phd survey methodology umich

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Leah Christian, Christopher Hansen, Martha McRoy and Zoe Slowinski- Texting in Mixed-Mode Studies: Results from Recent Research Experimentation

Texting in Mixed-Mode Studies: Results from Recent Research Experimentation Using multiple modes of contact has been found to increase participation in surveys over a single contact mode. Text messaging has emerged as a new mode to contact survey participants in mixed-mode survey designs, especially for surveys that include web and/or phone data collection. However, it has been unclear how to best combine text messages with mailings and other outreach contacts to improve response…

Frauke Kreuter - Leveraging AI for Survey Research - JPSM MPSDS Seminar

Frauke Kreuter Leveraging AI for Survey ResearchThis presentation scrutinizes the transformative potential of Large Language Models (LLMs) insurvey research, focusing on three critical areas: questionnaire design, synthetic data creation, and the role of LLMs as qualitative interviewers. In the domain of questionnaire design, the lecture delves into if and how LLMs can construct contextually accurate and highly effective survey items. However ,there are valid concerns about the model’s…

Rebecca Andridge - When “representative” surveys fail: Can a non-ignorable missingness mechanism explain bias in estimates of COVID-19 vaccine uptake? - JPSM MPSDS Seminar

JPSM MSPDS Seminar March 13, 2024 When “representative” surveys fail: Can a non-ignorable missingness mechanism explain bias in estimates of COVID-19 vaccine uptake? Recently, attention was drawn to the failure of two very large internet-based probability surveys to correctly estimate COVID-19 vaccine uptake in the U.S. in early 2021. Both the Delphi-Facebook COVID-19 Trends and Impact Survey (CTIS) and Census Household Pulse Survey (HPS) overestimated vaccine uptake…

6th Likert Symposium: Meeting Respondents Where They Are

6th Annual Likert Workshop Meeting Respondents Where They Are March 8,2024 Survey measurement is complicated. We strive to ask questions that respondents understand and can answer as we intend them to. But often there is a gulf between the ideal respondents we design questions for and the real respondents who answer them. The Sixth Annual Likert symposium features four presentations about the speakers' experiences incorporating the respondents’ perspective and circumstances into the…

Martin Slawski - Recent Developments and Open Problems in Post-Linkage Data Analysis - JPSM MPSDS Seminar

Recent Developments and Open Problems in Post-Linkage Data Analysis Record linkage and subsequent data analysis of the linked file with suitable propagation of uncertainty can be performed if the analyst also happens to be the linker or at least has comprehensive information about how the data were linked. However, it is rather common that the two processes are considered in a separate fashion, with the analyst being handed a linked file that is possibly subject to substantial…

Brady West - A Novel Methodology for Improving Applications of Modern Predictive Modeling Tools to Linked Data Sets Subject to Mismatch Error -JPSM MPSDS Seminar

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…

Michael Elliott - Using Synergies Between Survey Statistics and Causal Inference to Improve Transportability of Clinical Trials - JPSM MPSDS Seminar

Michael Elliott is a Professor of Biostatistics and a Research Professor at the Institute for Social Research. He received his Ph.D. in biostatistics in 1999 from the University of Michigan. Prior to joining the University of Michigan in 2005, he held an appointment as an Assistant Professor at the Department of Biostatistics and Epidemiology at the University of Pennsylvania School of Medicine. Dr. Elliott's research interests include the design and analysis of sample surveys, causal…

Lane Burgette - 2020 California Neighborhoods Count - JPSM MPSDS Seminar - November 8, 2023

2020 California Neighborhoods Count: A validation of U.S. Census Population Counts and Housing Characteristic Estimates within California In response to long-standing concerns about the accuracy of census data and about a possible undercount, we conducted the California Neighborhoods Count (CNC) study — the first-ever independent, survey-based enumeration to directly evaluate the accuracy of the U.S. Census Bureau's population totals for a subset of California census…

Jeremy Seeman - Flexible Formal Privacy for Public Data Curation - JPSM MPSDS Seminar - November 1, 2023

