This is a North Central University course (EDR 8205-2) week 2 assignemt: Analyze Non-Experimental (Non-Causal) Correlational Designs. It is written in APA format, has been graded by an instructor (A), and includes references. Most higher-education assignments are submitted to turnitin, so remember to paraphrase. Let us begin.

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Student: Orlanda Haynes Date: 05/20/2018
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EDR8205-2
Week 2 Assignment: Analyze Non-Experimental (Non-Causal) Correlational Designs
Hi Orlanda.
I enjoyed reading your week 2 analysis. You did well with showing your understanding of the article by
Beauvais, Steward, DeNisco, and Beauvais (2014).
Just giving you a bit of a recap to think about here, this study used a quantitative methodology and a
correlational design. That wording is important to take note of, as it is an example of how you
might identify a methodology and design in your own research.

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We use correlational designs when we want to see how two quantitative continuous variables are related.
Remember that with correlations we can never say that one thing causes another thing. In stats
we say correlation does not imply causation. However it is often extremely useful to know how
two variables are related. One of the big problems is making that leap to causation. We do not
know for sure that there is not a third variable, which we could call a lurking variable, doing the
causing. As a funny example of that I had a former stats professor in grad school who gave us
the example of going to bed with your shoes on being highly positively correlated with waking up
with a headache. Now of course we can all guess what the lurking variable is likely to be in that
case, the one actually doing the causing.
I gave you a few more comments below. Keep up the great work in the class!
Joanna

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Week 2 - Assignment: Analyze Non-Experimental (Non-Causal) Correlational Designs
The primary purpose of this assignment is to enhance Ed D students’ awareness of
correlation research designs including definition, strengths and weakness, best practice, and the
nature and characteristics of research questions. For illustration purpose, the author uses
Beauvais, Steward, DeNisco, and Beauvais’s (2014) article “Factors Related to Academic
Success Among Nursing Students: A Descriptive Correlational Research Study.” Correlation
research is nonexperimental. Researchers used the designs to (a) understand and describe
relationships between two or more variables, (b) to test for prediction variables (e.g., students’
grade point average and standardized test scores), and (c) to explore the predictive validity of
measuring tools (Lodico, Spaulding, & Voegtle, 2010). Unlike experiments which require
independent and dependent variables controlled by researchers, correlation variables are
changeable, measurable, and occur naturally, in most instance, as underlying constructs.
Research literature refers to them as covariables (Lodico, Spaulding, & Voegtle, 2010).
Moreover, correlations range from negative to positive. If both variables, for example,
increase simultaneously, a positive relationship exists (Creswell, 2015). A good example of this
phenomenon is the relationship between hours spent studying for an exam and a higher test
score. The opposite is true if a negative relationship exists. The correlation coefficient is a
measure of the strength and the direction of correlations; its denoted by the letter “r.” The range
is from -1.0 to +1.0; coefficients less than zero describe negative correlations and above this
margin denotes positive coefficients (Creswell, 2015; Lodico, Spaulding, & Voegtle, 2010).
Data collection and analysis encompass both quantitative and qualitative methods. (Lodico,
Spaulding, & Voegtle, 2010).
To present data, researchers usually use scatter plots, also known as scatter grams or

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scatter graphs, to present the data. Scatter plots display scores from each covariable on the x and
y-axis. The tool is essential to correlation designs primarily because they allow researchers to
analyze relationships among variables (e.g., existences, types, directions, and degrees) (Creswell,
2015; Lodico, Spaulding, & Voegtle, 2010). Beauvais, Stewart, DeNisco, and Beauvais (2014)
used a descriptive correlational design to explore and describe relationships between and among
emotional intelligence, psychological empowerment, resilience, spiritual well-being, and
academic success among. Descriptive correlational studies allow for descriptive analysis of
variables as well as the naturally occurring relationships among them (Creswell, 2015; Lodico,
Spaulding, & Voegtle, 2010).
Correlational Designs: Strengths and Weaknesses
Strengths. Correlation frameworks are cost effective means to gathering large data bases
and then figuring-out if relationship exists among or between variables and, if so, to what extent.
Researchers can employ an array of data collection and analysis strategies including quantitative
and qualitative tools (e.g., surveys, questionnaires, preexisting standardized test scores,
descriptive statistics). Results could be used as factors for making predictions, for research
recommendations, and for instrument predictive validity to name a few (Beauvais et al., 2014;
Black, 2012; Creswell, 2015). By choosing a research design that could show how variables are
naturally related to real-world experiences, Beauvais et al. (2014) discovered, among other
factors, that resilience, emotional intelligence, empowerment, and spiritual well-being, correlate
with academic success. These finding supported current literature that show students who
perform successfully in academic environments have higher emotional intelligence
than their counterparts do. And that academic success (as defined by students’ GPA)

