Applied Quantitative Methods for Coaching

Design single-case experimental designs (SCEDs) like A-B-A to test the effectiveness of specific interventions for a client.

Create customized repeated-measures tracking tools for clients to monitor key variables (e.g., relationship satisfaction, time allocation).

Apply Bayesian reasoning to conceptually update beliefs about intervention effectiveness as new client data comes in.

Select appropriate statistical tests (e.g., paired t-test) to analyze pre-post outcome data from specific interventions.

Construct compelling data visualizations (e.g., run charts, annotated graphs) to illustrate trends for non-technical clients.