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…

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JPSM/MPSM Seminar Series - Frauke Kreuter - December 12th 2018

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JPSM/MPSM Seminar Series - Lingxiao Wang - November 14th 2018

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JPSM/MPSM Seminar Series - Brady West - November 7th 2018

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Survey Methodology/Survey Research

FROM STUDY TO SKILLS

Survey Methodology studies sources of error in surveys—the bias and variability that affect the quality of survey data. As a field of knowledge, a profession, and a science, survey methodology seeks to link the principles of survey design, collection, processing, and analysis to an understanding of error.

Survey Methodology is an inherently multidisciplinary field. Achieving high quality survey results requires applying principles from traditional academic disciplines such as statistics and the social sciences. Statistics provides a quantitative foundation while social and cognitive psychology supplies the framework for understanding how human behavior affects accuracy in survey responses. Sociology and anthropology offer theories of social stratification and cultural diversity. Finally, computer science provides principles of database design and human-computer interaction.

Related fields include Statistics, Informatics, Sociology, Psychology, Economics, and Mathematics.

SKILLS AND ABILITIES

Every survey involves a number of decisions about design and implementation, and each decision can potentially affect the quality and validity of the results. How will the sample be chosen? What mode will be used to pose questions and collect answers? All surveys involve compromises, and the challenge for the researcher is to determine how best to use the available resources to produce, on balance, the best results.

As a student of survey methodology, you will be developing the following skills and abilities:

Analytical Skills

Applying methods to problems Projecting from data Reasoning critically Categorizing data Developing theories Designing systems for processing data

Quantitative Skills

Computer programming Mathematical modeling Designing questionnaires Developing sample forms Mathematical analysis Applying statistical packages Interpreting data from tables / charts

Problem-Solving Skills

Formulating problems Applying logic to problems Assessing needs Distinguishing relevant / extraneous information Interpreting data Understanding components of complex problems

Research and Communication Skills

Writing reports / publications Evaluating collected data Identifying areas for research Gathering data / processing Presenting alternative explanations Planning / designing project Translating theory into research plans

ADDITIONAL RESOURCES

To identify internships or job opportunities, visit Career Center Connector:  www.careercenter.umich.edu/article/career-center-connector

To begin connecting to professionals in fields that interest you, create your own LinkedIn account: www.careercenter.umich.edu/article/getting-started-linkedin

FROM SKILLS TO CAREER

Graduates at the Masters and PhD levels in Survey Methodology are in high demand in government, academic, and private industry jobs. Government agencies, such as those in the U. S. federal statistical system (e.g., Bureau of the Census, Bureau of Labor Statistics) spend approximately $4 billion annually on economic and social information collection and dissemination and employ 12,000 staff. The commercial sector presents job opportunities in survey research firms and market research, with annual gross revenues of $10-15 billion and more than 45,000 technical staff. Academic survey centers and, recently, survey methodology education programs, seek staff and faculty with specialization in survey methodology.

The following is a selected list of occupations compiled from information about Michigan graduates and from national data.

Economist Policy analyst Census Bureau analyst Interviewer Sales forecaster Operations research analyst Health policy analyst Risk management analyst

Quantitative Techniques

Statistician Survey statistician Program statistician Survey manager Research administrator

Employment specialist Opinion pollster Organizational consultant Hospital Risk Management

Research Assistant / Associate Research Scientist Legislative Researcher Professor Marketing research Lobbyist Media (TV, Radio, Newspaper, Internet)

For more career information, see O*NET at http://online.onetcenter.org/

ACADEMIC PROGRAMS

Several programs are housed within the Institute for Social Research which train and educate students and professionals in methodology and skills of social science research. Together, these programs offer courses covering most aspects of survey research with a wide variety of methodological and substantive topics.

