Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. Code-Free Machine Learning: Introduction to Concepts and Best Practices with Hands-on Experiences - Summer Institute in Survey Research Techniques (September 28, 2022 10:00am) https://events.umich.edu/event/95713 95713-21790777@events.umich.edu Event Begins: Wednesday, September 28, 2022 10:00am
Location: Off Campus Location
Organized By: Summer Institute in Survey Research Techniques

Code-Free Machine Learning: Introduction to Concepts and Best Practices with Hands-on Experiences

Course open for registration!
Open to all!

September 7 - October 5, 2022
10:00am-12:00pm
Wednesdays

Social scientists are increasingly interested in machine learning methods to glean scientific knowledge and actionable insights from designed and gathered data. Implementing machine learning, however, requires users to have programming skills. This can be a daunting challenge for many non-tech savvy researchers. This course aims to guide social scientists to explore how machine learning can be used for their research without learning how to code. This course uses a graphical user interface tool Orange to provide learners with hands-on experiences in implementing machine learning techniques including data cleaning, visualization, and fine-tuning of algorithmic models. The open-source tool Orange is built on popular Python packages, providing basically the same functions and performances as many data scientists would obtain by writing complicated code. The course demonstrates that researchers can utilize the power of machine learning without learning how to code and focus more on machine learning concepts and best practices as well as analytical model development and validation.

Prerequisite: You must have your own laptop or desktop with Orange installed to participate in this class. For installation instruction of Orange, see https://orangedatamining.com/

Not for academic credit.

Instructor: Jinseok Kim

All 2022 courses will be held in an alternative format.

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Class / Instruction Mon, 20 Jun 2022 14:53:44 -0400 2022-09-28T10:00:00-04:00 2022-09-28T12:00:00-04:00 Off Campus Location Summer Institute in Survey Research Techniques Class / Instruction
Data Collection Using Wearables, Sensors, and Apps in the Social, Behavioral, and Health Sciences - Summer Institute in Survey Research Techniques (September 28, 2022 11:00am) https://events.umich.edu/event/95724 95724-21790795@events.umich.edu Event Begins: Wednesday, September 28, 2022 11:00am
Location: Off Campus Location
Organized By: Summer Institute in Survey Research Techniques

Data Collection Using Wearables, Sensors, and Apps in the Social, Behavioral, and Health Sciences

Course open for registration!
Open to all!

September 21-30, 2022
11:00am-1:00pm
Wednesdays and Fridays

The recent proliferation of mobile technology allows researchers to collect objective health and behavioral data at increased intervals, in real time, and may also reduce participant burden. In this course, we will provide examples of the utility of and integration of wearables, sensors, and apps in research settings. Examples will include the use of wearable health devices to measure activity, apps for ecological momentary assessment, and smartphone sensors to measure sound and movement, among others. Additionally, this course will consider the integration of these new technologies into existing surveys and the quality of the data collected from the total survey error perspective. We will discuss considerations for assessing coverage, participation, and measurement error when integrating wearables, sensors, and apps in a research setting as well as the costs and privacy considerations when collecting these types of data. Participants will work in groups to discuss a research study design using new technology and have the opportunity for hands-on practice with sensor data.

Not for academic credit.

Instructors: Florian Keusch and Heidi Guyer

All 2022 courses will be held in an alternative remote format.

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Class / Instruction Wed, 22 Jun 2022 11:44:43 -0400 2022-09-28T11:00:00-04:00 2022-09-28T13:00:00-04:00 Off Campus Location Summer Institute in Survey Research Techniques Class / Instruction
Data Collection Using Wearables, Sensors, and Apps in the Social, Behavioral, and Health Sciences - Summer Institute in Survey Research Techniques (September 30, 2022 11:00am) https://events.umich.edu/event/95724 95724-21790797@events.umich.edu Event Begins: Friday, September 30, 2022 11:00am
Location: Off Campus Location
Organized By: Summer Institute in Survey Research Techniques

Data Collection Using Wearables, Sensors, and Apps in the Social, Behavioral, and Health Sciences

Course open for registration!
Open to all!

