This book introduces a new data analysis technique that addresses long standing criticisms of the current standard statistics. Observation Oriented Modelling presents the mathematics and techniques underlying the new method, discussing causality, modelling, and logical hypothesis testing. Examples of how to approach and interpret data using OOM are presented throughout the book, including analysis of several classic studies in psychology. These analyses are conducted using comprehensive software for the Windows operating system.
- Describes the problems that statistics are meant to answer, why popularly used statistics often fail to fully answer the question, and how OOM overcomes these obstacles
- Chapters include examples of statistical analysis using OOM
Foreword by Paul Barrett
Acknowledgments
Chapter 1: Introduction
Chapter 2: Data at its core
Chapter 3: Rotating deep structures
Chapter 4: Modeling with deep structures
Chapter 5: Statistics and Null Hypothesis Significance Testing
Chapter 6: Modeling and inferential statistics
Chapter 7: Models and effect sizes
Chapter 8: Measurement and additive structures
Chapter 9: Cause and Effect
Chapter 10: Coda
Introduces a new data analysis technique for psychological research, with an accompanying website with free software and instructional video on how to use technique
Traditional methods of data analysis in psychology have long been criticized by scientists and philosophers, the American Psychological Association, and the National Institute for Mental Health. However up to now, no reasonable alternative had been presented. Observation Oriented Modeling (Olc+
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