Course Description
CHIM408: Statistics for Healthcare Managers
This course introduces principles of statistics for use in health services. Topics include study design, sampling, description, summary, and hypothesis
testing. Students apply methods such as ANOVA, correlation, chi-square, and multiple regression using statistics software.
The applied approach develops knowledge and skills necessary to understand health services research and practice evidence-based management.
Course Focus and Scope
The learning goals are to equip you with the knowledge and skills necessary to organize, describe, display, and analyze quantitative healthcare
service data, and to interpret and effectively communicate findings produced by these analyses.
Through discussion, exercises, assignments and tests/exams, you are to will develop data literacy essential not only to performing analyses, but also
to understanding, interpreting, and critiquing those conducted by health professionals, policy makers, and researchers.
Disclaimer
While the Chang School makes every effort to provide course content that is accessible to all learners, there are elements in this course that may
not be fully accessible due to lack of alternative technology. The R and RStudio software are also not accessible for students using assistive
technology, and there is no comparable software application to date.
Course Objectives and/or Learning Outcomes
Upon successful completion of this course, you will be able to:
Describe types, characteristics, and distributions of quantitative data
Describe key principles of probability and sampling
Use commonly available software for statistical analyses and data display
Appropriately perform and interpret results of tests comparing means (t-test, ANOVA), association (chi-square) and correlation (correlation and
regression)
Critically evaluate the application and presentation of statistics by healthcare professionals, researchers, policy makers, and the media
Teaching Methods
Readings
Case studies
Tutorials for software
Instructor-monitored discussion boards
Outline of Topic Sequence
Introduction
Statistics in healthcare
Evidence-based decisions
Examples
How to use the software (Excel, R)
Descriptive Statistics
Measures of central tendency
Measures of variability
Measures of distribution
Data Visualization
Bar charts
Pie charts
Histograms
Scatterplots
Line plots
Probability and Probability Distributions
Basic probability
Normal distribution
Central limit theorem
Hypothesis Testing
One- and two-sample t tests for means
Paired and unpaired samples
One- and two-sample z tests for proportions
ANOVA and multiple comparisons
p values
Correlation and Regression
Coefficient of correlation
Simple linear regression
Multiple linear regression
Simple logistic regression
Regression assumptions
Interpreting regression output
Other Tests
Non-parametric
Chi-square
Course Text and Materials
Required Textbook(s)
Please view a list of the required materials for this course and instructions on how to order them from the Ryerson University Bookstore.
1. Scott, I., & Mazhindu, D. (2014). Statistics for healthcare professionals. London, UK: Sage.
2. OpenIntro Statistics, available for free online.
Further Readings or Resources
Wasserstein, R.L., & Lazar, N.A. (2016). The ASA's statement on p-values: context, process, and purpose. The American Statistician. DOI:
10.1080/00031305.2016.1154108
Goodman, S.N. (1999a). Toward evidence-based medical statistics 1: The P Value fallacy. Annals of internal medicine, 130. 995–1004.
Goodman, S.N. (1999b). Toward evidence-based medical statistics. 2: The Bayes Factor. Annals of internal medicine, 130. 1005–1013.
Goodman, S.N. (2008). A dirty dozen: Twelve p-value misconceptions. Seminars in hematology, 45. 135–140.
Ioannidis, J.P. (2005). Contradicted and initially stronger effects in highly cited clinical research. Journal of the American Medical
Association, 294. 218–228.
Ioannidis, J.P. (2008). Why most discovered true associations are inflated (with discussion). Epidemiology,
19. 640–658.
Sterne, J.A.C. (2002). Teaching hypothesis tests – Time for significant change? Statistics in medicine, 21. 985–994.
Course Schedule
Week 1 (May 1, 2023)
Module 1
Introduction
Topics
Installing software
Statistical vocabulary
Data types
Learning Objectives
After successfully completing this module, you should be able to:
Open R and use it do a little arithmetic
Identify the different types of data
Readings
Required
1. Chapters 1 and 2. Scott, Ian and Mazhindu, Debbie. 2014. Statistics for Healthcare Professionals. London, UK: Sage
2. Getting Started with the R Commander, by John Fox
3. R Commander an Introduction, by Natasha Karp
4. Guide to Using Microsoft Excel
Assignments
Assignment 1 is available in the D2L Brightspace Assignment area
Tests/Exams
There are no tests/exams this week
Week 2 (May 6, 2023)
Module 2
Describing and Visualizing Data
In this module, you will look at ways to understand the structure of a data set. You will consider different ways to visualize data, as well as statistics
calculated from the data that attempt to summarize the information it contains. The simplest summary statistic is the average, which is a
measurement of the centre of the data, but there are others as well.
