Reflections on Course Concepts
Given the readings and assignments in the course:
· Identify and briefly discuss two concepts in this course that you believe will be most applicable to the professional discipline you will enter upon completion of your degree program.
· What is the importance of these concepts to the professional discipline?
· How will you use these concepts in your future career?
Optional: Offer feedback on how the course and/or facilitation of the course can be improved.
Reflection is a mental process that challenges you to use critical thinking to examine the course information, analyze it carefully, make connections with previous knowledge and experience, and draw conclusions based on the resulting ideas. A well-cultivated critical thinker raises vital questions and problems, formulating them clearly and precisely; gathers and assesses relevant information, using abstract ideas to interpret it effectively; comes to well-reasoned conclusions and solutions, testing them against relevant criteria and standards; thinks open-mindedly within alternative systems of thought, recognizing and assessing, as need be, their assumptions, implications, and practical consequences; and communicates effectively with others in figuring out solutions to complex problems. (Paul & Elder, 2008)
In order to earn maximum credit, the comment should be more than your opinion, and more than a quick “off the top of your head” response. Be sure to support your statements, cite sources properly, cite within the text of your comments, and list your reference(s). The response must be a minimum of 250 words.
Paul, R. & Elder, L. (February 2008). The miniature guide to critical thinking concepts and tools. Foundation for Critical Thinking Press
Module 4 – SLP
Univariate Vs. Bivariate Analyses and Regression
Interpret the two models that appear below, and address the following additional questions as they pertain to each.
Diabetes (1 unit) = 1.3 + 2.4 (BMI) + 2.3 (family history diabetes) + 1.7 (gender) + 1.4 (age) + 1.7 (race) + 2.6 (income) + 3.4 (height), p<0.05
Allergies = 4.5 + 3.8 (Family History Allergies) + 2.1 (gender) + 1.4 (age) + 0.8 (race) + 1.5 (weight), p<0.05
1. What about confounding? Which of the variables are potential confounders?
2. Compare and contrast matching on potential confounders versus including them in a regression model.
SLP Assignment Expectations
Length: SLP assignments should be at least 2 pages (500 words) in length.
References: At least two references from academic sources must be included (e.g., peer-reviewed journal articles). You may use any required readings from this module for your two references. Quoted material should not exceed 10% of the total paper (since the focus of these assignments is critical thinking). Use your own words and build on the ideas of others. When material is copied verbatim from external sources, it MUST be enclosed in quotes. The references should be cited within the text and listed at the end of the assignment in the References section (APA formatting recommended).
Organization: Subheadings should be used to organize your paper according to question.
Format: APA formatting is recommended for this assignment. See Syllabus page for more information on APA formatting.
Grammar and Spelling: While no points are deducted for minor errors, assignments are expected to adhere to standards guidelines of grammar, spelling, punctuation, and syntax. Points may be deducted if grammar and spelling impact clarity.
Your assignment will not be graded until you have submitted an Originality Report with a Similarity Index (SI) score <20% (excluding direct quotes, quoted assignment instructions, and references). Papers not meeting this requirement by the end of the session will receive a score of 0 (grade of F). Do keep in mind that papers with a lower SI score may be returned for revisions. For example, if one paragraph accounting for only 10% of a paper is cut and pasted, the paper could be returned for revision, despite the low SI score. Please use the report and your SI score as a guide to improve the originality of your work.
The following items will be assessed in particular:
· Achievement of learning outcomes for SLP Assignment.
· Relevance: all content is connected to the question.
· Precision: specific question is addressed; statements, facts, and statistics are specific and accurate.
· Depth of discussion: points that lead to deeper issues are presented and integrated.
· Breadth: multiple perspectives, references, and issues/factors are considered.
· Evidence: points are well supported with facts, statistics, and references.
· Logic: presented discussion makes sense; conclusions are logically supported by premises, statements, or factual information.
· Clarity: writing is concise, understandable, and contains sufficient detail or examples.
· Objectivity: use of first person and subjective bias are avoided.
Module 4 – Background
Univariate Vs. Bivariate Analyses and Regression
Required Reading
Barrat, H. & Kirwan, M. (2009) Confounding, interactions, methods for assessment of effect modification. Health Knowledge. Retrieved from http://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/confounding-interactions-methods
Collier, W. Independent & dependent variables. University of North Carolina at Pembroke. Retrieved from http://www.uncp.edu/home/collierw/ivdv.htm
DeLong, E., Li, L., & Cook, A., (2014). Pairing matching vs.stratification in cluster – Randomized trial. NIH Collaboratory
LaMorte, W.W. & Sullivan, L. (2016). Confounding and effect measure modification. Retrieved from http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704-EP713_Confounding-EM/BS704-EP713_Confounding-EM5.html
Lowry, R. (2016). Simple logistical regression. VassarStats: Website for Statistical Computation. http://www.vassarstats.net/logreg1.html
Ludford, P.J. Linear regression. University of Minnesota, College of Science and Engineering. Retrieved from http://www-users.cs.umn.edu/~ludford/Stat_Guide/Linear_Regression.htm
McDonald, J.H.(2014) Logistic Regression. In Handbook of Biological Statistics.Retrieved from http://www.biostathandbook.com/simplelogistic.html
National Science Digital Library’s Computation Science Education Research Desk. (2016) Univariate data and bivariate data. Retrieved from http://www.shodor.org/interactivate/discussions/UnivariateBivariate/
National Science Digital Library’s Computation Science Education Research Desk. (2016). Graphing and interpreting bivariate data. Retrieved from http://www.shodor.org/interactivate/discussions/GraphingData/
Penn State. (2016). STAT507 Epidemiological Research Methods: 3.5 – Bias, Confounding, and Effect Modification. Retrieved from https://onlinecourses.science.psu.edu/stat507/node/34
Wunsch, G. (2007). Confounding and control. Demographic Research 16(4). Retrieved from http://www.demographic-research.org/Volumes/Vol16/4/16-4.pdf
Optional Resources
Purdue Online Writing Lab. (2018). General format. Retrieved from https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
Purdue Online Writing Lab. (2018). In-text citations: The basics. Retrieved from https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/in_text_citations_the_basics.html
Purdue Online Writing Lab. (2018). Reference list: Basic rules. Retrieved from https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/reference_list_basic_rules.html
Module 4 – Home
Univariate Vs. Bivariate Analyses and Regression
Modular Learning Outcomes
Upon successful completion of this module, the student will be able to satisfy the following outcomes:
· Case
· Distinguish between univariate and multivariate analysis.
· Distinguish between dependent and independent variables.
· Distinguish between logistic and linear regression.
· SLP
· Interpret the results of a regression analysis, both linear and logistic.
· Discuss the concept of confounding and note potential confounders in a hypothetical study.
· Assess the merits of matching on confounders versus adjusting for confounders by including them in a regression model.
· Discussion
· Identify confounders for known diseases.
Module Overview
Univariate versus Multivariate Analysis
Univariate analysis looks at how two variables relate to one another. It often examines whether there is an association between a potential risk factor, or background characteristic (e.g., smoking, gender, exercise), with an outcome or disease (e.g., lung cancer, breast cancer, diabetes). The analysis only involves the disease (or outcome) with the potential risk factor (or exposure). Multivariate analysis, on the other hand, examines more than one potential risk factor at the same time, and their potential association to the disease or outcome. For instance, one could examine the effects of smoking, gender, age, obesity, and diabetes together against a potential association with cardiovascular disease.
Dependent versus Independent Variables
In these cases, the outcome or disease status is the dependent variable, whereas any potential exposure or risk factor is an independent variable. Multivariate analysis most often looks at one dependent variable (disease or outcome status) and more than one independent variable (e.g., gender, race, income, medical history, etc.).
Confounder
A confounder is a variable that is linked with a disease (or outcome), is related with a risk factor (or exposure), and changes the relationship between the exposure and outcome. For instance, let’s say that obesity is a potential risk factor for diabetes. Then consider a third variable. A family history of diabetes is also a potential risk factor for diabetes and is related to obesity. If the addition of a third variable (family history of diabetes) changes the relationship between obesity and diabetes, then the third variable (family history of diabetes) is a confounder in this situation.
Logistical and Linear Regression
Unlike univariate analysis, regression models allow researchers to examine more than one independent variable at a time against a dependent variable. This means that confounders or demographic variables may be studied alongside the exposure and outcome variables to adjust for any potential bias that may arise due to background characteristics (e.g., difference by gender or race or income, etc.). Depending on the outcome variable, logistical regression is used for binary outcomes (e.g., disease status of “yes” or “no,” mortality data, etc.), whereas linear regression is used for continuous outcomes (e.g., blood pressure, bone mass density, fasting blood glucose, etc.).
Logistical and Linear models can be interpreted as follows:
Lung Cancer = 4.5 + 2.4 (smoking) + 1.7 (gender) + 2.3 (age) + 0.7 (race), p<0.05
After controlling for gender, age, and race, those with a history of smoking are 2.4 times more likely to have lung cancer than those who do not smoke (p<0.05). In this statement, lung cancer is the dependent variable, history of smoking is the independent variable of interest (the exposure), and gender, age, and race are the confounders. This is a logistical regression model, where the dependent variable is binary: lung cancer versus no lung cancer.