September 21-30, 2022
11:00am-1:00pm
Wednesdays and Fridays

The recent proliferation of mobile technology allows researchers to collect objective health and behavioral data at increased intervals, in real time, and may also reduce participant burden. In this course, we will provide examples of the utility of and integration of wearables, sensors, and apps in research settings. Examples will include the use of wearable health devices to measure activity, apps for ecological momentary assessment, and smartphone sensors to measure sound and movement, among others. Additionally, this course will consider the integration of these new technologies into existing surveys and the quality of the data collected from the total survey error perspective. We will discuss considerations for assessing coverage, participation, and measurement error when integrating wearables, sensors, and apps in a research setting as well as the costs and privacy considerations when collecting these types of data. Participants will work in groups to discuss a research study design using new technology and have the opportunity for hands-on practice with sensor data.

Not for academic credit.

Instructors: Florian Keusch and Heidi Guyer

All 2022 courses will be held in an alternative remote format.

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Class / Instruction Wed, 22 Jun 2022 11:44:43 -0400 2022-09-30T11:00:00-04:00 2022-09-30T13:00:00-04:00 Off Campus Location Summer Institute in Survey Research Techniques Class / Instruction
Code-Free Machine Learning: Introduction to Concepts and Best Practices with Hands-on Experiences - Summer Institute in Survey Research Techniques (October 5, 2022 10:00am) https://events.umich.edu/event/95713 95713-21790778@events.umich.edu Event Begins: Wednesday, October 5, 2022 10:00am
Location: Off Campus Location
Organized By: Summer Institute in Survey Research Techniques

Code-Free Machine Learning: Introduction to Concepts and Best Practices with Hands-on Experiences

Course open for registration!
Open to all!

September 7 - October 5, 2022
10:00am-12:00pm
Wednesdays

Social scientists are increasingly interested in machine learning methods to glean scientific knowledge and actionable insights from designed and gathered data. Implementing machine learning, however, requires users to have programming skills. This can be a daunting challenge for many non-tech savvy researchers. This course aims to guide social scientists to explore how machine learning can be used for their research without learning how to code. This course uses a graphical user interface tool Orange to provide learners with hands-on experiences in implementing machine learning techniques including data cleaning, visualization, and fine-tuning of algorithmic models. The open-source tool Orange is built on popular Python packages, providing basically the same functions and performances as many data scientists would obtain by writing complicated code. The course demonstrates that researchers can utilize the power of machine learning without learning how to code and focus more on machine learning concepts and best practices as well as analytical model development and validation.

Prerequisite: You must have your own laptop or desktop with Orange installed to participate in this class. For installation instruction of Orange, see https://orangedatamining.com/

Not for academic credit.

Instructor: Jinseok Kim

All 2022 courses will be held in an alternative format.

]]>
Class / Instruction Mon, 20 Jun 2022 14:53:44 -0400 2022-10-05T10:00:00-04:00 2022-10-05T12:00:00-04:00 Off Campus Location Summer Institute in Survey Research Techniques Class / Instruction
MPSDS JPSM Seminar Series - Should surveys produce more contextual features? Comparing contextual features by alternative definitions of neighborhoods (October 5, 2022 12:00pm) https://events.umich.edu/event/98386 98386-21796589@events.umich.edu Event Begins: Wednesday, October 5, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 5, 2022
12:00 - 1:00 pm

Should surveys produce more contextual features? Comparing contextual features by alternative definitions of neighborhoods.

Shiyu Zhang is a PhD candidate at the Michigan Program in Survey and Data Science. Before arriving at Michigan, she received master's degrees in immigration study, sociology and data science, and a bachelor's degree in psychology. Shiyu's dissertation focuses on the effect of adaptive survey design on estimates. She is also interested in collecting and using neighborhood features as auxiliary variables.

