Hierarchical regression and structural equation modeling: Two useful analyses for life course research

Andrea E. Berndt, Priscilla C. Williams

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)4-18
Number of pages15
JournalFamily and Community Health
Volume36
Issue number1
DOIs
StatePublished - Jan 1 2013

Keywords

  • analysis
  • life course research
  • statistics

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Fingerprint Dive into the research topics of 'Hierarchical regression and structural equation modeling: Two useful analyses for life course research'. Together they form a unique fingerprint.

Cite this