BZAN 6353: Research Design for Problems in Business Analytics
1. About BZAN 6353
BZAN 6353: Research Design for Problems in Business Analytics is a graduate-level course that teaches students how to translate business problems into research questions that can be investigated using analytics.
The course focuses on research methods, hypothesis development, data collection, statistical analysis, and experimental design. Students learn how to identify relevant variables, select appropriate research methods, and evaluate analytical models to support business decisions.
- Course format: Face-to-face
- Class time: Thursdays, 6:00 PM–9:00 PM
- Prerequisite: Graduate standing
2. What Do Students Learn in BZAN 6353?
Students learn how to develop rigorous research approaches for solving business problems using statistical and analytical methods.
By the end of the course, students should be able to:
- Translate business problems into research questions and testable hypotheses.
- Identify the constructs involved in research hypotheses and map them to measurable variables.
- Select appropriate primary and secondary data collection methods.
- Prepare and organize data for statistical analysis.
- Apply statistical methods, including significance testing and multivariate analysis.
- Design experiments and analyze experimental data.
- Understand and apply true experimental and quasi-experimental designs.
- Formulate and evaluate estimation models using data.
- Assess the accuracy and rigor of analytical models and research conclusions.
3. Main Topics Covered
The course progresses from foundational research concepts to advanced methods for causal inference and multivariate analysis.
| Topic | What It Covers |
|---|---|
| Research Methods and Conceptual Foundations | Research rigor, explanatory models, constructs, measurements, variables, and relationships. |
| Problem Formulation and Hypotheses | Developing research problems, defining hypotheses, and understanding multivariate approaches. |
| Causal Inference and Sampling | Randomness, randomization, representativeness, and non-probabilistic sampling. |
| Hypothesis Testing and Analysis of Variance | Testing research hypotheses and applying analysis of variance to business problems. |
| Survey Research | Survey methods, sampling, estimation, and survey design. |
| Measurement Reliability | Understanding reliability, measurement error, and standard errors. |
| Validity | Evaluating content, criterion, and construct validity. |
| Research Design | Developing research designs and addressing threats to research rigor. |
| Experimental Design | Understanding experimental design principles and methods for improving signal and reducing noise. |
| Quasi-Experimental Design | Exploring non-equivalent groups, regression discontinuity, and related methods. |
| Advanced Causal Inference | Evaluating research design issues and the rigor of regression-based inference. |
| Advanced Multivariate Regression | Applying advanced multivariate regression concepts to research problems. |
4. Course Structure
BZAN 6353 combines classroom instruction, application exercises, group work, quizzes, and a group research project.
The course is organized into five broad modules:
- Module 1: Research Methods – Conceptual Foundations
- Module 2: Causal Inference
- Module 3: Measurement Reliability and Validity
- Module 4: Research Design
- Module 5: Advanced Concepts in Causal Inference
Students apply research concepts to business problem contexts through exercises and group activities. The course culminates in a group project that requires students to work collaboratively on research-related deliverables.
5. Assignments and Assessments
Quizzes
Students complete three quizzes during the semester. Together, the quizzes account for 45% of the final grade.
| Quiz | Scheduled Date | Grade Weight |
|---|---|---|
| Quiz 1 | September 17, 2026 | 10% |
| Quiz 2 | November 5, 2026 | 15% |
| Quiz 3 | December 7, 2026 | 20% |
The syllabus identifies these dates as tentative. Missed or late quizzes generally cannot be made up unless an officially acceptable reason is provided and approved under applicable college policies. Unexcused missed assessments receive a score of zero.
Group Project
The group project is worth 30% of the final grade and is divided into two phases.
Students are assigned to groups of approximately four or five other students through a random assignment process. Group members collaborate on project deliverables, with the delivery schedule and submission method provided in advance.
Students also complete peer evaluations toward the end of the semester. These evaluations help determine each member’s adjusted project score based on their contributions.
Group Work
Group work accounts for 10% of the final grade. The course schedule includes application exercises and group activities that allow students to work through research methods and analytical problems.
Class Participation
Class participation accounts for 15% of the final grade. Students should expect to engage with course material and participate in classroom activities.