You will look at statistics that measure the centre of a data set, and statistics that measure how spread out a data set is.
Topics
Visualizing categorical data
Visualizing numerical data
Summary statistics and their interpretation
Learning Objectives
After successfully completing this module, you should be able to:
Create informative data visualizations using R or Excel
Calculate the following summary statistics and explain their interpretation: mean, median, quartiles, mode, variance, standard deviation, range,
interquartile range
Readings
Required
1. Chapters 3, 6, and 7. Scott, Ian and Mazhindu, Debbie. 2014. Statistics for Healthcare Professionals. London, UK: Sage
2. How to Lie with Charts, Harvard Business Review, December 2014
3. Making tables in APA (look at suggested readings and videos)
Assignments
No new assignments this week.
Tests/Exams
There are no tests/exams this week
Week 3 (May 13, 2023)
Module 3
Basics of Probability
This module is an introduction to probability. You will learn the definition of probability, and look at how to calculate some simple probabilities.
You will be introduced to the concepts of conditional probability and independence, and then look at Bayes’ Theorem, a powerful method to update
probabilities based on new information.
Topics
Definition of probability
Conditional probability
Independence
Contingency tables
Bayes’ Theorem
Learning Objectives
After successfully completing this module, you should be able to:
Calculate simple probabilities
Describe a process to determine if events are independent
Apply Bayes’ Theorem to revise probability estimates
Readings
Required
1. OpenIntro Statistics, Chapter 2
Assignments
Assignment 1 due
Assignment 2 is available in the D2L Brightspace Assignment area
See the Assignments section of the online module for more details.
Tests/Exams
There are no tests/exams this week
Week 4 (May 20, 2023)
Module 4
More Probability and an Introduction to Hypothesis Test
The ultimate goal for the next few modules is to be able to test hypotheses in a statistically rigorous manner.
Here is an example where a rigorous statistical test would be necessary. Suppose a test for a new blood pressure drug yields the following results:
One group of patients, call them Group 1, were given the drug for a month, and after the trial their systolic blood pressure averaged 119 with a
standard deviation of 14. Another group (Group 2) was given a placebo, and at the end of the month their systolic blood pressure averaged 126 with
a standard deviation of 12.
Is there enough evidence from this trial to conclude that the new drug is effective at lowering blood pressure? Group 1 did have a lower average
blood pressure at the end of the trial, but could that just be due to chance? How certain can you be that the observed difference is actually
meaningful?
Rigorous statistical testing can help give clear answers to these questions.
In this module, you will learn the process necessary to perform such tests and interpret the results. You will be introduced to hypothesis testing with a
few intuitive examples, while the statistical details will be covered in the next module.
This module begins with a discussion of the normal probability distribution and the Central Limit Theorem. These ideas form the theoretical
underpinning of hypothesis testing.
Topics
Probability distributions
Normal distributions
The sampling distributions and the Central Limit Theorem
Hypothesis testing
Null and alternative hypotheses
Type I and type II errors
Learning Objectives
After successfully completing this module, you should be able to:
Calculate probabilities from the normal distribution
Describe the hypothesis testing process
Identify type I and type II errors
Readings
Required
1. Chapters 9, 10, and 11. Scott, Ian and Mazhindu, Debbie. 2014. Statistics for Healthcare Professionals.London, UK: Sage
Assignments
No new assignments
Tests/Exams
There are no tests/exams this week
Week 5 (May 27, 2023)
Module 5
Hypothesis Testing: Comparing Means
In this module you will learn how to perform specific types of hypothesis tests. You will learn the theory behind them, how to do the calculations, and
how to interpret the results.
Topics
Z test for the mean
T test for the mean
Two-sample T test
Independent and dependent samples
P values
Learning Objectives
After successfully completing this module, you should be able to:
Perform a variety of hypothesis tests and interpret the results
Describe the p value and its limitations
Readings
Required
1. Chapter 12. Scott, Ian and Mazhindu, Debbie. 2014. Statistics for Healthcare Professionals. London, UK: Sage
2. Chapter 5, OpenIntro Statistics
3. “Prone breast forward intensity-modulated radiotherapy for Asian women with early left breast cancer: Factors for cardiac sparing and clinical
outcomes”, available on D2L
Assignments
Assignment 2 due
Assignment 3 is available in the D2L Brightspace Assignment area
See the Assignments section of the online module for more details.
