One Year Program: Required Courses

  • HIS 9241 - Interdisciplinary Issues in Health Information Science (included in the drop-down menu required course list)

    This course will provide an overview of issues in the creation, provision and retrieval of information in the health care system. A focus will be on describing the ‘actors’ in the health area, their information behaviour, and consideration of how services provided by information professionals and other information sources meet these needs. We will also consider, taking a critical approach, emerging issues in health care generally and health information specifically, and how these influence and are influenced by broader ethical, social, political, legal and economic considerations.

  • FIMS 9325 – Introduction to Health Informatics (this course is offered by FIMS and is also available to a limited number of students in other programs as an elective)

    Evidence-based healthcare (EBHC) and big data can play important roles in healthcare. EBHC’s main purpose is to increase and improve the use of evidence (i.e., data and information) by stakeholders (e.g., health practitioners, policy-makers, public health managers, etc.). As health data continues to grow, big data can also play an increasingly important role in different aspects of EBHC. Despite the emergence of these two areas, the role that health informatics (HI) can play in EBHC and the analysis, design, and evaluation of HI tools often receive little attention. HI tools permeate EBHC at every turn—e.g., data and text mining tools for evidence generation, distillation, or synthesis; decision support for incorporating evidence-based protocols into clinical workflow; or web-based visualization tools for gaining insight into patterns of data. As HI tools advance, explicit understanding of and investigation into, the relationship between HI tools and healthcare become increasingly vital. This course explores topics related to health informatics—with particular emphasis on different areas of health informatics, HI tools, big data in healthcare, analytics methods and their role in healthcare, health data presentation, and other new developments.

  • HEALTSCI 9623 - Perspectives in Knowledge Translation (as a jointly offered program with the Faculty of Health Science, this required course is external to FIMS)

    This course will examine the multidisciplinary theoretical perspectives related to knowledge translation, as well as explore the conditions under which some knowledge translation interventions are successful (or not). Leading-edge topics in the field, such as the appropriate definition evidence; knowledge brokers; networks; and the role of non-governmental organizations in knowledge translation will also be discussed.

  • HEALTSCI 9601 - Quantitative/Qualitative Research Methods (as a jointly offered program with the Faculty of Health Science, this required course is external to FIMS)

    This course provides students with an introduction to the common quantitative and qualitative approaches to research through topics such as design, sampling, measurement and interpretation. Students will engage in learning activities that focus on the basic concepts and terminology surrounding quantitative and qualitative research to lay the groundwork for further advanced understanding. Throughout this course students will evaluate the methodological features of qualitative and quantitative research studies in the published literature.

  • HIS 9100 - Knowledge Synthesis - included in the drop-down menu required course list

    This course serves to support students to integrate, through experiential learning, theoretical aspects of research that have been introduced and explored in other core Master of Health Information Science (MHIS) courses. Each student works with a mentor of their choice in an established program of research to gain practical experience in the application of research within the context of Health Information Science. A proposed learning plan will be negotiated with the mentor for depth, breadth, and rigor and alignment with the achievement of program outcomes and course content will be individualized to meet students’ needs and professional goals. Students may partner with another classmate or work individually on a research project. This experience may include some or all the following experiences: literature review development, program evaluation, quality improvement, primary or secondary data collection and/or analysis, development of proposals, and other aspects of the research process that are tailored for the individual student experience.

  • + 2 electives

One Year Program: Elective Courses

Health Information Science students are required to take two elective courses. Students can access elective courses to build content or research expertise to suit their future goals. Possibilities for elective courses are wide-ranging and span multiple programs and Faculties.

Research Methods courses from Health Sciences

More details on Faculty of Health Sciences courses are available at:

HS 9602a – Qualitative Research Methods in Health Sciences
This course provides a comprehensive introduction to the qualitative paradigm and its current and potential applications in health and rehabilitation sciences. The philosophical assumptions that form an integral part of the qualitative paradigm will be examined, as will the assumptions underlying various qualitative schools of inquiry (e.g., grounded theory, phenomenology, ethnography, action research, narrative). Key considerations in the critical evaluation and design of qualitative studies within several schools of inquiry relevant to health and rehabilitation sciences will be addressed. Students will have opportunities to engage in critical analysis of qualitative research; discuss ethical issues related to the conduct of qualitative research; and engage in the process of proposal development within a group. 0.5 credit course

