Sentences with phrase «error covariance»

The error covariance matrix at level 1 (among the different measures of child problems) was modeled as unstructured.

Last, to account for the temporal correlation between observations at the within - person level, models were fit using a first - order autoregressive error covariance structure (see Singer & Willett, 2003).

All CFA models with error covariance were better than their counterparts with no covariance.

Estimating background error covariance parameters and assessing their impact in the OSTIA system Read more»

Nevertheless, the optimal model among all different two - factor models tested was the ESEM model with error covariances.

Not exact matches

This is why, in our modeling efforts, we do massive multivariate, longitudinal analyses in order to exploit the covariance structure of student data over grades and subjects to dampen the errors of measurement in individual student test scores.

We estimate the overall extent of test measurement error and how this varies across students using the covariance structure of student test scores across grades in New York City from 1999 to 2007.

We analyzed data using the LISREL 8.80 analysis of covariance structure approach to path analysis and maximum likelihood estimates.42 We used four goodness - of - fit statistics to assess the fit of our path model with the data: the Root Mean Square Error of Approximation test (RMSEA), the Norm - fit index (NFI), the adjusted Goodness of Fit index (GFI) and the mean Root Mean Square Residual (RMR).

They chose principal component analysis (PCA) to overcome the estimation errors inherent in sample covariance matrices.

Much of this debate seems to be confusing «measurement error» with a spatial covariance of land - use and temperature trends (someone with a Phd: is «heteroscadasticity» a correct term for this?).

Thus, when there is a systematic bias (not just random variation), creating a positive covariance between the error values, you can calculate differences much more accurately than the uncertainty in individual values.

That equation is only correct when errors are random — i.e., the covariance between the errors in X and Y is zero.

The difference between iid and LTP, for example, is confined to the covariance matrices of the corresponding error distribution (the covariance matrix corresponding to iid data is simply an identify matrix multiplied by a scalar variance; for LTP, the off - diagonal elements are non-zero and non-vanishing).

I suspect there is a near singularity caused by some subtle identification problem in the model — the symptom of this is that standard errors grow almost linearly through the variables possibly reflecting the difficulty of inverting an ill - conditioned near singular covariance matrix.

If the correlation is negative (i.e., one error being positive means the other error is negative, i.e., covariance is negative), then the error in A+B will be less than the error in either A or B alone.

An information metric to quantify AOS model errors in the climate is proposed here which incorporates both coarse - grained mean model errors as well as covariance ratios in a transformation invariant fashion.

Modification indices for covariances among measurement errors suggested that allowing the items «run one or more red lights» and «speed through a yellow light» to correlate would substantially improve the model fit (and it also made sense conceptually that these two items were related).

The model was expanded to included analysis of covariance within the structural equation modelling framework in order to correct for measurement error and adjusting for the imbalance in scores across the intervention and control group at the baseline.

λ1 considers that all of an item variance is error and that only the inter-item covariances reflect true variability; λ2 is a modification of λ1 that considers the square root of the sums of squares of the off diagonal elements; λ3 is equivalent with Cronbach's alpha; λ4 is the greatest split - half reliability; λ5 is a modification of λ1 that replaces the diagonal values with twice the square root of the maximum (across items) of the sums of squared inter-item covariances; λ6 considers the variance of errors (Revelle and Zinbarg 2009, pp. 147 — 149)

To account for this significant correlation, a covariance path between the error term of these two variables was added when estimating the hypothesized model presented in Figure 1.

The fit of the model was evaluated in terms of three fit indices: (a) the chi - square statistic, which compares the observed covariance structure to the covariance structure specified by the model; (b) the comparative fit index (CFI), which compares the hypothesized model to a null model with no paths or latent variables; and (c) the root mean square error of approximation (RMSEA), which estimates the degree to which the covariance structure observed in the data deviates from that specified in the model.