Online Training Resources

The Office of Behavioral and Social Sciences Research (OBSSR) has identified training needs in several areas relevant to the behavioral and social sciences.  

Good Clinical Practice for Social and Behavioral Research eLearning Course

In September 2016, the NIH issued a Policy on Good Clinical Practice (GCP) Training for NIH Awardees Involved in NIH-funded Clinical Trials. GCP is an international ethical and scientific quality standard for designing, conducting, recording and reporting clinical trials. The principles of GCP help assure the safety, integrity, and quality of clinical trials. Investigators and clinical trial staff who are competent in GCP principles will be better able to assure that the rights, safety, and well-being of human subjects are protected; that clinical trials are conducted in accordance with approved plans and with rigor and integrity; and that data derived from clinical trials are reliable. For accessibility issues, please contact OBSSRNews@mail.nih.gov.

Extramural Researchers can go here to take the course.
NIH Employees can go here to take the course. (NIH login required)
Educational Facilities can Download the Good Clinical Practices for Social and Behavioral Sciences Course for your educational facility's Learning Management System (LMS). 

TADA-BSSR Webinar: Integrating Data Analytics into the Social and Behavioral Science Research Lifecycle

Presenters: Hannah L. F. Cooper, Sc.D., Rollins School of Public Health, Emory University; Lance Waller, Ph.D., Rollins School of Public Health, Emory University

May 20, 2021 | Video Recording | Webinar Description: The presenters explore the ways that elements of Cook and Campbell’s validity framework can strengthen analyses of “big data” that are designed to study, monitor, and intervene in drug-related harms. They also discuss the relevance of specific threats to internal validity, external validity, and statistical conclusion validity to these analyses, and strategies to minimize these threats.

TADA BSSR Webinar: Translating Domain Knowledge into Mechanistic Process Models

Presenters: Eric Hekler, University of California San Diego; Misha Pavel, Northeastern University; Donna Spruijt-Metz, University of Southern California

March 18, 2021 | Video Recording | Webinar Description: Translating Domain Knowledge into Mechanistic Process Models: Illustrating the Need with a Just-in-Time Adaptive Intervention

TADA-BSSR Webinar: Analyzing Complex Behavioral, Social and Population Health Data for COVID-19

Presenter: Carl T. Bergstrom, Ph.D., University of Washington

January 21, 2021 | Video Recording | Webinar Description: Selection bias occurs when the way a statistical sample is obtained prevents the sample form accurately represent the population about which one wishes to draw inferences. Straightforward as the issue may seem, selection bias is among the most pernicious perils of statistical inference. In this lecture I will discuss some of the many ways that selection bias and related phenomena, from right censoring to the Will Rogers effect, can arise in medical research and beyond. I’ll draw upon a range examples including recent studies on Covid-19.

TADA-BSSR Webinar: Analyzing Complex Behavioral, Social and Population Health Data for COVID-19 & New Opportunities for BSSR Data Science Training

Presenters: Lorene Nelson, Ph.D., M.S., Stanford University School of Medicine; Lance Waller, Ph.D., Emory University; Mick Tilford, Ph.D., University of Arkansas for Medical Sciences; Lucila Ohno-Machado, M.D., Ph.D., University of California, San Diego; and Maria Glymour, Sc.D., M.S., University of California, San Francisco

October 15, 2020 | Video Recording | Webinar Description: This video is a recording of the inaugural edition of the TADA-BSSR Webinar Series which features a brief introduction of the new NIH/OBBSR sponsored training program and highlights some of the exciting ongoing work at five of the training sites involving complex data related to the COVID-19 pandemic.

Training Webinar Cover

OBSSR Methodology Seminar: Text Mining for Behavioral and Social Sciences Research

August 9, 2019 | NIH Videocast Archive | Seminar Description: The age of Big Data has ushered in a wave of high-volume digital information and much of it is text based. Text mining is a form of data mining that involves collecting and analyzing large volumes of textual data to reveal patterns and relationships. Techniques for mining can be used to extract key concepts, spot trends, summarize content from documents and gain semantic understanding, and index and search text for use in predictive analytics. Text mining has become an important research process with many different commercial and academic applications, and it is becoming more widely applied in social science studies. This one-day methodology seminar sponsored by OBSSR presented a basic introductory overview of principles and techniques of text mining for behavioral and social research and showcased some innovative health research examples. 

OBSSR Methodology Seminar: Predictive Modeling for Behavioral and Social Sciences Health Research

October 12, 2018 | NIH Videocast Archive | Seminar Description: This one-day methodology seminar showcased principles and techniques for prediction modeling from machine learning via specific case examples presented by scientists who are applying predictive algorithms to health-related behavioral and social sciences data.

OBSSR Methodology Workshop: Emerging Non-Traditional Survey Data Collections

August 25, 2017 | NIH Videocast Archive  | Seminar Description: Surveys via in-person interviews, telephone interviews, and mail questionnaires have been a traditional mainstay in the behavioral and social sciences for many decades. Vast technological and societal changes are driving inevitable change in the world of health research data collection. The past decade has seen an extraordinary expansion in the range of new technologies being applied in survey research. This seminar focused on health research involving emerging, non-traditional survey methodologies including: online non-probability panel surveys; survey participant recruitment via social media platforms; opportunities for using text messaging or SMS to recruit and engage survey participants for health research; "passive data collection” obtained from mobile devices or sensor devices"; and the value of social media or web data in the context of health survey research.