The SRC Summer Internship Program offers a paid summer internship for undergraduates who have completed sophomore year (or higher) and graduate students with an interest in social science research. http://www.src.isr.umich.edu/content.aspx?urlid=1039

Program in Survey Methodology offers programs of study at the doctoral, master’s and certificate levels. www.psm.isr.umich.edu , 734-764-0038

Survey Research Center (SRC) Summer Institute in Survey Research Techniques provides rigorous and high quality graduate training in all phases of survey research. It is also open to advanced undergraduates. www.si.isr.umich.edu , 734-764-6585

Inter-University Consortium for Political and Social Research (ICPSR) Summer Program in Quantitative Methods is recognized throughout the world as the preeminent forum for basic and advanced training in the methodologies and technologies of social science research.  www.icpsr.umich.edu/sumprog , 734-763-7400

For more information, contact:

Institute for Social Research 426 Thompson Street Ann Arbor, MI 48106-1248 734-615-4883 www.isr.umich.edu

The Career Center 3200 Student Activities Building 734-764-7460 www.careercenter.umich.edu http://www.facebook.com/careercenter.umich http://twitter.com/careercenter

The Career Guide series was developed by the University of Michigan Career Center, Division of Student Affairs, in cooperation with the Institute for Social Research. ©2013 Regents of the University of Michigan

Research Professor/Research Associate Professor/Research Assistant Professor (Open Rank) – Survey Methodology Program

How to apply.

Interested applicants will submit a cover letter describing their scholarly activities, any funded research programs and plans, and interest in the Survey Research Center; a curriculum vitae; list of references; and up to three recent publications; and a diversity, equity, and inclusion (DEI) statement. All applicants must submit their applications on our online applications site, Interfolio, at: https://apply.interfolio.com/125734

Please direct questions or inquiries to [email protected] . Reference # 234823.

Review of applications will begin immediately and will continue until the position is filled.

The Survey Research Center (SRC) in the Institute for Social Research at the University of Michigan has conducted investigator-initiated, survey-based research on theoretical and applied problems of both social and scientific importance for over 70 years ( https://src.isr.umich.edu ). SRC has over 250 research and support staff and research volumes of about $85 million per year.

SRC is the site of a large group of PhD-level survey methodologists that includes Fred Conrad, Mick Couper, Michael Elliott, Steven Heeringa, Sunghee Lee, Roderick Little, Trivellore Raghunathan, Yajuan Si, James Wagner, and Brady West. Together they form the Survey Methodology Program (SMP), which pursues cutting-edge statistical and methodological research with investigator-initiated research grants. SMP also houses the Michigan Program in Survey and Data Science (MPSDS), a graduate degree-granting program at the University of Michigan. MPSDS has both a Masters and PhD program, and offers the opportunity for the candidate to both advise and provide support to graduate students for shared research goals. It is anticipated that the candidate will be teaching in the MPSDS program for up to a maximum of 50% effort during the academic year.

The center also contains the Survey Research Operations (SRO) unit, which has over 150 technical staff working on applied design and implementation of large complex sample surveys with advanced data collection technologies. These include large-scale ongoing longitudinal surveys, one-time complex mixed mode designs (face-to-face, web, mail, telephone, bodily fluid samples, administrative records), and development of large-scale survey software capabilities. The candidate will be expected to provide regular technical guidance to SRO, and also encouraged to conduct methodological research that may benefit SRO operations. More generally, candidates will be able to pursue their own research interests through external funding and collaborate with other scientists in ongoing research programs at the University of Michigan and beyond.

The center invites applications from (and nominations of) outstanding candidates for an open rank Research Professor position. Suitable candidates at the Research Assistant Professor level will be recent doctoral graduates from programs in the quantitative and computational social sciences. Candidates at this level should have a demonstrated interest in innovative survey methodology and primary data collection; evidence of a developing research agenda; and an initial record of research dissemination in this area. Suitable candidates at the Research Associate Professor or Research Professor ranks will have an established record of accomplishment and excellence in obtaining research funding and publishing original and innovative research in survey methodology that preferably incorporates new data sources (e.g., social media data, administrative data, biosocial data, sensor data). An established teaching record defined by consistently positive student evaluations would also be preferable for all levels but is not required.

