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This book covers the science of measuring the invisible building blocks of thought processes that are useful for understanding humans, including technology users, media consumers, and consumers of goods and services. It brings together key notions from the psychometric tradition of measuring attitudes, couples them with findings of studies that have investigated what works and what doesn't work when using self-report measures in the context of surveys and experiments, explains and connects the preceding in a non-technical manner, and extends what we have learned from over 100 years of self-report measurement science for extracting opinion from social media text content and the potential role that AI can play in assisting opinion extraction.
This book provides:
- An explanation of what self-report measurement entails for beginners.
- A clear set of assumptions that need to be made in order for self-report measurement to provide the researcher with useful information;
- A set of principles of sound self-report measurement practice based on science that relates to the assumptions that are needed;
- A mindset that can be used by researchers using self-report measurement in the contexts of surveys and experiments and another mindset that can be used by those who are trying to extract opinion from social media text content;
- A road map for quantifying the errors associated with self-report measurement.
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