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Darla Moore School of Business

Program Structure

All doctoral students complete a rigorous program which emphasizes three main aspects: academic coursework, dissertation preparation, and graduate assistantship. Together, these aspects provide a cohesive process which focuses on academic training, structured time to conduct research, and practical mentoring in research and teaching.

The Ph.D. in Business Administration Program with a concentration in marketing consists of a total of 60 credit hours and typically takes five years to complete.


Coursework (first two years)*

During the first part of the program you will focus on completing the required 48 credit hours of coursework. This requirement typically takes two years to complete and may extend into the third year, given available course sequence or special academic circumstances.

The coursework is completed in three stages, which can be taken in any order. The information below represents courses typically included in a program of study, however, course content may vary depending on a student’s unique academic and professional background, research interests or guidance provided by the academic advisory committee.

Courses focus on learning specialized tools for conducting research.

  • Advanced Statistics for Business I
  • Advanced Statistics for Business II

  • Consumer Behavior Specialization
    • Basic Quantitative Methods in the Analysis of Behavioral Data I
    • Research Approaches to Human Behavior
    • Multivariate Analysis of Behavioral Data
    • Psychological Tests and Measurement
  • Quantitative Marketing Specialization
    • Econometrics and Regression I
    • Econometrics and Regression II
    • Hierarchical Lineal Modeling
    • Survival Analysis

Courses focus on studying and conducting literature review of the subject matter.

  • Seminar in Marketing Strategy
  • Topics in Consumer Research
  • Concepts and Theories in Consumer Research
  • Marketing Models
  • Research Methods and Philosophies in Marketing

Courses focus on complementary subjects from various disciplines that enhance the student’s body of knowledge. Possible courses include but are not limited to the following:

  • Current Issues in Organizational Behavior
  • Survey of Social Psychology
  • Research Approaches to Human Behavior
  • Seminar in Judgment and Decision Making
  • Advanced SAS Programming
  • Intro to Bayesian Data Analysis
  • Machine Learning

* Students who hold an earned master’s degree are eligible, pending their academic advisory committee approval, to waive up to six semester hours of coursework. Such courses are typically waived from the cognate course requirement. This opportunity does not typically translate into reducing the typical length of the program (five years) but does provide the student with important time to focus on non-coursework activities.

Research and Dissertation Preparation (Remaining three years)

Under the guidance of your dissertation advisor and committee, years three, four and five will be dedicated to planning, conducting and finalizing your dissertation research. This ongoing academic work accounts for the remaining 12 credit hours required for graduation.

At the end of the program, you will deliver your official document and present (i.e., defend) your dissertation before your dissertation committee. In addition to your required dissertation, and in most cases as part of your graduate assistantship, you will have a close working relationship with multiple faculty in the department, often participating in various research projects.

Both the dissertation, as well as various opportunities to participate in faculty-led research, play an integral part in the academic and professional training of all Ph.D. students. These opportunities provide students with research experience as they prepare for their future academic research career.

Graduate Assistantship

The Graduate Assistantship provides you with important experience and practical training in activities critical to your future academic career. During your progression in the program, graduate assistantship responsibilities may include:

  • Faculty Research Support: students provide research support as they work with faculty on important research initiatives.
  • Faculty Teaching Support: students act as teaching assistants, providing support to faculty on preparing and delivering a class.
  • Teaching: after completing their coursework but before graduation, students are required to teach at least one course at the undergraduate level (supervised by their dissertation advisor).

Progression Milestones

As you progress through the program, you will be required to achieve the following milestones that will allow you to meet the degree requirements established by both the academic department and the university’s graduate school.

During the first two years of the program, you’ll will work closely with your Ph.D. coordinator and Ph.D. Advisory Committee to create and update your program or study. The Ph.D. faculty coordinator acts as your main advisor during the first two years, although it is expected that all students interact, work and network with other Ph.D. faculty.

This milestone consists of an oral exam presented before the committee and is based on all courses taken during the first year of the program.

