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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.
Cartogram or Mapograph: Statistical maps are also used to represent data like density of population indifferent states in the country or different countries in the world or th
The interest rate on the three year loan is 0.087. Whereas the interest rate on the two year loan is 0.085 as given in A. Suppose that the liquidity premium at t=1 is 0.002 and tha
why we use dummy variable
In a study of outcomes for patients who had been in the Intensive care Unit (ICU) at a large hospital, the records from last 150 patients who had been in the ICU for more than one
What is a null hypothesis? ..
Analysis of variance allows us to test whether the differences among more than two sample means are significant or not. This technique overcomes the drawback of the method used in
There may be two values which occur with the same maximum frequency. The distribution is then called bimodal. In a bimodal distribution, the value of mode cannot be determined with
Differentiate between data and database
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-
In reduced rank regression (RRR), the dependent variables are first submitted to a PCA and the scores of the units are then used as dependent variables in a series of
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