EDR 8205 Week 2 Assignment: Analyze Non-Experimental (Non-Causal) Correlational Designs
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This document serves as a guide for Northcentral University students on how to complete assignments, specifically focusing on correlation research designs. It highlights the importance of academic integrity, outlines strengths and weaknesses of correlational studies, and emphasizes best practices for conducting such research. The document also illustrates these concepts using the study by Beauvais et al. (2014) about factors related to academic success among nursing students.
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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).](https://image.slidesharecdn.com/correlation-assignment-190221043915/85/EDR-8205-Week-2-Assignment-Analyze-Non-Experimental-Non-Causal-Correlational-Designs-6-320.jpg)
EDR 8205 Week 2 Assignment: Analyze Non-Experimental (Non-Causal) Correlational Designs
- 1. OHAYNESEDR8205-2 1 NORTHCENTRAL UNIVERSITY ASSIGNMENT COVER SHEET Student: Orlanda Haynes Date: 05/20/2018 THIS FORM MUST BE COMPLETELY FILLED IN Follow these procedures: If requested by your instructor, please include an assignment cover sheet. This will become the first page of your assignment. In addition, your assignment header should include your last name, first initial, course code, dash, and assignment number. This should be left justified, with the page number right justified. For example: DoeJXXX0000-1 1 Save a copy of your assignments: You may need to re-submit an assignment at your instructor’s request. Make sure you save your files in accessible location. Academic integrity: All work submitted in each course must be your own original work. This includes all assignments, exams, term papers, and other projects required by your instructor. Knowingly submitting another person’s work as your own, without properly citing the source of the work, is considered plagiarism. This will result in an unsatisfactory grade for the work submitted or for the entire course. It may also result in academic dismissal from the University. 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.
- 2. OHAYNESEDR8205-2 2 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
- 3. OHAYNESEDR8205-2 3 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
- 4. OHAYNESEDR8205-2 4 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)
- 5. OHAYNESEDR8205-2 5 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).
- 6. OHAYNESEDR8205-2 6 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).
- 7. OHAYNESEDR8205-2 7 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.