Applicants must include a statement in their cover letter that addresses suitability for this position, including experience with innovative research in survey methodology or other primary data collection. Applicants who have experience only in secondary analysis of survey data will not be considered. Applicants will also be expected to live within commuting distance of Ann Arbor, Michigan, as on-site work will be an expectation for this position.

Successful candidates are expected to demonstrate knowledge and interest in survey methodology, including (for example) topics in questionnaire design, innovative approaches to measuring and reducing nonresponse and measurement error, mode effects, web survey methodology, responsive and adaptive design, longitudinal survey design and analysis, total survey error, interviewer effects, uses of big data for conducting social research, statistical privacy protection, network analysis, natural language processing, integrating survey data with other new data sources (e.g., administrative, biosocial, or social media data), and other areas related to the fundamentals of data collection from human populations. Candidates are also expected to engage in regular teaching and mentoring activities. We are interested in researchers who would thrive in our entrepreneurial, interdisciplinary, collegial, yet highly autonomous culture. Individuals from groups historically under-represented in the social sciences are strongly encouraged to apply.

Required Qualifications

Applicants must have a doctoral degree in the quantitative social sciences or related disciplines.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.

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Faculty Profile

Michael R. Elliott, PhD

• Professor, Biostatistics
• Research Professor, Survey Methodology
• Courses Taught Courses Taught
• Graduation Cap: Google Scholar
• PDF: Curriculum Vitae
• Book: Michigan Research Experts

Michael Elliott is a Professor of Biostatistics at the University of Michigan School of Public Health and Research Professor at the Institute for Social Research. He received his PhD in biostatistics in 1999 from the University of Michigan. Prior to joining the University of Michigan in 2005, he held an appointment as an Assistant Professor at the Department of Biostatistics and Epidemiology at the University of Pennsylvania School of Medicine. Dr. Elliott's statistical research interests focus around the broad topic of "missing data," including the design and analysis of sample surveys, causal and counterfactual inference, and latent variable models. He has worked closely with collaborators in injury research, pediatrics, women's health, the social determinants of physical and mental health, and smoking cessation research. Dr. Elliott has served as an Associate Editor for the Journal of the Royal Statistical Society, Series C and the Journal of the American Statistical Association, and as an Associate Editor and Editor of the Journal of Survey Statistics and Methodology. He was Associate Chair of Academic Affairs for the Department from 2018-2021.

• PhD, University of Michigan, 1999
• MS, University of Michigan, 1997
• BA, University of Chicago, 1985

Research Interests & Projects

• Survey design and analysis, missing data, causal inference, longitudinal data
• Developing model-based Bayesian approaches that complement traditional design-based analyses of complex sample survey data.
• Combining probability and non-probability samples.
• Developing methods to assess surrogate markers using causal inference (determining to what degree easy-to-observe biomarkers are on causal pathway for outcomes that are expensive or time-consuming to observe).
• Developing methods to adjust for bias due to "treatment by indication" in observational settings, where preliminary outcomes may drive treatment decisions.
• Improving the generalizability of clinical trials by incorproating information from relevant probability samples.
• Developing models that focus on variability structures rather than, or in addition to, mean structures to predict health outcomes.

Research Projects: Lead biostatistician on the Michigan cohort for the Environmental Influences on Child Health Outcomes (ECHO), a national study of children for the National Institutes of Health.

Worked in women's health issues, including studies designed to understand the onset of menopause and to predict and ultimately treat health problems that accompany the menopausal transition.

I collaborate with researchers at the Institute for Social Research on environmental effects on health outcomes, including factors such as work stress and built environment on health in later life.

I collaborate with researchers at the University of Michigan Transportation Research Institute on a wide variety of topics related to driving behavior and driver safety, including work on a large naturalistic driving study funded though the Michigan Institute for Data Science (MIDAS).

I collaborate with smoking cessation researchers, in particular to assess the impact of various legal restrictions on smoking behavior.