An important methodological challenge in studying neighborhood effects is how to geographically define “neighborhoods” and create contextual features to characterize the areas. In quantitative research that uses survey data, contextual features are commonly defined by census geographies like census tracts and block groups. However, the literature has called for expanding the definition of neighborhoods beyond census boundaries and exploring contextual features in geographic areas more relevant to the studied individuals.
In this research, we compare social and built environment features of neighborhoods based on three geographic definitions (i.e., census tracts, residential buffers, and respondent-informed neighborhoods). We evaluate how the alternatively defined measures influence the detected associations between contextual features and health outcomes. Our findings suggest that the neighborhood definition matters. Therefore, other than simply offering linkages to census boundaries based on participants’ geocoded location, surveys may enrich the data and support further research by producing and releasing case-specific contextual features.

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 (MPSDS). 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.

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Workshop / Seminar Mon, 26 Sep 2022 11:43:15 -0400 2022-10-05T12:00:00-04:00 2022-10-05T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Workshop / Seminar Flyer for Should surveys produce more contextual features? Comparing contextual features by alternative definitions of neighborhoods.
Best Practices in Data Management (October 11, 2022 1:00pm) https://events.umich.edu/event/95981 95981-21792050@events.umich.edu Event Begins: Tuesday, October 11, 2022 1:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for this virtual lecture from the 2022 Blalock Lecture Series from the ICPSR Summer Program in Quantitative Methods of Social Science.

The 2022 Blalock Lecture Series includes 17 lectures on social science topics and data. Completely virtual, free, and open to the public. All lectures will be held from 7:30-9:00 pm ET. Share freely with friends and colleagues.

Find the full list of 2022 Blalock Lectures at https://myumi.ch/ICPSR2022Blalocks.

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Lecture / Discussion Thu, 21 Jul 2022 19:50:49 -0400 2022-10-11T13:00:00-04:00 2022-10-11T14:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Lecture / Discussion
MPSDS JPSM Seminar Series - Evaluating Pre-Election Polling Estimates using a New Measure of Non-Ignorable Selection Bias (October 12, 2022 12:00pm) https://events.umich.edu/event/98434 98434-21796653@events.umich.edu Event Begins: Wednesday, October 12, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 12, 2022, 12:00-1:00 pm

Brady T. West is a Research Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He earned his PhD from the Michigan Program in Survey and Data Science in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, selection bias in surveys, responsive/adaptive survey design, interviewer effects, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Third Edition, Chapman Hall/CRC Press, 2022), and he is a co-author of a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), the second edition of which was published by CRC Press in June 2017. He was elected as a Fellow of the American Statistical Association in 2022.

Among the numerous explanations that have been offered for recent errors in pre-election polls, selection bias due to non-ignorable partisan nonresponse bias, where the probability of responding to a poll is a function of the candidate preference that a poll is attempting to measure (even after conditioning on other relevant covariates used for weighting adjustments), has received relatively less focus in the academic literature. Under this type of selection mechanism, estimates of candidate preferences based on individual or aggregated polls may be subject to significant bias, even after standard weighting adjustments. Until recently, methods for measuring and adjusting for this type of non-ignorable selection bias have been unavailable. Fortunately, recent developments in the methodological literature have provided political researchers with easy-to-use measures of non-ignorable selection bias. In this study, we apply a new measure that has been developed specifically for estimated proportions to this challenging problem. We analyze data from 18 different pre-election polls: nine different telephone polls conducted in eight different states prior to the U.S. Presidential election in 2020, and nine different pre-election polls conducted either online or via telephone in Great Britain prior to the 2015 General Election. We rigorously evaluate the ability of this new measure to detect and adjust for selection bias in estimates of the proportion of likely voters that will vote for a specific candidate, using official outcomes from each election as benchmarks and alternative data sources for estimating key characteristics of the likely voter populations in each context.

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.

