Each student plays an active role in the design of his/her program of study.
Although there are a number of required courses for all Ph.D. students, each program of study is individually tailored to meet the student’s particular needs and goals. Input from faculty and fellow students can be valuable. Courses should be chosen to enhance substantive knowledge and research skills.
The program of study is not cast in stone once it has been approved and filed with the Ph.D. program office. However, any changes must be approved in writing by the unit coordinator and the Ph.D. program office.
The rate of progress in the Ph.D. program may be a factor in the allocation of competitive awards made to students. These awards include fellowships as well as graduate research and teaching assistantships. As a general guideline, students are expected to register for a minimum of nine hours each semester (with the exclusion of summer quarter) until graduation. Many students will opt to register for more hours than the minimum. Failure to follow the program of study may be interpreted as a lack of progress.
Required Courses – 42 Semester Total Hours
- Research Methods (15 hours)
- Major (18 hours)
- Secondary Area (9 hours)
Research Methods Requirements
(15 semester hours)
The satisfactory completion of no fewer than 15 graduate-level semester hours constitutes minimum preparation in research methods. The Ph.D. Coordinator may approve substitutions for any of the following research methods requirements.
Approved Graduate Level Statistics Course
MGS 9940 – Design of Experiments
This course examines epistemologies and methods that lie at the heart of experimental research. It covers validation of experimental instruments, internal and external validity, and statistical conclusion validity derived through the family of ANOVA techniques, regression, and structural equation modeling. Students learn how to properly design an experiment and how to handle problems that come up in actually conducting experiments.
MGS 9950 – Regression Analysis
The focus of the course is on regression as an inferential tool for conducting empirical research. As such, in-depth coverage is given to the topics of parameter estimation, hypothesis testing, and residual analysis. Multicollinearity diagnostics and remedies are discussed, and several special topics are covered.
MGS 9960 – Multivariate Data Analysis
Multivariate data analysis is illustrated for data reduction, quasi-experimentation, and true experimentation. Critical assessment of published research is the key goal. Among various techniques covered are multivariate hypothesis testing, principal components analysis, factor analysis, cluster analysis, discriminant analysis, canonical analysis, multivariate analysis of variance, and multivariate analysis of covariance.
BA 9260 – Theory Development
Students understand how to develop theory and surface a theoretical contribution. They understand the distinction between identifying a business problem and a scientific problem, and the approaches to achieve rigor and relevance. They learn about the elements of a theory and the approaches to build theory. They understand the distinction between process and variance models, and the importance of achieving correspondence between theoretical arguments and model specification. They develop an understanding about how to leverage context and time in building theory, and about multi-dimensional constructs and multi-level models. Cumulatively, they develop the skills and understanding to formulate a research question, synthesize the literature, build a theory, and specify a model.
BA 9280 – Quantitative Research Methods in Business
This course develops skills in designing, evaluating, and understanding quantitative methods and methodologies for research in the social science paradigm. Students also acquire skills in developing research proposals, supporting methodological choices, and understanding how to successfully publish their work. The course is intended for students across the business disciplines.
BA 9300 – Qualitative Research Methods in Business
This course helps develop knowledge and skills in the application and use of qualitative research techniques. The course provides a survey of the methodological literature on qualitative research methods paired with appropriate article-length exemplars in the disparate business disciplines. This course covers a variety of different research strategies including case study, ethnography, grounded theory, and action research. In addition, students acquire skills in developing a research design, and qualitative date collection and analysis techniques, and authoring research manuscripts.
BA 9330E – Philosophy of Science for Business Research
The objective of this 1.5-credit-hour seminar is to prepare a new doctoral student for a career of conducting and reading academic research in business. For the purposes of this course, the field of business will be broadly defined to include all disciplines from accountancy and finance to marketing and managerial sciences. As such, this seminar is designed to be taken in the first year of a Ph.D. program at the J. Mack Robinson College of Business. We will begin by covering classic readings on the philosophy of science. The purpose of these early readings is to provide an historical and conceptual foundation for academic research in business. Next, we will cover key topics related to the development of theory and the testing of theory with empirical data. Thereafter, we will cover highly influential papers that demonstrate the progression of theory and research insights in business. It is highly recommended that Ph.D. students follow up this introductory seminar with the 3-credit-hour doctoral seminar BA 9260: Theory Development.
BA 9340 E – Advanced Psychometrics
This course focuses on varied ways to develop and define theoretical constructs, diverse approaches for developing measures of constructs, statistical methods relevant to establishing construct validity. Current issues and topics related to academic publishing will be emphasized.
BA 9520E – Principles of Multi-level Methods and Modeling
The goal of the course is to gain familiarity in multi-level approaches, including data analysis, theoretical considerations and study design. The course emphasizes conceptual, operational and interpretational skill development, in all areas of inquiry where multi-level approaches can be valuable.
IFI 8650 Image and Text Analytics with Deep Neural Networks
Text documents and images have proven to be useful complements to structured data in different research fields such as marketing, information management, real estate, accounting, finance, operations management etc. This course studies how to use deep neural network methods to analyze text documents and images to solve business related problems.
IFI 9000 Research Methods with Analytics
This course introduces analytics methods for research. In particular, basic methods of machine learning, text and image analytics will be introduced. The applications of established and recent developments of these methods in solving practical business problems will be studied.
Quantitative and Economic Foundations
Students entering the Ph.D. program are presumed to have background and current knowledge in:
- multi-variable calculus – including multiple integration, partial derivatives and infinite series
- matrix algebra – including linear transformations, vector differentiation and eigenstructures
- computer skills for empirical research – including statistical packages and the use of data tapes and files
- macroeconomics and microeconomics through the intermediate level
New students will want to discuss any deficiencies in their academic background with their Ph.D. coordinator. Students lacking English communication skills may be required to take special English courses as foundation coursework. A proficiency test is administered at the special orientation program for international students. Any English courses assigned as a result of this test must be taken during the student’s first semester of coursework.