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does not correlate with students’ overall emotional intelligence.
Weakness. Correlation research does not explain or establishes cause-and-effect
relationships; the nature of the research is to determine if a correlation exists between two or
more covariables and to what extent (Creswell, 2015; Lodico, Spaulding, & Voegtle, 2010).
Beauvais et al. (2014) noted some design weakness in their study, including a limited definition
of academic success (e.g., included only students’ GPA as a measure of academic performance),
convenience sampling and a small sample size (e.g., nursing students who attended a private
Catholic university), and reliance on students’ self-reported data.
Best Practice: Correlation ResearchDesigns
When research professionals want to know if a correlation exists between two or more
variables, best practice (BP) recommends a correlation design (Creswell, 2015; Lodico,
Spaulding, & Voegtle, 2010). With a rise in attrition rates among nursing students, Beauvais et
al. (2014) research aim was to explore and describe relationships among related factors and their
underlying constructs as well as to address the problems by making recommendations. Although
most education researchers use convenience sampling, BP suggests, among other factors, the use
of random sampling, a minimum of 30 participants, and a heterogeneous sample. Primarily
because using a larger sample allows for generalizing findings to the general or the larger target
population, and a wider range of scores allows researchers to avoid “restriction of range” issues
(Black, 2012; Creswell, 2015). In addition, BP recommends the use of a correlation matrix and
scatter plots to present findings. The tools, in part, help simplify presentation of results more
easily than other means (e.g., statistical symbols and letters replace written text, variables are
numbered to show correlations, and asterisks are used to show statistically significant
correlations) (Lodico, Spaulding, & Voegtle, 2010).

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A Sample Correlation ResearchScenario and Question
Mr. Blackwell, an English high school teacher, reported that students who scored higher
on their homework assignments also scored higher on the final exams. He thus posed the
following question: Is there a relationship between the amount of time his students spend on
homework each week and the final exam? To collect data, he asked his students to report how
many minutes they spent each week on homework. Then, with the data in hand, he created a
table (e.g., each students’ name, their prior final exam scores [x-axis], and the minutes [y-axis]
that each student spent on homework each week) and a scatterplot to determine whether a
relationship between the two variables existed and to present the findings. As with Beauvais et
al. (2014) research interest, Mr. Blackwell’s research aim was to explore and understand a real-
world experience.
Conclusion
The goal of this assignment is to help doctorial students in the Ed D program expand their
knowledge and skills about correlation research, including the definition, strengths and
weaknesses, best practices, and the nature and characteristics of research questions. For
illustration purpose, the author uses Beauvais, Steward, DeNisco, and Beauvais’s (2014) article
“Factors Related to Academic Success Among Nursing Students: A Descriptive Correlational
Research Study.” Researchers used correlation designs to (a) understand and describe
relationships between two or more variables, (b) to test for prediction variables (e.g., students’
grade point average and standardized test scores), and (c) to explore the predictive validity of
measuring tools (Lodico, Spaulding, & Voegtle, 2010).

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References
Beauvais, A. M., Stewart, J. G., DeNisco, S., Beauvais, J. E. (2014). Factors related to academic
success among nursing students: A descriptive correlational research study. Nurse
Education Today 34, 918–923. http://dx.doi.org/10.1016/j.nedt.2013.12.005
Black, T. (2012). Doing quantitative research in the social sciences: An integrated approach to
research design, measurement, and statistics. Thousand Oaks, CA: Sage Publications.
Creswell, John W. (2015). Educational research: Planning, conducting, and evaluating
quantitative and qualitative research (5th Ed.). Boston, MA: Pearson.
Lodico, M., Spaulding, D., & Voegtle, K. (2010). Methods in educational research: From theory
to practice (Laureate Education, Inc., custom ed.). San Francisco: John Wiley &Sons.