patterns of linear regression. After confirming
that the samples and data met the requirements for eligibility
such as a linear relationship and normal distribution, the
researcher found a correlation value of .75 which according to
Tanner (2011) is considerably high. Correlation values of −1.0
or +1.0 (r = -1 and r = 1, respectively) are indicative of a
perfect relationship and a correlation value of “0” (r = 0)
indicates absolutely no relationship. Should the researcher wish
to create a visual representation of the relationship between I.Q.
and G.P.A he could enter the data into a scatterplot with one set
of data on the y axis and the other on the x axis. With a
correlation value of .75 a clear picture of the strong positive
relationship would appear with the two variables mainly moving
together from the lower left of the plot to the upper right.
Conversely, if the relationship had been negative the pattern
would move from the upper left of the plot to the lower right.
Generally speaking, the less “scatter” in the plot, the stronger
the relationship regardless of whether the values are positive or
negative (Tanner, 2011).
Despite the strong correlation the researcher has established in
this example, it is important to keep in mind that correlation
alone is not sufficient evidence of causation. While it is true
that students with higher I.Q. levels often have high grade point
averages, there are other factors believed to contribute to the
relationship including the availability of educational resources
such as tutoring, strong organization skills, diet, stress levels,
and adequate parental supervision and guidance (Bernard,

Nalbone, Hecker, & Degges-White, 2015). In fact, correlation
simply implies that two variables are related in some way. To
effectively determine causation, one would need to isolate the
related variables through methods such as randomized
controlled trials (RCTs) which greatly reduce the possibility of
skewed findings due to confounding variables. The
identification of a strong correlating relationship between high
I.Q. and high G.P.A. may not be enough to establish causation
but there are plenty of beneficial ways that the relationship can
be used. For example, it could be used as a platform on which to
build effective after-school programs that offer nutritional
information and resources, tutoring services, and parent-student
study sessions to economically-disadvantaged students.
Essay Three
In this section of my final exam I will provide the data set for
each group and fulfil the requirements of the essay prompts as a
means of demonstrating both my comprehension of the concepts
and terms used and my ability to effectively organize data and
interpret results. The information provided in this section can
also be found in the appendices at the end of the paper.A
researcher has compiled a list of reaction times for 20
individuals who have completed a memory-related assessment
test. This is the data set produced: (See Appendix A)
2.2, 4.7, 7.3, 4.1, 9.5, 15.2, 4.3, 9.5, 2.7, 3.1, 9.2, 2.9, 8.2, 7.6,
3.5, 2.5, 9.3, 4.8, 8.5, 8.1
The sum which is the total of the entire data set is ∑x = 127.2
The data is to be divided into two groups of 10; group A
contains the lowest 10 reaction times (See Appendix B) and
group B contains the 10 highest reaction times (See Appendix
C).
The sum of group A is 34.8 and for group B the sum is 92.4.
The mean, which is the sum of the data set divided by the
number of items in the set, for group A is M = 3.48 and for
group B is M = 9.24. The mode, which is the value that appears
most frequently within the set, for group A is Mo = 2.2, 2.5,
2.7, 2.9, 3.1, 3.5, 4.1, 4.3, 4.8, 4.7 and for group B the mode is

Mo = 9.5.
The median represents the middle score when a set of data is
arranged in order. The median for group A is Mdn = 3.3 and for
group B the median is Mdn = 8.85. The standard deviation of a
data set is the square root of the variance, for group A the
standard deviation is s = 0.94 and for group B it is s = 2.2. The
range for group A is r = 2.6 and for group B is r = 7.9. Range
marks the difference between the highest and lowest values
within the set. Skewness refers to the symmetry within the set
and for group A is sk = 0.15 while group B skew is sk = 1.95.
Finally, kurtosis refers to how spread out the data in a set are.
The kurtosis of group A is kur = 1.4 and for group B is kur =
5.7.
The descriptive values of each data set vary significantly. There
are several reasons for these variations which include factors
such as outliers. Outliers are extreme scores that stand out from
the rest of the scores in a data set. For example, in group A the
mode consisted of every number in the data set because they
each appeared a single time within the set but in group B the
mode was simply 9.5 because this number appeared twice within
the set. The sums of each set where significantly different from
one another because one set was comprised of the lowest values
of the original set and the other group contained all the greatest
values.
Next the values of group A (See Appendix D) and group B (See
Appendix E) were doubled and produced the following results:
The sum for group A is now 60 and for group B is 185. The
mean for group A is M = 3.3 and M = 9.24 for group B. The
mode values for both groups remained the same with group A
being Mo = 2.2, 2.5, 2.7, 2.9, 3.1, 3.5, 4.1, 4.3, 4.8, 4.7 and
group B at Mo = 9.5. The median for group A is Mdn = 3.1and
for group B is Mdn = 8.85. The group A standard deviation is s
= .084 and s = 2.18 for group B. Range for group A is r = 2.5
and r = 7.9 for group B. After doubling, the skew for group A
was sk = 0.30 and for group B was sk = 2. Kurtosis value for
group A became kur = 1.64 and for group B kur = 6.02. Upon

