Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. Sharing Data with the National Addiction & HIV Data Archive Program (NAHDAP): Meeting the Requirements of the New NIH Data Sharing Policy (December 5, 2022 1:00pm) https://events.umich.edu/event/101349 101349-21801251@events.umich.edu Event Begins: Monday, December 5, 2022 1:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

The National Addiction & HIV Data Archive Program (NAHDAP) is a data archive at ICPSR, the University of Michigan, which facilitates research on drug addiction and HIV infection by acquiring, enhancing, preserving, and sharing data produced by research grants, particularly those funded by the National Institute on Drug Abuse (NIDA). NAHDAP staff work with researchers to safely share their data, including sensitive data, for long-term preservation and secure access by the research community. NAHDAP provides services to help grantees meet the data sharing requirements for their current NIH grants, and to help future grantees plan to meet data sharing requirements for upcoming grants, particularly in light of the new NIH Data Management and Sharing Policy.

In this webinar, you will learn:
● What services NAHDAP provides and what types of data are a good fit for NAHDAP
● Why you should consider sharing your data with NAHDAP
● How NAHDAP protects sensitive or restricted data
● How you can use NAHDAP to meet the data sharing requirements of your NIH grant
● How to write an NIH Data Management and Sharing Plan as a social or behavioral scientist using NAHDAP

Who should attend?
This webinar is free and open to the public. It will be most useful for current or prospective NIH grantees, with a research focus on drug use, addiction, and/or HIV topics, who expect to share data in the future.

Register here: https://myumi.ch/9P24g

]]>
Presentation Mon, 14 Nov 2022 16:21:34 -0500 2022-12-05T13:00:00-05:00 2022-12-05T14:00:00-05:00 Off Campus Location Institute for Social Research Presentation This image includes the title, date/time, and registration link of the webinar. It also includes a stock image of two people smiling.
MPSDS JPSM Seminar Series - The Role of Data Collection in Population Science: Contemporary Studies from ABCD to HBCD (January 20, 2023 2:00pm) https://events.umich.edu/event/103756 103756-21807773@events.umich.edu Event Begins: Friday, January 20, 2023 2:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
February 1, 2023
12:00 - 1:00 EST

The Role of Data Collection in Population Science: Contemporary Studies from ABCD to HBCD

Abstract

Recently nationwide consortiums of multiple research sites have conducted multi-modal, longitudinal cohort studies and provided unprecedented data sources for population science research. For example, the Adolescent Brain Cognitive Development (ABCD) Study has collected data from 11,880 children ages 9-10 across 21 U.S. research sites, as the largest long-term study of brain development and child health; and the Healthy Brain and Child Development (HBCD) Study will enroll 7,500 pregnant women across 25 research sites and follow them from pregnancy through early childhood, as the largest long-term study of early brain and child development in the U.S. Both studies aim to reflect the sociodemographic diversity of the target population to enable characterization of natural variability and trajectories. Without probability sampling as the touchstone for randomization-based inferences, the data quality and analysis validity require rigorous evaluations and potentially rely on untestable assumptions. The data collection process also presents various challenges during practical operation.

In this talk, I look into both inference and design schemes to study the impact of data collection on population science. First, using the ABCD study as an example of secondary data analysis, I discuss inference approaches focusing on multilevel regression and poststratification for population generalizability and latent subgroup detection for population heterogeneity in brain activity and association studies. Second, I introduce the HBCD study design. HBCD also aims to include individuals demographically and behaviorally similar to those in the substance exposure group, but without exposure, to enable valid causal inference in a non-experimental study design. I discuss our proposed weighting, matching, and modeling strategies to leverage analysis goals to inform the design and dashboard monitoring for adaptive sample enrollment.

Bio

Yajuan Si is a Research Associate Professor in the Institute for Social Research at the University of Michigan. Dr Si’s research lies in cutting-edge methodology development in streams of Bayesian statistics, linking design- and model-based approaches for survey inference, missing data analysis, confidentiality protection involving the creation and analysis of synthetic datasets, and causal inference with observational data.

Michigan Program in Survey and Data Science (MPSDS)
The University of Michigan Program in Survey Methodology was established in 2001 seeking to train future generations of survey and data scientists. In 2021, we changed our name to the Michigan Program in Survey and Data Science. Our curriculum is concerned with a broad set of data sources including survey data, but also including social media posts, sensor data, and administrative records, as well as analytic methods for working with these new data sources. And we bring to data science a focus on data quality — which is not at the center of traditional data science. The new name speaks to what we teach and work on at the intersection of social research and data. The program offers doctorate and master of science degrees and a certificate through the University of Michigan. The program's home is the Institute for Social Research, the world's largest academically-based social science research institute.

Summer Institute in Survey Research Techniques (SISRT)
The mission of the Summer Institute is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948, and has offered such courses every summer since. Graduate-level courses through the Program in Survey and Data Science are offered from June 5 through July 28 and available to enroll in as a Summer Scholar.

The Summer Institute uses the sample survey as the basic instrument for the scientific measurement of human activity. It presents sample survey methods in courses designed to meet the educational needs of those specializing in social and behavioral research such as professionals in business, public health, natural resources, law, medicine, nursing, social work, and many other domains of study.

