Book contents
- Frontmatter
- Contents
- Contributors to this volume
- Foreword
- Preface
- 1 Studying individual development: problems and methods
- 2 Modeling individual and average human growth data from childhood to adulthood
- 3 Intraindividual variability in older adults' depression scores: some implications for developmental theory and longitudinal research
- 4 Now you see it, now you don't – some considerations on multiple regression
- 5 Differential development of health in a life-span perspective
- 6 Assessing change in a cohort-longitudinal study with hierarchical data
- 7 Statistical and conceptual models of ‘turning points’ in developmental processes
- 8 Qualitative analyses of individual differences in intra- individual change: examples from cognitive development
- 9 Application of correspondence analysis to a longitudinal study of cognitive development
- 10 Event-history models in social mobility research
- 11 Behavioral genetic concepts in longitudinal analyses
- 12 Genetic and environmental factors in a developmental perspective
- 13 Structural equation models for studying intellectual development
- 14 Longitudinal studies for discrete data based on latent structure models
- 15 Stability and change in patterns of extrinsic adjustment problems
- Index
12 - Genetic and environmental factors in a developmental perspective
Published online by Cambridge University Press: 27 April 2010
- Frontmatter
- Contents
- Contributors to this volume
- Foreword
- Preface
- 1 Studying individual development: problems and methods
- 2 Modeling individual and average human growth data from childhood to adulthood
- 3 Intraindividual variability in older adults' depression scores: some implications for developmental theory and longitudinal research
- 4 Now you see it, now you don't – some considerations on multiple regression
- 5 Differential development of health in a life-span perspective
- 6 Assessing change in a cohort-longitudinal study with hierarchical data
- 7 Statistical and conceptual models of ‘turning points’ in developmental processes
- 8 Qualitative analyses of individual differences in intra- individual change: examples from cognitive development
- 9 Application of correspondence analysis to a longitudinal study of cognitive development
- 10 Event-history models in social mobility research
- 11 Behavioral genetic concepts in longitudinal analyses
- 12 Genetic and environmental factors in a developmental perspective
- 13 Structural equation models for studying intellectual development
- 14 Longitudinal studies for discrete data based on latent structure models
- 15 Stability and change in patterns of extrinsic adjustment problems
- Index
Summary
INTRODUCTION
Developmental behavior genetics is concerned with the diverse ways in which genetic and environmental processes are involved in changes as well as continuity in development (Plomin, 1986; DeFries & Fulker, 1986). During ontogenesis, observed (phenotypic) change of a quantitative character may be due to distinct subsets of genes turning on and off, whereas continuity, on the other hand, may be caused by stable environmental causes. In contrast to the popular point of view, then, genetically determined characters are not always stable, nor are longitudinally stable characters always due to hereditary influences. Only through carefully designed longitudinal investigation of phenotypic changes in genetically related individuals can the dynamic patterns of genetic and environmental influences be disentangled.
In the following we shall mainly be concerned with a particular type of genetic model for the analysis of longitudinal phenotypic data, namely the simplex model (Jöreskog, 1970). The genetic simplex model is a genuine time series model and therefore can explain the characteristic time-dependent patternings of serial correlation (autocorrelation) as observed in longitudinal studies. It was already shown by Cronbach (1967) that common factor analysis of autocorrelation matrices will yield spurious, i.e. invalid, results. Consequently, recent efforts in the genetic modeling of longitudinal data have put particular emphasis on the elaboration of simplex models in this context (Boomsma & Molenaar, 1987a; Eaves, Hewitt & Heath, 1988).
- Type
- Chapter
- Information
- Problems and Methods in Longitudinal ResearchStability and Change, pp. 250 - 273Publisher: Cambridge University PressPrint publication year: 1991
- 10
- Cited by