doubling the differences in values became more obvious.
The article I will be critiquing for the second part of my final
exam is an article from 2010 titled “Parenthood: A Contributing
Factor to Childhood Obesity”.
Introduction
This article is in response to data from 2004 by The National
Health and Nutrition Examination Survey (NHANES) which
suggested that national obesity rates among children increased
from 6.5% in 1980 to 17.1% in 2004. The authors of the
selected article analyzed the information provided by the
NHANES to identify specific predictors which might play a
pivotal role in the rising childhood obesity epidemic. Variables
such as race, physical inactivity, economic standing, single and
double parent households, and parental involvement were
considered and analyzed. The secondary objective of the authors
was to assess differences in dietary and blood cholesterol of
children from single parent versus dual-parent households. They
hypothesized (H1) that in a national population, children from
single parent households would have higher obesity rates than
children from dual-parent households. The null hypothesis (H0)
would be that no differences in obesity rates between children
from single and dual parent households would be observed.
Methods
The authors believed the data provided by the NHANES was
ideal for their study as it matched the information of children to
their parents and included a large sample population of 1,000
participants between the ages of 6-11 years. The 1000 children
came from 219 households with single-parents (N = 219) and
780 with two-parents (N = 780). Gender distribution between
the two groups was equal. The physical variables included in
analysis for the children were age, gender, race, height, weight,
and body mass index (BMI). Obesity was determined according
to the guidelines provided by the Center for Disease Control
and Prevention (CDC) which categorizes a child as obese when
they fall above the 95th percentile for their age, height, and

weight. Recall data of dietary intake for the previous 24 hour
period were collected and included the consumption of total
energy, carbohydrates, proteins, total fat, saturated fat, and
mono/poly unsaturated fatty acids (Huffman, Kanikireddy, &
Patel, 2010). The variables analyzed for the parents included
gender, marital status, level of education, and annual family
income.
Results
Initial findings showed that children from single parent
households were significantly more overweight (p < 0.01) than
children from dual-parent households (p < 0.05), and that black
children were heavier than children from all other racial
backgrounds (p < 0.04). These preliminary findings suggested
to researchers that their hypothesis was correct. To investigate
these relationships further, researchers randomly selected a
sample population from the NHANES study and asked them to
participate on a smaller scale study. Following the original
study design used in the NHANES survey, researchers also
included in-depth interviews of each participant and the parent
or parents with whom they lived. In the follow-up study
researchers used BMI as the primary measurement of obesity
when comparing children from single and dual-parent
households. Then a one-way analysis of variance (ANOVA) was
performed to compare the BMI status of children from different
racial groups living in single-parent households. To test the
association of obesity and parental status, logistic regression
models were performed with obese versus not obese as the
dependent variable (DV), single-parent household as the
independent variable (IV) and age, income race and education
as the covariates (CV). The final sampled weight and Taylor’s
linearization methods were used for variance estimation.
Statistical analyses were conducted and the database was
imported for logistic regression analyses. For all methods of
analysis, p < 0.05 was considered significant acids (Huffman,
Kanikireddy, & Patel, 2010). Findings from the subsequent
study showed that the proportion of overweight children from