Selected Publications

Tan, Y. V., Flannagan, C.A.C., Elliott, M.R. (2021). "Accounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study," Statistics in Medicine , 40, 2613-2625.

Elliott, M.R ., Zhao, Z., Muhkerjee, B., Kayna, A., Needham, B.L. (2020). "Methods to Account for Uncertainty in Latent Class Assignments when using Latent Classes as Predictors in Regression Models, with Application to Acculturation Strategy," Epidemiology , 31, 194-204.

Zhou, T., Elliott, M.R. , Little, R.J.A. (2019). "Penalized Spline of Propensity Methods for Treatment Comparison," Journal of the American Statistical Association (with discussion) , 114, 1-38.

Elliott, M.R. , Valliant, R. (2017). Inference for Non-probability Samples. Statistical Science , 32, 249-264.

Zhou, H., Elliott, M.R. , Raghunathan, T.E. (2016). A Two-Step Semiparametric Method to Accommodate Sampling Weights in Multiple Imputation. Biometrics 72, 242-252.

Jiang, B., Elliott, M.R. , Sammel, M.D., Wang, N. (2015). Joint Modeling of Cross-Sectional Health Outcomes and Longitudinal Predictors via Mixtures of Means and Variances. Biometrics , 71, 487-497.

Elliott, M.R. , Conlon, A.S.C., Li, Y., Kaciroti, N., Taylor, J.M.G. (2015). Surrogacy Marker Paradox Measures in Meta-Analytic Settings. Biostatistics , 16, 400-12.

Elliott, M.R. , Sammel, M.D., Faul, J. (2012). Variability as a Predictor of Health in Longitudinal Studies. Statistics in Medicine , 31, 2745-2756.

Elliott, M.R. , Raghunathan, T.E., Li, Y. (2010). Bayesian Inference for Causal Mediation Effects Using Principal Stratification with Dichotomous Mediators and Outcomes. Biostatistics , 11, 353-372.

Lin, J.Y., Ten Have T.R., Elliott, M.R. (2009). Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance. Biometrics , 65, 505-513.

Elliott, M.R. (2009). Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models. Journal of Official Statistics , 25, 1-20.

Elliott, M.R. , Little, R.J.A. (2005). A Bayesian Approach to 2000 Census Evaluation using A.C.E. Survey Data and Demographic Analysis. Journal of the American Statistical Association , 100, 380-388.

Contact Information

M4124 SPHII 1415 Washington Heights Ann Arbor, MI 48109 Email: [email protected] Office: 734-647-5160 For media inquiries: [email protected]  

Areas of Expertise: Aging ,   Biostatistics ,   Child Health ,   Clinical Trials ,   COVID-19 ,   Health Policy ,   Maternal Health ,   Substance Use ,   Women’s Health

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Survey Data Collection and Analytics

Description.

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data.

Faculty for this specialisation comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection firm Westat, founded by the National Science Foundation and the Interagency Consortium of Statistical Policy in the U.S. to educate the next generation of survey researchers, survey statisticians, and survey methodologists. In addition to this specialization we offer short courses, a summer school, certificates, master degrees as well as PhD programs.

U-M Credit Eligible

Instructors.

Frederick Conrad

Research Professor, Survey Methodology

Institute for Social Research

Frauke Kreuter

Professor, Joint Program in Survey Methodology

James Lepkowski

Research Professor

Courses (4)

Data Collection: Online, Telephone and Face-to-face

Questionnaire Design for Social Surveys

Sampling People, Networks and Records

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The Summer Institute in Survey Research Techniques, housed at the University of Michigan's Institute for Social Research (ISR), supports U-M's...

View Webinar here The Sampling Program for Survey Statisticians, founded by Leslie Kish, will be offered as part of the...

The Summer Institute for Survey Research Techniques course are currently offered in an alternative remote format except for the Sampling...

  Offered in partnership with the Summer Institute in Survey Research Techniques, the RSD webinars combine an explanation of the...