]]>
Lecture / Discussion Fri, 20 Jan 2023 14:51:50 -0500 2023-01-20T14:00:00-05:00 2023-01-20T15:00:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
MPSDS JPSM Seminar Series - The Role of Data Collection in Population Science: Contemporary Studies from ABCD to HBCD (January 20, 2023 2:00pm) https://events.umich.edu/event/103756 103756-21807774@events.umich.edu Event Begins: Friday, January 20, 2023 2:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
February 1, 2023
12:00 - 1:00 EST

The Role of Data Collection in Population Science: Contemporary Studies from ABCD to HBCD

Abstract

Recently nationwide consortiums of multiple research sites have conducted multi-modal, longitudinal cohort studies and provided unprecedented data sources for population science research. For example, the Adolescent Brain Cognitive Development (ABCD) Study has collected data from 11,880 children ages 9-10 across 21 U.S. research sites, as the largest long-term study of brain development and child health; and the Healthy Brain and Child Development (HBCD) Study will enroll 7,500 pregnant women across 25 research sites and follow them from pregnancy through early childhood, as the largest long-term study of early brain and child development in the U.S. Both studies aim to reflect the sociodemographic diversity of the target population to enable characterization of natural variability and trajectories. Without probability sampling as the touchstone for randomization-based inferences, the data quality and analysis validity require rigorous evaluations and potentially rely on untestable assumptions. The data collection process also presents various challenges during practical operation.

In this talk, I look into both inference and design schemes to study the impact of data collection on population science. First, using the ABCD study as an example of secondary data analysis, I discuss inference approaches focusing on multilevel regression and poststratification for population generalizability and latent subgroup detection for population heterogeneity in brain activity and association studies. Second, I introduce the HBCD study design. HBCD also aims to include individuals demographically and behaviorally similar to those in the substance exposure group, but without exposure, to enable valid causal inference in a non-experimental study design. I discuss our proposed weighting, matching, and modeling strategies to leverage analysis goals to inform the design and dashboard monitoring for adaptive sample enrollment.

Bio

Yajuan Si is a Research Associate Professor in the Institute for Social Research at the University of Michigan. Dr Si’s research lies in cutting-edge methodology development in streams of Bayesian statistics, linking design- and model-based approaches for survey inference, missing data analysis, confidentiality protection involving the creation and analysis of synthetic datasets, and causal inference with observational data.

Michigan Program in Survey and Data Science (MPSDS)
The University of Michigan Program in Survey Methodology was established in 2001 seeking to train future generations of survey and data scientists. In 2021, we changed our name to the Michigan Program in Survey and Data Science. Our curriculum is concerned with a broad set of data sources including survey data, but also including social media posts, sensor data, and administrative records, as well as analytic methods for working with these new data sources. And we bring to data science a focus on data quality — which is not at the center of traditional data science. The new name speaks to what we teach and work on at the intersection of social research and data. The program offers doctorate and master of science degrees and a certificate through the University of Michigan. The program's home is the Institute for Social Research, the world's largest academically-based social science research institute.

Summer Institute in Survey Research Techniques (SISRT)
The mission of the Summer Institute is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948, and has offered such courses every summer since. Graduate-level courses through the Program in Survey and Data Science are offered from June 5 through July 28 and available to enroll in as a Summer Scholar.

The Summer Institute uses the sample survey as the basic instrument for the scientific measurement of human activity. It presents sample survey methods in courses designed to meet the educational needs of those specializing in social and behavioral research such as professionals in business, public health, natural resources, law, medicine, nursing, social work, and many other domains of study.

]]>
Lecture / Discussion Fri, 20 Jan 2023 14:51:50 -0500 2023-01-20T14:00:00-05:00 2023-01-20T15:00:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
Ahead of the Curve featuring Dr. Sanjay Gupta (February 1, 2023 4:00pm) https://events.umich.edu/event/103680 103680-21807637@events.umich.edu Event Begins: Wednesday, February 1, 2023 4:00pm
Location: Off Campus Location
Organized By: School of Public Health

Dr. Sanjay Gupta has become synonymous with health communications over the past two decades in his roles as CNN's Chief Medical Correspondent, podcast host, and author. The two-time University of Michigan graduate ('90, MD '93) continues to work as a practicing neurosurgeon in Atlanta as well. Gupta will join Dean DuBois Bowman for a conversation on leadership, communication, and trust during this edition of the "Ahead of the Curve" speaker series. The event will be streamed, and is free and open to the public. Please register to receive the streaming link.

]]>
Livestream / Virtual Tue, 31 Jan 2023 11:34:25 -0500 2023-02-01T16:00:00-05:00 2023-02-01T17:00:00-05:00 Off Campus Location School of Public Health Livestream / Virtual Dr. Sanjay Gupta
MPSDS JPSM Seminar Series - The Evolution of the Use of Models in Survey Sampling (February 15, 2023 12:00pm) https://events.umich.edu/event/103587 103587-21807518@events.umich.edu Event Begins: Wednesday, February 15, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
February 15, 2023
12:00 - 1:00 EST

Richard Valliant, PhD, is a research professor emeritus at the Institute for Social Research, University of Michigan, and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an associate editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology.

The Evolution of the Use of Models in Survey Sampling

The use of models in survey estimation has evolved over the last five (or more) decades. This talk will trace some of the developments over time and attempt to review some of the history. Consideration of models for estimating descriptive statistics began as early as the 1940's when Cochran and Jessen proposed linear regression estimators of means. These were early examples of model-assisted estimation since the properties of the Cochran-Jessen estimators were calculated with respect to a random sampling distribution. Model-thinking was used informally through the 1960's to form ratio and linear regression estimators that could in some applications reduce design variances.

