Factor analysis of data identifies the underlying factors that explain the correlation among a set of variables. It is used to identify a new, smaller use of uncorrelated variables to replace the original set of correlated variables. It also identifies a smaller set of salient variables for use in subsequent research. It is used in various types of research including product research, customer satisfaction, market segmentation and consumer profiling.

An analytical procedure that can be used for identifying the number and nature of constructs underlying a set of measures

multivariate statistical technique, which reduces a large number of questions in a topic area to a smaller number of basic factors.

Data reduction technique that analyzes the relationships among a set of variables in order to identify clusters that vary together as a group. Each cluster consists of several measures of the same underlying dimension (or factor) and the common information can be largely or entirely explained by a single composite of the several variables in the cluster

A quantitative research tool used to consolidate variables (see Variable). Factor analysis identifies patterns in the data— for example, answering "5" to Question 14 may be correlated to answering "1" to Question 26. Factor analysis groups these patterns and creates new consolidated variables.

A multi-variate, data reduction technique. It aims to summarise a large number of variables with a small number of factors.

Technique for identifying patterns intrinsic in a set of data. A computer program proposes “factors” as sets of variables that are related because respondents who score high on one variable tend to score high on related variables. Each factor is similar to a regression line, with variables coming close (“loading on”) various factors. The researcher must select factors based on statistical characteristics and meaningfulness and label them.

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 variable that account for the interrelations observed in the data

a type of statistical review that defines relationships among variables

a data analysis technique used to group survey items into dimensions and eliminate superfluous items.

A statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.

A body of statistical techniques concerned with study of interrelationships among a certain set of variables--none of which is given the special status of a criterion variable.

A statistical procedure for reducing the dimensionality of a data set by approximating the given variables as linear combinations of factors than are uncorrelated with each other. Factor analysis is very similar to principal component analysis and can also be found in most statistical packages. In fact, the underlying computations are often identical and the major difference is in how the results are presented and interpreted. In principal component analysis, the emphasis is on the reduction of dimensionality and the factors are almost always uncorrelated with each other. In factor analysis, the emphasis is on understanding what the factors mean in terms of the original problem, and factors may be rotated and may actually be correlated with each other if that leads to a greater understanding of the problem. See [Rayment Joreskog 1996] for more details.

Factor analysis is a statistical multivariate procedure used to analyze the intercorrelations or covariance of variables. The method results in the identification of a reduced number of factors needed to explain the intercorrelations of variables.

A statistical technique used to determine the natural ordering of complex data/information. It is a particularly useful technique to pinpoint respondents' mental models by factor analysing how they have used ratings either for questionnaires or for assessment purposes. The items/questions that are rated in similar ways by people cluster together to create 'factors'. Therefore, if looking for factors within a personality questionnaire you might find factors emerging like extroversion or sensitivity.

a statistical technique reducing large data sets to the smallest number of ‘factors' required to ‘explain' the pattern of relationships in the data. It has fierce advocates and vehement critics. The former see it as neutral and scientific. The latter argue that it generates statistical abstractions, which have little explanatory value and are not necessarily psychologically meaningful.

a statistical procedure that identifies how well similar variables group together, such as items that comprise a scale on a test.

several types of statistical approaches that can be used to analyze interrelationships among a large number of variables and to explain these variables in terms of their underlying dimensions (factors). It involves finding patterns among the variations in the values of several variables. See: variable.

an approach that, like cluster analysis, identifies relationships without using an outcome (dependent) variable. Grouping related characteristics instead of related people, factor analysis reveals unobserved "dimensions" that underlie a larger number of observed variables. This technique can either identify a subset of variables to represent these dimensions, or derive new variables that are composites of the original variables associated with each dimension. In either case, subsequent analyses (e.g. regression or cluster) can benefit from variable reduction.

Used to examine relationships in a set of data to identify underlying factors or constructs that explain most of the variation in the original data set. Factors are usually uncorrelated with each other. Factor scores can be calculated and used in order to eliminate the problem of collinearity in data and reduce the number of variables.

a statistical technique used to (1) estimate factors or latent variables, or (2) reduce the dimensionality of a large number of variables to a fewer number of factors.

A statistical method for studying the interrelations among various tests, the object of which is to discover what the tests have in common and whether these communalities can be ascribed to one or several factors that run through all or some of these tests.

A statistical procedure that seeks to explain a certain phenomenon, such as the return on a common stock, in terms of the behavior of a set of predictive factors.

An advanced statistical method for analyzing the relationships among a set of items or indicators to determine the factors or dimensions that underlie them.

Factor analysis, including variations such as component analysis and common factor analysis, is a statistical approach that can be used to analyze interrelationships among a large number of variables and to explain these variables in terms of their common underlying dimensions (factors). The objective is to find a way of condensing the information contained in a number of original variables into a smaller set of variates (factors) with a minimum loss of information (Hair et al., 1995).

A statistical method used in test construction and in interpreting scores from batteries of tests. The method enables the investigator to compute the minimum number of determiners (factors) required to account for the intercorrelations among the scores on the tests making up the battery.

Factor analysis is a statistical technique used to explain variability among observed random variables in terms of fewer unobserved random variables called factors. The observed variables are modeled as linear combinations of the factors, plus "error" terms. Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.