The objectives of Core C are to (a) facilitate acquisition of data and project specific data for Projects 1-4, (b) develop tools and procedures for ensuring and monitoring the accuracy and confidentiality of all collected data, (c) develop tools and procedures to aid the Principal Investigator, the Executive Committee, and the Scientific Advisory Board in providing scientific oversight and administration of the Texas Center for Learning Disabilities, (d) facilitate communication and sharing of data among investigators through state of the art computer networking, (e) provide statistical analysis of Project 1-4 hypotheses, and (f) collaborate with investigators in the conceptualization of and to conduct cross-project, integrative analyses from the current projects, as well as those that might involve data from the current projects and data collected in the prior award. Dr. David Francis, an experienced methodologist, is the Core Director. He will supervise all activities of Core C, working closely with other experienced methodologists (Stuebing, Cirino, Roberts) to establish data bases, supervise quality control, and perform statistical analyses. The resources of the Texas Institute for Measurement, Evaluation, and Statistics (TIMES) are extensive and will be available to the Center. The Core has the capacity for sophisticated data management and complex statistical analysis, with an established track record in this area. All of the project publications reflect usage of Core C for data management and analysis over the past 5 years.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Specialized Center (P50)
Project #
2P50HD052117-06
Application #
8264856
Study Section
Special Emphasis Panel (ZHD1-DSR-H (53))
Project Start
Project End
Budget Start
2011-12-01
Budget End
2012-11-30
Support Year
6
Fiscal Year
2012
Total Cost
$245,857
Indirect Cost
$96,035
Name
University of Houston
Department
Type
DUNS #
036837920
City
Houston
State
TX
Country
United States
Zip Code
77204
Huang, Jin; Vaughn, Michael G; Kremer, Kristen P (2016) Breastfeeding and child development outcomes: an investigation of the nurturing hypothesis. Matern Child Nutr 12:757-67
Williams, Kelly J; Walker, Melodee A; Vaughn, Sharon et al. (2016) A Synthesis of Reading and Spelling Interventions and Their Effects on Spelling Outcomes for Students With Learning Disabilities. J Learn Disabil :
Cirino, Paul T; Miciak, Jeremy; Gerst, Elyssa et al. (2016) Executive Function, Self-Regulated Learning, and Reading Comprehension: A Training Study. J Learn Disabil :
Hodge, David R; Salas-Wright, Christopher P; Vaughn, Michael G (2016) Behavioral Risk Profiles of Homeschooled Adolescents in the United States: A Nationally Representative Examination of Substance Use Related Outcomes. Subst Use Misuse :1-13
Dimitriadis, Stavros I; Laskaris, Nikolaos A; Simos, Panagiotis G et al. (2016) Greater Repertoire and Temporal Variability of Cross-Frequency Coupling (CFC) Modes in Resting-State Neuromagnetic Recordings among Children with Reading Difficulties. Front Hum Neurosci 10:163
Fletcher, Jack M; Miciak, Jeremy (2016) Comprehensive Cognitive Assessments are not Necessary for the Identification and Treatment of Learning Disabilities. Arch Clin Neuropsychol :
Huang, Jin; Vaughn, Michael G (2016) Household Food Insecurity and Children's Behaviour Problems: New Evidence from a Trajectories-Based Study. Br J Soc Work 46:993-1008
Taylor, W Pat; Miciak, Jeremy; Fletcher, Jack M et al. (2016) Cognitive Discrepancy Models for Specific Learning Disabilities Identification: Simulations of Psychometric Limitations. Psychol Assess :
Wanzek, Jeanne; Vaughn, Sharon; Scammacca, Nancy et al. (2016) Meta-Analyses of the Effects of Tier 2 Type Reading Interventions in Grades K-3. Educ Psychol Rev 28:551-576
Barnes, Marcia A; Stuebing, Karla; Fletcher, Jack M et al. (2016) Cognitive Difficulties in Struggling Comprehenders and their Relation to Reading Comprehension: A Comparison of Group Selection and Regression-Based Models. J Res Educ Eff 9:153-172

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