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Workshop / Seminar Tue, 20 Sep 2022 11:50:22 -0400 2022-10-12T12:00:00-04:00 2022-10-12T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Workshop / Seminar Flyer for Evaluating Pre-Election Polling Estimates using a New Measure of Non-Ignorable Selection Bias
Information Session Webinar- Michigan Program in Survey and Data Science (MPSDS) (October 12, 2022 3:00pm) https://events.umich.edu/event/98336 98336-21796508@events.umich.edu Event Begins: Wednesday, October 12, 2022 3:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

Wednesday, October 12, 2002
3:00 - 4:00pm
Registration is required.

Please join us October 12, 2022 to learn about the Michigan Program in Survey and Data Science. The speaker will be Dr. Brady West.

Advance registration is required, https://bit.ly/3d3upwR

The Michigan Program in Survey and Data Science (MPSDS) offers graduate degrees that combine ideas and techniques for producing and analyzing data about humans and our society. Joint us to launch your career in this exciting and rewarding field in which scientists interpret the world through data.

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.

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Presentation Thu, 08 Sep 2022 14:38:06 -0400 2022-10-12T15:00:00-04:00 2022-10-12T16:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Presentation MPSDS Informational Session Webinar
MPSDS JPSM Seminar Series - How to Draw a Nationally-Representative Sample: Updating and Reassessing Monitoring the Future's Sampling Procedures (October 19, 2022 12:00pm) https://events.umich.edu/event/98438 98438-21796659@events.umich.edu Event Begins: Wednesday, October 19, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 19, 2022
12:00 - 1:00 pm EDT

Professor Richard Miech is Principal Investigator of Monitoring the Future, which since 1975 has drawn annual, nationally-representative samples of adolescents and tracked trends in adolescent drug use. His work focuses on trends in substance use, with an emphasis on disentangling how these trends vary by age, historical period, and birth cohort membership.

The national estimates of drug use from Monitoring the Future (MTF) serve as a gold standard in the field and are a key source of information for research, U.S. policymakers, and nonprofit organizations that seek to reduce teen drug use. For sample selection MTF uses a multistage, random sampling procedure that consists of (1) selection of a specific geographic areas, (2) selection of one or more high schools in each area, and (3) selection of students within each school. MTF has recently begun a revisit and overhaul of its sampling procedures, which were developed more than three decades ago. In this talk Professor Miech discusses this overhaul, including sampling challenges and issues that have arisen over the years, as well as opportunities to streamline and improve MTF sampling with new technology.

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.

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Workshop / Seminar Tue, 20 Sep 2022 11:51:08 -0400 2022-10-19T12:00:00-04:00 2022-10-19T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Workshop / Seminar Flyer for https://www.src.isr.umich.edu/people/richard-miech/
MPSDS JPSM Seminar Series - Would electoral research show different findings if we replaced probability face-to-face surveys with other types of surveys? (October 26, 2022 12:00pm) https://events.umich.edu/event/99079 99079-21797546@events.umich.edu Event Begins: Wednesday, October 26, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
October 26, 2022
12:00 - 1:00 pm

Would electoral research show different findings if we replaced probability face-to-face surveys with nonprobability online surveys?

Hannah Bucher is a PhD student in survey research at the University of Mannheim and a research associate at GESIS - Leibniz-Institute for the Social Sciences at the German Longitudinal Election Study (GLES). Her research focuses on (non)probability online surveys.

As respondents of nonprobability online surveys are self-selected, it is often questioned whether results are comparable with those of probability face-to-face surveys. In this paper, I compare a nonprobability online survey and a probability face-to-face survey by the German Longitudinal Election Study (GLES) in terms of estimation of benchmark statistics; distributions in 80 variables covering measures of political attitudes and behavior; and differences in results of multivariate analyses through a multimodel comparison with individual-level voter turnout as the dependent variable. The probability face-to-face survey performs slightly better in estimating characteristics with external benchmarks. There are substantial differences in numerous variables and their associations in multivariate models. Thus, switching from a probability face-to-face survey to a nonprobability online survey affects empirical findings on individual-level voter turnout and the conclusions drawn therefrom.