In a 1963 Australian Journal of Statistics paper, Brewer presented results for a ratio estimator that were entirely based on a super population model. Royall (Biometrika 1970 and later papers) formalized the theory for a more general prediction approach using linear models. Since that time, the use of models is ubiquitous in the survey estimation literature and has been extended to nonparametric, empirical likelihood, Bayesian, small area, machine learning, and other approaches. There remains a considerable gap between the more advanced techniques in the literature and the methods commonly used in practice.

In parallel to the model developments, the design-based, randomization approach was dominating official statistics in the US largely due to the efforts of Morris Hansen and his colleagues at the US Census Bureau. In 1937 Hansen and others at the Census Bureau designed a follow-on sample survey to a special census of the employed and partially employed because response to the census was incomplete and felt to be inaccurate. The sample estimates were judged to be more trustworthy than those of the census itself. This began Hansen’s career-long devotion to random sampling as the only trustworthy method for obtaining samples from finite populations and for making inferences.

Model-assisted estimation, as discussed in the 1992 book by Särndal, Swensson, and Wretman is a type of compromise where models are used to construct estimators while a randomization distribution is used to compute properties like means and variances. This thinking has led to the popularity of doubly robust approaches where the goal is to have estimators with good properties with respect to both a randomization and a model distribution.

The field has now reached a troubling crossroads in which response rates to many types of surveys have plummeted and nonprobability datasets are touted as a way of obtaining reasonable quality data at low cost. Sophisticated model-based mathematical methods have been developed for estimation from nonprobability samples. In some applications, e.g., administrative data files that are incomplete due to late reporting, these methods may work well. However, in others the quality of nonprobability sample data is irremediably bad as illustrated by Kennedy in her 2022 Hansen lecture. In some situations, we are back in Morris' 1937 situation where standard approaches no longer work. Methods are needed to evaluate whether acceptable estimates can be made from the most suspect data sets. Nonetheless. nonprobability datasets are readily available now, and it is up to the statistical profession to develop good methods for using them.

Michigan Program in Survey and Data Science (MPSDS)
The University of Michigan Program in Survey Methodology was established in 2001 seeking to train future generations of survey and data scientists. In 2021, we changed our name to the Michigan Program in Survey and Data Science. Our curriculum is concerned with a broad set of data sources including survey data, but also including social media posts, sensor data, and administrative records, as well as analytic methods for working with these new data sources. And we bring to data science a focus on data quality — which is not at the center of traditional data science. The new name speaks to what we teach and work on at the intersection of social research and data. The program offers doctorate and master of science degrees and a certificate through the University of Michigan. The program's home is the Institute for Social Research, the world's largest academically-based social science research institute.

Summer Institute in Survey Research Techniques (SISRT)
The mission of the Summer Institute is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948, and has offered such courses every summer since. Graduate-level courses through the Program in Survey and Data Science are offered from June 5 through July 28 and available to enroll in as a Summer Scholar.

]]>
Lecture / Discussion Wed, 18 Jan 2023 15:55:19 -0500 2023-02-15T12:00:00-05:00 2023-02-15T13:00:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
MPSDS JPSM Seminar Series - Network Size: Measurement and Errors (March 8, 2023 12:00pm) https://events.umich.edu/event/104021 104021-21808283@events.umich.edu Event Begins: Wednesday, March 8, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
March 8, 2023
12:00 - 1:00 EST

Abstract
Respondent driven sampling (RDS) is a sampling method that leverages the respondents' networks to reach more members of the target population. In RDS, the size of the respondents' social network (also known as personal network size (PNS), or respondent's degree) is important in both the study operations and in estimation. A commonly used estimation of degree is the self-reported data from the interview, which typically has substantial measurement error, and, specifically, is found to be frequently rounded to a multiple of five. Measurement error in the PNS can introduce biased estimates for RDS, especially if the misreporting of the degree is associated with the outcome to be estimated.

This brown bag will present two related studies on the measurement of PNS. The first study uses two sets of data; 1) semi-structured in-depth interviews conducted over Zoom with 19 adult respondents of various ages, gender identities (transgender, nonbinary, cisgender), race, and sexual orientations (gay, lesbian, bi), 2) an RDS web survey targeting the adult LGBT population (n = 394). Thematic analysis conducted on the semi-structured interview transcripts showed a large variation in how respondents define "knowing" someone; for some respondents, it covers a larger network than the "recruitable" network (the network of people respondents are likely to think of recruiting to an RDS study). Meanwhile, the web-RDS shows that the more restrictive PNS questions yielded more realistic ranges for a "recruitable" network, with less proportion of rounded responses on the more restrictive PNS questions.

Motivated by the desire to improve the degree estimation in RDS, the second study presents a latent variable model to make inferences about participants’ actual degrees and potential reporting behaviors. Specifically, individual-level degree estimation will be obtained by revealing the association between the actual degree and relevant personal characteristics and blending their response to “How many [a particular sub-population] do you know in the target population?” Simulation studies demonstrate that the proposed method delivers sensible estimations about the individual degree.

Bios
Ai Rene Ong works at American Institutes for Research (AIR) as a Researcher/Survey Methodologist in the area of Education Statistics. She graduated with a PhD in Survey Methodology from the University of Michigan in 2022. Her dissertation research was on the measurement of network size and the mechanism of peer recruitment in Respondent Driven Sampling — a sampling method typically used for hard-to-sample populations.