77th Annual Summer Institute

June 3 – July 26, 2024

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 .

All 2024 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 2024 course schedule ) although no formal academic credit is actually earned.  Registration is open until the week before a course is scheduled to start.

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 US Eastern 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.

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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• Prospective Students
• Programs of Study
• Survey and Data Science Master's Program| University of Michigan

Compare U-M to Other Survey and Data Science Programs

Why two U-M grad students believe the University of Michigan has the best master’s in data science and survey science.

A Master’s Degree That Gives You Career Options

You have a lot of choices when it comes to pursuing a master’s degree. But the University of Michigan is one of the few institutions in the world to offer a survey and data science master’s degree program.

Our graduate program in survey and data science teaches you how to harness the power of survey data and new, non-designed data to gain insights, understand the public’s opinions, behaviors, and beliefs in order to inform policy. It’s the kind of graduate education that will help you innovate in socially valuable work over the course of your career.

Without survey and data science master’s program rankings, it can be a challenge to find the right degree. Learn more from two of our students and see why they chose Michigan.

Comparing top data science graduate programs requires more than survey methods

For Chendi Zhao, a native of Beijing, China, a recommendation from a faculty member led her to U-M’s survey and data science master’s program.

“My professor thought the program would offer the best training and fit my academic goals,” said Zhao. “As I checked out the program, I was impressed by the great teachers, students with different backgrounds, and cutting-edge research programs.”

Curtiss Engstrom, of Iron River, Wisconsin, looked beyond the lack of survey and data science master’s program rankings. After considering both high-rated public institutions and private, Ivy League schools, he knew U-M’s graduate program would position him well to pursue a career educating the next generation of survey researchers.

“I wanted a graduate program that not only focused on survey methodology and data science but also emphasized applications in the field,” said Engstrom. “The survey and data science master’s degree at U-M really felt right to me.”

So, what exactly makes the U-M program one of the best master’s in data science and survey science programs? Let’s take a closer look.

1. You will be hard pressed to find a master’s program that gives you the best of both worlds — jobs in survey methods and data science.

You might have discovered top data science graduate programs. Or you might be looking at master’s degrees that focus on social research methods. But at Michigan, you are trained to work with both kinds of data in one program that marries the two fields. While our master’s program has three concentrations — data science , social and psychological science , and survey statistics — graduate students gain a broad spectrum of skills and knowledge.

“One aspect I love about the Michigan Program in Survey and Data Science is that everyone is required to take graduate courses within the other two tracks, so we don’t focus solely on one aspect of the subject matter,” says Zhao. “Instead, you gain a more comprehensive and detailed knowledge of the field.”

U-M realizes it’s important to provide graduate students with knowledge and expertise in survey methodology and its intersection with data science. Engstrom, who is now pursuing Michigan’s PhD program in survey and data science , says U-M takes a unique approach when it comes to defining data science.

“Michigan’s program stands out because of their emphasis on applications of data science into the survey world,” says Engstrom. “They teach us not only what data science is, but also how it changes based upon how we collect data.”

2. Other programs are too big to know your name. At U-M, you will have personalized, graduate-level support during your studies.

Students like Engstrom say size is one of the top reasons to pursue the University of Michigan’s graduate degree in survey and data science. Sure, the university is big, but the program is small enough to offer opportunities for meaningful and valuable discussions with instructors both in and outside the classroom.

“Michigan’s master’s program is smaller than some others that I looked into, including Princeton, North Carolina, and others. The faculty not only get to know you, but they work with you and help you on any project — even if you’re not in one of their classes or labs,” said Engstrom. “The faculty and staff care if you succeed and are available to answer questions.”

In fact, Michigan’s survey and data science master’s program rarely has classes of more than 20 students. This means you will have more access to some of the best master’s in data science and survey methodology faculty members.

For Zhao, who researched many of the top data science graduate programs, especially with a focus on survey methodology, Michigan stood out because of the personalized environment. It didn’t matter that there aren’t survey and data science master’s program rankings.

