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Erin H. Arruda
University of California, Los Angeles
4Publications
2H-index
29Citations
Publications 4
Newest
#1Laura Wray-Lake (UCLA: University of California, Los Angeles)H-Index: 15
#2Erin H. Arruda (UCLA: University of California, Los Angeles)H-Index: 2
Last.David A. Hopkins (BC: Boston College)H-Index: 4
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This article examines effects of political party affiliation on U.S. young adults’ political participation across age and historical time. Using national U.S. longitudinal Monitoring the Future dat...
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#1Andrew J. Fuligni (UCLA: University of California, Los Angeles)H-Index: 52
#2Erin H. Arruda (UCLA: University of California, Los Angeles)H-Index: 2
Last.Nancy A. Gonzales (ASU: Arizona State University)H-Index: 40
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To inform public health recommendations for adolescent sleep, the amounts of sleep associated with the highest levels of academic achievement and mental health were examined. The degree to which daily variability in sleep duration represents an underappreciated but functionally significant sleep behavior also was tested. A total of 421 adolescents (Mage = 15.03 years) with Mexican-American backgrounds reported nightly sleep times for 2 weeks; approximately 80% repeated the same protocol 1 year l...
22 CitationsSource
#1Erin H. Arruda (UCLA: University of California, Los Angeles)H-Index: 2
#2Peter M. Bentler (UCLA: University of California, Los Angeles)H-Index: 81
Ill conditioning of covariance and weight matrices used in structural equation modeling (SEM) is a possible source of inadequate performance of SEM statistics in nonasymptotic samples. A maximum a posteriori (MAP) covariance matrix is proposed for weight matrix regularization in normal theory generalized least squares (GLS) estimation. Maximum likelihood (ML), GLS, and regularized GLS test statistics (RGLS and rGLS) are studied by simulation in a 15-variable, 3-factor model with 15 levels of sam...
5 CitationsSource
#1Jennifer L. Krull (UCLA: University of California, Los Angeles)H-Index: 27
#2Erin H. Arruda (UCLA: University of California, Los Angeles)H-Index: 2
Growth curve modeling is a statistical analysis technique for longitudinal data that captures both intra-individual (“within person”) change over time and inter-individual (“between person”) variability in such change. Such models are more flexible and powerful than traditional ANOVA and MANOVA approaches to repeated measures. Growth models are extremely valuable for describing or making inferences about the shape, direction, and rate of change that best characterize a population trajectory as w...
2 CitationsSource
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