Yibo Wang is a 3rd year Ph.D. candidate from the department of Biostatistics. She is now working with Dr. Sunghee Lee and Dr. Michael Elliott on measurement estimation in Respondent Driven Sampling

Michigan Program in Survey and Data Science (MPSDS)
The University of Michigan Program in Survey Methodology was established in 2001 seeking to train future generations of survey and data scientists. In 2021, we changed our name to the Michigan Program in Survey and Data Science. Our curriculum is concerned with a broad set of data sources including survey data, but also including social media posts, sensor data, and administrative records, as well as analytic methods for working with these new data sources. And we bring to data science a focus on data quality — which is not at the center of traditional data science. The new name speaks to what we teach and work on at the intersection of social research and data. The program offers doctorate and master of science degrees and a certificate through the University of Michigan. The program's home is the Institute for Social Research, the world's largest academically-based social science research institute.

Summer Institute in Survey Research Techniques (SISRT)
The mission of the Summer Institute is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948, and has offered such courses every summer since. Graduate-level courses through the Program in Survey and Data Science are offered from June 5 through July 28 and available to enroll in as a Summer Scholar.

The Summer Institute uses the sample survey as the basic instrument for the scientific measurement of human activity. It presents sample survey methods in courses designed to meet the educational needs of those specializing in social and behavioral research such as professionals in business, public health, natural resources, law, medicine, nursing, social work, and many other domains of study.

]]>
Lecture / Discussion Wed, 25 Jan 2023 14:08:47 -0500 2023-03-08T12:00:00-05:00 2023-03-08T13:00:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
MPSDS JPSM Seminar Series - How to ask for consent to data linkage: Things we’ve learnt (March 15, 2023 12:00pm) https://events.umich.edu/event/104312 104312-21808815@events.umich.edu Event Begins: Wednesday, March 15, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
March 15, 2023
12:00 - 1:00 EST

The Zoom call will be locked 10 minutes after the start of the presentation.

Annette Jäckle is Professor of Survey Methodology at the Institute for Social and Economic Research at the University of Essex, UK and Associate Director of Innovations and Co-Investigator of the UK Household Longitudinal Study: Understanding Society. Her research interests are in methodology of data collection for longitudinal studies, mixed mode data collection, questionnaire design, respondent consent to data linkage, and new ways of using mobile devices for survey data collection.

Abstract
Data linkage usually requires informed consent of respondents, whether for legal or ethical reasons. A common problem is that when consent questions are asked in self-completion surveys, respondents are much less likely to consent than when they are asked for consent in interviewer administered surveys. In the existing literature, predictors of consent are mostly inconsistent, between studies, but also between different consents asked within one study. In addition, experiments with the wording of consent questions have often had no or inconsistent effects. Why is this? And what can be done to increase informed consent to data linkage? This presentation provides an overview of what we have learnt from qualitative in-depth interviews and a series of experiments implemented in two UK probability household panels (the Understanding Society Innovation Panel and COVID-19 study) and in the UK PopulusLive online access panel. We address the following questions. (1) How do respondents decide whether to consent to data linkage? (2) Why are respondents less likely to consent in web than CAPI surveys? (3) How best to ask for multiple consents within a survey? (4) Which wording and formats affect informed consent and why? We end the overview with a summary of the practical implications for how best to ask for consent to data linkage.

Michigan Program in Survey and Data Science (MPSDS)
The University of Michigan Program in Survey Methodology was established in 2001 seeking to train future generations of survey and data scientists. In 2021, we changed our name to the Michigan Program in Survey and Data Science. Our curriculum is concerned with a broad set of data sources including survey data, but also including social media posts, sensor data, and administrative records, as well as analytic methods for working with these new data sources. And we bring to data science a focus on data quality — which is not at the center of traditional data science. The new name speaks to what we teach and work on at the intersection of social research and data. The program offers doctorate and master of science degrees and a certificate through the University of Michigan. The program's home is the Institute for Social Research, the world's largest academically-based social science research institute.

Summer Institute in Survey Research Techniques (SISRT)
The mission of the Summer Institute is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948, and has offered such courses every summer since. Graduate-level courses through the Program in Survey and Data Science are offered from June 5 through July 28 and available to enroll in as a Summer Scholar.

The Summer Institute uses the sample survey as the basic instrument for the scientific measurement of human activity. It presents sample survey methods in courses designed to meet the educational needs of those specializing in social and behavioral research such as professionals in business, public health, natural resources, law, medicine, nursing, social work, and many other domains of study.

]]>
Lecture / Discussion Wed, 15 Mar 2023 08:14:34 -0400 2023-03-15T12:00:00-04:00 2023-03-15T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
Virtual Webinar: The US COVID-19 County Policy Database: a novel resource to support pandemic-related research (March 23, 2023 3:00pm) https://events.umich.edu/event/105335 105335-21811573@events.umich.edu Event Begins: Thursday, March 23, 2023 3:00pm
Location:
Organized By: Inter-university Consortium for Political and Social Research

It is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. While prior work has focused on characterizing and analyzing the effects of federal and state policies, there has been limited systematic evaluation of variation in U.S. local (i.e., county) COVID-19-related policies. This virtual webinar introduces the U.S. COVID-19 County Policy (UCCP) Database, whose objective is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies. Data collection is still ongoing for this NIH and PCORI-funded database. Dr. Hamad will describe the data collection methods and some preliminary results. Register for this virtual webinar here: https://myumi.ch/NkmZk

]]>
Workshop / Seminar Wed, 22 Feb 2023 16:21:25 -0500 2023-03-23T15:00:00-04:00 2023-03-23T16:00:00-04:00 Inter-university Consortium for Political and Social Research Workshop / Seminar A campaign photo that includes information about the Webinar
MPSDS JPSM Seminar Series - Assessing Cross-Cultural Comparability of Self-Rated Health and Its Conceptualization through Web Probing (April 5, 2023 12:00pm) https://events.umich.edu/event/103497 103497-21807352@events.umich.edu Event Begins: Wednesday, April 5, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
April 5, 2022
12:00 - 1:00 EST

Stephanie Morales is a second-year Ph.D. student at the University of Michigan's Program in Survey and Data Science. She holds a BA in Psychology and an MA in Sociology. She is interested in cross-cultural surveys, measurement error in data collection with racial/ethnic minorities, and adaptive survey design.