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.

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.

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Workshop / Seminar Wed, 26 Oct 2022 12:35:50 -0400 2022-10-26T12:00:00-04:00 2022-10-26T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Workshop / Seminar Flyer
MPSDS JPSM Seminar Series - A Multivariate Stopping Rule for Survey Data Collection (November 2, 2022 12:00pm) https://events.umich.edu/event/98854 98854-21797269@events.umich.edu Event Begins: Wednesday, November 2, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
November 2, 2022
12:00 - 1:00 EDT

Xinyu Zhang
A Multivariate Stopping Rule for Survey Data Collection

Bio
Xinyu Zhang is a PhD candidate studying survey and data science at the University of Michigan. He is primarily interested in responsive survey designs, survey nonresponse, and machine learning techniques. His dissertation topic is using models to inform responsive survey designs.

Abstract
Surveys are experiencing declining response rates. With more and more effort expended to combat these declining response rates, the cost of large-scale surveys has continued to rise. Recent technological developments in survey data collection have allowed the survey designer to make near-real-time intervention decisions. Stopping rules are one of the interventions often considered to improve the efficiency of data collection. Stopping some cases essentially reallocates effort from stopped cases to others, but most previously proposed stopping rules have only considered single estimates. In multipurpose surveys, there may be data quality objectives that must be met for multiple estimates with constraints on costs. We introduce a stopping rule that accounts for the cost and the quality of one or more estimates. The proposed stopping rule is illustrated via simulation using data from the Health and Retirement Study.

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.

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.

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Workshop / Seminar Tue, 01 Nov 2022 13:40:59 -0400 2022-11-02T12:00:00-04:00 2022-11-02T13:00:00-04:00 Off Campus Location Michigan Program in Survey and Data Science Workshop / Seminar Flyer
MPSDS JPSM Seminar Series - Accounting for Non-ignorable Sampling and Nonresponse in Statistical Matching (November 9, 2022 12:00pm) https://events.umich.edu/event/100348 100348-21799633@events.umich.edu Event Begins: Wednesday, November 9, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
November 9, 2022
12:00 - 1:00 EST

Accounting for Non-ignorable Sampling and Nonresponse in Statistical Matching

Danny Pfeffermann retired as the National Statistician and Director General of Israel's CBS. He is Professor Emeritus of Statistics at the Hebrew University of Jerusalem and Professor of Social Statistics at the University of Southampton. His main research areas are: Analytic inference from complex sample surveys; Seasonal adjustment and trend estimation; Small area estimation; Inference under informative sampling and nonresponse and more recently; Mode effects and Proxy surveys.

Professor Pfeffermann published about 80 articles in leading statistical journals and co-edited the two-volume handbook on Sample Surveys. He is Fellow of the American Statistical Association (ASA), the International Statistical Institute (ISI) and the Institute of Mathematical Statistics (IMS), and recipient of several international awards.

Abstract
Data for statistical analysis is often available from different samples, with each sample containing measurements on only some of the variables of interest. Statistical matching attempts to generate a fused database containing matched measurements on all the target variables. In this article, we consider the use of statistical matching when the samples are drawn by informative sampling designs and are subject to not missing at random nonresponse. The problem with ignoring the sampling process and nonresponse is that the distribution of the data observed for the responding units can be very different from the distribution holding for the population data, which may distort the inference process and result in a matched database that misrepresents the joint distribution in the population. Our proposed methodology employs the empirical likelihood approach and is shown to perform well in a simulation experiment and when applied to real sample data.

**Joint paper with Daniela Marella, to appear in International Statistical Review

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.

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.

