Sentences with phrase «covariance structure»
The phrase "covariance structure" refers to how different variables are related to each other in a set of data. It indicates how much change in one variable can be associated with changes in another variable. It helps to understand the pattern and strength of the relationships between variables. Full definition
Factor analyses of autistic traits in clinical ASD and community samples, using a variety of ASD measurement tools, generally indicate that multiple factors account for the observed covariance structure of ASD symptoms and traits (Happé and Ronald 2008; Mandy and Skuse 2008).
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[jounal] Hu, L. J. / 1999 / Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternative / Structural Equation Modeling 6: 1 ~ 55
Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.
[jounal] Willet, J.B / 1994 / Using covariance structure analysis to detect correlates and predictors of individual change over time.
We analyzed data using the LISREL 8 analysis of covariance structure approach to path analysis and maximum likelihood estimates.175
[jounal] Breckler, S. J. / 1990 / Application of covariance structure modeling in psychology: Cause for concern / Psychological Bulletin 107: 260 ~ 273
I might argue that these are long - winded ways of generating proxy series with certain kinds of temporal and spatial covariance structures, but there's much to be said for testing methods on some standard -LSB-...]
Final models control for dependence over time assuming autoregressive covariance structure that provided the best fit of the models.
ARIMA models that fit the data from 1880 to 1988 have a very complex covariance structure, which results in very wide CIs for the 2005 forecasts.
Marginal logistic regression models were fitted for repeated - measures data (eg, well - child visits) using generalized estimating equations with working - independence covariance structures.28
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).
The mean - variance model is not of much use, as the variance - covariance structure of asset class retuns will change for significant periods of time from the long term structure.
Analysis of longitudinal studies with missing data using covariance structure modeling with full - information maximum likelihood
[jounal] Hu, L. / 1999 / Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives / Structural Equation Modeling 6: 1 ~ 55
Using covariance structure analysis to detect correlates and predictors of individual change over time
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.
Significance tests and goodness of fit in the analysis of covariance structures.
Our researchers have expertise in computer adaptive testing, item response theory, covariance structure analysis, meta - analyses, hierarchical modeling, causal analysis, computational statistics, and natural language processing (NLP).
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.
Given the differences in the covariance structure of returns across alternative equity styles (price movements are not perfectly correlated), it is reasonable to question whether tactical allocation strategies are effective across alternative equity styles.
Kaufmann and Stern (1997) examine the lagged - covariance structure of hemispheric mean temperature and find it consistent with unequal anthropogenic aerosol forcing in the two hemispheres.
When examining the covariance structure, the best fit was first - order autoregressive (r = − 0.26, p <.05).
After choosing the covariance structure, we next examined the random - effects component of our model.
Cross-domain analyses of change over time: Combining growth modeling and covariance structure analysis
Three likelihood - based methods for mean and covariance structure analysis with nonnormal missing data