Book contents
- The Cambridge Encyclopedia of Child Development
- Child Development
- Copyright page
- Dedication
- Contents
- Contributors
- Editorial preface
- Foreword
- Acknowledgments: External Reviewers
- Introduction Study of child development: an interdisciplinary enterprise
- Part I Theories of development
- Part II Methods in child development research
- Biomechanics
- Cross-cultural comparisons
- Cross-species comparisons
- Developmental testing
- Experimental methods
- Eye tracking
- Fetal and neonatal magnetoencephalography
- Fetal ultrasonography
- Head-mounted eye tracking
- Interviewing children for research
- Magnetic resonance imaging (MRI)
- Functional near-infrared spectroscopy
- Direct observational methods
- Parent and teacher rating scales
- Qualitative methods
- Self-reports and peer ratings
- Epidemiological designs
- Longitudinal and cross-sectional designs
- Twin and related designs
- Bayesian data analysis
- Connectionist modeling
- Individual and group differences in developmental functions
- Indicators of accuracy for screening tests
- Multilevel modeling
- Structural equation modeling
- Time-series analysis
- Ethical considerations in studies with children
- Part III Prenatal development and the newborn
- Part IV Perceptual and cognitive development
- Part V Language and communication development
- Part VI Social and emotional development
- Part VII Motor and related development
- Part VIII Postnatal brain development
- Part IX Developmental pathology
- Part X Crossing the borders
- Part XI Speculations about future directions
- Book part
- Author Index
- Subject Index
- Plate Section (PDF Only)
- References
Structural equation modeling
from Part II - Methods in child development research
Published online by Cambridge University Press: 26 October 2017
Book contents
- The Cambridge Encyclopedia of Child Development
- Child Development
- Copyright page
- Dedication
- Contents
- Contributors
- Editorial preface
- Foreword
- Acknowledgments: External Reviewers
- Introduction Study of child development: an interdisciplinary enterprise
- Part I Theories of development
- Part II Methods in child development research
- Biomechanics
- Cross-cultural comparisons
- Cross-species comparisons
- Developmental testing
- Experimental methods
- Eye tracking
- Fetal and neonatal magnetoencephalography
- Fetal ultrasonography
- Head-mounted eye tracking
- Interviewing children for research
- Magnetic resonance imaging (MRI)
- Functional near-infrared spectroscopy
- Direct observational methods
- Parent and teacher rating scales
- Qualitative methods
- Self-reports and peer ratings
- Epidemiological designs
- Longitudinal and cross-sectional designs
- Twin and related designs
- Bayesian data analysis
- Connectionist modeling
- Individual and group differences in developmental functions
- Indicators of accuracy for screening tests
- Multilevel modeling
- Structural equation modeling
- Time-series analysis
- Ethical considerations in studies with children
- Part III Prenatal development and the newborn
- Part IV Perceptual and cognitive development
- Part V Language and communication development
- Part VI Social and emotional development
- Part VII Motor and related development
- Part VIII Postnatal brain development
- Part IX Developmental pathology
- Part X Crossing the borders
- Part XI Speculations about future directions
- Book part
- Author Index
- Subject Index
- Plate Section (PDF Only)
- References
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- The Cambridge Encyclopedia of Child Development , pp. 196 - 200Publisher: Cambridge University PressPrint publication year: 2017
References
Further reading
Grimm, K.J., Zhang, Z., Hamagami, F., & Mazzocco, M.M. (2013). Modeling nonlinear change via latent change and latent acceleration frameworks: Examining velocity and acceleration of growth trajectories. Multivariate Behavioral Research, 48, 117–143.Google Scholar
Hoyle, R.H. (2012). Handbook of structural equation modeling. New York, NY: Guilford Press.Google Scholar
Laursen, B., Little, T., & Card, N. (2011). Handbook of developmental research methods. New York, NY: Guilford Press.Google Scholar
References
Bollen, K.A., & Curran, P.J. (2006). Latent curve analysis: A structural equation perspective. Hoboken, NJ: Wiley.Google Scholar
Collins, L.M., & Sayer, A.G. (2001). New methods for the analysis of change. Washington, DC: American Psychological Association Press.CrossRefGoogle Scholar
Grimm, K.J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82, 1357–1371.Google Scholar
Kline, R.B. (2011). Principles and practice of structural equation modeling. New York, NY: Guilford Press.Google Scholar
Little, T.D. (2013). Longitudinal structural equation modeling. New York, NY: Guilford Press.Google Scholar
Loehlin, J.C. (2004). Latent variable models: An introduction to factor, path, and structural analysis. Mahwah, NJ: Erlbaum.Google Scholar
McArdle, J.J. (2001). A latent difference score approach to longitudinal dynamic structural analysis. In Cudeck, R., du Toit, S., & Sorbom, D. (Eds.), Structural equation modeling: Present and future (pp. 342–380). Lincolnwood, IL: Scientific Software International.Google Scholar
McArdle, J.J., & Grimm, K.J. (2010). An empirical example of change analysis by linking longitudinal item response data from multiple tests. In von Davier, A. (Ed.), Statistical models for test equating, scaling, and linking (pp. 71–88). New York, NY: Springer Science + Business Media.Google Scholar
McArdle, J.J., Grimm, K.J., Hamagami, F., Bowles, R.P., & Meredith, W. (2009). Modeling lifespan growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement. Psychological Methods, 14, 126–149.Google Scholar
McArdle, J.J., & Hamagami, F. (2001). Linear dynamic analyses of incomplete longitudinal data. In Collins, L. & Sayer, A. (Eds.), New methods for the analysis of change (pp. 137–176). Washington, DC: American Psychological Association Press.Google Scholar
McArdle, J.J., & Nesselroade, J.R. (2014). Longitudinal data analysis using structural equation models. Washington, DC: American Psychological Association Press.Google Scholar
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122.CrossRefGoogle Scholar
Singer, J.D., & Willett, J.B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.Google Scholar