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Confirmatory factor analysis (CFA) seeks to determine whether 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 first generates one (or a few) model(s) of an underlying explanatory structure (i.e., a construct) which is often expressed as a graph. The researcher's ri priori assumption is that each factor (the number and labels of which may be specified hpriori) is associated with a specified subset of indicator variibles. A minimum requirement of confirmatory factor analysis is that one IiypotheSize beforehand the number of faCtors in the model, but usually also the researcher will posit expectations about which 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. The correlations between the dependent variables are fitted to this structure. Models are evaluated by comparing how well they fit the data. Variations over CFA are called structural equation modelling (SEM), LISREL, or EQS.
The State Department of Taxation wishes to investigate the effect of experience, x, on the amount of time, y, required to fill out Form ST 1040AVG, the state income-averaging form.
Statistician is searching the \home ground" effect and is studying 20 football games, of which 14 were won by the home team and 6 by the visitors. Therefore the game is a Bernoulli
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Normal Distribution Meaning: According to ya Lun Chou There perfectly smooth and symmetrical curve, resulting from the expansion of the binomial (p+q) n when n approac
how to analyzePractice-Based Evidence Back to the Future
objective of the testing stochastic regression
A consumer preference study involving three different bottle designs (A, B, and C) for the jumbo size of a new liquid detergent was carried out using a randomized block experimenta
Multivariate analysis of variance (MANOVA) is a technique to assess group differences across multiple metric dependent variables simultaneously, based on a set of categorical (non-
Andrews ‘Plots A graphical display of multivariate data in which an observation, x0 = [x1, x2, . . . , xq] is represented can be represented in the form of function A set
(1) Assume we categorize voters in a city as havingless educationand those havingmoreeducation. Those with less education have less than a college degree; those with more education
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