6. Grading Breakdown
The final course grade is determined by the following components:
| Grade Component | Weight |
|---|---|
| Quiz 1 | 10% |
| Quiz 2 | 15% |
| Quiz 3 | 20% |
| Group Project | 30% |
| Group Work | 10% |
| Class Participation | 15% |
| Total | 100% |
The group project and quizzes together account for 75% of the final grade, making consistent preparation and project participation important throughout the semester.
7. Readings and Learning Materials
The required textbook is:
- Research Methods: The Essential Knowledge Base, 2nd edition (2015), by William M. K. Trochim, James P. Donnelly, and Kanika Arora, published by Cengage Learning.
The course also draws on instructor notes, teaching excerpts, and selected material from other research methods texts, including Foundations of Behavioral Research by Fred N. Kerlinger.
Students are encouraged to use software capable of running linear regressions and testing correlations.
Recommended software includes:
- Microsoft Excel with StatTools: Recommended for Windows users.
- StatPlus: Recommended for Mac users working with Excel spreadsheets.
- Other quantitative analytics tools: Students may use other suitable software for quantitative analysis.
8. Weekly Course Topics
The Fall 2026 schedule moves from conceptual research foundations to advanced statistical and experimental methods.
| Date | Topics and Activities |
|---|---|
| August 27 | Research methods, conceptual foundations, rigor in analytics, explanatory research, constructs, measurements, variables, relationships, and associations |
| September 3 | Application exercises involving constructs, operationalizing variables, measurement models, measurement errors, and explanatory models |
| September 10 | Formulating research problems and hypotheses; multivariate approaches; causal inference, sampling, and randomness |
| September 17 | Hypothesis testing and analysis of variance; Quiz 1 |
| September 24 | Application exercises involving analysis of variance, hypotheses, and random samples |
| October 1 | Foundations of survey research, survey design, sampling, estimation methods, and Group Project Phase 1 activities |
| October 8 | Measurement reliability, reliability measures, standard errors, and measurement theory |
| October 15 | Validity, including content, criterion, and construct validity |
| October 22 | Research design, application exercises, and Group Project Phase 1 due |
| October 29 | Experimental design foundations, experimental classifications, and Group Project Phase 2 activities |
| November 5 | Quasi-experimental designs, non-equivalent groups, regression discontinuity, and Quiz 2 |
| November 12 | Advanced causal inference, regression-based research design, and threats to research rigor |
| November 19 | Advanced topics in multivariate regression and group work |
| November 26 | Makeup class, only if needed |
| November 27 | Tentative Group Project Phase 2 deadline |
| December 7 | Quiz 3 |
The syllabus describes the lecture outline and several assessment dates as tentative. Students should check course communications for confirmed dates and any schedule changes.
9. Skills Developed
BZAN 6353 helps students develop research and analytical skills that can be applied to business problems.
- Research problem formulation: Turning business challenges into researchable questions.
- Hypothesis development: Creating testable propositions that guide analytical investigations.
- Variable measurement: Connecting theoretical constructs to measurable variables.
- Data collection: Selecting and applying primary and secondary data collection methods.
- Statistical analysis: Using significance tests, analysis of variance, and multivariate methods.
- Experimental design: Planning experiments and understanding quasi-experimental approaches.
- Model evaluation: Assessing estimation models and the accuracy of research findings.
- Critical analysis: Identifying threats to research validity and rigor.
- Team collaboration: Working with group members to develop research project deliverables.
10. What Should Students Expect?
Students should expect a research-focused course that combines theoretical foundations with practical applications. Rather than concentrating only on statistical calculations, the course emphasizes how to design a research process that can answer meaningful business questions.
Class sessions include conceptual instruction, application exercises, and group activities. Students will encounter research terminology and methods related to measurement, sampling, causal inference, experimental design, and multivariate analysis.
The group project requires sustained collaboration over multiple phases. Students should be prepared to contribute to their assigned group and participate in peer evaluation.
Attendance and keeping up with course materials are important. The syllabus states that students are responsible for obtaining materials distributed during missed classes. Missed or late assessments are subject to strict rules, with makeups generally unavailable unless an officially acceptable reason applies.
