Sentences with phrase «equation modeling analyses»

Structural equation modeling analyses generally favored the reciprocal model over each of the unidirectional models.

The structural equation modeling analyses revealed that HIV - related stigma had a positive direct effect on problem behaviors of vulnerable children, while HIV - related stigma and low education aspiration had direct negative effects on school adjustment among both orphans and vulnerable children.

Multi-group structural equation modeling analyses were used to analyze the data.

Our hypothesized predictive model received partial support based on structural equation modeling analyses.

The associations among male - perpetrated partner violence, wives» psychological distress and children's behaviour problems: A structural equation modeling analysis.

Independent Cluster Model Confirmatory Factor Analysis (ICM - CFA), Bifactor Confirmatory Factor Analysis and Exploratory Structural Equation Modeling Analysis followed in the second sample (CFA1 Sample), testing seven alternative solutions.

Not exact matches

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.

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.

The research employed associational methodology and used Structured Equation Modeling statistics, specifically, Pathways Analysis, to analyze a variety of variables and their relative influence upon mathematics achievement for the fourth - and eighth - grade NAEP scores.

Having done various sorts of modeling (simulation, population models, stability analyses, fractal models, statistical models) and having seen people who just throw any old equation in to make something work, I don't believe anything about a «model» unless there is a clear explication of it and unless it works well.

Point two suggested an alternative between «This needs to be demonstrated either in the context of a more comprehensive scale analysis that includes the Navier Stokes equations» and «numerical model simulations using mesoscale or weather or climate models.»

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!

Sea level from equations (3.3) and (3.4) is shown by the blue curves in figure 2, including comparison (figure 2c) with the Late Pleistocene sea - level record of Rohling et al. [47], which is based on analysis of Red Sea sediments, and comparison (figure 2b) with the sea - level chronology of de Boer et al. [46], which is based on ice sheet modelling with the δ18O data of Zachos et al. [4] as a principal input driving the ice sheet model.

Quick note, Stephan Lewandowsky built upon correlation matrices like mine by using factor analysis and structural - equation modeling (SEM).

Climate models are not defined by only the statistical analysis but mainly based on the theoretical equations unlike the time series analysis in econometrics.

This allows the appropriate cost benefit analysis (maybe this is getting to far into politics) that should be significantly more useful for the main debate than these temperature predictions that we have and takes many unpredictable factors out of the equation and if we have a full chain of logic it should be easier to find — because time as opposed to amount of carbon related models leave you asking questions like «what will happen to technology»

Dr. Judd has made major contributions to the literatures on mediation (including mediated moderation and moderated mediation), latent variable analysis and structural equation modeling, models of interdependence, moderation, and generalizing effects by treating stimuli as random factors.

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.

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.

The results of mediation analysis using structural equation modeling showed that maternal problems in reciprocal social behavior directly increased infantile aggression (estimate = 0.100, 95 % CI [0.011, 0.186]-RRB-, and indirectly increased infantile aggression via maternal postpartum depressive symptoms (estimate = 0.027, 95 % CI [0.010, 0.054]-RRB-, even after controlling for covariates.

Influence of perceived motivational climate on achievement goals in physical education: A structural equation mixture modeling analysis.

Topics Include Exploratory Data Analysis, Multiple Regression, Logistic Regression, Correlation, Multivariate Analysis Of Variance (manova), Factorial Analysis Of Variance (anova), Factor Analysis And Principal Components, Discriminant Analysis, Structural Equation Modeling, And Emerging Data Analysis Techniques.

Operationalization was tested using confirmatory factor analyses and causal hypotheses were evaluated by means of structural equation modeling.

To do this, we repeated the previous analyses except we also entered the dummy code indicating whether wives were using HCs at relationship formation to account for variance in the intercept and current HC status slope estimates in the second level of the model to create the current HC status × HC status at relationship formation interaction with the following equation (Eq.

Under the a priori hypothesis that each scale measures a distinct latent trait, construct validity was assessed by confirmatory factor analysis (CFA) for each scale respectively, using structured equation models.

Sex - difference analyses, multivariate structural equation modeling, and graphic productions were performed with R (version 3.1.2) and AMOS (version 22).

The final version of the instrument was then cross-validated with the Bonn sample, again using a confirmatory factor analysis through a structural equation model.

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.

QuestionPro has donated its advanced survey analytics platform, providing MaxDiff Scaling, Conjoint Analysis, Structural Equation Models, and Data Segmentation.

[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

She has technical expertise in a wide range of statistical techniques used in the social sciences, including structural equation modeling, confirmatory factor analysis and MIMIC approaches to measurement, path modeling, regression analysis (e.g., linear, logistic, Poisson), latent class analysis, hierarchical linear models (including growth curve modeling), latent transition analysis, mixture modeling, item response theory, as well as more commonly used techniques drawing from classical test theory (e.g., reliability analysis through Cronbach's alpha, exploratory factor analysis, uni - and multivariate regression, correlation, ANOVA, etc).

[jounal] Hu, L. / 1999 / Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives / Structural Equation Modeling 6: 1 ~ 55

Westfall and Yarkoni (2016) explain how to conduct incremental validity analyses correctly with structural equation modeling.

As our variables came from couples, we used structural equation modeling and included husbands» and wives» data in one analysis akin to the actor — partner interdependence model (Kenny, Kashy, & Cook, 2006).

Structural equation modeling was used to conduct dyadic analyses on the variables.

Structural Equation Modeling (SEM) is another method that allows for multidimensional analysis of scales.

To examine the independent contribution of program participation on program outcomes (parenting stress, parenting behaviors, and mental health), in all analyses separate regression models were constructed in which mothers» age and baseline measures of mental health were introduced into the regression equation first.

Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis.

We conducted analyses using structural equation modeling procedures, and attempted to control method - varieance biases through the use of multiple informants and multiple methods.

Because of skewness and the ordered categorical nature of our variables, we estimated α within a structural equation model framework, which resulted in higher α coefficients.20 Our ω reliability analyses yielded results consistent with previous studies reporting ω reliabilities for preschool and school - age SDQs.9, 16

Analyses were undertaken within a structural equation modelling framework that allowed for an ordinal treatment of well - being and personality items, and latent variable modelling of longitudinal data on emotional adjustment.

Fit Statistics for the Confirmatory Factor Analyses of the Friend and Peer Attribution Questionnaires and Structural Equation Models

Analyses using structural equation modeling (SEM) indicated HIV seropositivity was positively correlated with depression and negatively correlated with positive social support and effective family functioning.

Exploratory factor analysis in the structural equation modeling context revealed three different therapist use - of - self orientations: Transpersonal, Contextual, and Instrumental.

Dyadic data analysis with structural equation modeling is used to determine the respective contributions of each respondent's predictors (i.e., actor effects) and his / her spouse's or partner's predictors (i.e., partner effects).

Cross-lagged analyses using structural equation modeling supported a reciprocal causality model involving self - criticism (but not dependency) among girls (but not boys).

«Application of confirmatory factor analysis and structural equation modeling in sport / exercise psychology,» in Handbook of Sport Psychology, eds G. Tenenbaum and R. C. Eklund (New York, NY: Wiley), 774 — 798.

Assessing model fit: caveats and recommendations for confirmatory factor analysis and exploratory structural equation modeling.

CFA, confirmatory factor analysis; ESEM, exploratory structural equation modeling; S - factor, specific factors.

To examine the cross-effects between adolescents» perceptions of the quality of their relationships with parents and friends over time, we conducted path analyses with cross-lagged effects by means of structural equation modeling.