Assessing Cross-Cultural Comparability of Self-Rated Health and Its Conceptualization through Web Probing

Self-rated health (SRH) is a widely used question across different fields, as it is simple to administer yet has been shown to predict mortality. SRH asks respondents to rate their overall health typically using Likert-type response scales (i.e., excellent, very good, good, fair, poor). Although SRH is commonly used, few studies have examined its conceptualization from the respondents’ point of view and even less so for differences in its conceptualization across diverse populations. We aim to assess the comparability of SRH across different cultural groups by investigating the factors that respondents consider when responding to the SRH question. We included an open-ended probe asking what respondents thought when responding to SRH in web surveys conducted in five countries: Great Britain, Germany, the U.S., Spain, and Mexico. In the U.S., we targeted six racial/ethnic and linguistic groups: English-dominant Koreans, Korean-dominant Koreans, English-dominant Latinos, Spanish-dominant Latinos, non-Latino Black Americans, and non-Latino White Americans. One novelty of our study is allowing multiple attribute codes (e.g., health behaviors, illness) per respondent and tone (e.g., in the direction of positive or negative health or neutral) of the probing responses for each attribute, allowing us 1) to assess respondents’ thinking process holistically and 2) to examine whether and how respondents mix attributes. Our study compares the number of reported attributes and tone by cultural groups and integrates SRH responses in the analysis. This study aims to provide a deeper understanding of SRH by revealing the cognitive processes among diverse populations and is expected to shed light on its cross-cultural comparability.

Michigan Program in Survey and Data Science (MPSDS)
The University of Michigan Program in Survey Methodology was established in 2001 seeking to train future generations of survey and data scientists. In 2021, we changed our name to the Michigan Program in Survey and Data Science. Our curriculum is concerned with a broad set of data sources including survey data, but also including social media posts, sensor data, and administrative records, as well as analytic methods for working with these new data sources. And we bring to data science a focus on data quality — which is not at the center of traditional data science. The new name speaks to what we teach and work on at the intersection of social research and data. The program offers doctorate and master of science degrees and a certificate through the University of Michigan. The program's home is the Institute for Social Research, the world's largest academically-based social science research institute.

Summer Institute in Survey Research Techniques (SISRT)
The mission of the Summer Institute is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948, and has offered such courses every summer since. Graduate-level courses through the Program in Survey and Data Science are offered from June 5 through July 28 and available to enroll in as a Summer Scholar.

The Summer Institute uses the sample survey as the basic instrument for the scientific measurement of human activity. It presents sample survey methods in courses designed to meet the educational needs of those specializing in social and behavioral research such as professionals in business, public health, natural resources, law, medicine, nursing, social work, and many other domains of study.

]]>
Lecture / Discussion Mon, 16 Jan 2023 17:00:12 -0500 2023-04-05T12:00:00-04:00 2023-04-05T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
Analyzing Data on Arts and Culture in Large-scale Health, Education, and Labor Studies (April 18, 2023 1:00pm) https://events.umich.edu/event/106098 106098-21813746@events.umich.edu Event Begins: Tuesday, April 18, 2023 1:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

This webinar, hosted by the National Archive of Data on Arts & Culture (NADAC) and moderated by Melissa Menzer, a Senior Program Analyst in the Office of Research & Analysis at the NEA, will introduce participants to NEA research priority areas, their research grant funding opportunities, and several examples of research projects funded by the NEA research awards that use datasets archived or cataloged in NADAC. Presentations from the webinar panelists will cover the Health and Retirement Study, various datasets from the National Center of Education Statistics, and the Strategic National Arts Alumni Project (SNAAP). Panelists will share their successes and challenges working with these datasets and/or working with data repositories to analyze data. Panelists include Jennifer Novak-Leonard (University of Illinois at Urbana-Champaign), Kenneth Elpus (University of Maryland School of Music), and Hei Wan (Karen) Mak (World Health Organization Collaborating Centre on Arts and Health). This webinar is free and open to the public. To register, go to https://myumi.ch/1A6pb.

]]>
Presentation Sun, 12 Mar 2023 23:18:32 -0400 2023-04-18T13:00:00-04:00 2023-04-18T14:30:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Webinar: Analyzing Data on Arts and Culture in Large-scale Health, Education, and Labor Studies
Michigan Genomics Initiative Symposium 2023 (September 29, 2023 10:00am) https://events.umich.edu/event/108642 108642-21820240@events.umich.edu Event Begins: Friday, September 29, 2023 10:00am
Location: Public Health I (Vaughan Building)
Organized By: Precision Health

The Michigan Genomics Initiative (MGI) Symposium is an annual event for faculty, researchers & students at U-M; a platform for fostering collaboration, sharing knowledge, and exploring the latest advancements in leveraging MGI data to improve human health.

Building upon the success of our previous symposiums, we’re implementing some new ideas too, including trainee lightning talks and collaboration pitches.

Please join us for our 3rd annual MGI Symposium!

