Training

Training

Featured trainings

Video

California Health Interview Survey (CHIS) Data User Training: August 2024

The California Health Interview Survey (CHIS) hosted a data user training webinar to demonstrate how to use AskCHIS™ Neighborhood Edition (NE). This training highlights the refreshed dashboard (now in Tableau) and creating maps and tables.
Man on monitor screen, looking at AskCHIS NE dashboard

Video

Data Access Center (DAC) Online: How to Submit a Project Application

We are excited to announce that the new version of DAC Online is now live!

Screenshot that reads "Data Access Center (DAC) Online Project Application Overview"

Video

California Health Interview Survey (CHIS) Data User Training: October 2023

Interested in learning how to access California Health Interview Survey (CHIS) data? Following the CHIS annual data release on October 4, 2023, the UCLA Center for Health Policy Research hosted a data user training webinar to demonstrate how to use CHIS' free online data tools.
Photo of First slide of Training webinar with Jacob Rosalez

Search trainings

Legal and Regulatory Guidance for Racial/Ethnic Data Disaggregation in Health Data Sets

Legal and Regulatory Guidance for Racial/Ethnic Data Disaggregation in Health Data Sets

The National Network of Health Surveys, part of the UCLA CHPR, offers a series of workshops designed to improve the disaggregation of race and ethnicity measures in health data sources. Its goal is to boost the number of subpopulation categories made available to key constituencies working to improve health equity.

Imputation Methods for Increasing Racial/Ethnic Data Disaggregation

Imputation Methods for Increasing Racial/Ethnic Data Disaggregation

Surveys offer first-person, self-reported data. But sometimes, survey data contains missing data in some response sets. Imputation is a technique for replacing missing data with a substitute value that retains the integrity of the full data set. This workshop discusses imputation strategies for racial/ethnic data disaggregation.

Disaggregating Data Decision-making: Who, What, When?

Disaggregating Data Decision-making: Who, What, When?

This panel discusses key considerations when choosing to expand racial/ethnic categories in health data sets. Researchers shared their decision-making on what categories to include, what question-wording gets at the information desired, and what conditions should be present to trigger expanded racial/ethnic data disaggregation.

Strategies in Mitigating Disclosure Risk in Disaggregated Racial/Ethnic Data

Strategies in Mitigating Disclosure Risk in Disaggregated Racial/Ethnic Data

This presentation reviews several commonly used statistical disclosure limitation (SDL) techniques that are used to protect sensitive government data. The presenter demonstrates these techniques in practice by presenting case studies and shares useful resources for further guidance on available techniques used to protect its data.

Collection and Reporting of Data on the Multiracial Population

Collection and Reporting of Data on the Multiracial Population

This seminar discusses trends in capturing multiracial identity and the implications for researchers. As more surveys allow respondents to identify with the multiple races that reflect their identity, data managers are working on strategies for developing the best methods to most accurately reflect the population.

Coding and Machine-Learning Strategies for Disaggregated Racial/Ethnic Data

Coding and Machine-Learning Strategies for Disaggregated Racial/Ethnic Data

This seminar helps the audience understand the capacity for machine learning, addresses the barriers to data disaggregation, and discusses what to consider when using technology as a solution to improve your data disaggregation.

Advanced Weighting Strategies for Disaggregated Racial/Ethnic Data

Advanced Weighting Strategies for Disaggregated Racial/Ethnic Data

This seminar discusses the ways in which survey weighting processes can and cannot be used to improve the representativeness of data on small and disaggregated populations within population surveys.

Strategies in Mitigating Disclosure Risk in Disaggregated Racial/Ethnic Data

User Experience Accessing Disaggregated Racial/Ethnic Data

This presentation reviews commonly used statistical disclosure limitation (SDL) techniques that are used to protect sensitive government data, demonstrates these techniques in practice, and shares several useful resources that the audience can refer to for further guidance on available techniques used to protect its data.

Question Wording & Response Sets for Disaggregated Racial/Ethnic Data

Question Wording & Response Sets for Disaggregated Racial/Ethnic Data

Disaggregated race/ethnicity data is needed to expose health inequities and inform policies and programs to address those inequities. In this webinar, four researchers talk about data disaggregation and working with collecting data for populations in New York, one of the most diverse populations in the U.S. and in the world.

Disaggregated Racial/Ethnic Data: Considerations for Data Collection and Processing

Disaggregated Racial/Ethnic Data: Considerations for Data Collection and Processing

The workshop explores effective strategies to ensure your data represents the true diversity of your population. The discussion includes decision-making approaches, community engagement strategies, and opportunities to discuss specific project needs.