Search Results

Annotation: This short presentation discusses the information needs of MCH professionals and identifies distinct online resources to address those needs, from pop and professional sources such as Google, PubMed, and Wikipedia to grant-supported resources that address MCHB topical programs and initiatives. Topics include data warehouses, research centers, epidemiology sites, professional and membership organizations

Learning Objectives: • Identify information needs of professionals • Explain the differences between types of online resources • Differentiate between trusted and questionable online resources • Understand where to go to find additional resources

Annotation: This webinar highlights 3 resources that can assist researchers in using early care and education administrative data. Additionally, it provides researchers' perspectives based on experience throughout their projects. The first resource examines the benefits of and strategies for developing collaborative partnerships with researchers and state agencies. The second resource was created to help researchers determine the feasibility of using administrative data by posing questions related to data policies and procedures, data contacts and coordination, and data usability. The third resource presents topics to consider when preparing to analyze administrative data to address child care and early education research questions.

Annotation: In a world where data are proliferating as never before, more policymakers are relying on research evidence to serve the public good. What are the research methods that offer the most useful data to policymakers in this rapidly changing landscape? Find out in #EvidenceInsight, a new video series from Mathematica Policy Research. Randomized controlled trials (RCTs) are considered the gold standard of rigorous research design. For decades, Mathematica researchers have designed, executed, and replicated large-scale RCTs in many different policy and program areas. Today, greater availability of high quality administrative data—along with an abundance of emerging technologies—have increased demand for faster program evaluation with equally robust results. This demand, coupled with shrinking resources, has motivated researchers to consider new methods that are more efficient and less expensive than RCTs, but just as reliable. This video series is designed to help policymakers who need access to strong evidence. After a brief video describing the series, additional videos address these topics: Bayesian Methods: A Faster, Probabilistic Approach to Research Design. Adaptive Randomization: A Fresh Perspective on Traditional Research Design. Rapid-Cycle Evaluation: Determining What Works in Less Time. Predictive Analytics: Transforming Decision Making in Three Steps.

Annotation: This learning bundle uses the CDC framework as a conceptual model to organize learning opportunities. It presents introductions to the six steps of program evaluation in short video podcasts. You can also download materials from the CDC about each step. After reviewing the introductory material, you can access additional learning opportunities to gain knowledge and skills related to each step of the framework. For additional resources this learning bundle also includes an Evaluation Toolkit developed by NCEMCH that includes an evaluation primer, a collection of key resources, and an interactive Choose-and-Use tool to assist users in finding instructions on how to conduct evaluations and examples of successful evaluations from the field.

Annotation: This webinar presents database and coding typology, and illustrates how the database can be used to integrate multiple studies at the item level to examine adolescent predictors of adult health outcomes.

Learning Objectives:

Annotation: Where are infections spreading? How many people will be affected? What are some different ways to stop the spread of an epidemic? In a process known as modeling, scientists analyze data using complex mathematical methods to provide answers to these and other questions during an emergency response. Models provide the foresight that can help decision makers better prepare for the future.

Learning Objectives:

Annotation: This webinar on evidence-informed decision-making (EIDM) draws on The National Collaborating Centre for Methods and Tools (NCCMT)'s seven-step process, with reference to methods and tools for each step that are specific to mental health practice. The National Collaborating Centre for Methods and Tools (NCCMT) is one of six National Collaborating Centres for Public Health in Canada with a collective mandate to strengthen public health in the country.

Annotation: This webinar presents an in-depth analysis of instrumental variables (IV) based methods for inference in public health research. Topics such as the motivation and intuition of instrumental variables analysis, how to define IV, IV assumptions, interpretation of the IV effect estimate and IV assumption violations are addressed.

Learning Objectives: • Understand the situations in which instrumental variables can be useful in health research • Describe the assumptions and interpretations for instrumental variables based effect estimates • Learn examples of instrumental variables used in health research, including policy differences, genetic variants, and other examples • Learn basic concepts for implementing instrumental variables analyses and understand whether the methods are feasible in your research setting

Annotation: Leading with the cold hard facts can be a reassuring method to know you’re making the best decisions for your organization. But this can be challenging at times when you have to discern between “good” data and “bad” data. Harnessing methods for data analysis is easier said than done, but it can make all the difference in leading your organization. This course is led by Dr. Henry Thibodeaux, Assessment and Evaluations Leader in the Office of Personnel Management, and Allen Schweyer, Executive Director of Talent Management and Leadership University. The course comprises an overview and introduction, 5 lessons, and a post-course survey.

Learning Objectives: • Discern the difference between correlation and causation. • Understand the importance of framing data analysis with precise questions and objectives. • Learn to distinguish “good” data from “bad” data. • Gain familiarity with several common data analysis techniques and where they should be used.

Continuing Education: GovLoop is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors.

Annotation: This introductory video explains the basics of performance measurement; the importance of performance measurement; when to use performance measures; and how to develop good performance measures.

Learning Objectives: • Define performance measurement. • Learn why performance measurement is important. • Understand when to use performance measures. • Discuss steps to developing good performance measures.