on best practices for reporting factor analysis results and where exploratory factor analysis sits within the broader framework of statistical data reduction. This discussion will include tools such as discriminant analysis, canonical correlation, and partial least squares regression.
Upon completing this book, I hope that you are comfortable with the basics of exploratory factor analysis, and that you are aware of some of the exciting extensions available for use with it. Factor analysis is a powerful tool that can help us understand the latent structure underlying a set of observed data. It includes a set of statistical procedures that can be quite subtle to use and interpret. Indeed, it is not hyperbole to say that successfully using factor analysis involves as much art as it does science. Thus, it is important that when we do make use of this tool, we do so with a good sense for what it can and cannot do, and with one eye fixed firmly on the theoretical underpinnings that should serve as our foundation. With these caveats in mind, let’s dive in.
Конец ознакомительного фрагмента.
Текст предоставлен ООО «ЛитРес».
Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.
Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.