]]>
Conference / Symposium Thu, 10 Aug 2023 10:15:49 -0400 2023-09-29T10:00:00-04:00 2023-09-29T15:00:00-04:00 Public Health I (Vaughan Building) Precision Health Conference / Symposium Scan the QR code to learn more and/or register
MPSDS JPSM Seminar Series - Using Partially Synthetic Frames to Evaluate Alternative Sample Designs for Estimating a Rare Business Characteristic (October 4, 2023 1:00pm) https://events.umich.edu/event/113114 113114-21830116@events.umich.edu Event Begins: Wednesday, October 4, 2023 1:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 4, 2023
12:00 - 1:00 pm EDT

In person, room 1070 Institute for Social Research, and via Zoom. The Zoom call will be locked 10 minutes after the start of the presentation.

Using Partially Synthetic Frames to Evaluate Alternative Sample Designs for Estimating a Rare Business Characteristic

Katherine Jenny Thompson, U.S. Census Bureau
Hang Joon Kim (University of Cincinnati)
Stephen Kaputa (U.S. Census Bureau)

In the “traditional'” finite population sampling framework, the sample designer has a complete list (frame) of eligible units with classification information and auxiliary variables related to surveyed characteristics. In our setting, the frame auxiliary variables are weakly related to the survey characteristic, which is not present for most units. Hence, using frame auxiliary variables to assess survey design efficacy can be misleading. Instead, we propose generating multiple partially synthetic frames, modeling characteristic values for each unit on the frame, then drawing repeated samples from each synthetic frame using the candidate sample design(s) to assess finite sample performance for each design within and between the synthetic frames. Focusing on establishment survey data, we illustrate our proposed approach on a subset of industries surveyed annually by the Business Enterprise Research and Development Survey.

Katherine Jenny Thompson is the Senior Mathematical Statistician in the Economic Directorate of the Census Bureau. Jenny holds a masters of science degree in Applied Statistics from the George Washington University and an bachelor or arts degree in Mathematics from Oberlin College. She is an American Statistical Association (ASA) Fellow, an elected member of the International Statistics Institute, and the Vice President Elect of the ASA. She is the Survey Statistics Editor-in-Chief of the Journal of Survey Statistics and Methodology and an Associate Editor for the Journal of Official Statistics. She has published papers on a variety of topics related to complex surveys in several journals, including the Journal of Official Statistics, Journal of the Royal Statistical Society (Series A), Survey Methodology, Annals of Applied Statistics, International Statistical Review, Journal of Survey Sampling and Methodology, and Public Opinion Quarterly.

]]>
Lecture / Discussion Tue, 26 Sep 2023 13:43:03 -0400 2023-10-04T13:00:00-04:00 2023-10-04T14:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
MPSDS JPSM Seminar Series - New data, new questions, old problems? Online behavioral data in social science research (October 11, 2023 12:00pm) https://events.umich.edu/event/113445 113445-21831024@events.umich.edu Event Begins: Wednesday, October 11, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 11, 2023
12:00 - 1:00 pm EDT

In person, room 1070 Institute for Social Research, and via Zoom. The Zoom call will be locked 10 minutes after the start of the presentation.

New data, new questions, old problems? Online behavioral data in social science research

Records of individuals’ online activities obtained from devices like personal computers and smartphones have received a lot of interest in the social sciences in recent years. Many have praised such data for allowing fine-grained observations of individuals’ online activities which would be impossible with more traditional data sources such as surveys. Recent work, however, warns that many data quality aspects of these novel data are so far poorly under- stood. As the number of observations can quickly reach several millions, researchers seem tempted to treat online behavioral data as gold standard, ignore what their data may be missing, and which other systematic biases may be present. In this talk, I present both applied and methodological work using online behavioral data in a typical social science setting. First, using within-between random effects models, I show how online behavioral data combined with a panel survey allows us to understand the effects of news media consumption from populist alternative news platforms on individuals’ political attitudes. Second, I show that online behavioral data, although containing detailed records of individuals’ social media use, are far from being complete. Using hidden Markov models, combined online behavioral data, survey records, and donated social media data, I show that the online behavioral data seem to completely fail in capturing social media use for about one third of the sample. I emphasize the need for researchers to navigate the complexities of online behavioral data, highlighting potentials and limitations.

Ruben Bach is a Research Fellow at the Mannheim Centre for European Social Research, University of Mannheim, Germany. His research is concerned with data quality in social science data products and applied computational social science (media consumption, political attitudes, socially responsible AI). In the fall of 2023, he is a visitor with the Department of Statistics and Actuarial Science, University of Waterloo, Ontario.

]]>
Lecture / Discussion Tue, 10 Oct 2023 12:29:09 -0400 2023-10-11T12:00:00-04:00 2023-10-11T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
MPSDS JPSM Seminar Series - Implementing and Adjusting a Non-probability Web Survey: Experiences of EVENs (Survey on the Impact of COVID19 on Ethnic Minorities in the United Kingdom) (October 18, 2023 12:00pm) https://events.umich.edu/event/113847 113847-21831814@events.umich.edu Event Begins: Wednesday, October 18, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 11, 2023
12:00 - 1:00 pm EDT

In person, room 1070 Institute for Social Research and via Zoom.
The Zoom call will be locked 10 minutes after the start of the presentation.

