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Handbook of Research Methods in Developmental Science
Douglas M. Teti (Edited by), Hobart Harrington Cleveland (Edited by), Kelly L. Rulison (Edited by)
9781119880820, Wiley
Paperback / softback, published 26 March 2026
688 pages
24.4 x 17.3 x 4.3 cm, 1.247 kg
Presents cutting-edge research methods in developmental science The Handbook of Research Methods in Developmental Science delivers a fully revised and expanded exploration of the latest methodologies in human development research. Twenty-four entirely new chapters by renowned experts introduce innovative approaches that reflect the dynamic evolution of developmental science methodologies. Part of the Blackwell Handbooks of Research Methods in Psychology series, this authoritative resource is indispensable for those seeking to apply state-of-the-art methods to the study of human development. The second edition of the Handbook builds upon the strengths of its predecessor while incorporating significant updates to address emerging research challenges. It offers a comprehensive review of traditional and contemporary developmental research designs, including cross-sectional, longitudinal, and quasi-experimental methods, as well as advanced topics such as the Multiphasic Optimization Strategy (MOST) framework, time-varying effect modeling, and integrative data analysis. Additionally, the authors present fresh insights into causal inference, observational methods, and intervention research, providing a more nuanced understanding of how developmental scientists can study change over time. Capturing the latest theoretical and technological advancements in the discipline, the Handbook of Research Methods in Developmental Science: The Handbook of Research Methods in Developmental Science, Second Edition, is an essential resource for graduate students, researchers, and professionals in developmental science, psychology, and human development. It is particularly relevant for courses in research methods, developmental psychology, and intervention science within psychology, education, and social sciences degree programs.
Author Bios viii Preface xxiv Part I Developmental Designs, Sampling, and Causal Inference 1 1 Cross- Sectional, Longitudinal, and Retrospective Designs: General Utility and Threats to Validity 3 2 Experimental Design in Developmental Science 31 3 Strengthening Causal Inference Using Sibling Designs 57 4 Quasi- Experimental Designs for Causal Inference About Intervention Effects: Addressing Threats to Validity from a Graphical Models Perspective 79 5 Measurement Burst Designs in Developmental Science 110 6 Obtaining and Keeping Your Sample: Participant Recruitment and Panel Maintenance 129 7 On Sampling and Measurement: A Case Study of the Association Between Loneliness and Cognitive Function in the Survey of Health Aging and Retirement in Europe 146 Part II Construct Measurement in Developmental and Family Science 181 8 Conceptualizing and Measuring the Family Context 183 9 Direct and Indirect Observational Methods in Developmental and Family Science 200 10 Assessing Parenting in Context: Naturalistic Assessment, Domain Specificity, and Early Development 220 11 Maximizing Cultural Validity in the Study of Child Development 250 12 Introduction to Qualitative Methods in Developmental Science 276 Part III Advances in Developmental Intervention 301 13 The Multiphase Optimization Strategy (MOST): A Framework to Develop More Effective, Affordable, Scalable, and Efficient Interventions 303 14 The Sequential Multiple Assignment Randomized Trial (SMART) Design 329 15 Adaptive Intervention Designs for Studies of Human Development 349 16 Economic Evaluation of Developmental Interventions: Issues and Best Practices 370 17 Bringing Interventions to Scale: Research Methods for Dissemination and Implementation Science 389 Part IV Data Analysis and Methods in Developmental Science 411 18 It’s All Regression: Fundamentals of Developmental Data Analysis 413 19 Modern Causal Mediation and Longitudinal Models for Developmental Data 453 20 A Rosetta Stone for Modeling Change: Connections among Multilevel Models, Structural Equation Models, and Multilevel Structural Equation Models 483 21 Integrative Data Analysis in Developmental Science: A Primer 516 22 Propensity Score Methods in Quasi- Experimental Research 544 23 Applications of Social Network Analysis in Developmental Science 560 24 Time- Varying Effect Modeling to Address Novel Questions in Developmental Research 583 Author Index 612 Subject Index 643
Ekjyot K. Saini and Douglas M. Teti
Robert Hepach
Gabriel L. Schlomer, Olivia C. Robertson, and Kristine Marceau Copyrighted Material
Patrick Sheehan, Muwon Kwon, and Peter M. Steiner
Daisy V. Zavala, Giselle A. Ferguson, Giancarlo Pasquini, and Stacey B. Scott
Carina Cornesse and Bella Struminskaya
Ashton M. Verdery and Mara Getz Sheftel
Gregory M. Fosco and Devin Malloy McCauley
Rena L. Repetti
Douglas M. Teti
Suzanne Gaskins
Debbie Kim and Rachel Carly Feldman
Kelly L. Rulison
Xiaoxi Yan, Yifan Cui, and Bibhas Chakraborty
Contents vii
Timothy R. Brick
Lawrie Green Daniel (Max) Crowley
Gitanjali Shrestha and Brittany Cooper
Michael J. Rovine, Emily M. Weiss, and Paul A. McDermott
Matthew J. Valente, Judith J. M. Rijnhart, and David P. MacKinnon
Lesa Hoffman
Veronica T. Cole, Conor H. Lacey, and Lydia F. Bierce
Donna L. Coffman
Andrea Vest Ettekal and Jimi Adams
Stephanie T. Lanza and Anna K. Hochgraf
Subject Areas: Psychology [JM]
