Todd D. Little, PhD is a Professor and Director of the Research, Evaluation, Measurement, and Statistics program at Texas Tech University (TTU) where, in 2013, he became the founding director of the Institute for Measurement, Methodology, Analysis and Policy (IMMAP). The IMMAP at TTU is a university-designated research and support center that provides expert consulting and assistance on all manner of data collection, data management, and advanced statistical analyses. Little is internationally recognized for his quantitative work on various aspects of applied SEM (e.g., modern missing data treatments, indicator selection, parceling, modeling developmental processes) as well as his substantive developmental research (e.g., action-control processes and motivation, coping, and self-regulation). Prior to joining TTU, Little has guided quantitative training and provided consultation to students, staff, and faculty at the Max Planck Institute for Human Development’s Center for Lifespan Studies (1991-1998), Yale University’s Department of Psychology (1998-2002), and researchers at KU (2002-2013, including as director of the RDA unit in the Lifespan Institute and as director of the Center for Research Methods and Data Analysis). In 2001, Little was elected to membership in the Society for Multivariate Experimental Psychology, a restricted-membership society of quantitative specialists in the behavioral and social sciences. In 2009, he was elected President of APA’s Division 5 (Evaluation, Measurement, and Statistics). He founded, organizes, and teaches in the internationally renowned ‘Stats Camps’ each June (see statscamp.org for details of the summer training programs) and has given over 150 workshops and talks on methodology topics around the world. He is a fellow in APA, APS, and AAAS. In 2013, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development.
As an interdisciplinary-oriented collaborator, Little has published with over 340 persons from around the world in over 67 different peer-reviewed journals. His work has garnered over 29,388 citations with an h-index of 85 and an i10-index of 195. He published Longitudinal Structural Equation Modeling in 2013 and he has edited five books related to methodology including the Oxford Handbook of Quantitative Methods and the Guildford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card). Little has served on numerous grant review panels for federal agencies such as NSF, NIH, and IES and private foundations such as the Jacobs foundation. He has been principal investigator or co-principal investigator on over 40 grants and contracts and he have served as a statistical consultant on over 85 grants and contracts. In the conduct of his collaborative research, he has participated in the development of over 12 different measurement tools, including the CAMI, the Multi-CAM, the BALES, the BISC, the I FEEL, and the form/function decomposition of aggression.
Little, T. D., Bovaird, J. A., & Card, N. A. (Eds.) (2007). Modeling contextual effects in longitudinal studies. Mahwah, NJ: LEA
Longitudinal data are critical for understanding how individuals change across time. Researchers are faced with a complex task when modeling the contexts in which longitudinal processes unfold. Modeling Contextual Effects in Longitudinal Studies reviews the challenges and alternative approaches to modeling these influences and provides methodologies and data analytic strategies for behavioral and social science researchers.
Card, N. A., Selig, J. P., Little, T. D. (Eds.) (2008). Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences. New York, NY: Routledge
Lee, J., Little, T. D., & Preacher, K.J. (2010). Partial factorial invariance in crosscultural research. In E. Davidov, P. Schmidt & J. Billiet (Eds.), Cross-cultural data analysis: Methods and applications. New York: Guilford press. Structural equation modeling (SEM) has been suggested as a single, integrated framework for testing cross-cultural group differences. Recently, particular forms of SEM have been widely used to detect the items that function differently for different groups (i.e., differential item functioning; DIF). Accordingly, the primary goal of this chapter is to discuss some methodological issues that may arise when researchers conduct SEM-based DIF analysis.
Little, T. D. (2013). Longitudinal Structural Equation Modeling. New York: Guilford press.
Featuring actual datasets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any repeated-measures study. Initial chapters lay the groundwork for modeling a longitudinal change process, from measurement, design, and specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical “how-to-do-it” details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation. User-friendly features include equation boxes that clearly explain the elements in every equation, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website https://www.depts.ttu.edu/immap/Books.php provides datasets for all of the examples.
Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to today’s complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences.
Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.