You will be asked to present your first paper under the guidance of one or more faculty members and the Ph.D. faculty coordinator. This serves as the first experience in developing a research paper at the doctoral level. 

You will be asked to present your second paper under the guidance of one or more faculty members and the Ph.D. faculty coordinator. This paper may or may not be linked to their first-year paper.

This milestone consists of a written exam and an oral exam presented before the committee. The exam focuses on two important aspects:

  • Evaluate the student’s mastery of coursework
  • Evaluate the student’s ability to conduct the research required to complete a Ph.D.

After two years of ongoing interaction with faculty in the department, and in consultation with the Ph.D. faculty coordinator, you will select your dissertation advisor (advisor’s consent needed) and committee. This team will serve as your main support system and will provide guidance on completing your dissertation.

You’ will provide initial ideas on both the dissertation topic and the proposed methodology to use in the research process. The proposal consists of both a written document and a presentation before your dissertation committee.

This is the final milestone in earning the Ph.D. degree. You will provide the final dissertation document to the dissertation committee and conduct a formal presentation of its content and findings. Once approved by the committee, the final dissertation document is sent to the Graduate School for final approval and publication.

Course Descriptions

For your reference, the following list provides a general description of each of the courses outlined in the coursework section above.

The development and application of advanced statistical methods to problems in business. Topics include application of estimation and hypothesis testing in both univariate and multivariate cases.

The structure and analysis of experimental and research designs with applications to business problems.

Quantitative methods for graduate students in psychology and other behavioral sciences. Emphasizes logical/intuitive understanding of the basic techniques, focuses heavily on the application of these methods to psychological research. Three lecture/discussion hours and a one-hour scheduled lab per week.

Nonquantitative aspects of research methodology and experimental design in laboratory and field settings. A critical investigation of artifacts and ethical issues in behavioral research.

Advanced topics in multiple-variable research. Topics include multiple linear regression, polynomial regression, canonical correlation, discriminant function, and the analysis of variance using orthogonal polynomials and multidimensional scaling, both metric and nonmetric approaches.

Introduction to the theory and practice of measuring psychological attributes. Emphasis on test construction in a laboratory setting. Hands-on experience in designing, administering and analyzing psychological tests and measures.

A treatment of single equation estimating techniques for the simple linear model, various nonlinear models and the general linear model.

Topics in generalized least squares, autocorrelation, distributed lag models, principal components, identification and simultaneous estimating techniques.

Advanced quantitative methods course in multilevel data analysis. Covers theoretical grounding, applications in the social sciences and model building

Methods for the analysis of survival data in the biomedical setting. Underlying concepts; standard parametric and nonparametric methods for one or several samples; concomitant variables; and the proportional hazards model.

Doctoral seminar investigating emerging paradigms and theory regarding the role of marketing within the firm and the effects of marketing mix variables on consumer behavior and firm performance.

Doctoral seminar involving intensive study and criticism of the current consumer research literature.

Doctoral seminar exploring concepts, theories and research methods relevant to understanding consumer behavior.

Doctoral seminar covering advances in marketing science models, including brand choice, product development, media choice and other models.

Doctoral seminar covering research methods and philosophies that underpin knowledge generation in marketing.

An advanced seminar focusing upon reading, synthesis and critical evaluation of current research in business and management.

Issues, research and theories in social psychology.

Nonquantitative aspects of research methodology and experimental design in laboratory and field settings. A critical investigation of artifacts and ethical issues in behavioral research.

Research and theories of processes in judgment, choice and decision making.

Advanced programming techniques in SAS, including database management, macro language and efficient programming practices.

Principles of Bayesian statistics including: one- and multi-sample analyses; Bayesian linear models; Monte Carlo approaches; prior elicitation; hypothesis testing and model selection; hierarchical models; selected advanced models; and statistical packages such as WinBUGS and R.

Fundamentals of machine learning including rote learning, learning from examples, learning from observations and learning by analogy; knowledge acquisition for expert systems.


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