By the end of this chapter, you will be able to do the following:

12.1 Formulate the goals of using market basket analysis (MBA).

12.2 Understand the benefits of using MBA, including how it allows for inductive theorizing, can address contingency relationships, does not rely on assumptions that are often violated when using regression-based techniques, allows for the use of data often considered “unusable” and “messy,” can help build dynamic theories, is suited to examine relationships across levels of analysis, and is practitioner-friendly.

12.3 Arrange the steps involved in market basket analysis, including determining the suitability of MBA, defining the “transactions,” collecting data, checking MBA requirements, and deriving association rules and their strength.

12.4 Formulate the goals of Experience Sampling Methodology (ESM).

12.5 Understand the benefits of ESM, including capturing dynamic person-bysituation interactions over time, enhancing ecological validity, and allowing for an examination of between- and within-person variability.

12.6 Describe the steps necessary to design and implement ESM, including determining the sample size, scheduling, signaling devices, recruiting participants, and data analysis.

12.7 Propose the advantages of Bayesian analysis over frequentist analysis, including using prior knowledge, the joint distribution of parameters, assessment of null hypothesis, ability to test complex models, unbalanced or small sample sizes, multiple comparisons, and power analysis and replication probability.

12.8 Set up the steps to implement Bayesian multiple linear regression, including establishing the prior distribution, computing the posterior distribution, accepting the null value, and summarizing Bayesian analysis’s rich information.