title: Hierarchical regression and structural equation modeling: two useful analyses for life course research.
abstract: This article reviews the life course perspective and considers various life course hypotheses such as trajectories, transitions, critical periods, sequencing, duration, and cumulative effects. Hierarchical regression and structural equation modeling are suggested as analyses to use in life course research. Secondary analysis was performed on the Early Head Start Research and Evaluation Study, 1996-2010, to illustrate their strengths and challenges. Models investigated the influence of mother and infant characteristics and of parent-child dysfunction at 14 and 24 months to children's cognitive outcomes at 36 months. Findings were interpreted and discussed in the context of life course hypotheses.
authors.affiliation: School of Nursing, Family and Community Health Systems Department, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA. Berndt@uthscsa.edu
authors.forename: Andrea E
authors.initials: AE
authors.lastname: Berndt
authors.forename: Priscilla C
authors.initials: PC
authors.lastname: Williams
mesh_terms: D002675:Child, Preschool; D003072:Cognition Disorders / Q000175:diagnosis / Q000523:psychology*; D018479:Early Intervention, Educational; D005260:Female; D006302:Health Services Research; D006801:Humans; D007223:Infant; D008016:Life Change Events; D008297:Male; D009034:Mother-Child Relations; D015999:Multivariate Analysis; D009769:Object Attachment; D012044:Regression Analysis