Sentences with phrase «variable measurement models»

Repeated measures of both teachers and students are planned over a three - year period, with annual analysis making use of latent variable measurement models and accounting for the multilevel and longitudinal structure of the data.

Not exact matches

This may affect the ability to infer which thermoregulatory variables are being monitored in the presently proposed anticipatory regulation model, because it is the timing of the changes in work rate that is essential, and rectal temperature may not provide the necessary resolution of measurement.

Infant age at weight measurement was the time variable, and age squared was the quadratic term included in the model.

First, a linear regression model was constructed using the latest postnatal weight measurement in grams as the dependent variable and using the breastfeeding medication group (fluoxetine: yes / no) as the independent variable of interest.

A new paper explores model cloud and precipitation transitions in a highly variable meteorological environment observed during the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Midlatitude Continental Convective Clouds Experiment (MC3E) field campaign.

A measurement model provides a mathematical (probabilistic) connection between observations and amounts of an underlying but unobservable variable.

Since we used statistical models that included many observable school -, teacher -, and class - level variables — such as school and class size, teachers» levels of education and experience, and schools» demographic makeup — it is clear that the things that make schools and teachers effective defy easy measurement.

MI developed a conceptual model / theory of change to organize the study variables and guide all measurement work.

I can determine the standard uncertainty for all the measured variables from statistics It is falsifiable — i can move a body at a certain velocity for a certain time and measure the traveled distance If the traveled distance does not fit with calculated distance within the uncertainty calculated by using the international standard Guide to the expression of Uncertainty in Measurement the model might be wrong.

The model variables that are evaluated against all sorts of observations and measurements range from solar radiation and precipitation rates, air and sea surface temperatures, cloud properties and distributions, winds, river runoff, ocean currents, ice cover, albedos, even the maximum soil depth reached by plant roots (seriously!).

The problems with AGW science are two-fold; first, we are trying to confine nature into a scientific test, when we still do not know all the variables, and secondly, we are trying to extrapolate the data and models far beyond the current measurements and understanding.

The resulting best - estimate temperature data product for Lauder is expected to be valuable for satellite and model validation as measurements of atmospheric essential climate variables are sparse in the Southern Hemisphere.

Every measurement of key climatic variables has indicated that the «everything else being equal» lab experiments reflected in the models is not realized in the dynamic and chaotic climate.

Climometry, modeled after astrometry, would be the science of measurement of climate variables.

Empirical models work with real measurements where every variable has a range of normal values within a given statistical distribution.

The researchers used historical measurements to learn how the ice had changed, then modeled climate variables to untangle the causes.

Ice core measurements of beryllium indicate a less variable TSI while modelling from solar magnetic flux show a greater decrease in TSI during the Maunder Minimum.

It is more plausible that the models are running hot, that the aerosol data is botched, that the TOA data has issues etc etc because we are trying to estimate highly spatially variable values for all global quantities with very few measurements.

ECMWF's approach to reanalysis combines measurements of temperature and other meteorological variables with a global weather model to provide a complete picture of the regional patterns of climate.

Our estimates of key climate model uncertainties are constrained by observations of the climate system for the period 1906 - 1995, 7 and uncertainty in emissions reflect errors in measurement of current emissions and expert judgment about variables that influence key economic projections.

First, following the strategy proposed by Muthén and Curran (1997) and Hess (2000) trajectories of each construct were modeled using a two - factor LGM: the intercept (with the factor loadings of four observed variables, corresponding to four measurement waves, set at 1) and the slope factor (with the factor loadings of 0, 1, 6, and 12, corresponding to the number of months that passed since the pretest).

Correlations among variables are provided in Table I based on an initial confirmatory factor analysis of a 5 - factor, correlated measurement model (χ2 = 550.35, df = 125, p <.01, RMSEA = 0.05, and CFI = 0.93).

Structural equation modeling (SEM) allows for the simultaneous examination of the relationships between latent constructs defined by multiple measures as well as directly observed variables (e.g., gender, HbA1C) while reducing the effect of measurement error on results.