EXPLORATORY FACTOR ANALYSIS SPSS

factor analysis

A process in which the values of observed data are expressed as functions of a number of possible causes in order to find which are the most important
(factor analytic) factor analytical: of or relating to or the product of factor analysis
Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors.
any of several methods for reducing correlational data to a smaller number of dimensions or factors; beginning with a correlation matrix a small number of components or factors are extracted that are regarded as the basic variables that account for the interrelations observed in the data

exploratory

In the afternoon RiverBend Academy offers classes that meet 1-3 times per week. These classes are designed to offer background information and generate project ideas.
serving in or intended for exploration or discovery; “an exploratory operation”; “exploratory reconnaissance”; “digging an exploratory well in the Gulf of Mexico”; “exploratory talks between diplomats”
study: a study investigating an entirely new area of research. Unlike replications, an exploratory study does not follow directly from an existing study. (p. 469)
Relating to or involving exploration or investigation

spss

Supplementary Pertussis Surveillance System
SPSS is a computer program used for statistical analysis. Between 2009 and 2010 the premier software for SPSS was called PASW (Predictive Analytics SoftWare) Statistics. The company announced July 28, 2009 that it was being acquired by IBM for US$1.2 billion.
A software program that facilitates quantitative analysis.

exploratory factor analysis spss – An Easy

An Easy Guide to Factor Analysis

Factor analysis is a statistical technique widely used in psychology and the social sciences. With the advent of powerful computers, factor analysis and other multivariate methods are now available to many more people. An Easy Guide to Factor Analysis presents and explains factor analysis as clearly and simply as possible. The author, Paul Kline, carefully defines all statistical terms and demonstrates step-by-step how to work out a simple example of principal components analysis and rotation. He further explains other methods of factor analysis, including confirmatory and path analysis, and concludes with a discussion of the use of the technique with various examples.
An Easy Guide to Factor Analysis is the clearest, most comprehensible introduction to factor analysis for students. All those who need to use statistics in psychology and the social sciences will find it invaluable.
Paul Kline is Professor of Psychometrics at the University of Exeter. He has been using and teaching factor analysis for thirty years. His previous books include Intelligence: the psychometric view (Routledge 1990) and The Handbook of Psychological Testing (Routledge 1992).

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What are the most important factors that are also unpredictable?

Early notes from the factor analysis at New Optimists Food Forum at University of Warwick, 23rd May 2012. These are the factors which are rated high for importance (closer to 10) and low for predictability (closer to 1).

exploratory factor analysis spss

Exploratory Factor Analysis (EFA) has played a major role in research conducted in the social sciences for more than 100 years, dating back to the pioneering work of Spearman on mental abilities. Since that time, EFA has become one of the most commonly used quantitative methods in many of the social sciences, including psychology, business, sociology, education, political science, and communications. To a lesser extent, it has also been utilized within the physical and biological sciences. Despite its long and widespread usage in many domains, numerous aspects of the underlying theory and application of EFA are poorly understood by researchers. Indeed, perhaps no widely used quantitative method requires more decisions on the part of a researcher and offers as wide an array of procedural options as EFA does.

This book provides a non-mathematical introduction to the underlying theory of EFA and reviews the key decisions that must be made in its implementation. Among the issues discussed are the use of confirmatory versus exploratory factor analysis, the use of principal components analysis versus common factor analysis, procedures for determining the appropriate number of factors, and methods for rotating factor solutions. Explanations and illustrations of the application of different factor analytic procedures are provided for analyses using common statistical packages (SPSS and SAS), as well as a free package available on the web (Comprehensive Exploratory Factor Analysis). In addition, practical instructions are provided for conducting a number of useful factor analytic procedures not included in the statistical packages.