Tests/Exams
There are no tests/exams this week
Week 6 (June 3, 2023)
Module 6
More Hypothesis Testing and ANOVA (Comparing More Than Two Groups)
The t tests of the previous module compared the means of two groups. In this module you learn about similar tests for proportions. An example of
where this might be useful is a comparison of disease prevalence between two ethnic groups.
You will also learn about the ANOVA test, which is a statistical method to compare the means of more than two groups.
Topics
Z test for the proportion
Z test for comparing two proportions
ANOVA tests
Learning Objectives
After successfully completing this module, you should be able to:
Perform a z test for the proportion using R commander and interpret the results
Perform a z test comparing two proportions using R commander and interpret the results
Perform a one-way ANOVA using R commander and interpret the results
Readings
Required
1. Scott and Mazhindu, Chapter 13 (up to and including box 13.7 on page 133)
2. OpenIntro Statistics, Chapter 6 (up to and including section 6.2.3)
3. “Meeting patient’s expectations in primary care consultations in Lithuania,” available in the online module
Assignments
No new assignments
Tests/Exams
There are no tests/exams this week
Week 7 (June 10, 2023)
Module 7
Correlation and Regression
In this module you will learn about statistical methods used to understand the relationship between two variables. This type of analysis is useful to
address questions such as:
How are obesity and health care outcomes related?
How do staffing levels affect patient satisfaction?
Topics
Correlation
Simple linear regression
Learning Objectives
After successfully completing this module, you should be able to:
Calculate and interpret the correlation coefficient
Perform simple linear regression using Excel or R and interpret the output
Demonstrate the correct use of a simple linear regression model to make predictions
Readings
Required
1. Chapter 17. Scott, Ian and Mazhindu, Debbie. 2014. Statistics for Healthcare Professionals. London, UK: Sage
Assignments
Assignment 3 due
Assignment 4 is available in the D2L Brightspace Assignment area
See the Assignments section of the online module for more details.
Tests/Exams
There are no tests/exams this week
Week 8 (June 17, 2023)
Module 8
Multiple Regression
Multiple regression is an extension of simple regression that uses more than one predictor variable. This technique allows you to account for the fact
that an outcome may depend on several different variables. For example, length of stay in the ICU may depend on several factors, including the age
of the patient, the severity of their medical issue, and their current level of health.
Topics
Multiple regression
Learning Objectives
After successfully completing this module, you should be able to:
Perform multiple linear regression using Excel and interpret the output
Use a multiple linear regression model to make predictions
Readings
Required
1. OpenIntro Statistics, Chapters 8.1, 8.2, and 8.3
2. Hikmet, N., Bhattacherjee, A., Menachemi, N., Kayhan, V.O., & Brooks, R.G. (2008). The role of organizational factors in the adoption of
healthcare information technology in Florida hospitals. Health Care Management Science, 11(1), 1–9.
Assignments
No new assignments
Tests/Exams
There are no tests/exams this week
Week 9 (June 24, 2023)
Module 9
Simple Logistic Regression
Topics
Applications of logistic regression
Learning Objectives
Identify situations that call for logistic regression
Describe criteria to be considered in the appropriate use of logistic regression.
Interpret the output of simple logistic regression
Readings
Supplemental
1. OpenIntro Statistics, Chapter 8.4
Assignments
Assignment 4 due
Assignment 5 is available in the D2L Brightspace Assignment area
See the Assignments section of the online module for more details.
Tests/Exams
There are no tests/exams this week
Week 10 (July 1, 2023)
Module 10
Other Tests
In this module you will learn about non-parametric tests and the chi-square test.
A simple way to think about non-parametric tests is that they are more general but less powerful than the tests you have learned about so far (the t
test for means, for example). All of the tests you have seen have required some assumptions on the distribution of your data. Non-parametric tests do
not make these assumptions, and therefore can apply to data with any distribution.
The price of this generality is a less powerful test. Non-parametric tests are particularly useful when you are dealing with ordinal or nominal data.
The chi-square test is used to test whether there is an association between two variables. For example, you might be interested in knowing if a
particular medical condition is more common among a certain ethnic group or demographic.
