Todd D. Little - US grants

Increasingly, researchers are discussing and examining the potential influences on human development of families, peers, schools, neighborhoods, communities, and so on. This increased attention to modeling ecological influences highlights a growing need to understand how best to represent and model contextual impacts on human development. This conference will bring together leading developmentalists, quantitative scholars, and statisticians to address ecological/contextual effects as direct influences on development, mediators and/or moderators of change, nested hierarchical levels of influence, and as dynamic systems. In addition to the primary themes of ecological impacts, common methodological concerns will be addressed, including: measurement issues; incomplete data; sample heterogeneity (a person-centered approach); and power and sample size considerations. Each conference participant is explicitly asked to address at least one of the primary themes, consider the methodological concerns, and relate each to the developmental applications, thus allowing for a) appropriate conceptualizations and analyses of ecological factors, b) increased awareness and inclusion of ecological factors in future research, and c) expanded scientific knowledge on the complex interplay among all the factors that contribute to human development. Approximately twenty-five scholars will participate as presenters or discussants with considerable meeting time allocated for round-table discussions of the themes and issues. The 2-day intensive retreat format, to be held in Tempe, Arizona, on March 4-6, 2004, allows significant cross-fertilization of ideas so that each presenter's work will be refined and extended as a result of their direct participation in the conference. The final product is an edited volume to consist of 12-15 chapters, 4-6 commentaries and discussions pieces, and a supporting web site.

Increasingly, researchers are discussing and examining the influence of interpersonal interactions on the development of individuals. Interpersonal interactions can be roughly divided into those occurring within dyadic contexts (e.g., parent-child, romantic couples, peer-to-peer, etc.) and those occurring in group contexts (families, cliques, groups, etc). Supported by NSF funds, Dr. Little and Dr. Card will organize a conference that will bring together developmentalists, quantitative scholars, and statisticians to address (a) how best to conceptualize the role of interactive processes in development and (b) how to appropriately analyze the interdependence that is inherent in dyadic and group data. Each conference participant will consider one or more methodological concern and relate each to one or more developmental application. Approximately twenty scholars will participate as presenters or discussants. The open 2-day conference format, which will be held at the University of Kansas in spring of 2006, allows significant cross-fertilization of ideas so that each presenter's work will be refined and extended as a result of their direct participation in the conference. The final product will be an edited volume to consist of approximately 20 chapters and a supporting web site.

By pairing specific techniques with relevant applications in developmental science, the broader impacts of this conference and resulting edited book will be to: (a) advance conceptualizations of developmental processes to explicitly consider their occurrence within dyadic and group contexts, (b) increase awareness among developmental researchers of the analytic approaches that are needed for analyzing the interdependence that is inherent in dyadic and group data, and (c) extend these analytic methods to specifically address the needs of developmental science (e.g., longitudinal extensions).

The central objective of the Research Design and Analysis Core (RDAC) is to provide MRDDRC[unreadable] investigators with the most up-to-date, state of the art data analysis resources to help them make[unreadable] the most of their data. Through collaborative services provided to MRDDRC investigators, the RDAC[unreadable] will enhance the validity, rigor, and impact of their research.[unreadable] Areas of expertise covered by Core personnel include research design and data management,[unreadable] advanced quantitative and statistical analysis, and bioinformatics. These services are provided across a[unreadable] wide spectrum of disciplines, methods, and techniques and are tailored to the specific needs and level[unreadable] of the individual investigator. In assisting researchers across different disciplines, the Core promotes[unreadable] collaboration and fosters dialogue by sharing techniques, solutions, and ideas among investigators and[unreadable] by providing opportunities for researchers to meet in educational settings that highlight current best[unreadable] practices. The Core is also generative in its own right through the development and refinement of[unreadable] design and analysis techniques.

