Qualitative Analysis and Interpretation

Course Number
CHL5115H
Series
5100 (Social and Behavioural Health Science)
Format
Seminar
Course Instructor(s)
Brenda Gladstone

Course Description

This is an advanced graduate-level course in qualitative research methodology that focuses on the theory, techniques and issues of data analysis and interpretation. The course is designed for students taking qualitative approaches to their thesis research i.e. using both qualitative forms of data and qualitative (non-numeric, interpretive) forms of analysis. Ideally students should be in the late data gathering and analysis phase of their research, although students at the proposal writing and pre-data collection stage also benefit from the course. The course aims to give students knowledge and experience in concrete analysis practices, but also to enhance their ability to articulate and address the core theoretical and methodological issues of qualitative inquiry. Although the topics discussed are generic to qualitative methodology, the literature and class instruction draw heavily on the field of health, and on the instructor’s own disciplinary background in the sociology of health and illness and childhood, and substantive topic area of mental health.

This course is part of CQ‟s Essentials of Qualitative Research curriculum. CQ is an extra-departmental unit in the Dalla Lana School of Public Health also supported by the Faculties of Kinesiology and Physical Education, Nursing, Pharmacy, Social Work, and the Rehabilitation Sciences Institute.

Course Objectives

This course aims to develop in students a deeper marvel for, enjoyment of, and skill in qualitative research. At the end of the course students should have made significant progress towards being able to understand and articulate:

  1. What it means to critically analyze and interpret qualitative data, including the difference between value-added analysis and primary description.
  2. The role, place, significance and timing of theory in the analysis process
  3. The implications for analysis and interpretation of the data generation, transformation and management process
  4. The complexity and implications of the interpretation of ‘meaning’
  5. The role of the researcher in analysis, and the significance of standpoint
  6. The notion, practice and significance of methodological reflexivity, and its role in the research process
  7. The constitutive effects of writing on the analysis, and the different ways of representing the results of qualitative inquiry and their implications
  8. Issues associated with judging research quality in qualitative inquiry
  9. The importance of being able to write and articulate convincingly the nature, value, and limitations of your analytic process and of qualitative methodology more generally.

Methods of Assessment

There are three (3) requirements for this course. All are designed to facilitate students’ own research-in-progress (accommodating different interests, topics, and stage of research) while developing generic methodological knowledge and skills.

In the interest of fairness, extensions of submission dates are not normally granted, so please organize yourself to submit assignments by the due dates.  Should a request for an extension become unavoidable, please negotiate this with the course instructor one week prior to the due date. A penalty of 2% per day will be applied to late assignments. 

Assignment # 1 Reflection Paper 20% of final grade
Assignment # 2 Reflection Paper 30 % of final grade
Assignment # 3 Major Paper 50 % of final grade

 Class Schedule

  1.       Introduction

Introduction to interpretive qualitative analysis; varieties of analytic approaches; ‘value-added’ analysis; place in the research process; key features; exemplar; overview of course; how to ‘do’ the course; resources.

  1.                Key considerations in analysis & interpretation

What is (not) ‘analysis’? Significance of: the researcher, theoretical perspective, how data are produced, and context. Core concepts and assumptions; double (triple) hermeneutics; the ‘everything is data’ maxim.

  1.                Data transformations

From in vivo to tape to transcription to analysis: what is lost and changed; politics and practicalities; implications for interpretation.

  1.                Reading and interrogating data

Meaning and its interpretation; notion of ‘analytic devices’; making strange; reflexivity as resource; different approaches to understanding data; layered, relational, narrative readings; contradiction; negation; counter-imaging.

  1.                Coding

Theory, practice, implications; types of codes; codebooks, coding as means not end.

*Assignment 1 due (submit in class) 

  1.                Working with and beyond codes

Capturing the gestalt; reconstituting, re-contextualizing & summarizing data.

NOTE: No Class  (Reading Week)

  1.                Conceptualizing I

Analytic memoing; analytic generalization; types/levels of concepts; generating concepts.

  1.                Conceptualizing II

Developing, situating and linking concepts; pursuing hunches; comparison; thought operations; situational analysis. 

  1.                Analyzing different types of data: Visual data 

Explore insights generated by analytic questioning of images, their production, and intended/imagined audiences: three interrelated meaning-making sites 

*Assignment 2 due (submit in class)

  1.                       Theorizing

Different sites, types, sources and uses of theory and their combination; transforming data and concepts into ‘findings’; abductive thinking; linking macro and micro level data/ideas.

  1.                       Writing I: The Story

Writing as analysis; finding the story; strategies and approaches; audience; the politics of representation; positioning the story; taking sides.

  1.                Writing II: The Words; Describing the analytic process

Significance of word-craft and grammar; providing evidence; confidentiality; incorporating literature.

Writing about method of analysis; claiming your own inventions; key contested issues including scientific legitimacy and authority; issues of quality and rigor.

General Requirements

Students taking this course need to have:

  • Knowledge of the theoretical and philosophical foundations of qualitative inquiry, and of data generation, is expected (e.g., CHL5131, JRP1000, SWK6307, or equivalent)
  • Prior training and/or experience with qualitative research.
  • Their own data/research plan to use in the course.

Permission from the instructor is required for enrollment. A maximum of 15 students can be in the course at one time. Audits are not generally accepted. Priority is given to students in departments/faculties that are ‘contributing’ members in CQ (see pg. 9), and to those with optimal backgrounds and current research situations for benefiting and contributing to the course.