This module will present several examples of these tests that you can work through yourself to gain a better understanding of how they work and how
they differ.
Topics
Non-parametric tests
Mann-Whitney U test
Wilcoxon two-sample test
Kruskal-Wallis H test
Spearman rank correlation
Chi-square test
Learning Objectives
After successfully completing this module, you should be able to:
Determine when a non-parametric test is appropriate
Perform a non-parametric test and interpret the results
Determine when a chi-square test is appropriate
Perform a chi-square test and interpret the results
Readings
Required
1. Chapters 15 and 16. Scott, I., and Mazhindu, D. (2014). Statistics for healthcare professionals. London, UK: Sage
Assignments
No new assignments
Tests/Exams
There are no tests/exams this week
Week 11 (July 8, 2023)
Module 11
Using Statistics Appropriately
In this module, you will learn about how statistics can be misused and misinterpreted.
This module focuses on a widely cited article about the reproducibility of results in Psychology. This article claimed, based on statistical analysis, that
over half of all results from psychological studies could not be replicated. A group of authors wrote a rebuttal to this article, claiming that it contained
statistical errors. The original authors replied to this rebuttal, and this set off much discussion back and forth between psychologists and social
scientists as well as statisticians.
You will need to read the original article, the rebuttal, the reply to the rebuttal, the reply to the reply to the rebuttal, and a summary of the controversy
by the rebuttal authors.
Your instructor will facilitate a discussion of the issues raised by these publications. For example, how is it possible that two sets of authors,
using statistical analysis of the same evidence, can come to two different conclusions to the same question?
To prepare for a robust discussion, it is important that you consider the guiding themes provided in the module content while studying the two articles.
Topics
Critical evaluation of health research
Learning Objectives
After successfully completing this module, you should be able to:
Assess the statistical validity of research results
Readings
Required
1. Chapter 8. Scott, I., & Mazhindu, D. (2014). Statistics for healthcare professionals. London, UK: Sage
2. Open Science Collaboration (2015, August). Estimating the reproducibility of psychological science. Science, 349(6251).
http://dx.doi.org/10.1126/science.aac4716.
3. Gilbert, D., King, G., Pettigrew, S., & Wilson, T. (2016, March) Comment on “Estimating the reproducibility of psychological science”. Science,
351(6277). http://dx.doi.org/10.1126/science.aad7243.
4. Anderson, C.J., Bahník, S., Barnett-Cowan, M., Bosco, F., Chandler, J., Chartier, C.R., et al. (2016, March 4). Response to Comment on
“Estimating the reproducibility of psychological science”. Science, 351(6277) http://dx.doi.org/10.1126/science.aad9163.
5. Gilbert, D., King, G., Pettigrew, S., & Wilson, T. (n.d.) A response to the reply to our technical comment on “Estimating the reproducibility of
psychological science”.
6. Gilbert, D., King, G., Pettigrew, S., & Wilson, T. (2016, March 7). A response to the reply to our technical comment on “More on “Estimating the
reproducibility of psychological science”.
Assignments
Assignment 5 due
See the Assignments section of the online module for more details.
Tests/Exams
There are no tests/exams this week
Week 12 (July 15, 2023)
Module 12
Review
This module will focus on providing reviews of concepts found to be particularly challenging to you. Prepare for this review by formulating specific and
clear questions of concepts, formulae, processes, or procedures you need help with in order to be able to meet the course learning outcomes and
prepare for assessments.
Week 13 (July 22, 2023)
Module 13
Final Exam
Assignments
Assignment 6 due
Tests/Exams
Available this week in D2L.
Marking Scheme
Assessment
%
Week Assigned
Week Due
Discussion board participation
15
1
As noted in each online
module
Assignment 1
15
1
3
Assignment 2
15
3
5
Assignment 3
10
5
7
Assignment 4
15
7
9
Assignment 5
10
9
11
Final Exam
20
13
13
Total
100%
Assignment Descriptions (See D2L for details)
Assignment 1 - Describing data
Assignment 2 - Displaying data
Assignment 3 - t-tests and ANOVA
Assignment 4 - Regression & correlation
Assignment 5 - Chi-square
Participation Details
The online discussion board is an excellent way to enhance your learning and practice critical thinking. Discussing content in an online environment
allows you to reflect before contributing and take time to consider other student postings. By providing opportunities for networking and community
building, the discussion board can reduce the feeling of isolation that sometimes occurs in online courses.
