Sentences with phrase «model analysis of variance»

Not exact matches

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.

An initial mixed model was fit by performing a split plot repeated measures analysis of variance (ANOVA) on the rank transformed energy values.

However, for the above model analysis, I have explicitly removed the 61 year cycle (and other higher frequency data) in order to examine the low frequency data, so I do not feel that your explanation is a likely explanation of the variance.

The IPCC's methodology relies unduly — indeed, almost exclusively — upon numerical analysis, even where the outputs of the models upon which it so heavily relies are manifestly and significantly at variance with theory or observation or both.

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.

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MBA plus career of experience in financial planning, analysis, building budgets and models in EXCEL, variance analysis, financial reporting & forecasting, ad hoc analysis, AP supervision, general accounting, journal entries, month end close, GL accruals, acc...

PROFILE Senior financial professional with extensive experience in accounting and financial operations and management, including cash management and financial forecasting, budgeting, financial modeling, financial statement preparation, variance analysis, internal controls, internal auditing, management of accounting personnel, and internal administration of 401 (k) and ESOP.

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.

To do this, we repeated the previous analyses except this time we additionally added husbands» facial attractiveness, and the HC status at relationship formation × husbands» facial attractiveness interaction to account for variance in the intercept and current HC status slope estimates in the second level of the model to create the crucial current HC status × HC status at relationship formation × husbands» facial attractiveness interaction and all lower level interactions with the following model (Eq.

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.

Statistical power analysis by Visscher [82] has addressed the questions of rejecting CE models within the classical twin design when the true model is ACE, i.e. there is a significant genetic variance.

All analyses will be undertaken using mixed - model repeated measures analysis of variance with planned contrasts.

In hierarchical regression analyses with the various ENRICH factor scores as dependent variables and job satisfaction as the independent variable in the first block, the two SSQ factors in the second block, and the WOC factors in the third block, between 24 and 38 % of the variance in seven of the nine ENRICH factors (not significant model for «Family & Friends» and «Marriage & Children») could be explained by the variation in all the independent variables with varying weight of the several independent variables (Table 3).

All analyses will be undertaken using mixed - model repeated measures analysis of variance, with measurement occasion as a within - groups factor, and experimental group as a between - groups factor.

Biometric analyses showed that these multi-source measures of childhood impulsivity and inattention were highly heritable, with genetic variance accounting for 70 - 80 % of the phenotypic variance in many of the models.

Given poor robustness of t - tests with very different group sizes, we used t ′ assuming lack of homogeneity of variance; control analysis was tested with general linear model (GLM) controlling for age, depressive symptoms, and self - rated health (df = 1).

Mplus v7.11 was used for all analyses.23 SDQ items were treated as ordinal, with weighted least - squares means and variance — adjusted estimation used.23 Given the χ2 statistic's propensity to reject good models when samples are large and / or complex, the comparative fit index (CFI) and root mean square error of approximation (RMSEA) were used to assess model fit.

Regression analyses revealed that although a preoccupied working model of attachment and withdrawal coping explained variance in symptomatology, relationship stressors were more predictive of poor psychological adaptation.

Three nested models with increased degrees of constraint were compared in multigroup analyses (fathers versus mothers): We specified a first model of configural invariance, in which the parameters (factor loadings, item intercepts, residual variances, factor variances, and covariance) were freely estimated in each group, whereas the factor means were constrained to zero in both groups.

We examined differences in diary scales (secure, avoidant, resistant, and coherence) as they related to age at placement and foster parent attachment, using hierarchical linear modeling and analyses of variance.

Although the workshop does not require any prior knowledge or experience with multilevel modeling, participants are expected to have a working knowledge of multiple regression and analysis of variance, as well as SPSS.