Implementing and Adjusting a Non-probability Web Survey: Experiences of EVENs (Survey on the Impact of COVID19 on Ethnic Minorities in the United Kingdom)

Natalie Shlomo
Professor of Social Statistics, University of Manchester

This is joint work with Andrea Aparcio-Castro, Daniel Ellingworth, Angelo Moretti, Harry Taylor, Nissa Finney and James Nazroo

We discuss the challenges of implementing and adjusting a large-scale non-probability web survey. For the application, we focus on the 2021 Evidence for Equality National Survey (EVENS) which was led by the Centre on Dynamics of Ethnicity (CoDE) at the University of Manchester in the United Kingdom, in partnership with Ipsos-MORI. The aim was to understand the impact of the COVID19 pandemic on ethnic and religious minority groups in the UK. Standard probability-based surveys, even with ethnic minority group boosts, do not have the sample sizes required to obtain reliable estimates for small group statistics. We therefore designed a non-probability web survey of ethnic minority groups to overcome these limitations. We formed partnerships with community organizations and used innovative recruitment strategies, including digital and social media. Daily monitoring of the data collection against desired sample sizes and R-indicator calculations allowed the team to focus attention on the recruitment of specific groups in a responsive data collection mode. We also supplemented the sample with existing members in both established non-probability and probability-based panels in the UK. We describe the measures applied to improve the quality of the collected data and the statistical adjustments to correct for selection and coverage biases based on estimating the probability of participation in the non-probability sample using combined probability reference samples followed by calibration to auxiliary information from the UK Census 2021. We demonstrate how a pseudo-population bootstrap approach can be designed to obtain bootstrap weights to allow for statistical analyses and inference.

Natalie Shlomo is Professor of Social Statistics at the University of Manchester and publishes widely in the area of survey statistics, including small area estimation, adaptive survey designs, non-probability sampling, confidentiality and privacy, data linkage and integration. She has over 70 publications and refereed book chapters and a track record of generating external funding for her research. She is an elected member of the International Statistical Institute (ISI), a fellow of the Royal Statistical Society, a fellow of the Academy of Social Sciences and President 2023-2025 of the International Association of Survey Statisticians. She also serves on national and international Methodology Advisory Boards at National Statistical Institutes.

Homepage: https://www.research.manchester.ac.uk/portal/natalie.shlomo.html

]]>
Lecture / Discussion Wed, 11 Oct 2023 14:15:21 -0400 2023-10-18T12:00:00-04:00 2023-10-18T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
MPSDS JPSM Seminar Series - Investigating the quality of digital trace and data donation (October 25, 2023 12:00pm) https://events.umich.edu/event/114041 114041-21832242@events.umich.edu Event Begins: Wednesday, October 25, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 25, 2023
12:00 - 1:00 pm EDT

In person, room 1070 Institute for Social Research, and via Zoom. The Zoom call will be locked 10 minutes after the start of the presentation.

Investigating the quality of digital trace and data donation

Challenges to traditional survey data collection such as increased costs and decreasing non-response are leading survey researchers to explore new forms of data. Recently, two types of data have received increased focus as a possible replacements or enhancements of surveys: digital trace data and data donation. Digital trace data refers to data produced while individuals interact with digital platforms, such as apps and websites. Data donation, on the other hand, refers to the acquisition of data from online platforms, such as Facebook or Google, directly from users. In a recent study we use an experimental design in a non-probability panel in Germany to explore non-response bias in data donated from Facebook as well measurement error in digital trace data from PCs and mobile phones.

Alexandru Cernat is an associate professor in the social statistics department at the University of Manchester. He has a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods and the Cathie Marsh Institute. His research and teaching focus on: survey methodology, longitudinal data, measurement error, latent variable modelling, new forms of data and missing data. You can find out more about him and his research at: www.alexcernat.com

]]>
Lecture / Discussion Mon, 16 Oct 2023 14:39:03 -0400 2023-10-25T12:00:00-04:00 2023-10-25T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
Planning Data Archiving (and Reuse) with RWJF’s Health & Medical Care Archive at ICPSR (October 26, 2023 1:00pm) https://events.umich.edu/event/114143 114143-21832392@events.umich.edu Event Begins: Thursday, October 26, 2023 1:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Established in 1985, the HMCA is the archival home to over 220 studies produced by RWJF-funded research projects. HMCA provides both data curation services and user support for reuse of research data selected by RWJF to be included in the archive. In this webinar, you will learn about the benefits of archiving data at HMCA and best practices for managing and preparing data for reuse. This webinar is targeted to those researchers and teams interested in learning more about the data archiving process and is open to those “required” to archive RWJF-funded data, as well as others interested in learning more about data management to improve data reuse.

]]>
Presentation Tue, 17 Oct 2023 21:58:33 -0400 2023-10-26T13:00:00-04:00 2023-10-26T14:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR webinar "Webinar: Planning Data Archiving (and Reuse)"
PSC Brownbag Series: Discordance in chromosomal and self-reported sex in the UK Biobank: Implications for transgender- and intersex-inclusive data collection (October 30, 2023 12:00pm) https://events.umich.edu/event/111279 111279-21826617@events.umich.edu Event Begins: Monday, October 30, 2023 12:00pm
Location: Institute For Social Research
Organized By: Population Studies Center

The PSC Brown Bag Series runs live and on Zoom this year, Mondays from noon to 1.

Social epidemiologist Kate Duchowny (University of Michigan, SRC) presents this brown bag seminar:

"Discordance in chromosomal and self-reported sex in the UK Biobank: Implications for transgender- and intersex-inclusive data collection"


Despite recent calls to distinguish between sex and gender, these constructs are often assessed in isolation or are used interchangeably. In this talk, she will present data that quantifies the disagreement between chromosomal and self-reported sex and identifies potential reasons for discordance using data from the UK Biobank. She and coauthors show that among approximately 200 individuals with sex discordance, 71% of discordances were explained by intersex traits or transgender identity. These findings imply that health and clinical researchers have a unique opportunity to advance the rigor of scientific research as well as the health and well-being of transgender, intersex, and nonbinary people, who have long been excluded from and overlooked in clinical and survey research.