More details on discussion activities are listed in the “Getting Around in the Course”, the “How to Work Your Way Through the Modules”, and the
“Setting Norms and Expectations” sections of Module 1 online. Also, there is an example discussion board contribution provided in the Module 1
discussion section for your reference.
Please also review the “Etiquette Guidelines” and “Issues Awareness” sections below which outline the minimum conduct expectations for discussion
board behaviour.
Discussion boards in this course are comprised to two separate requirements: (1) original student posts; (2) comments on posts by other students.
The due dates for each requirement are:
Posts must be made by Friday 11:59 p.m. of the specified week.
Comments must be made by Sunday 11:59 p.m. of the specified week.
Etiquette Guidelines
Treat online forums as academic, public-speaking places. Post comments in the same way you would speak in a traditional classroom – politely
and respectfully. Forums are a place for discussion and debate about the content you are studying. They are a way of getting to know the
abilities and strengths of your peers and instructor(s) and an opportunity to share your views and ideas.
Respect diversity. There will be multiple perspectives and experiences shared relating to course content and subject matter practice. You may
disagree with someone’s perspective or have a different one, but positioning any perspective as “right” or “wrong” should be avoided.
Read and respond to peer postings. If someone comments on your thread or asks a question, monitor and reply.
Keep criticism constructive and positive. Reference course readings and content to make suggestions or recommendations.
Participate frequently. You may be assessed for attendance and participation via weekly forums.
Be concise. You, your instructor and your peers have many posts to read each week. Unless your instructor states otherwise, keep your initial
postings and responses brief and meaningful (one to two short paragraphs) and include references and links.
Issues Awareness
Discussion forums can sometimes move off topic; avoid tangents and assist with redirection to keep postings contextual.
The instructor is the course expert and will address any incorrect information in forums with guidance and support as needed.
Inappropriate forum behaviour should be reported to the instructor immediately. Allow the instructor time to respond and take action. Do not
engage an inappropriate peer directly.
Your instructor may provide a separate course Q & A forum. This is the ideal place to post general questions about assignments and schedules
and to seek clarification on forum issues. Your peers may have similar questions, so it will benefit everyone to ask publicly. Personal issues
should be communicated with your instructor outside of this forum.
You may also have a course “coffee shop” where you can socialize with peers about non-course topics. The Etiquette Guidelines above apply
to this social area and your instructor will check in to ensure that all students are using the forum appropriately.
Your instructor may opt to form smaller groups out of the larger class to reduce the number of posts each student must read or to enable group
assignments.
Missed Term Work or Examinations and Course Repeats
Missed Term Work or Examinations
Students are expected to complete all assignments, tests, and exams within the time frames and by the dates indicated in this outline. Exemption or
deferral of an assignment, term test, or final examination is only permitted for a medical or personal emergency or religious observance (the request
must be received within the first two weeks of the course). The instructor must be notified by email prior to the due date or test/exam date or as
soon as possible after the date, and the appropriate documentation must be submitted. For absence on medical or religious-observance grounds,
official forms may be downloaded from the Ryerson website or picked up from The Chang School at Heaslip House, 297 Victoria Street, Main Floor.
Course Repeats
Senate GPA Policy prevents students from taking a course more than three times. For the complete GPA Policy, see Policy 46 on the Ryerson
Senate Policies website.
Plagiarism
The Ryerson Student Code of Academic Conduct defines plagiarism and the sanctions against students who plagiarize. All Chang School students
are strongly encouraged to go to the academic integrity website and complete the tutorial on plagiarism.
Other Course Information
From Ryerson University Policy
Ryerson University is a learning, teaching, and work community of students, faculty and staff, committed to providing a civil and safe environment
which is respectful of the rights, responsibilities, well-being, and dignity of all of its members.
Ryerson Student Email
All students in full- and part-time graduate and undergraduate degree programs and all continuing education students are required to activate and
maintain their Ryerson online identity at ryerson.ca/accounts in order to regularly access Ryerson’s email (either Rmail or Gmail), RAMSS, the
my.ryerson.ca portal and learning system, and other systems by which they will receive official university communications.
Student Support
If you are experiencing technical or administrative issues with your course, help is available from Student Support for Distance Courses via email
at distance@ryerson.ca or by phone from Monday to Friday, 9 a.m.–5 p.m., at (416) 979-5315.