Address; Analysis, Data; Animals; Area; Arts; Behavioral; Bio-Informatics; Bioinformatics; Biological; Biometrics; Biometry; Biometry and Biostatistics; Biostatistics; Characteristics; Chemicals; Child Development Disorders; Clinical Trials; Clinical Trials Design; Clinical Trials, Monitoring; Clinical Trials, Unspecified; Collaborations; Connectionist Models; Consultations; Data; Data Analyses; Data Banks; Data Bases; Data Collection; Data Set; Data Sources; Databank, Electronic; Databanks; Database, Electronic; Databases; Dataset; Development; Developmental Disabilities; Discipline; Documentation; Educational workshop; Ensure; Epidemiologic Research; Epidemiologic Studies; Epidemiological Studies; Epidemiology Research; Fostering; Gene Expression; Genomics; Hand; Human; Human Resources; Human, General; IDDRC; IDDRP; Image; Individual; Intellectual and Developmental Disabilities Research Centers; Investigators; Laboratories; Learning; MRDD Research Center; MRDDRC; Man (Taxonomy); Man, Modern; Manpower; Measurement; Mental Retardation; Mental Retardation and Developmental Disabilities Research Centers; Methods and Techniques; Methods, Other; Monitoring Clinical Trials; Neural Network Models; Neural Network Simulation; Neural Networks (Computer); Perceptrons; Plant Roots; Play; Procedures; Programs (PT); Programs [Publication Type]; Proteomics; Research; Research Design; Research Personnel; Research Resources; Researchers; Resources; Retrieval; Role; Sample Size; Security; Services; Solutions; Sources, Data; Study Type; Systems Analyses; Systems Analysis; Techniques; Training and Education; Workshop; analytical method; analytical tool; biobehavioral measurement; clinical data repository; clinical data warehouse; clinical investigation; data management; data repository; design; designing; experiment; experimental research; experimental study; imaging; neural network (computer simulation of nervous system); personnel; programs; relational database; research study; root; social role; statistics/biometry; study design; tool

This project will develop and evaluate a research methodology known as a "planned missing research design." This statistical technique enables scientists who conduct longitudinal studies to obtain higher quality results (greater statistical certainty and/or broader ability to generalize findings) using the same amount of research resources. This technique has been used with great effectiveness in certain specific contexts in the past. The proposed project will provide guidelines and procedures that will enable researchers to use the technique in all areas of developmental science in which a study evaluates how participants change over time.

Planned missing data designs have tremendous potential to streamline how we conduct basic research in the social, behavioral, and educational sciences. Strong statistical theory supports the idea that carefully planned patterns of intentionally missing information will not bias or weaken the accuracy of research conclusions. When properly constructed a planned missing design can save 33% or more of the costs associated with data collection. They can also reduce the fatigue and demand on participants which can increase the overall quality of the data that is collected from each participant. In fact, planned missing research designs have been recommended for decades as efficient ways to collect expensive data (e.g., one-on-one assessments) and time-intensive data (e.g., large questionnaire protocols and repeated measures). Planned missing data designs can be used to manage cost, improve data quality, reduce fatigue of participants and test-retest effect, as well as increase statistical power to detect effects of interest. Statisticians are certain of these benefits; researchers and stakeholders, however, remain skeptical. Outside the ideal conditions that statisticians espouse for such designs, are they practical for the conditions faced in real-world research? Statistical proofs are just a starting point for answering important questions about the practical nature of these designs. This research will provide compelling guidance to researchers for using planned missing research designs in social, behavioral, and educational research.

To validate their utility for practical researchers, planned missing designs will be thoroughly studied under varying conditions of real-world data. The goal of this sponsored research is to examine precisely under what conditions such designs can be useful and the conditions in which caution would be warranted. Previous studies on planned missing data designs have all been small scale simulation studies that demonstrate proof of concept but do not provide practical guidance to researchers. This funded project will conduct a series of well-conceived large-scale Monte Carlo simulation studies to explore the utility of planned missing data designs under a wide variety of conditions including extreme data conditions and complex longitudinal designs. This project will vary the critical features of planned missing designs that will inform researchers on when, how, and why to use planned missing elements in their research designs. The results of this project fully detail the boundaries of when this design is useful and when it should be avoided or modified. By so doing, the results of this work will be paradigm shifting for research across the social, behavioral, and educational sciences because real-world researchers and stakeholders will now understand their utility and applicability. As a result, this funded project will impact and change the future of best-practice methodology in all these research areas.

The scientific validity of any behavioral, biological, or biobehavioral study lies with the conditions under which data have been collected, and the quality of analyses that allow data to be understood or interpreted. The Research Design and Analysis Core (RDAC) is charged with the critical task of applying optimal experimental design and analytical methods to realize the full informational potential of data collected as part of KIDDRC projects. RDAC was established in 1995 to meet increasing demands by KIDDRC principal Investigators (Pls) for consultafion in complex designs and access to advanced statisfical expertise. In 2002, RDAC expanded to include bioinformatics support[unreadable]necessitated by microarray services instituted in Core D[unreadable]and the first bioinformatics services at KU were born. Since our last renewal, the core has confinued to expand its activities by providing new services in support of our research Themes. We now are poised to assume significant new responsibilities in analyzing data from cutting edge technologies and clinical trials.