Sentences with phrase «linear modeling analyses»

In Study 1 (rural community), results of hierarchical linear modeling analyses showed that compared to the control condition (Head Start as usual), EBP produced greater increases in emotion knowledge and emotion regulation and greater decreases in children's negative emotion expressions, aggression, anxious / depressed behavior, and negative peer and adult interactions.

Personality change over 40 years of adulthood: Hierarchical linear modeling analyses of two longitudinal samples

Results of hierarchical linear modeling analyses confirmed the first two hypotheses and partially supported the third.

Additional Contribution: Steve Malone, PhD, provided statistical assistance on the hierarchical linear modeling analyses.

Hierarchical linear modeling analyses of the NEO-PI-R scales in the Baltimore Longitudinal Study of Aging

The color scale represents the value of the t statistic for the comparison between meals (n = 11) by using general linear model analyses as described in Subjects and Methods.

In hierarchical linear modelling analyses, the intervention was more effective than no intervention for reducing peer reports of aggression (p = 0.03) and hyperactive and disruptive behaviour (p = 0.02)(table ⇓); no difference was seen for peer reports of prosocial behaviour or ratings of most liked children.

Hierarchical linear model analyses revealed that, at the individual level, Mexican - American adolescents» who reported more chronic peer victimization incidents across the two - weeks also reported heightened distress and academic problems.

Not exact matches

Their models of linear development (most famously W.W. Roster's «five stages» of economic development) were gradually replaced by more complex analyses of economies as «systems», in which complex institutional constraints could distort or prevent convergence.

It speaks of operations research, systems analysis, technological forecasting, information theory, game theory, simulation techniques, decision theory, Delphi method, cross-impact matrix analysis, statistical time - series, stochastic models, linear programming, input - output economics, computer based command and control systems, and so on.

Confirmation of the chemistry results came through statistical analysis of the data from the experiments, which comprised «linear mixed modelling of quantified volatile compounds and generalised linear modelling of counts of mosquitoes,» says Stephen Powers, a data analyst at Rothamsted.

Fifteen years later, MIT researchers presented the Quantum Linear Systems Algorithm (QLSA), that promised to bring the same type of efficiency to systems of linear equations — whose solution is crucial to image processing, video processing, signal processing, robot control, weather modeling, genetic analysis and population analysis, to name just a few applications.

Population structure was evaluated by principal component analysis to infer continuous axes of genetic variation, and single linear regression models were applied.

Tests for trend with the use of simple linear regression analysis were performed by modeling the median values of each fiber category as a continuous variable.

The analyses were performed by using linear mixed - effects models on the log scale of hormones.

The analyses were performed by using generalized linear mixed models.

The relationship between an athlete personal best in competition and back squat, bench press and power clean 1RM was determined via general linear model polynomial contrast analysis and regression for a group of 53 collegiate elite level throwers (24 males and 29 females); data analysis showed significant linear and quadratic trends for distance and 1RM power clean for both male (linear: p ≤ 0.001, quadratic: p ≤ 0.003) and female (linear: p ≤ 0.001, quadratic: p = 0.001) suggesting how the use of Olympic - style weightlifting movements — the clean, in this particular case, but more in general explosive, fast, athletic - like movements — can be a much better alternative for sport - specific testing for shot putters (Judge, et al, 2013).

In this course, students will learn how to use a set of quantitative methods referred to as the general linear model — regression, correlation, analysis of variance, and analysis of covariance — to address these and other questions that arise in educational, psychological, and social research.

In February of 2011, CUNY's Office of Institutional Research and Assessment, headed by University Dean David Crook, released critical data (obtained by Director of Policy Analysis Colin Chellman using linear probability models and logistic regression) demonstrating that, all else being equal (i.e., taking into account all measurable demographic and performance characteristics), CUNY's transfer students were at a disadvantage in terms of graduation compared to native students.

To be sure, statewide analyses can provide accurate estimates of the impact of school resources — but only if the analyst includes within the statistical model all the factors that affect student performance and, in the standard linear regression model generally favored by RAND, if these factors have a constant, additive effect on student achievement.

Specific statistical areas of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic regression techniques.

Hierarchical linear models: Applications and data analysis methods (2nd ed.).