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Lecture / Discussion Mon, 17 Oct 2022 17:18:24 -0400 2022-11-09T12:00:00-05:00 2022-11-09T13:00:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer for Accounting for Non-ignorable Sampling and Nonresponse in Statistical Matching
Information Session Webinar- Michigan Program in Survey and Data Science (MPSDS) (November 10, 2022 9:30am) https://events.umich.edu/event/100543 100543-21800056@events.umich.edu Event Begins: Thursday, November 10, 2022 9:30am
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

Thursday, November 10, 2022
9:30 - 10:30am (EST)
Registration is required, https://tinyurl.com/422xdvdp

Please join us to learn about the University of Michigan Program in Survey and Data Science.

The Michigan Program in Survey and Data Science (MPSDS) offers graduate degrees that combine ideas and techniques for producing and analyzing data about humans and our society. Join us to launch your career in this exciting and rewarding field in which scientists interpret the world through data.

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.

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Lecture / Discussion Fri, 21 Oct 2022 11:06:53 -0400 2022-11-10T09:30:00-05:00 2022-11-10T10:30:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion MPSDS Informational Session Webinar
MPSDS JPSM Seminar Series - Utility of Commercial Data for Sampling Population Subgroups: A Case of Health and Retirement Study (November 16, 2022 12:00pm) https://events.umich.edu/event/101145 101145-21800872@events.umich.edu Event Begins: Wednesday, November 16, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
November 16, 2022
12:00 - 1:00 EST

Sunghee Lee is a Research Associate Professor at Survey Research Center, University of Michigan. Her research focuses on sampling and measurement issues with hard-to-survey population subgroups as well as racial, ethnic, and linguistic minorities.

Chendi Zhao is a Research Assistant and first-year Ph.D. student in the Program in Survey and Data Science

Anqi Liu is a master’s student in MPSDS at the University of Michigan. She works closely with Dr. Sunghee Lee on the Health and Retirement Study sampling.

Abstract
A standard approach for targeting population subgroups in household surveys is to sample general population and then to screen for eligible households. This becomes increasingly costly as the subgroup accounts for a small proportion of the population, which is the case for the Health and Retirement Study (HRS). HRS is a population-based longitudinal study of adults ages 50 and older in the U.S. and maintains its representativeness by adding a new age cohort every 6 years. In 2016, HRS targeted those born between 1960 and 1965 with an additional goal of oversampling racial/ethnic minorities. This group is less than 10% of the population. In order to increase the efficiency of screening, HRS had traditionally used probability proportionate size sampling in its area-probability sample with the age-eligible population size as a measure of size as well as stratification based on the race/ethnicity distribution of area sampling units. For 2016, HRS sampling additionally used stratification at the address level by enhancing the population of addresses in the sample areas with commercial data. This study examines the utility of commercial data for increasing efficiency with a focus on its availability and accuracy by analyzing a dataset that combines sampling frame data, screening data, main survey data as well as external data from the American Community Survey.

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.

SISRT
The Annual Summer Institute in Survey Research Techniques
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.

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Lecture / Discussion Mon, 07 Nov 2022 18:46:54 -0500 2022-11-16T12:00:00-05:00 2022-11-16T13:00:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
MPSDS JPSM Seminar Series - Effect of Branching Middle Responses in Dichotomous Polar Scales in Web Surveys (November 30, 2022 12:00pm) https://events.umich.edu/event/101568 101568-21801526@events.umich.edu Event Begins: Wednesday, November 30, 2022 12:00pm
Location: Off Campus Location
Organized By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series
November 30, 2022
12:00 - 1:00 EST

Effect of Branching Middle Responses in Dichotomous Polar Scales in Web Surveys

In telephone surveys, 11 to 49% of respondents would select a middle alternative when it is offered although they would not volunteer it if it were not mentioned in dichotomous bipolar questions. Furthermore, offering a middle option led to differences in response effects that are related to respondent characteristics, including social desirability bias and satisficing effects. While a question form that branches middle responses has been shown to have a lower validity compared to offered form in telephone surveys, potentially, branched question form can motivate respondents to spend extra time and effort in giving a response in the absence of an interviewer. Therefore, differences in validity and reliability of responses to branched question form compared to offered form is a research interest in general population web surveys. This study tests the validity and the reliability to branched question form in a general population survey using a randomized experiment. The branched question form did not change validity and reliability of responses and reduced the satisficing behavior based on the proxies compared to the offered form.