BMI (1 unit) = 3.9 + 3.4 (high fasting glucose) + 1.5 (gender) + 1.3 (age) + 2.7 (race), p<0.05
After controlling for gender, age, and race, a one unit increase in BMI is 3.4 times more likely in those with a high fasting glucose level than those with a lower fasting glucose level. In a linear regression model, the dependent variable is continuous and results are measured in units. The dependent variable here is body mass index (BMI), the independent variable is fasting glucose levels (high versus low), and the potential confounders are gender, age, and race.
NEWS – Information from prof
In this module you will learn:
1. Differentiate between logistic and linear regression.
2. Interpret the results from the two models (logistic versus linear) that are provided
In the Case assignment you will:
1. Distinguish between univariate and multivariate analysis.
2. Distinguish between dependent and independent variables.
3. Distinguish between logistic and linear regression.
In The SLP assignment you will:
1. Interpret the results of a regression analysis, both linear and logistic.
2. Discuss the concept of confounding and note potential confounders in a hypothetical study.
3. Assess the merits of matching on confounders versus adjusting for confounders by including them in a regression model.
In the Discussion you will Identify confounders for known diseases.
In more details
Case
Using the materials in the module homepage and in the background section, please address the following:
· What is the difference between “univariate” and “multivariate” analyses? (1 page)
· Define and contrast dependent versus independent variables. (1 page)
· Describe the difference between logistic regression and linear regression. What types of variables are used for the dependent variable? (1 page)
SLP
Interpret the two models that appear below, and address the following additional questions as they pertain to each.
Diabetes (1 unit) = 1.3 + 2.4 (BMI) + 2.3 (family history diabetes) + 1.7 (gender) + 1.4 (age) + 1.7 (race) + 2.6 (income) + 3.4 (height), p<0.05
Allergies = 4.5 + 3.8 (Family History Allergies) + 2.1 (gender) + 1.4 (age) + 0.8 (race) + 1.5 (weight), p<0.05
· What about confounding? Which of the variables are potential confounders?
· Compare and contrast matching on potential confounders versus including them in a regression model.
Discussion:
· Confounders Discussion
Discussion Topic
Actions for ‘Confounders Discussion’
Updated
Locate and describe a potential confounder linked with a disease. For instance, what is a potential confounder for obesity and diabetes? For smoking and lung cancer?
· Reflection Discussion
Discussion Topic
Actions for ‘Reflection Discussion’
Updated
Given the readings and assignments in the course, identify and briefly discuss two concepts that you believe will be most applicable to the professional discipline you will enter upon the completion of your degree program.
Required Reading
Barrat, H. & Kirwan, M. (2009) Confounding, interactions, methods for assessment of effect modification. Health Knowledge. Retrieved from http://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/confounding-interactions-methods
Collier, W. Independent & dependent variables. University of North Carolina at Pembroke. Retrieved from http://www.uncp.edu/home/collierw/ivdv.htm
DeLong, E., Li, L., & Cook, A., (2014). Pairing matching vs.stratification in cluster – Randomized trial. NIH Collaboratory
LaMorte, W.W. & Sullivan, L. (2016). Confounding and effect measure modification. Retrieved from http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704-EP713_Confounding-EM/BS704-EP713_Confounding-EM5.html
Lowry, R. (2016). Simple logistical regression. VassarStats: Website for Statistical Computation. http://www.vassarstats.net/logreg1.html
Ludford, P.J. Linear regression. University of Minnesota, College of Science and Engineering. Retrieved from http://www-users.cs.umn.edu/~ludford/Stat_Guide/Linear_Regression.htm
McDonald, J.H.(2014) Logistic Regression. In Handbook of Biological Statistics.Retrieved from http://www.biostathandbook.com/simplelogistic.html
National Science Digital Library’s Computation Science Education Research Desk. (2016) Univariate data and bivariate data. Retrieved from http://www.shodor.org/interactivate/discussions/UnivariateBivariate/
National Science Digital Library’s Computation Science Education Research Desk. (2016). Graphing and interpreting bivariate data. Retrieved from http://www.shodor.org/interactivate/discussions/GraphingData/
Penn State. (2016). STAT507 Epidemiological Research Methods: 3.5 – Bias, Confounding, and Effect Modification. Retrieved from https://onlinecourses.science.psu.edu/stat507/node/34
Wunsch, G. (2007). Confounding and control. Demographic Research 16(4). Retrieved from http://www.demographic-research.org/Volumes/Vol16/4/16-4.pdf
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Reflections on Course Concepts was first posted on August 31, 2019 at 6:11 pm.
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