Factor Analysis: Statnotes, from North Carolina State Univers...
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Saved by 6 people (0 private), first by anonymouse user on 2008-10-14
- Gaotsin on 2009-05-12 - Tags no_tag
- Bigenhoc on 2009-04-28 - Tags research , statistics
- Jessen on 2009-04-12 - Tags SEM
- Zhangti3 on 2009-04-11 - Tags no_tag
- Doug_y on 2009-02-08 - Tags Statistics , DataProcessing , Computation , LinearAlgebra , FA
Public Sticky notes
To establish that multiple tests measure the same factor, thereby giving justification for administering fewer tests.
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To validate a scale or index by demonstrating that its constituent items load on the same factor, and to drop proposed scale items which cross-load on more than one factor.
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To select a subset of variables from a larger set, based on which original variables have the highest correlations with the principal component factors.
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Similarly, factor analysis takes as input a number of measures and tests, analogous to the bumps and shapes. Those that move together are considered a single thing, which it labels a factor.
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in factor analysis the researcher is assuming that there is a "child" out there in the form of an underlying factor, and he or she takes simultaneous movement (correlation) as evidence of its existence.
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Factor analysis is part of the general linear model (GLM) family of procedures and makes many of the same assumptions as multiple regression:
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Factor analysis generates a table in which the rows are the observed raw indicator variables and the columns are the factors or latent variables which explain as much of the variance in these variables as possible.
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onfirmatory factor analysis (CFA) seeks to determine if the number of factors and the loadings of measured (indicator) variables on them conform to what is expected on the basis of pre-established theory.
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The researcher's à priori assumption is that each factor (the number and labels of which may be specified à priori) is associated with a specified subset of indicator variables.
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he researcher seeks to determine, for instance, if measures created to represent a latent variable really belong together.
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Ideally, the researcher wants a "simple factor structure," with all main loadings greater than .70 and no cross-loadings greater than .40
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The eigenvalue for a given factor measures the variance in all the variables which is accounted for by that factor.
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