Research Methods and Statistics

All students, regardless of specialization, will choose research methods electives. The elective courses available are sufficiently diverse that students from differing specializations may select courses that are most appropriate to their area.

The Alberta School offers a two-term course sequence (MGTSC 705, Multivariate Data Analysis I, and MGTSC 706, Multivariate Data Analysis II), which most PhD students take. The Alberta School also offers a course in qualitative research methods (BUS 701) and is planning to introduce a course in philosophy of research and experimental methods. In addition, a wide range of other statistics and research design courses are available in other Faculties. Students choose from these according to their research needs and goals.

The following methods courses for doctoral students are offered within the Alberta School of Business:

ACCTG 701 Introduction to Accounting Research

A survey/history of accounting thought, introducing the major research approaches in accounting. Open to all doctoral students or with written permission of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.

BUS 701 Qualitative Methodology for Business Research

Examines qualitative research methods as they apply to business research. Includes: the terrain and history of qualitative research, exploring different approaches to qualitative research, designing qualitative research, strategies of inquiry, qualitative data analysis, writing up research, and professional and ethical issues. Prerequisite: Registration in Business PhD Program or written permission of instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.

BUS 715 Experimental Design for Behavioural Science

The objective of this course is to provide students with an understanding of the essential principles and techniques for conducting scientific experiments on human behaviour. It is tailored for individuals with an interest in doing research using experimental methods in areas such as psychology, judgement and decision making, consumer behaviour, behavioural economics and finance, organizational behaviour, and human performance. Classes are conducted in an interactive seminar format, with extensive discussion of concrete examples, challenges, and solutions. Prerequisites: Registration in the Business PhD Program or permission of instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.

MGTSC 705 Multivariate Data Analysis I

An overview of multivariate data analysis normally taken by students in the first year of the Business PhD program. The course is designed to bring students to the point where they are comfortable with commonly used data analysis techniques available in most statistical software packages. Students are expected to complete exercises in data analysis and in solving proofs of the major results. Topics will include univariate analysis, bivariate analysis, multiple linear regression, and analysis of variance. It is expected that students have as background at least (a) one semester of calculus; (b) one semester of linear algebra, and (c) two semesters introduction to probability, probability distributions and statistical inference. Prerequisite: Registration in Business PhD Program or written permission of instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.

MGTSC 707 Applied Business Analysis of Time Series and Panel Data

This course is organized into two parts. Part I covers univariate and multivariate time domain models of stationary and nonstationary time series. Topics covered include univariate time series models, unit root tests, time series regression modeling, systems of regression equations, vector autoregressive models for multivariate time series and cointegration. In Part II the course introduces the issues and opportunities that arise with panel data and the main statistical techniques used for its analysis. Topics covered include fixed-effects models, random-effects models, dynamic models and limited dependent variable models. Throughout the course, the emphasis will be on how to use S-plus and Stata to estimate panel data and time series models. There is relatively less emphasis on statistical theory. Evaluation in the course is based on homework assignments and a term project. Prerequisite: MGTSC 705 or equivalent.

SEM 706 Seminar in Quantitative Research Methods (in development)

Quantitative methods is an empirics-focused seminar that is intended to sharpen the student's ability to design and use quantitative and mixed methods in behavioural studies, as well as to broaden the student's knowledge of exemplary research in methods in this domain of research. The course complements standard regression or ANOVA course taken by students, and is particularly tailored for students of organization, strategy, and entrepreneurship. Prerequisite: Registration in Business PhD Program at the University of Alberta or written permission of instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.

Other appropriate research methods courses may be substituted with approval of the supervisor. The following table lists some of the past courses taken by our students: 

  • ECON 506 Applied Econometrics 
  • ECON 509 Time Series Methods in Financial Econometrics
  • ECON 598 Econometric Theory and Applications
  • ECON 599 Applied Econometrics
  • ECON 608 Topics in Econometrics
  • PSYCO 531 Design and Analysis in Psychological Research I
  • PSYCO 532 Design and Analysis in Psychological Research II
  • SOC 509 Multi-Variable Sociological Analysis
  • SOC 515 Quantitative Methods in Social Research
  • SOC 518 Qualitative Methods in Social Research
  • SOC 519 Comparative and Historical Methods in Sociological Research
  • SOC 533 Research Design
  • SOC 609 Multivariate Analysis
  • SOC 615 Advanced Methods of Sociological Inquiry
  • SOC 616 Structural Equation Modelling with LISREL
  • STAT 5xx Graduate Courses in Statistics