Z. Tuba Suzer Gurtekin is an Assistant Research Scientist within the Institute for Social Research (ISR) at the University of Michigan. She is the scientific leader of the Surveys of Consumers, which conducts monthly national surveys of American households to understand consumer expectations and how those expectations impact their spending and saving behavior. Her research experience has included development of alternative sample, methodology and questionnaire designs, data collection and analysis methods for a general population in parallel survey modes. In addition to her work through the Surveys of Consumers, she also currently serves on the Board of Associate Editors of CDC’s Preventing Chronic Disease Journal. 608.82She teaches survey sampling and survey methodology in University of Michigan’s Clinical Research Design and Statistical Analysis Program (OJOC CRDSA).

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.

SISRT
June 5 – July 28, 2023

The Summer Institute in Survey Research Techniques is a teaching program of the Survey Research Center at the Institute for Social Research. It is located on the central campus of the University of Michigan at 426 Thompson Street in Ann Arbor. The summer courses are select offerings from the Michigan Program in Survey and Data Science, and can be used to pursue a doctorate, master of science and a certificate in survey methodology.

All 2023 courses will be offered in an alternative remote format with the exception of the Sampling Program for Survey Statisticians. Payment of Summer Scholar and workshop fees must be made in full before you will be officially registered for class. Fees are based on total “course hours” (assigned to each course as shown in the section on description of courses and on the 2023 course schedule) although no formal academic credit is actually earned.

Our courses this summer will be offered primarily by two-way, live video through a platform that supports lectures and group work. In some cases, courses are offered in a flipped format in which lectures are video recorded for students to watch on-demand and then meet with their instructor by two-way live video to discuss the lectures, readings, and problem sets. All classes are scheduled in Eastern Standard Time Zone.

We have been offering courses in remote formats for many years through our connection with the graduate programs at the Universities of Michigan and Maryland which share all courses by live classroom-to-classroom video. In the COVID era, our transition to entirely remote instruction has been straightforward and brought the students’ experience very close to that of a place-based classroom.

We understand that some participants were looking forward to visiting Ann Arbor, networking and participating in social activities. As an alternative, we are planning several virtual social and networking activities in which participants will meet informally (by live video) with their instructors and just with each other in small groups to discuss various topics, some related to courses and some not. This will give participants a chance get to know each other as well as instructors outside the “classroom.” We’re excited to work with you as we learn how to best connect with each other remotely.

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Lecture / Discussion Tue, 22 Nov 2022 13:25:54 -0500 2022-11-30T12:00:00-05:00 2022-11-30T13:00:00-05:00 Off Campus Location Michigan Program in Survey and Data Science Lecture / Discussion Flyer
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

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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.
Love Data Week 2023 (February 13, 2023 8:00am) https://events.umich.edu/event/92579 92579-21692650@events.umich.edu Event Begins: Monday, February 13, 2023 8:00am
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Save the date! Love Data Week takes place February 13-17, 2023. Sign up for email announcements and more: https://myumi.ch/ICPSRlovedata23

Join the conversation on social media! Use #LoveData23

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Other Fri, 18 Feb 2022 13:55:54 -0500 2023-02-13T08:00:00-05:00 2023-02-13T18:00:00-05:00 Off Campus Location Inter-university Consortium for Political and Social Research Other Love Data Week 2023 save the date
Love Data Week 2023 (February 14, 2023 8:00am) https://events.umich.edu/event/92579 92579-21692651@events.umich.edu Event Begins: Tuesday, February 14, 2023 8:00am
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Save the date! Love Data Week takes place February 13-17, 2023. Sign up for email announcements and more: https://myumi.ch/ICPSRlovedata23