“I wanted a graduate program that had small cohorts of students, so everyone can get the best training,” said Zhao.

Small classes also mean that our faculty members have the time to mentor you and help you grow as a leader. For example, they may offer valuable feedback on your resume or CV, help you prepare for job interviews, or connect you with potential employers and career opportunities after graduating from one of the best master’s in data science and survey science programs in the country.

3. What other survey and data science master’s program has our name recognition? At U-M, your master’s degree will be well-respected by future employers.

You already know that survey and data science master’s program rankings aren’t available, but the University of Michigan is the No. 3 public university in the United States, according to U.S. News & World Report. It’s a ranking that carries a lot of weight in general and especially in the survey and data science industry. Zhao says that Michigan’s top-tier status played an influential role in her decision-making process.

“Michigan is one of the most highly ranked schools in the world, with excellent graduate school programs,” said Zhao. “It’s a place where people can fully focus on their academics and experience a thriving and energetic campus culture at the same time.”

When Zhao graduates from the University of Michigan, she will also benefit from one of the largest and most successful alumni networks in the world at more than 600,000 strong. She expects a master’s degree from one of the top data science graduate programs that focuses on survey methods to open doors to success.

“I hope to gain the knowledge and practical skills to be prepared for my future career,” said Zhao, whose goal is to work in an academic survey center. “I want to apply my sociology and survey methodology background in a fulfilling way.”

4. You won’t find a master’s program with more research opportunities in one location.

No academic institution does more survey and data science research than the University of Michigan. Through the U-M Institute for Social Research (ISR), which is the world’s oldest academic survey research organization, our graduate students can pursue research projects that interest them. With over 300 research scientists in more than 20 academic disciplines, the ISR offers ample opportunities to study and practice survey methods and to apply them to and with data science.

Through ISR, Engstrom has been able to pursue his passions in graduate research at the Center for the Study of Drugs, Alcohol, Substance, and Health (DASH) in the U-M School of Nursing. He’s worked on research of sexual and gender minorities and measurement differences in surveys, how measurement differences can affect estimates of health outcomes, and the use of agree/disagree questions in questionnaire design.

“I’ve learned patience and cooperation but more importantly how imperative collaboration is in research,” says Engstrom. “I am not going to know everything about a certain topic. That’s why discussing uncertainty with others about a topic is key to making research as great as possible.”

As a result, students like Engstrom gain experience and expertise that will help them stand out in the competitive field of survey and data science.

You, too, can stand out to future employers by pursuing a master’s degree in survey and data science from the University of Michigan.

Get started on your survey and data science master’s degree

Ready to reap the benefits of earning a master’s degree in survey and data science? Call us at 734-647-0038 or fill out a request for more information .

Submit your graduate college application online today for the Master of Survey and Data Science degree program from the University of Michigan.

Explore financial options

Put your survey and data science master’s degree within reach by exploring your options in paying for graduate school .

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• Prospective Students

Certificate in Survey and Data Science

• Course Descriptions

The Certificate program is designed for graduate students in departments other than the survey and data science program. While not required, it is preferred that students be enrolled in a graduate degree program at the University of Michigan or another graduate institution, or have completed a Masters or PhD within the last 5 years to be eligible to receive a Certificate in Survey and Data Science.

The Certificate in Survey and Data Science is intended to be a part-time program. International students need to be enrolled in a full time program within the University to obtain and maintain F-1 visa status, the Certificate Program is not suited for international students who are not already enrolled as a full time student in another program.

Certificate Schedule                                                                                                                                  

A sequence of courses comprising a minimum of 15 hours is required. Certificate courses should be completed within a two-year period.

