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Testing mediation using multiple regression and structural equation modeling analyses in secondary data.

Spencer D Li1

  • 1Department of Sociology, University of Macau, Taipa, Macau, People's Republic of China. spencerli@umac.mo

Evaluation Review
|September 16, 2011
PubMed
Summary
This summary is machine-generated.

Mediation analysis using secondary data in child development research is feasible. Multiple regression and structural equation modeling (SEM) are effective statistical methods for testing mediated effects in these studies.

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Area of Science:

  • Child and Adolescent Development
  • Developmental Psychology
  • Sociology

Background:

  • Secondary data sets offer valuable resources for mediation analysis in child development.
  • Understanding mediated effects is crucial for explaining complex relationships in adolescent research.

Purpose of the Study:

  • To provide an overview of statistical methods for mediation analysis in secondary data.
  • To illustrate the application and utility of multiple regression and structural equation modeling (SEM).

Main Methods:

  • The study reviews two primary statistical techniques: multiple regression and SEM.
  • Empirical examples demonstrate the practical application of these methods for analyzing mediated effects.

Main Results:

  • Multiple regression and SEM are suitable for analyzing mediation in child and adolescent development research using secondary data.
  • Case studies highlight the specific contexts where each method is most effectively applied.

Conclusions:

  • Mediation analysis is a viable approach for exploring complex pathways in child and adolescent development using existing data.
  • The choice between multiple regression and SEM depends on the specific research question and data structure.