HS 9707a (formerly HS 9600) – Linear Regression in Health & Rehabilitation Sciences
This course is an introduction to linear regression for health sciences, examining simple regression, multiple regression, the use of categorical independent variables, and the fitting of interaction terms. Although formulas are given and calculations are presented, the principal orientation of the course is conceptual rather than mathematical. 0.5 credit course. 
Please note: HS 9601 is recommended as a precursor to this course

HS 9708 – Advanced Topics in Qualitative Research
This course will give learners the opportunity to learn how to rigorously and systematically analyse qualitative data in the form of interview transcripts from a study on women's experience of aging and osteoporosis. The course will begin with a review of the three key qualitative approaches or research designs used in the health sciences (grounded theory, qualitative case study and phenomenology). Next, we will highlight how the approaches are shaped by specific research paradigms (post-positivism, interpretivism/constructivism or critical theory). A key concept for this course is that by combining a qualitative approach with a paradigm will produce nine sub-approaches, each with its own guidelines on what constitutes an appropriate research questions, how to identify and select data sources and types, determining sample size, analyzing/interpreting data and presenting findings. Learners will be expected to select one qualitative sub-approach as the framework for their work in this course. To assist learners carry out their analysis projects, we will use the Qualitative Decision Points Matrix© to guide the research decisions they make. By the end of the course, learners will have designed and completed a rigorous and systematic secondary data analysis project and present their work orally and in writing. The grading format consists of observable contributions to group learning (15%), two papers (60%) and an oral presentation (25%). HS 9602 is recommended as a precursor to this course

HS 9709b – ANOVA Based Analysis in Health & Rehabilitation Sciences
This course will explore ANOVA based methods of data analysis, including t test, ANOVA, ANCOVA, Split-plot ANOVA, Factorial ANOVA, and MANOVA. 0.5 credit course. Please note: HS 9601 is recommended as a precursor to this course

HS 9730b – Philosophical Foundations of Qualitative Research
This course provides an introduction to philosophical foundations of qualitative research with a particular focus on interpretive and critical paradigms of inquiry. Assumptions about what constitutes knowledge (epistemology), the nature of existence (ontology), and means for gaining knowledge (methodology) within different knowledge paradigms are considered. Students examine philosophical and theoretical perspectives that underpin various schools to qualitative inquiry and identify perspectives relevant to the coherent and rigorous design of research. Within this course, students explore perspectives that relate to their own research interests; expand their familiarity with the specialized terminology adopted in qualitative research; consider approaches to representing, writing and publishing qualitative research; and investigate implications for the design and evaluation of qualitative research in health and social care. This course is highly recommended for doctoral level students completing a qualitative research dissertation, and is open to highly motivated master's level students wishing to deepen their research knowledge.

Research Methods courses from other Faculties

Taking courses outside of FIMS and FHS normally requires special permission. They may also have additional requirements or rules, depending on the home Faculty/Department. This is a list of examples, but is not an exhaustive collection.

Department of Epidemiology and Biostatistics

Epidemiology 9530B - Health Economics
This course is designed to give students a solid background in health economics and its application in the field of health and medicine. The course objectives are to provide the student with an understanding of the theoretical economic foundation of health economics and methods for the economic evaluation of health interventions. The topics to be covered are: microeconomic tools for health economics, production of health, demand for healthcare and health insurance, market failure in the health sector, measures of costs, measures of health outcomes, discounting, cost-minimization analysis, cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis, uncertainty in economic evaluation, decision-analytic models, Markov models, sensitivity analysis, and Monte Carlo simulation. This course will also provide the student with a hands-on experience in conducting economic evaluation using TreeAge Pro software package. Prerequisites: Some background in Statistics (e.g., E9509a: Principles of Biostatistics) or instructor’s permission.

Epidemiology 9531B - Methods and Issues in Program and Policy Evaluation in Health and Human Services
The purpose of this course is to familiarize students with the major issues in the fields of health and human services program and policy evaluation. Students will develop an understanding of the theoretical frameworks used for evaluative research, validity issues in evaluative research, and the multi-methods, theory-driven approach to evaluation. Students will also develop an understanding of the relative value of different designs that can be applied to evaluation research. Students will have the opportunity to develop their theoretical, methodological, and interpretive skills through various examples and applications and through the development of a proposal on an evaluation question of interest to them. Half course; one term.