single-parent households (41%) was greater than children from
dual-parent households (31%). Participants from dual-parent
households had significantly (p < 0.01) lower BMI (19.2 ± 5.4)
than participants living in single-parent households (21.5 ± 6.5).
Total calories consumed were marginally significant (p < 0.06)
between participants of single-parent households (1910 ± 24)
and dual-parent households (1,860 ± 25). Mean low-density
lipoprotein (LDL) level, the cholesterol considered “bad” by the
medical and nutritional community was also higher among
participants from single-parent household (91 ± 1.5) compared
to participants from dual-parent households (88 ± 1.4; p < 0.05).
Total fat and saturated fat intakes (g/day) were higher (p <
0.05) for single-parent participants (9.1 ± 6.2; 3.3 ± 1.0) than
for participants from two-parent households (8.6 ± 5.2; 3.2 ±
1.1). African-American youths were found to be more
overweight and had higher (p < 0.04) BMI (20.4 ± 2.2) than
White youths (19.2 ± 2.2) and youths from all other racial
backgrounds (18.9 ± 3). Through the use of binary logistic
regression models, researchers determined that the odds ratio
(OR) for the likelihood of obesity for children in single-parent
households was significantly higher (OR: 1.72 (1.24, 2.38), p =
0.001) than the OR for obesity in kids from dual-parent homes.
Co-variables such as parental income, levels of education, race,
and age were also used in the logistic regression models.
Researchers believed the findings to support their original
hypothesis.
Discussion
The findings of the smaller, subsequent study aligned with the
findings from nearly every other study previously conducted
regarding childhood obesity and household construct. One
exception was a study in which findings suggested children
from foster homes or living with grandparents had lower rates
of obesity.
Overall I believe this study was conducted ethically with most
of the important variables accounted for. Any limitations or

flaws I noted were small and possibly insignificant but included
the omission of information about how many households were
single-parent due to a deceased second parent and the overall
mental health of the parent(s) within the study. These variables
could cause a slight skew in finding interpretations.
Nevertheless, this study and its findings can be instrumental in
the construction of programs designed to lower childhood
obesity rates on a global scale. Doctors, therapists, and school
administrators might also benefit from this study in the
assessment and detection of at-risk children.
In conclusion, the importance of statistics within the social and
behavioral sciences is far more significant than many people,
mainly students, often recognize. Without the studies and
research efforts of those in the field of statistical analysis, many
helping professionals would not have the tools or knowledge to
treat their patients and clients or assist them in learning to live
a healthy, autonomous life. This course has been a challenge for
me but I believe I have gained a basic knowledge of statistics
and more importantly, a better understanding of how great a
contribution statistics has made in the field I have chosen to
dedicate my life.
References
Bernard, J. M., Nalbone, D. P., Hecker, L. L., & Degges-White,
S. E. (2015). Co-Parenting factors that contribute to academic
success. European Scientific Journal, 241.
http://search.ebscohost.com.proxy-
library.ashford.edu/login.aspx?direct=true&db=edsgao&AN=eds
gcl.424568329&site=eds-live
Center for Disease Control and Prevention. (2015, October 2).
Key facts about seasonal
Flu vaccine| seasonal influenza (Flu) | CDC. Retrieved from
http://www.cdc.gov/flu/protect/keyfacts.htm
Furey, E. (2016) Stem and leaf plot generator [Online calculator
resource] Retrieved from http://www.CalculatorSoup.com
Huffman, F. G., Kanikireddy, S., & Patel, M. (2010).

Parenthood: A contributing factor to childhood obesity.
International Journal of Environmental Research and Public
Health, 7(7), 2800-2810. doi:10.3390/ijerph7072800
Tanner, D. (2011). Statistics for the Behavioral & Social
Sciences San Diego, CA: Bridgepoint Education, Inc.
Appendix A
Original Data Set/Essay 3
(Source: Furey, 2016)
Appendix B
Data Set/Essay 3 (Group A)
Appendix C
Data Set/Essay 3 (Group B)
Appendix D
Data Set/Essay 3 (Group A doubled)
Appendix E
Data Set/Essay 3 (Group B doubled)

Project Concept Document 9/9
Project Concept Document (PCD)
<Project Title>
Contents
1Purpose of Project Concept Document (PCD)3
2Document and Concept Summary3
3Project Concept Statement4
4Project Sponsorship, Team and Key Stakeholders Analysis4
5Project Value Measurement Framework5
6Project Description7
Project Purpose, High-Level Scope, Time Estimate and Costs7
Background and Circumstances7
Estimated Business Benefits7
Proposed
Solution
(s) Approach7
7Estimated Resource Requirements7
8Potential Project Risks / Constraints8
9Project Sponsor’s (Governance) Authorization to Proceed9

Purpose of Project Concept Document (PCD)
The PCD document serves several purposes:
· Project initiation document – provides initial assessment for
project governance decision to Approve/Reject/Put-on-Hold
proposed project,
· Clearly states the business problem that needs to be solved,
· Defines the projects strategic objectives
· Suggests an initial approach to solve the business problem,
· Defines high-level project success criterion of Customers /
Sponsor,
· Communicates the perceived priority and strategic alignment
of the project to Lahey Health Governance.
· Serves as sponsor’s and PPM Governance authorization to
proceed (Decision Gate #1) to project Concept and Definition
phases.
Document and Concept Summary
Functional Area Priority Score
# (x) Project For FY13/14, Score ____ Priority : H - M -
L
Date Submitted
dd/mmm/yyyy