• SurvMeth 600 - Fundamentals of Survey Methodology (3 credit hours)
• SurvMeth 621 - Fundamentals of Data Collection I (3 credit hours)
• SurvMeth 622 - Fundamentals of Data Collection II (3 credit hours)
• SurvMeth 625 - Applied Sampling (3 credit hours)
• Approved Survey Methodology (SurvMeth) Elective (3 credits)

Certificate requirements

Application Process

• For current Rackham students:   Rackham’s Dual Admission  online form.  This includes uploading the  Rackham Pre-Approval Form for Dual Admissions form. 
• For non-Rackham U-M students:   Rackham's online application . Submit Rackham’s required materials/transcripts, and pay the application fee. Graduate Certificate applicants do not need to submit new test scores, but a final official copy of undergraduate transcripts/credentials must be submitted to Rackham. 

Application deadline

• July 1 for study beginning fall term

Financial Aid:

Students enrolled only in the certificate program are not eligible for federal financial aid.  A student must be enrolled in a full degree program to be eligible for federal financial aid.

Contact  MPSDS  program offices  for details on the certificate program curriculum.

Bildverarbeitung mit MATLAB

Wenden Sie praktische Bildverarbeitungs-Workflows auf Bilder aus verschiedenen Branchen an. Machen Sie sich mit verschiedenen Ansätzen zur Problemlösung vertraut und vertiefen Sie Ihre Kenntnisse der Grundlagen der Bildverarbeitung mit MATLAB®.

• Course Overview

Familiarize yourself with the course.

Working with Image Data

Import, visualize, and extract information from different image types and image data types.

• Introduction
• Extracting Metadata from Image Files
• Representing Different Image Types
• Converting Image Data Types
• Working with Binary Images

Preprocessing

Preprocess images to improve algorithms: enhancing contrast, noise removal techniques, block processing, and quality metrics.

• Adjusting Contrast
• Block Processing
• Filtering Noise
• Quality Metrics
• Background Subtraction

Color Segmentation

Use color spaces, regions of interest, and the Color Thresholder app to segment images based on color.

• Color Thresholding
• Color Spaces
• The Color Thresholder App
• Segment Based on a Region of Interest

Texture Segmentation

Use range, entropy, and standard deviation filters to separate regions based on texture.

• Texture Filters

Improving Segmentations

Refine your segmentation with morphological operations. Automate segmentation from a seed mask using iterative techniques.

• Cleaning Binary Masks
• Growing Segmentations with Active Contours
• Growing Segmentations with the Fast Marching Method
• Using the Image Segmenter App

Finding and Analyzing Objects

Separate overlapping objects in your segmentation. Label objects and measure their properties, such as area and perimeter.

• Working with Connected Components of a Mask
• Separating Overlapping Objects with Watershed Segmentation
• Measuring Shape Properties

Detecting Edges and Shapes

Detect edges of objects and identify lines and circles in an image.

• Detecting Edges
• Detecting Circles
• Detecting Lines

Batch Processing

Process large numbers of files using the Image Batch Processor app and image datastores.

• Using the Image Batch Processor App
• Batch Processing with Image Datastores

Aligning Images with Image Registration

Register images using phase correlation, control points, and feature matching.

• Applying Geometric Transformations
• Estimating a Geometric Transformation
• Mapping Control Points
• Matching Image Features

Learn next steps and give feedback on the course.

• Additional Resources

Format:Kurs zum Selbststudium

Sprache:Deutsch

Sprache Praktische Übungen mit automatischem Feedback Zugang zu MATLAB über Ihren Webbrowser Teilbarer Fortschrittsbericht und teilbares Kurszertifikat Computer Vision Onramp

Erlernen Sie die Grundlagen von Computer Vision und entwickeln Sie einen Objekt-Detektor und -Tracker.

Deep Learning Onramp

Machen Sie die ersten Schritte mit Deep-Learning-Methoden für die Bilderkennung.

Signal Processing Onramp

Eine interaktive Einführung in Signalverarbeitungsmethoden für die Spektralanalyse.

Image Processing Onramp

Erlernen Sie die Grundlagen praktischer Bildverarbeitungstechniken in MATLAB.

Zellen und Strukturen

Verwenden Sie Zellarrays und Strukturen, um Daten unterschiedlicher Größe und unterschiedlichen Typs zu speichern.