This link has been bookmarked by 21 people . It was first bookmarked on 14 Oct 2008, by kpreader.
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26 Feb 11
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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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Confirmatory 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. Indicator variables are selected on the basis of prior theory and factor analysis is used to see if they load as predicted on the expected number of factors. 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. A minimum requirement of confirmatory factor analysis is that one hypothesize beforehand the number of factors in the model, but usually also the researcher will posit expectations about which indicator variables will load on which factors (Kim and Mueller, 1978b: 55). The researcher seeks to determine, for instance, if measures created to represent a latent variable really belong together.
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20 Sep 10
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18 Jun 10
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02 Jun 10
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31 May 10
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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: linear relationships, interval or near-interval data, untruncated variables, proper specification (relevant variables included, extraneous ones excluded), lack of high multicollinearity, and multivariate normality for purposes of significance testing.
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KMO overall should be .60 or higher to proceed with factor analysis
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rop the indicator variables with the lowest individual KMO statistic values, until KMO overall rises above .60
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The diagonal elements on the Anti-image correlation matrix are the KMO individual statistics for each variable
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27 Mar 10
George Bradford"Overview
Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of factors and as such is a "non-dependent" procedure (that is, it does not assume a dependent variable is specified). Factor analysis could be used for any of the following purposes: " -
09 Mar 10
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21 Oct 09
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04 Jun 09
ictlogistNotes from the the workshop Citizen Politics: Are the New Media Reshaping Political Engagement? held in Barcelona, Spain, on May 28-30th, 2009. More notes on this event: citizen_politics_2009.
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12 May 09
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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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28 Apr 09
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12 Apr 09
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11 Apr 09
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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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11 Mar 09
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08 Feb 09
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31 Oct 08
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14 Oct 08
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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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