Running head: OVERVIEW 1 OVERVIEW 3 Articles Overview Yoanka Rodriguez South University May 2017 Articles Overview Quantitative Qualitative Articles summary The article by Bortz, Ashkenazi, and Melnikov (2015) has addressed the problem of organ donation. The authors were interested to learn about the motivation of those individuals who sign the donor card. Comparative analysis of values and beliefs of those who agree and disagree to donate demonstrated that people with better education agree to sign the document. Dinkel and Schmidt (2015) have discussed the strategies of primary prevention in incarcerated women. They have indicated the main health-related concerns in this population. Research problem To identify the difference in mentality between those willing and not willing to donate organs. To identify the incarcerated women’s health-related education needs. Purpose statement Comparative analysis of “spirituality, purpose in life, and attitudes toward organ donation” in people willing and not willing to sign the donor card (Bortz et al., 2015, p. 33). Analysis of health educational needs in imprisoned women with the use of an interview. Hypothesis/research questions Personal beliefs, cultural peculiarities, spirituality, and values influence the decision to sign the donor card. “What are the top ten health education needs in imprisoned women?” (Dinkel & Schmidt, 2015, p. 230). Significance to nursing Organ donation is an important aspect of health care. Educating the right attitude to organ donation in broad populations, health care professionals will help save millions of lives. Nursing professionals as direct care providers constantly working with patients have the key role in this objective. Therefore, they need informational support on how to approach people in the most effective way. The article provides many important findings to help in this area. The number of incarcerated females is ever growing in the United States. This population is identified as a vulnerable group due to increased morbidity and mortality. This research has helped to understand how primary prevention can be implemented to help them. Two details to support the study being quantitative or qualitative This study is quantitative because (1) the research process was organized to test measurable relationships between variables and (2) inferential statistics was used. This study is qualitative because (1) it uses an interview to collect the data and (2) it aims to generate the theory as for the best practice health teaching for incarcerated women. References Bortz, A., Ashkenazi, T., & Melnikov, S. (2015). Spirituality as a predictive factor for signing an organ donor car.

1. Running head: OVERVIEW
1
OVERVIEW
3
Articles Overview
Yoanka Rodriguez
South University
May 2017
Articles Overview
Quantitative

2. Qualitative
Articles summary
The article by Bortz, Ashkenazi, and Melnikov (2015) has
addressed the problem of organ donation. The authors were
interested to learn about the motivation of those individuals
who sign the donor card. Comparative analysis of values and
beliefs of those who agree and disagree to donate demonstrated
that people with better education agree to sign the document.
Dinkel and Schmidt (2015) have discussed the strategies of
primary prevention in incarcerated women. They have indicated
the main health-related concerns in this population.
Research problem
To identify the difference in mentality between those willing
and not willing to donate organs.
To identify the incarcerated women’s health-related education
needs.
Purpose statement
Comparative analysis of “spirituality, purpose in life, and
attitudes toward organ donation” in people willing and not
willing to sign the donor card (Bortz et al., 2015, p. 33).
Analysis of health educational needs in imprisoned women with
the use of an interview.
Hypothesis/research questions
Personal beliefs, cultural peculiarities, spirituality, and values
influence the decision to sign the donor card.
“What are the top ten health education needs in imprisoned
women?” (Dinkel & Schmidt, 2015, p. 230).
Significance to nursing

3. Organ donation is an important aspect of health care. Educating
the right attitude to organ donation in broad populations, health
care professionals will help save millions of lives. Nursing
professionals as direct care providers constantly working with
patients have the key role in this objective. Therefore, they
need informational support on how to approach people in the
most effective way. The article provides many important
findings to help in this area.
The number of incarcerated females is ever growing in the
United States. This population is identified as a vulnerable
group due to increased morbidity and mortality. This research
has helped to understand how primary prevention can be
implemented to help them.
Two details to support the study being quantitative or
qualitative
This study is quantitative because (1) the research process was
organized to test measurable relationships between variables
and (2) inferential statistics was used.
This study is qualitative because (1) it uses an interview to
collect the data and (2) it aims to generate the theory as for the
best practice health teaching for incarcerated women.
References
Bortz, A., Ashkenazi, T., & Melnikov, S. (2015). Spirituality as
a predictive factor for signing an organ donor card. Journal of
Nursing Scholarship, 47(1), 25-33. doi: 10.1111/jnu.12107
Dinkel, S., & Schmidt, K. (2015). Health education needs of
incarcerated women. Journal of Nursing Scholarship, 46(4),
229-234. doi: 10.1111/jnu.12079

