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
2 - Modeling individual and average human growth data from childhood to adulthood
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
The first longitudinal growth study dates back to 1759 when Count de Montbeillard measured the body length of his son from birth to 18 years (Scammon, 1927; Tanner, 1962). Actually, when studying growth, there are two basically different approaches: longitudinal and crosssectional studies. In longitudinal growth studies, we measure the same children over several years at regular intervals (as was done by de Montbeillard) in order to be able to establish individual growth patterns. In cross-sectional growth studies, we measure children of different ages only once. A plot of the average height obtained at each age (or age group) depicts the average growth pattern in the sample. One should realize that the shape of the curve seen in an average growth pattern is different from the shape of individual growth curves (Hauspie, 1989). The information provided by the longitudinal and cross-sectional approaches is quite different. Both methods have their advantages and limitations. Whether the data concerns individual or average growth patterns, we are dealing with a series of measures of size (height or average height, for example) at particular ages, either precise chronological ages (in case of longitudinal studies) or mid-points of age classes (in case of cross-sectional studies). However, the researcher is quite often interested in determining the underlying continuous process of growth, from which he wants to derive certain characteristics, such as the age at maximum velocity at adolescence, for example.
- Type
- Chapter
- Information
- Problems and Methods in Longitudinal ResearchStability and Change, pp. 28 - 46Publisher: Cambridge University PressPrint publication year: 1991
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