Epidemiology 9547B - Survey Research Methods
This course provides an overview of the procedures involved in designing and executing health surveys. Topics to be covered include sampling techniques (addressed at a conceptual level and with basic sampling statistics), data collection strategies with their advantages and disadvantages, questionnaire construction, survey implementation, sampling and non-sampling errors, and several other practical aspects of conducting health surveys. Prerequisite: introductory statistics course; Epidemiology 9509a. Half course; one term.

Epidemiology 9550B - Population Health Surveillance
This course covers in-depth methods of measuring and analyzing mortality and morbidity at the population level. The material is presented within the contexts of international and community health. Prerequisite: Epidemiology 9551a and one of Epidemiology 9509a or Epidemiology 9510a or equivalents are highly recommended. Half course; one term.

From the Department of Sociology

*4441/9331 Population Research Methods  
This course introduces students to the field of population studies and the tools used by demographers to study the size, structure, and dynamics of human populations. It covers the collection, evaluation, and analysis of demographic data; census and vital registration systems; morbidity, disability, mortality, fertility, and migration; life table construction; and population projections.  We will also discuss how demographic methods can be used to study other topics, such as education, health disparities, disability, and prison populations, in order to provide an understanding of how these methods are applied outside the field of traditional demography. This course is open to students from other disciplines.

*4400/9001 Introduction to Multivariate Statistics
After a review of basic statistics, the course introduces students to popular multivariate techniques such as multiple regression, analysis of variance, path analysis, and logistic regression. The emphasis will be on using these techniques in social science research and on practical applications with the software SPSS.

9007 Advanced Multivariate Statistics  
In this course we will cover the most common statistical techniques in the practice of sociology - linear regression, logistic regression, and survival analysis (event history analysis). We will discuss the uses of these techniques and the assumptions that we make when using them. Throughout the course, we will discuss how to develop an answerable research question, how to choose the best modeling strategy for that question, and how to interpret the results of quantitative analysis in light of relevant hypotheses. There will also be an applied portion of the class held in the computer lab, where we will talk through basic issues that come up when working with data, such as missing data; saving data, code, and output; and making tables. The last portion of the course will focus on writing about multivariate analysis - communicating questions, methods, and results clearly.

From the Department of Psychology

(Note their rules: Non-Psych grad students must contact course instructors who may wish to know the student's background before allowing enrollment. In addition, non-Psych grad students should indicate their interest in a Psychology course by sending an email message to Val Van Domelen at Only Val or the student's graduate program assistant may officially add the course to the student's academic record, if the course is not full and the instructor permits. *Full=Please do not contact the course instructor.)

Psychology 9545A. Test Construction and Survey Design
This course is intended for psychology graduate students who need to develop test instruments such as questionnaires, short performance scales, observation schedules, interview checklists etc. for their current research or practice. Students should know in advance what variables/factors they are intending to measure (e.g., resiliency, motivation, well-being) and be familiar with the relevant research and assessment issues. Students should also have completed at least a foundational course in psychometrics as well as intermediate statistics and be familiar with statistical packages such as SPSS. It is expected that students will complete the basic scale development and have sufficient data to demonstrate the psychometric integrity and usefulness of the measure. While each project will stand alone, common themes such as item writing, reliability and validity, and norming will be discussed in the larger group, creating a richer and collaborative/supportive learning opportunity. Students interested in applying to this course require the approval of the instructor and should meet with him/her to determine the 'goodness of fit'. Half course (0.5); one term.

Psychology 9040A. Scientific Computing 
The goal of this one-semester graduate seminar is to provide you with skills in scientific computing - tools and techniques that you can use in your own research. We will focus on learning to think about experiments and data in a computational framework, and we will learn to implement specific algorithms using a high-level programming language (mainly Python although we will see some C and R code as well; Matlab is also a possibility if you want to substitute on your own.) Learning how to program will significantly enhance your ability to conduct scientific research today and in the future. Programming skills will provide you with the ability to go beyond what is available in pre-packaged analysis tools, and code your own custom data processing, analysis and visualization pipelines. Half course (0.5); one term.

HIS Course Directory

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