Niam Yaraghi

Modern Experimental Designs for Business Research

Course Notes for BTE 693: Computational Methods and Field Experiments. Version 1.0, September 2026.

These are the notes for BTE 693, the doctoral course on experimental design that I teach at the University of Miami. Each chapter is one session, in the order the course runs: from what secondary data cannot tell you, through validity, power and sample size, to the designs that adapt as they go (MOST, SMART, just-in-time adaptive interventions and micro-randomized trials), and on to conjoint analysis, the problem of scaling an experiment, and the use of large language models in experimental research. The worked examples are in SAS, with the code and output shown.

Contents

  1. Preface
  2. Part 1 Foundations of Experimental Research
    1. 1
      Foundations: From Secondary Data to Experiments

      Secondary data analysis leverages existing data sources to infer causal relationships. While it offers scalability and cost efficiency, it is particularly susceptible to biases and relies on strong assumptions for causal

    2. 2
      Validity and the Spectrum of Experiment Types

      In the previous session, we discussed the foundational principles of experimental design, focusing on randomization, replication, and control as tools to address biases such as selection bias and omitted variable bias. We highlighted how these principles ensure internal validity…

    3. 3
      Hypothesis Testing, Statistical Power, and Sample Size

      In the previous session, we explored the use of online platforms like MTurk in experimental research, emphasizing their potential to enhance participant diversity, lower costs, and increase scalability. We discussed key challenges such as response noise, attentiveness, and…

  3. Part 2 Optimizing Interventions
    1. 4
      The Multiphase Optimization Strategy and Factorial Designs

      In the previous session, we examined essential components of experimental design, particularly focusing on sample size determination and statistical power. These elements ensure that studies are designed with sufficient observations to detect meaningful effects while minimizing…

    2. 5
      MOST in Practice: A Worked Example and Fractional Factorial Designs

      In our last session, we introduced the Multiphase Optimization Strategy (MOST) and explored how it differs from traditional randomized controlled trials (RCTs). While RCTs evaluate an intervention as a whole, MOST focuses on optimizing individual components before assembling the…

    3. 6
      Sequential Multiple Assignment Randomized Trials (SMART)

      In the previous session, we explored the Multiphase Optimization Strategy (MOST). The focus was on designing, optimizing, and evaluating an experiment before testing it through a randomized controlled trial (RCT). MOST is an excellent approach for developing experiments that are…

  4. Part 3 Adaptive and Real-Time Designs
    1. 7
      Just-in-Time Adaptive Interventions (JITAIs)

      In our previous sessions, we explored the Multiphase Optimization Strategy (MOST) and Sequential Multiple Assignment Randomized Trials (SMART) in great detail. MOST is a framework that helps us build and fine-tune interventions by identifying their most effective components.…

    2. 8
      Micro-Randomized Trials (MRTs)

      In the previous session we learned how SMART optimizes the sequence of interventions and how JITAIs take this further by delivering real-time, context-sensitive interventions (e.g., via mobile devices), often relying on SMART-like decision rules to determine ’when’ and ’what’ to…

  5. Part 4 Preferences, Scale, and New Tools
    1. 9
      Conjoint Analysis and Preference Elicitation

      Imagine you’re designing a stress reduction app with three components: mindfulness exercises, social and peer support, and physical exercise. Randomized Controlled Trials (RCTs) can tell you if the app reduces stress, Multiphase Optimization Strategy (MOST) can optimize which…

    2. 10
      Scaling Experiments and Heterogeneous Treatment Effects

      Throughout this course, we have explored a comprehensive range of experimental designs, building a robust foundation for conducting rigorous scientific research. We began with randomized controlled trials (RCTs), the gold standard for establishing causality, and progressed to…

    3. 11
      Large Language Models in Experimental Research

      So far, we’ve been discussing various experimental designs intended to study human behaviors and decision-making directly. However, with the rapid advancements and widespread adoption of Large Language Models (LLMs) and Generative AI, we’re now at an exciting turning point. In…

  6. Appendix A. Research Ethics and IRB Training

Download and cite

PDF
Version 1.0, September 2026 (143 pages, 1 MB).
Cite as
Yaraghi, N. (2026). Modern Experimental Designs for Business Research: Course Notes for BTE 693. Version 1.0. University of Miami. https://niamyaraghi.com/experimental-designs
Licence
CC BY-NC-ND 4.0: free to read, share and use in teaching with attribution; not for sale or alteration.
Corrections
Send them to niamyaraghi@miami.edu.