Comparing models to observations is perfectly fine, but the comparison has to be apples - with - apples and the analysis has to be a little more sophisticated than saying «look at the lines» (or «linear striations»).

sea ice, arctic, antarctic, climate change, global warming, general linear model, dummy variable, regression, deseasonalized trend, trend analysis

ozone, ozone depletion, dobson spectrophotometer, ozone ground stations, total column ozone, genera linear model, dummy coding, trend analysis, deseasonalized trends, antarctic ozone hole, hydrofluorocarbons, greenhouse gases, climate change, stratosphere, troposphere, atmospheric chemistry

tropical cyclones, climate change, global warming, extreme weather, hurricanes, typhoons, trend analysis, general linear model, applied statistics, accumulated cyclone energy, ACE index, cyclone activity, trend analysis

An analysis of the US refining sector, based on linear programming (LP) modeling, finds that refining plausibly high volumes of Canadian oil sands crudes in US refineries in 2025 would lead to a modest increase in refinery CO2 emissions (ranging between 5.4 % to 9.3 %) from a 2010 baseline, depending upon... Read more →

In other words, the analysis neglects structural uncertainty about the adequacy of the assumed linear model, and the parameter uncertainty the analysis does take into account is strongly reduced by models that are «bad» by this model - data mismatch metric.

Further quantitative analyses of species environment relationships suggested the use of linear regression models.

Furthermore, we found no relationship between δ13C and δ18O (linear mixed effect model, coefficient = − 0.012, df = 82, p = 0.925) when we structured the analysis by individual, a result that might have indicated metabolic effects on carbon and oxygen isotopes (e.g. [23]-RRB-.

This stock / (yearly absorption) analysis avoids all the pitfalls of the assumed equilibrium between absorption and out - gassing that is postulated by all the compartment models with constant inputs and outputs that lead to a set of linear equation and by Laplace transform to expressions like the Bern or Hamburg formulas; there is no equilibrium because as said more CO2 implies more green plants eating more and so on; the references in note 19 show even James Hansen and Francey (figure 17 F) admits (now) that their carbon cycle is wrong!

Schwartz's analysis depends on assuming that the global temperature time series has a single time scale, and modelling it as a linear trend plus an AR (1) process.

He used the Institutional Analysis and Development (IAD) framework to diagnose APB's linear program model for family law clinics.

Determined appropriate multiple linear regression model areas from analysis of surface hydrography, topography, and the location of monitoring wells

Performed advanced statistical analysis (univariate and multivariate analysis of variance, cluster and path analysis, principle component and factor analysis, analysis of covariance, survival & longitudinal analysis, logistic and linear regression modeling), created customized reports and presentation quality data summary tables and figures.

Executed inferential statistical analysis through constructing regression models such as multivariate linear

Substance use problem severity, mental health symptoms, and trauma symptoms were measured at baseline, and follow - up data were analyzed with prospective meta - analysis and hierarchical linear modeling.

«A comprehensive meta - analysis of Triple P - Positive Parenting Program using hierarchical linear modeling: Effectiveness and moderating variables».

Data analysis will follow the intention - to - treat principle using (generalised) linear mixed models.

Changes in rates of child diagnoses from baseline to 3 months as a function of mother's remission and subsequently mother's level of response were analyzed using a repeated measures analysis with binary response data, using generalized estimating equation (GEE) methods.27 A linear probability model with an identity link function (rather than a logit - link function) was used to model interactions on the additive scale28 and to model a dose - response function using rates (rather than odds) as the outcome measure because we considered risk differences to be a more relevant measure than odds ratios in our study.

In order to model the impact on children's total difficulties, an analysis of multiple linear regression was carried out by establishing Enter's models (at the same time putting the variables into the model).

Outcome analyses used SPSS (IBM SPSS Statistics, IBM Corporation; Predictive Analytics Software [PASW] 18) and HLM - 6.35 For child and parent outcomes, a piecewise growth curve modeling approach36 with an intercept representing baseline levels of functioning and 2 linear slope factors representing change over time was estimated for each family at the model's first level.

We test our hypothesis about the effects of human and social capital on student achievement using social network analysis and hierarchical linear modeling.

Multiple mediation analyses utilising linear and logistic regression models as appropriate were used to further investigate the extent to which the association between sports club membership and each of the three SES indicators was mediated by the three potential mediators.

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.

In the log - linear model, the analysis was carried out and estimates were obtained in terms of the logs of the incidence.

Cortical analyses across the surface were performed with general linear models as implemented in FreeSurfer, and results were displayed on a semiinflated template brain that was thresholded by a conventional criterion for correction for multiple comparisons (FDR at 5 % level)(58).

The general linear model procedure23 in SAS software was used to conduct the analysis of covariance and obtain the least square means (ie, the means adjusted for baseline score).

This model was preferable to a 4 - factor classification structure with all interactions because of the low incidence of some outcomes for certain subclasses, compromising the stability of the Poisson log - linear models used in the analysis.

This procedure extends the general linear model to allow for analysis of repeated measurements.