Kate Duchowny (she/her) is a Research Assistant Professor in the Survey Research Center at the University of Michigan's Institute for Social Research. As a social epidemiologist, her research focuses on the measurement, determinants, and consequences of compromised muscle health among older adults. Her overarching research goal seeks to inform interventions by bridging the social, environmental, and biological determinants of musculoskeletal health and physical functioning across the life course. She is particularly interested in identifying novel biomarkers that aid in our understanding of how social experiences become biologically embedded to produce and exacerbate health inequities.

Join us in person at ISR (Thompson Street) Room 1430.

Or online: Join Zoom Meeting
https://umich.zoom.us/j/95418610585?pwd=Z0cvdkF1T0R2cG1lRDEvVmlnbVdlZz09

Meeting ID: 954 1861 0585
Passcode: 818420
One tap mobile
+13017158592,,95418610585# US (Washington DC)
+13092053325,,95418610585# US

Dial by your location
+1 301 715 8592 US (Washington DC)
+1 309 205 3325 US
+1 312 626 6799 US (Chicago)
+1 646 876 9923 US (New York)
+1 646 931 3860 US
+1 564 217 2000 US
+1 669 444 9171 US
+1 669 900 6833 US (San Jose)
+1 719 359 4580 US
+1 253 215 8782 US (Tacoma)
+1 346 248 7799 US (Houston)
+1 386 347 5053 US
+1 647 374 4685 Canada
+1 647 558 0588 Canada
+1 778 907 2071 Canada
+1 780 666 0144 Canada
+1 204 272 7920 Canada
+1 438 809 7799 Canada
+1 587 328 1099 Canada
Meeting ID: 954 1861 0585
Find your local number: https://umich.zoom.us/u/aCRAyuQaT

Join by SIP
95418610585@zoomcrc.com

Join by H.323
162.255.37.11 (US West)
162.255.36.11 (US East)
115.114.131.7 (India Mumbai)
115.114.115.7 (India Hyderabad)
213.19.144.110 (Amsterdam Netherlands)
213.244.140.110 (Germany)
103.122.166.55 (Australia Sydney)
103.122.167.55 (Australia Melbourne)
149.137.40.110 (Singapore)
64.211.144.160 (Brazil)
149.137.68.253 (Mexico)
69.174.57.160 (Canada Toronto)
65.39.152.160 (Canada Vancouver)
207.226.132.110 (Japan Tokyo)
149.137.24.110 (Japan Osaka)
Meeting ID: 954 1861 0585
Passcode: 818420

]]>
Workshop / Seminar Thu, 31 Aug 2023 11:28:25 -0400 2023-10-30T12:00:00-04:00 2023-10-30T13:00:00-04:00 Institute For Social Research Population Studies Center Workshop / Seminar PSC Brownbag Series: Discordance in chromosomal and self-reported sex in the UK Biobank: Implications for transgender- and intersex-inclusive data collection
MPSDS JPSM Seminar Series - Flexible Formal Privacy for Public Data Curation (November 1, 2023 12:00pm) https://events.umich.edu/event/114344 114344-21832762@events.umich.edu Event Begins: Wednesday, November 1, 2023 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
November 1, 2023
12;00 - 1:00 pm EDT

In person, room 1070 Institute for Social Research, and via Zoom.
The Zoom call will be locked 10 minutes after the start of the presentation.

Flexible Formal Privacy for Public Data Curation

Researchers rely extensively on public datasets disseminated by official statistics agencies, universities, non-governmental organizations, and other data curators. With the increasing availability of data and computing power comes increased threats to privacy, as published statistics can more easily be used to reconstruct sensitive personal data. Formal privacy (FP) methods, like differential privacy (DP), provably limit such information leakage by injecting carefully chosen randomized noise into published statistics. However, the way DP accounts for privacy degradation requires this noise be injected into every statistic dependent on the confidential dataset. This fails to reflect data curator needs, social, legal or ethical requirements, and complex dependency structures between public and confidential datasets. In this talk, I'll discuss statistical methodology that addresses these problems. We propose a FP framework with novel characterizations of disclosure risk when assessing collections of statistics wherein only some statistics are published with DP guarantees. We demonstrate FP properties maintained by our proposed framework, propose data release mechanisms which satisfy our proposed definition, and prove the optimality properties of downstream statistical estimators based on these mechanism outputs. For this talk, I'll discuss a few end-to-end data analysis examples in public health and surveys, showing how theoretical trade-offs between privacy, utility, and computation time manifest in practice when assessing disclosure risks and statistical utility. I'll conclude with a discussion on the implications of this work for survey researchers, focusing on opportunities to incorporate privacy by design in survey planning, experimental design, and other data collection operations.

Jeremy Seeman is a Michigan Data Science Fellow at the Michigan Institute for Data Science (MIDAS) and MPSDS. He recently graduated with his PhD in statistics from Penn State University. Jeremy's research focuses on statistical data privacy, quantitative methods in the social sciences, and social values in data governance. He is the recipient of the U.S Census Bureau Dissertation Fellowship and the ASA Pride Scholarship. Prior to joining Penn State, Jeremy completed his BS in Physics and MS in Statistics at the University of Chicago, where he was a research fellow at the Center for Data Science and Public Policy.

]]>
Lecture / Discussion Mon, 23 Oct 2023 14:35:41 -0400 2023-11-01T12:00:00-04:00 2023-11-01T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer