Sentences with phrase «factor model fit»

That is, the three - factor model fit equally well for both Latino and Asian American adolescents.

Confirmatory factor analyses indicated that while the hypothesized three - factor model fit significantly better than an alternative one - factor model, the fit indices associated with the three - factor model were below satisfactory cutoffs, thus tempering conclusions that the best fitting structure was found and highlighting the need for additional research.

We compared one - and three - factor models for long - term mate preferences; the three - factor model fit the data better than a one - factor modeli (Table 2).

Although there are some criticisms of using parceling techniques (see Little et al., 2002, for a review), showing that the one - factor model fit worse than the data, with or without the use of item parceling, would be stronger evidence for supporting the two - factor structure of the PNS - J.

Not exact matches

«It seems directors are right when they contest that these firms are employing a one - size fits all model and are not allowing for customization and not measuring qualitative factors.

As mentioned above, size and style of your heated shelf are two of the most critical factors to consider when choosing a model fit for you and your establishment.

They then tested for differences in model fits using annual nest count data to see how the model performed with respect to environmental factors.

In contrast to previous studies of Proteaceae in Australia and South Africa, the best - fit model for predicting the number of cluster roots in this study did not contain any soil P factor; foliar P levels correlated with cluster root formation.

Models are unreliable» [Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observedModels are unreliable» [Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observedModels] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observedmodels more or less agree with the observed data.

What struck me most about the attendant was his honesty — based on the factors I was seeking, he suggested that the base - level 4Runner model (the SR5 w / third row) fit my needs more than the more expensive Sport, Trail, or Limited versions.

Between the numerous brands and models on the market, there are several key factors that will determine which vehicle best fits your needs.

In addition, the R - squared of around 0.78 indicated that IEMG in this single - factor model was a sub-optimal fit for the fund.

Scaling factors near 1 imply that the models» expected fingerprints fit reasonably well to the observations.

If solar is enhanced (by some factor like stratospheric and / or cloud responses), then the model fits the Moberg reconstruction, while with lower solar variability, the low - variability reconstructions are within the model margins...

For examples deep ocean currents and clouds are not well modelled by any of the IPCC models, all of which employ fudge factors to make the models fit.

Webster, «It is becoming very prevalent in modeling the temperature time series data to factor in the ENSO and fit all the yearly fluctuations.

Seeing this comparison has me wondering how else the historical temperature reconstuctions could be used to rate, tune or even create improved models, eg, scale factors to better fit model to historical record, and / or create ensemble models (as is done in the machine learning world (*)-RRB-.

Myth: Models are full of «fudge factors» or assumptions that make them fit with data collected in today's climate; there's no way to know if those same assumption can be made in a world with increased carbon dioxide.

The problem, of course, is that we don't understand all the internal and external drivers to any reasonable specificity to make those kinds of claims — asserted human impacts are unscientifically divined by creating fudge factors that make models fit, without actually * knowing * how realistic those models are (or more to the point, * knowing * how unrealistic those models are.

Models are unreliable» [Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observedModels are unreliable» [Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observedModels] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observedmodels more or less agree with the observed data.

All it takes is for the scientists to go back and re-adjust their models to fit and account for new factors that were missed.

Having driven and written about the BMW Active e, Tesla Model S, Honda Fit EV, and Nissan Leaf, I know and understand that EVs need to balance acceleration, cost, range and other factors.

The MIT model permits one to systematically vary the model's climate sensitivity (by varying the strength of the cloud feedback) and rate of mixing of heat into the deep ocean and determine how the goodness - of - fit with observations depends on these factors.

It is a lot better than using a «fudge factor» calling it sensitivity to make a model fit the data.

If you were claiming that some or most of the climate models are just curve fitting (implication: full of fudge factors), then I would readily agree.

Regularisation factors are then used to trade off fit to the data against smoothing constraints, which in the extreme case would give a pure dipole model and linear time evolution.

Irannejad et al. (2003) developed a statistical methodology to fit monthly fluxes from a large number of climate models to a simple linear statistical model, depending on factors such as monthly net radiation and surface relative humidity.

What I did was create a multiple regression model with all of the factors and found the best fit for natural variability.

Quantitative model fitting of temp data estimates of the last 11,500 years, assuming the suite of factors persists, comes closest to supporting (d).

Further it is shown that carbon cycle modelling based on non-equilibrium models, remote from observed reality and chemical laws, made to fit non-representative data through the use of non-linear ocean evasion «buffer» correction factors constructed from a pre-conceived idea, constitute a circular argument and with no scientific validity.