4. Meaningfulness vs. Statistical Significance
Meaningfulness vs. Statistical Significance
Program Transcript
MATT JONES: Statistical significance deals with the critical
value of a statistic.
And in a certain philosophy, making a determination of whether
the null
hypothesis is rejected or you fail to reject the null hypothesis.
That is statistical
significance.

5. Meaningfulness is taking that statistic and determining it's
applicability out in the
real world. So all too often, as researchers, we get caught up in
chasing
statistical significance. And certainly while that's part of
statistics, we also want to
focus on the tie back to the real world.
So while we might find statistical significance in a very large
sample, once we
look at the effect that's present there, or the strength of the
relationship, or the
magnitude of the difference, they all might be extremely small.
So while we might
have quote unquote, "highly statistically significant results,
the effect,
relationship, or differences are rather small and almost
meaningless in the
applied real world."
It's important to understand the difference between statistical
significance and
meaningfulness. Because as a critical consumer of research,
when you're
evaluating claims, you want to match up the actual statistics
with the claims
being made. So if a claim is being made of how large an effect
was or how
important something is, you want to go back to the statistical
analysis and see if
that is indeed the case.
So just because something says there was a statistically
significant result doesn't
necessarily mean you can make claims about how profound an
impact that it

6. might have out in the real world. Because that might be an
overreach of what the
statistics were actually doing. Statistics will tell you only a
small little piece of the
pie. It's up to us as researchers to put the results into a
specific context and
understand that alignment between the statistics and the results
with a possible
misalignment.
History has given us a lot of examples of where statistics
and statements don't
necessarily match up. It's important to have a critical eye when
it comes to
statistical significance and meaningfulness to evaluate results,
not only of other
researchers, but also your own. And when you're making a
claim about an effect
or a relationship or an intervention, we're able to make that
claim with some
degree of authority. So just because you have statistical
significance, i.e. a P
value below 0.01, that doesn't necessarily mean the magnitude
of the effect was
strong where there's a large difference between two groups
when in fact it might
be very small.
©2016 Laureate Education, Inc. 1

7. Meaningfulness vs. Statistical Significance
The danger in not understanding the difference between
statistical significance
and meaningfulness is our potential to overreach with results.
So in an era of big
data with extremely large sample sizes, it's very common to
find statistical
significance. But when in fact if we look at the results, the
differences might be
minute, the effect size might be very small, the strength of the
relationship might
be very small. So speaking from a purely statistical
standpoint, there might be an
effect that's present. But if we were to change policy and spend
money based
upon those results, it might not be the most practical or logical
way to proceed.
©2016 Laureate Education, Inc. 2
Statistical Significance and Meaningfulness
A research paper claims a meaningful contribution to the
literature based on finding statistically significant relationships
between predictor and response variables. In the footnotes, you
see the following statement, “given this research was
exploratory in nature, traditional levels of significance to reject
the null hypotheses were relaxed to the .10 level.”
Statistical significance deals with a critical value of a
statistic and making a determination of whether the null
hypothesis is rejected or you fail to reject the null hypothesis
(Laureate Education (Producer), 2016f). Meaningfulness is the
taking that statistic and determining its applicability out in the

