Design Research uses scientific methods to evaluate designs and build design theories. This book starts with recognizable questions in Design Research, such as A/B testing, how users learn to operate a device and why computer-generated faces are eerie. Using a broad range of examples, efficient research designs are presented together with statistical models and many visualizations.

With the tidy R approach, producing publication-ready statistical reports is straight-forward and even non-programmers can learn this in just one day. Hundreds of illustrations, tables, simulations and models are presented with full R code and data included. Using Bayesian linear models, multi-level models and generalized linear models, an extensive statistical framework is introduced, covering a huge variety of research situations and yet, building on only a handful of basic concepts. Unique solutions to recurring problems are presented, such as psychometric multi-level models, beta regression for rating scales and ExGaussian regression for response times. A think-first approach is promoted for model building, as much as the quantitative interpretation of results, stimulating readers to think about data generating processes, as well as rational decision making. New Statistics for Design Researchers: A Bayesian Workflow in Tidy R targets scientists, industrial researchers and students in a range of disciplines, such as Human Factors, Applied Psychology, Communication Science, Industrial Design, Computer Science and Social Robotics. Statistical concepts are introduced in a problem-oriented way and with minimal formalism. Included primers on R and Bayesian statistics provide entry point for all backgrounds. A dedicated chapter on model criticism and comparison is a valuable addition for the seasoned scientist.

Takes a problem-oriented approach to introducing a modern statistical framework Illustrates a broad range of situations in design research, using 19 case studies (all data included) and 90 models. Promotes the Bayesian approach for rational decision making in applied research Presents the New Statistics approach with emphasis on quantification of impact factors Teaches the basics of Tidy R in just one day and introduces more advanced programming techniques along the way

Autorentext

Martin Schmettow studied psychology and business computing. He's worked as a scientist and consultant at a German Software Engineering research institute. His current research is on advanced statistical approaches for measuring, planning and controlling usability evaluation studies.

Inhalt
Part I: Preparations.- Introduction.- Getting started with R.- Elements of Bayesian statistics.- Part II: Models.- Basic Linear Models.- Multi-predictor models.- Multi-level models.- Generalized Linear Models.- Working with models.- Appendix: Cases