Gitta H Lubke

Biographic Data

ID5761080
NAMEGitta H Lubke
GIVEN NAMESGitta H
FAMILY NAMELubke
SIGNATURELUBKE G H
AFFILIATIONSUniversity of Notre Dame
ORCID0000-0003-2472-9771
VERIFIEDYes
TOTAL WORKS11
TOTAL CITATIONS0
WORKS AS AUTHOR11
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2001
LATEST PUBLICATION YEAR2020
H-INDEX0

  • Harmonizing behavioral outcomes across studies, raters, and countries

    Open AccessJustin M Luningham, Anne M Hendriks et al.ARTICLEJournal of Child Psychology and…2020

    BACKGROUND: Aggression in children has genetic and environmental causes. Studies of aggression can pool existing datasets to include more complex models of social effects. Such analyses require large datasets with harmonized outcome measures. Here, we made use of a reference panel for phenotype data to harmonize multiple aggression measures in school-aged children to jointly analyze data from five large twin cohorts. METHODS: Individual level agg…

  • Genetic and environmental contributions to the development of childhood aggression

    Open AccessGitta H Lubke, Daniel B McArtor et al.ARTICLEDevelopmental Psychology2018References: 3

    Longitudinal data from a large sample of twins participating in the Netherlands Twin Register (n = 42,827, age range 3-16) were analyzed to investigate the genetic and environmental contributions to childhood aggression. Genetic auto-regressive (simplex) models were used to assess whether the same genes are involved or whether new genes come into play as children grow up. The authors compared 2 different simplex models to disentangle potentially …

  • A Gradient-Boosting Machine for Hierarchically Clustered Data

    Patrick J Miller, Daniel B McArtor et al.ARTICLEMultivariate Behavioral Research2017

    "A Gradient-Boosting Machine for Hierarchically Clustered Data." Multivariate Behavioral Research, 52(1), p. 117

  • Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data

    Zhenqiu Lu, Zhenqiu Laura Lu et al.ARTICLEMultivariate Behavioral Research2011

    (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent c…

  • Distinguishing Between Latent Classes and Continuous Factors with Categorical Outcomes

    Gitta H Lubke, Gitta Lubke et al.ARTICLEMultivariate Behavioral Research2008

    Factor mixture models (FMM's) are latent variable models with categorical and continuous latent variables which can be used as a model-based approach to clustering. A previous paper covered the results of a simulation study showing that in the absence of model violations, it is usually possible to choose the correct model when fitting a series of models with different numbers of classes and factors within class. The response format in the first s…

  • Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters

    Gitta H Lubke, Gitta Lubke et al.ARTICLEStructural Equation Modeling: A…2007

    Factor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model performance such as parameter …

  • Investigating population heterogeneity with factor mixture models.

    Gitta H Lubke, Bengt MuthénARTICLEPsychological Methods2005

    Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved popu…

  • Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons

    Gitta H Lubke, Bengt Muthén et al.ARTICLEStructural Equation Modeling: A…2004

    Treating Likert rating scale data as continuous outcomes in confirmatory factor analysis violates the assumption of multivariate normality. Given certain requirements pertaining to the number of categories, skewness, size of the factor loadings, and so forth, it seems nevertheless possible to recover true parameter values if the data stem from a single homogeneous population. It is shown that, in a multigroup context, an analysis of Likert data u…

  • Distinguishing Between Latent Classes and Continuous Factors

    Gitta H Lubke, Gitta Lubke et al.

    Latent variable models exist with continuous, categorical, or both types of latent variables. The role of latent variables is to account for systematic patterns in the observed responses. This article has two goals: (a) to establish whether, based on observed responses, it can be decided that an underlying latent variable is continuous or categorical, and (b) to quantify the effect of sample size and class proportions on making this distinction. …

  • Extending Cart-based Multiple Imputation with Gradient Boosting Machines

    Justin M Luningham, Gitta H Lubke

    "Extending CART-based Multiple Imputation with Gradient Boosting Machines." Multivariate Behavioral Research, 53(1), pp. 143–144

  • Investigating Group Differences on Cognitive Tests Using Spearman's Hypothesis

    Gitta H Lubke, Conor V Dolan et al.

    Jensen has posited a research method to investigate group differences in cognitive tests. This method consists of first extracting a general intelligence factor by means of exploratory factor analysis. Secondly, similarity of factor loadings across groups is evaluated in an attempt to ensure that the same constructs are measured. Finally, the correlation is computed between the loadings of the tests on the general intelligence factor and the mean…