Project/Program Name
Project Sponsor(s)
Business Case Author(s)
Department/Business Unit
Project Portfolio Category
Estimated Project Size
S – M – L
Date Governance Approved/Rejected/On-Hold
Project Concept Statement
What are the overall Lahey Health objectives that this project
will satisfy?
Example:
“To be the healthcare industry leader in our ability to provide
real-time billing information to our patients.”
Note: The concept statement is more strategic in nature than a
POS (Project Objective Statement), which specifically addresses
project scope, schedule and resource goals. The POS is

typically created in the PDW (Project Initiation/Definition
Workshops).
Project Sponsorship, Team and Key Stakeholders Analysis
Name
Project Role
Contact Information
Impacted How?
Business Line Executive Project Sponsor (VP and above)
Proposed Business Lead
Proposed Project Manager/Lead
Proposed Technical Manager/Lead

Key Project Stakeholders (Physicians, Clinics, Facilities)
Clinical/Business/IT/SMEs, Etc.
[Identify the Project Sponsor, proposed team members, and key
stakeholders. Identify their project role, how they will be
impacted by the project and contact information.]
Project Value Measurement Framework
The goal of all Lahey Health projects is to provide value to our
established strategic goals. Please fill out the following value
measurement matrix. 1. Provide a self-reported ranking for
each strategic goal category using the scoring rubric for each
category, 2. Provide a brief explanation or reference for your
self-reported score. Note: The final score will assist project
governance to determine relative ranking for each project.
Proposed Project Value Scoring Criterion & Ranking
Scoring Guidelines:

Yes
No
Regulatory / Compliance
Is the project “Mandated” by a regulatory body?
Justification: [Please provide reference or justification for this
decision]
Scoring Guidelines:
Requestor Rank 0-5
Governance Weighting (%)
Category Score
Patient Safety / Assessed Risk to Lahey Health
Will the project improve the quality of care for our patients and
improve patient satisfaction?
5 = Yes, significantly improves patient safety or reduces risk to
Lahey Health (quantifiable evidence exists)
3 = Yes, medium impact (some quantifiable improvement)
1 = Yes, minor impact (hard to quantify)
0 = No impact

ranking]
Superior Patient Experience / Satisfaction
Does this project improve our patients’ care experience at
Lahey Health?
5 = Yes, significantly improves care experience (quantifiable
evidence exists)
0 = No impact
ranking]
Margin Enhancement / Operational Efficiency / Cost
Effectiveness
Will the project reduce expenses or enhance revenue and
improve efficiency for how we care for our patients?

5 = Yes, significantly improves operational efficiency
(quantifiable evidence exists)
0 = No impact
ranking]
Lahey Health “System-wide Integration”
Will the project help grow our business or create new facilities
and / or services?)
5 = Yes, significantly facilitates system wide integration
(quantifiable evidence exists)
0 = No impact
ranking]

Lahey Health System Strategic Growth
Will the project support the strategic goals of Lahey Health?
5 = Yes, significantly improves strategic growth (quantifiable
evidence exists)
0 = No impact
ranking]
Project DescriptionProject Purpose, High-Level Scope, Time
Estimate and Costs
Provide a high-level summary of business/clinical problem
being solved or opportunity begin pursued by this project.
Briefly describe the proposed project/initiative and its purpose.
Provide a high level overview of expected project deliverables

(Scope),estimated or desired delivery time-frames (e.g. 2-4
months) and high-level cost estimates [Note: These are only
high-level – the detailed scope, schedule and cost will be
developed in the Project Definition Phase.]
Background and Circumstances
Provide a description of the environment and/or circumstances
that have given rise to the project. Examples may include issues
of quality, efficiency, cost, customer service, competition,
business integration, regulatory requirement etc. What has given
rise or triggered the need for this project?
Estimated Business Benefits
Summary of expected business benefits for Lahey Health to do
this project.
Think in terms of meeting the customers’ [clinicians’ or
patients’] needs, generating new revenues, increasing
operational efficiencies, reducing manpower, etc. Estimated
business benefits will form the basis for identifying critical
success factors for the project.
Proposed

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