8. real world (Laureate Education (Producer), 2016f).
The statement “given this research was exploratory in
nature, traditional levels of significance to reject the null
hypotheses were relaxed to the .10 level,” The statement notates
that a qualitative research study was completed using the
exploratory research tool, and exploratory research is often used
to generate formal hypotheses. Formal hypotheses is an idea
which is suggested as a possible explanation of a particular
situation or condition, but which has not yet been proved to be
correct. The traditional level of significance is the probability
of rejecting the null hypothesis in a statistical test when it is
true. Null hypothesis contradicts the research hypothesis and
states that there is no difference between the population means
and some specified value (p-value). The p-value threshold is
<0.05 which is extremely strict; therefore, to reduce the extreme
strictness by relaxing the p-value to <0.10. The predictability is
greater in weakness.
References
Laureate Education (Producer). (2016f). Meaningfulness vs.
statistical significance [Video file]. Baltimore, MD: Author.
© 2016 Laureate Education, Inc. Page 1 of 2
Week 5
Scenarios
1. The p-value was slightly above conventional threshold, but
was described as
“rapidly approaching significance” (i.e., p =.06).

9. An independent samples t test was used to determine whether
student satisfaction
levels in a quantitative reasoning course differed between the
traditional classroom
and on-line environments. The samples consisted of students in
four face-to-face
classes at a traditional state university (n = 65) and four online
classes offered at
the same university (n = 69). Students reported their level of
satisfaction on a five-
point scale, with higher values indicating higher levels of
satisfaction. Since the
study was exploratory in nature, levels of significance were
relaxed to the .10 level.
The test was significant t(132) = 1.8, p = .074, wherein students
in the face-to-face
class reported lower levels of satisfaction (M = 3.39, SD = 1.8)
than did those in the
online sections (M = 3.89, SD = 1.4). We therefore conclude
that on average,
students in online quantitative reasoning classes have higher
levels of satisfaction.
The results of this study are significant because they provide
educators with
evidence of what medium works better in producing
quantitatively knowledgeable
practitioners.
2. A results report that does not find any effect and also has
small sample size
(possibly no effect detected due to lack of power).
A one-way analysis of variance was used to test whether a
relationship exists
between educational attainment and race. The dependent

10. variable of education
was measured as number of years of education completed. The
race factor had
three attributes of European American (n = 36), African
American (n = 23) and
Hispanic (n = 18). Descriptive statistics indicate that on
average, European
Americans have higher levels of education (M = 16.4, SD =
4.6), with African
Americans slightly trailing (M = 15.5, SD = 6.8) and Hispanics
having on average
lower levels of educational attainment (M = 13.3, SD = 6.1).
The ANOVA was not
significant F (2,74) = 1.789, p = .175, indicating there are no
differences in
educational attainment across these three races in the
population. The results of
this study are significant because they shed light on the current
social conversation
about inequality.
3. Statistical significance is found in a study, but the effect in
reality is very small (i.e.,
there was a very minor difference in attitude between men and
women). Were the
results meaningful?
An independent samples t test was conducted to determine
whether differences
exist between men and women on cultural competency scores.
The samples
consisted of 663 women and 650 men taken from a convenience
sample of public,
private, and non-profit organizations. Each participant was
administered an
instrument that measured his or her current levels of cultural

11. competency. The
© 2016 Laureate Education, Inc. Page 2 of 2
cultural competency score ranges from 0 to 10, with higher
scores indicating higher
levels of cultural competency. The descriptive statistics indicate
women have
higher levels of cultural competency (M = 9.2, SD = 3.2) than
men (M = 8.9, SD =
2.1). The results were significant t (1311) = 2.0, p <.05,
indicating that women are
more culturally competent than are men. These results tell us
that gender-specific
interventions targeted toward men may assist in bolstering
cultural competency.
4. A study has results that seem fine, but there is no clear
association to social
change. What is missing?
A correlation test was conducted to determine whether a
relationship exists
between level of income and job satisfaction. The sample
consisted of 432
employees equally represented across public, private, and non-
profit sectors. The
results of the test demonstrate a strong positive correlation
between the two
variables, r =.87, p < .01, showing that as level of income
increases, job
satisfaction increases as well.