Join the conversation on social media! Use #LoveData23

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Other Fri, 18 Feb 2022 13:55:54 -0500 2023-02-14T08:00:00-05:00 2023-02-14T18:00:00-05:00 Off Campus Location Inter-university Consortium for Political and Social Research Other Love Data Week 2023 save the date
Loving Longitudinal Data: Added Value Access to NACDA Collections Using the NACDA Colectica Portal (February 14, 2023 2:00pm) https://events.umich.edu/event/103635 103635-21807580@events.umich.edu Event Begins: Tuesday, February 14, 2023 2:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

ICPSR will host a webinar February 14 from 2-3pm EST (11am PST) featuring the NACDA team.

The National Archive of Computerized Data on Aging (NACDA) hosts collections funded by the National Institute on Aging, and with the NIA's support provides preservation and access to data from the Midlife in the United States study (MIDUS), the National Social Life, Health, and Aging Project, and many more longitudinal data collections.

During this webinar, we will:
-Provide an overview of NACDA and the NACDA Colectica Portal
-Describe the benefits of accessing NACDA through the portal and the NACDA website.
Participants will also have the opportunity to provide feedback and ask questions.

Registration is required. This webinar is free and open to the public. This webinar will be recorded and the recording will be sent to all registrants. Accessibility Accommodations: Communication Access Real-time Translation (CART) will be provided, if requested at least 2 weeks in advance of the webinar - please email icpsr-nacda@umich.edu.

Zoom FAQ for Attendees: http://myumi.ch/kx2oo

Registration link: https://myumi.ch/2m59r

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Presentation Wed, 18 Jan 2023 18:21:28 -0500 2023-02-14T14:00:00-05:00 2023-02-14T15:00:00-05:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Love Data Week webinar with the National Archive of Computerized Data on Aging (NACDA)
Love Data Week 2023 (February 15, 2023 8:00am) https://events.umich.edu/event/92579 92579-21692652@events.umich.edu Event Begins: Wednesday, February 15, 2023 8:00am
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Save the date! Love Data Week takes place February 13-17, 2023. Sign up for email announcements and more: https://myumi.ch/ICPSRlovedata23

Join the conversation on social media! Use #LoveData23

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Other Fri, 18 Feb 2022 13:55:54 -0500 2023-02-15T08:00:00-05:00 2023-02-15T18:00:00-05:00 Off Campus Location Inter-university Consortium for Political and Social Research Other Love Data Week 2023 save the date
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.

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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
Love Data Week 2023 (February 16, 2023 8:00am) https://events.umich.edu/event/92579 92579-21692653@events.umich.edu Event Begins: Thursday, February 16, 2023 8:00am
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Save the date! Love Data Week takes place February 13-17, 2023. Sign up for email announcements and more: https://myumi.ch/ICPSRlovedata23

Join the conversation on social media! Use #LoveData23

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Other Fri, 18 Feb 2022 13:55:54 -0500 2023-02-16T08:00:00-05:00 2023-02-16T18:00:00-05:00 Off Campus Location Inter-university Consortium for Political and Social Research Other Love Data Week 2023 save the date
Love Data Week 2023 (February 17, 2023 8:00am) https://events.umich.edu/event/92579 92579-21692654@events.umich.edu Event Begins: Friday, February 17, 2023 8:00am
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Save the date! Love Data Week takes place February 13-17, 2023. Sign up for email announcements and more: https://myumi.ch/ICPSRlovedata23

Join the conversation on social media! Use #LoveData23

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Other Fri, 18 Feb 2022 13:55:54 -0500 2023-02-17T08:00:00-05:00 2023-02-17T18:00:00-05:00 Off Campus Location Inter-university Consortium for Political and Social Research Other Love Data Week 2023 save the date
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.

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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.

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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
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.

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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