Of course team meetings, mentoring, supervision and training all need to be factored in (and for the majority of law firms at present, a fully virtual firm is probably not the answer either), but at the same time the old model of a super-plush office in a premium city centre location, with space for all lawyers and support functions, plus huge meeting rooms etc is unlikely to be the best fit for many law firms either.

There are too many factors to consider to recommend a single model that fits every taste, so we've broken down our choices according to buying motivations.

iv) Assessment of model fit: Structural equation modeling was performed to produce a structural model using the factors finally adopted after exploratory factor analysis (maximum - likelihood estimation and promax rotation) as latent variables.

The best - fit model revealed one genetic risk factor for SxAnxDep acting at ages 8 — 9, 13 — 14, 16 — 17 and 19 — 20, and new sets of genetic risk factors «coming on line» in early adolescence, late adolescence and early adulthood.

A model that hypothesized three factors found to provide an excellent fit to the data and the factor analytic results are in agreement with analyses conducted in other researches using UCLA loneliness Scale.

The results of confirmatory factor analyses revealed that the two - factor model of the PNS - J fit better than the one - factor model, regardless of whether using items or parcels as indicators.

In general, model fit indexes in confirmatory factor analyses become worse as indicators of latent variables increase (Bandalos, 2002; Coffman & MacCallum, 2005; Gribbons & Hocevar, 1998; Little, Cunningham, Shahar, & Widaman, 2002; Marsh, Hau, Balla, & Grayson, 1998).

Afterward, confirmatory factor analyses using the 11 items of the PNS - J as indicators were performed to examine whether the two - factor model — 4 items loaded on the Desire for Structure factor and the other 7 items loaded on the Response to Lack of Structure factor — fits the data better than the one - factor model.

Confirmatory factor analysis (CFA): the one - factor model was conducted by confirmatory factor analysis giving unacceptable global fit indices.

Even if the one - factor model using the 11 items as indicators fit worse than the two - factor model with the data in Study 1, it is possible that these results were due not to the superiority of the two - factor model, but due to using too many indicators in the one - factor model.

Confirmatory factor analyses showed that the two - factor model of the PNS - J fit the data better than the one - factor model, as shown in the studies that validated the original PNS Scale.

to assess if the structure in six factors, highlighted by the exploratory factor analysis, fitted best with the data, as compared to alternative models.

model appears to better fit the data than an alternative model made up of two second - order factors (functional strategies and dysfunctional strategies) and of six first - order factors (emotion expression, task utility self - persuasion, negative self - talk, help seeking, brief attentional relaxation, and dysfunctional avoidance)(χ2 / df = 1.860, RMSEA = 0.027, SRMR = 0.033, CFI = 0.981, AGFI = 0.977).

Goodness - of - fit indices for the six - factor model with 14 items indicate a well - adjusted fit to the data (χ2 / df = 1.427, RMSEA = 0.019, SRMR = 0.021, CFI = 0.992, AGFI = 0.982) which confirms study 1's findings.

As shown in Table 2, the two - factor model (χ2 (43) = 75.77, p =.001, CFI =.922, TLI =.881, RMSEA =.056) fit the data better than the one - factor model (χ2 (44) = 108.09, p <.001, CFI =.848, TLI =.772, RMSEA =.077).

We implemented unadjusted and adjusted analyses (potential prognostic factors listed in table 2) of the outcomes (all quantitative) by using random effects linear regression models fitted by maximum likelihood estimation to allow for the correlation between the responses of participants from the same maternal and child health centre.29 We present means and standard deviations for each trial arm, along with the mean difference between arms, 95 % confidence intervals, and P values.

Consequently, two competing optimal models emerged: 1) the two - factor bifactor with covariances model, and 2) the two - factor ESEM with covariances model (see all fit statistics in Table 3 and Figure 1 for path diagram).

Regarding model fit, the single factor ICM - CFA (simple CFA) model performed poorly.

Results show that the fit of the model deteriorated as compared to the two - factor solution; (χ2 [df = 26, n = 1,647] = 154.54, CFI =.939, RMSEA =.055).

After model fitting, we computed the following types of effects for wave 1 risk factors on the wave 6 unsafe driving items: direct, indirect through a w6 latent construct, and double indirect through both wave 4 sensation seeking and a wave 6 latent construct.