Week 4: Introduction to Logistic Regression
For the most part, you have only been exposed to statistical methods that require a continual dependent variable. You have likely been thinking that there must be some way to predict myriad categorical variables that exist in social data. What if you want to predict the odds of whether a student passes or fails, a disease occurs or does not, or whether a recently released inmate offends or not? You will notice all of these examples are categorical and have a dichotomous outcome (yes/no).
Binary logistic regression will allow you to answer questions where you can predict the odds of an event occurring from a combination of categorical and continual variables. This week you explore categorical relationships including conditional probabilities, conditional odds, logits, odds ratios, relative risk, and slopes in the discussion. In the SPSS statistics assignment, you will conduct a simple logistic regression analysis (one independent variable and one dependent varaible) to begin to explore this statistical test and odds ratios.
Learning Objectives
Students will:
- Discuss categorical relationships including conditional probabilities, conditional odds, logits, odds ratios, relative risk, and slopes
- Apply binary logistic regression tests in a dataset
- Analyze binary logistic regression tests in a dataset
- Apply statistical software to analyze data
Learning Resources
Required Readings
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.
- Chapter 23, “Binary Logistic Regression” (pp. 1007–1050)
This chapter presents several simple examples of binary logistic regression. It also explains why researchers need to include probabilities along with odds when they report results.
Datasets
In this course, your instructor will provide you with one dataset (Guided Sample Dataset: Statistics Anxiety) that will be used in several of the tutorial videos. You are encouraged to reproduce the analyses performed in the video using this dataset so you can compare your answers and check your understanding of how to do each analysis in SPSS. You will also be provided with three large datasets for you to use in application of the statistics you’re learning each week. The Statistics Anxiety dataset is not for use in your weekly Assignments. Your instructor may also recommend using a different dataset from the ones we have provided here, which is fine. Your instructor will share these datasets in the Doc Sharing section of the classroom and as an Announcement.
In this course, your instructor will provide you with one dataset (Guided Sample Dataset: Statistics Anxiety) that will be used in several of the tutorial videos. You are encouraged to reproduce the analyses performed in the video using this dataset so you can compare your answers and check your understanding of how to do each analysis in SPSS. You will also be provided with three large datasets for you to use in application of the statistics you’re learning each week. The Statistics Anxiety dataset is not for use in your weekly Assignments. Your instructor may also recommend using a different dataset from the ones we have provided here, which is fine. Your instructor will share these datasets in the Doc Sharing section of the classroom and as an Announcement.
Required Media
Laureate Education (Producer). (2017l). Introduction to logistic regression [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 1 minute 15 seconds.
Begin each week of the course by viewing the Weekly Introduction video, in which experienced statistics Instructors, Dr. Matt Jones and Dr. Annie Pezalla, provide context for new learning, explain statistical methods in easy-to-understand language, and describe the learning activities you will need to complete.
Accessible player –Downloads– Download Video w/CC Download Audio Download Transcript
Laureate Education (Producer). (2017j). Introduction to binary logistic regression [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 1 minute 30 seconds.
Begin each week of the course by viewing the Weekly Introduction video, in which experienced statistics Instructors, Dr. Matt Jones and Dr. Annie Pezalla, provide context for new learning, explain statistical methods in easy-to-understand language, and describe the learning activities you will need to complete.
Accessible player –Downloads– Download Video w/CC Download Audio Download Transcript
Laureate Education (Producer). (2017e). Creating a contingency table in Microsoft Excel [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 10 minutes.
In this media program, Dr. Matt Jones demonstrates how to perform binary logistic regression in SPSS and calculate the odds ratio in Excel.
Accessible player –Downloads– Download Video w/CC Download Audio Download Transcript
Discussion: Logistic Regression Values
Important Note: This week contains a graded Discussion and an ungraded Collaboration Lab.
This week’s readings discuss conditional probabilities, conditional odds, logits, odds ratios, relative risk, and slopes. These can all be confusing terms but the good news is that all these values have some relationship to each other. Researchers have their own opinions on which values makes the most sense to report.
By Day 3
In a 2- to 3-paragraph post, construct a persuasive argument for the value (conditional probability, odds, odds ratio, etc.) that, intuitively, makes the most sense for you to report as a result to your audience. Be sure to provide a specific rationale for your choice.
By Day 5
Respond to a colleague’s post who has selected a different value. Describe whether or not your colleague’s post swayed your point of view. Does your opinion change when on reporting in different contexts? Why or why not?
Submission and Grading Information
- Grading Criteria
- To access your rubric:
- Week 4 Discussion Rubric
- Post by Day 3 and Respond by Day 5
- To participate in this Discussion:
- Week 4 Discussion
Collaboration Lab
Collaboration Labs are offered throughout the course as places of academic refuge, where you can ask questions, offer insights, and interact with the Instructor as they provide global feedback to the group based on trends, common problems, and common strengths in student posts.
As a peer, you are also encouraged to provide constructive, helpful feedback to your peers. Data analysts always benefit from the feedback of others. Your Collaboration Labs posts may be procedural (“How do I get the answer using the SPSS software?”), conceptual (“How does this relate to the other tests we have learned?”), or analytical (“What do my calculations actually mean in the context of my research question?”).
Although not mandatory, this is an opportunity to interact and practice as you navigate the assignments, so you are highly encouraged to take part in this activity. Full participation in activities like these is a statistically significant predictor of success.
Note that each week contains its own unique Collaboration Lab, so the Collaboration Lab should contain only posts related to this week’s topic and this week’s Assignments.
To participate in this Collaboration Lab:
Week 4 Collaboration Lab
Assignment: Simple Logistic Regression in SPSS (1 Independent Variable & 1 Dependent Variable)
Earlier this week, you practiced fitting simple regression models and, ideally, used the Collaboration Lab to ask, answer, and otherwise address any questions you had. In this Assignment, you apply what you learned to answer a social research question using logistic regression.
To prepare
- Review the datasets provided.
- Construct a research question based on one of those datasets.
- Use one independent variable and one dependent variable to conduct your simple logistic regression analysis.
- Remember that your dependent variable must be dichotomous/binary.
- Think about how you might use the odds ratio in your analysis to simplify the interpretation of your results.
By Day 7
The Assignment
Use SPSS to answer the research question you constructed. Write an analysis in APA format, including title page, references, and an appendix, that includes your data output and addresses each of the tasks listed above. The content should be 2–3 pages, including a discussion of whether the predictive relationship is statistically significant and the odds ratio and what it means. Your SPSS output should be included as an appendix.
Early in your Assignment, when you relate which dataset you analyzed, please include the mean of the following variables. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the General Social Survey Dataset, report the mean of Age. If you are using the HS Long Survey Dataset, report the mean of X1SES. See page 1032 in your Warner textbook for an excellent APA-compliant write-up of a binary logistic regression analysis.
Submission and Grading Information
To submit your completed Assignment for review and grading, do the following:
- Please save your Assignment using the naming convention “WK4Assgn+last name+first initial.(extension)” as the name.
- Click the Week 4 Assignment Rubric to review the Grading Criteria for the Assignment.
- Click the Week 4 Assignment link. You will also be able to “View Rubric” for grading criteria from this area.
- Next, from the Attach File area, click on the Browse My Computer button. Find the document you saved as “WK4Assgn+last name+first initial.(extension)” and click Open.
- If applicable: From the Plagiarism Tools area, click the checkbox for I agree to submit my paper(s) to the Global Reference Database.
- Click on the Submit button to complete your submission.