Sentences with phrase «longitudinal multilevel models»

Not exact matches

Individual growth curve models were developed for multilevel analysis and specifically designed for exploring longitudinal data on individual changes over time.23 Using this approach, we applied the MIXED procedure in SAS (SAS Institute) to account for the random effects of repeated measurements.24 To specify the correct model for our individual growth curves, we compared a series of MIXED models by evaluating the difference in deviance between nested models.23 Both fixed quadratic and cubic MIXED models fit our data well, but we selected the fixed quadratic MIXED model because the addition of a cubic time term was not statistically significant based on a log - likelihood ratio test.

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.

Combining longitudinal data, multilevel modeling and state - of - the - art measurement scales from The Lexile ® Framework for Reading and The Quantile ® Framework for Mathematics, Williamson (2016) premiered incremental velocity norms for average reading growth and average mathematics growth.

Two longitudinal analytic strategies, latent class analyses and multilevel modeling, are used to test these hypotheses.

Jennifer A. Theiss, Denise Haunani Solomon; Coupling Longitudinal Data and Multilevel Modeling to Examine the Antecedents and Consequences of Jealousy Experiences in Romantic Relationships: A Test of the Relational Turbulence Model, Human Communication Research, Volume 32, Issue 4, 1 October 2006, Pages 469 — 503, https://doi.org/10.1111/j.1468-2958.2006.00284.x

We used longitudinal data and multilevel modeling to examine how intimacy, relational uncertainty, and failed attempts at interdependence influence emotional, cognitive, and communicative responses to romantic jealousy, and how those experiences shape subsequent relationship characteristics.

To address the limited empirical research on the putative educational impact of such policies, this study used multilevel structural equation models to investigate the longitudinal associations between teacher evaluation and reward policies, and student mathematics achievement and dropout with a national sample of students (n = 7,779) attending one of 431 public high schools.

Longitudinal data from 315 older couples in which one partner had end - stage renal disease were analyzed using multilevel modeling.

In sum, given the results from our simulation study and the empirical applications, we conclude that the multilevel TAR model is a valuable addition to the available techniques for analyzing intensive longitudinal data.

Next, we used multilevel modeling to examine the longitudinal or lagged relations between predictor variables and metabolic control.

Drawing on longitudinal data from the Toledo Adolescent Relationships Study (TARS)(N = 1242) and multilevel modeling, analyses examine direct and indirect ways that traditional parenting practices, as well as parental histories of problematic behavior influence trajectories of offspring antisocial behavior.

The most appropriate statistical technique for nested data is multilevel modeling, which is useful in analyzing longitudinal data, as it effectively handles missing data, serial dependence among observations, and varying time periods between observations (Raudenbush & Bryk, 2002; Singer & Willett, 2003).