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RSD is on the road again!
by Jennifer Kelley - Friday, January 31, 2020, 5:04 PM
 

The RSD Program is traveling to the Population Association of America 2020 Annual Meeting in Washington D.C. on April 22, 2020!

Using Responsive and Adaptive Survey Design for Efficient Population Research

Presenters Include: William G. Axinn, University of Michigan James Wagner, University of ...

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Available courses

Instructor: Brady T. West

Date: July 21, 2021

Time: 9:00 a.m.-1:00 p.m.

This first webinar in a two-part series on implementing interventions in a responsive design framework will discuss a variety of potential RSD interventions. Many of these have been implemented experimentally, and the course will include evaluations of those experiments. The importance of experimental evaluations in early phases of RSD will be discussed. Methods for implementing interventions will also be discussed, including implementation of experiments aimed at evaluating new interventions. Strategies for implementing these interventions with both interviewer-mediated and self-administered (e.g., web and mail) surveys will be discussed. Methods for the evaluation of the results of the interventions (experimental and otherwise) will be considered. These evaluations will include measures of both costs and errors.   

RSD has financial support available to those who qualify.  

Not for academic credit.


Instructor: Brady T. West

Date: July 19, 2021

Time: 9:00 a.m.-1:00 p.m.

This first webinar in a two-part series on implementing interventions in a responsive design framework will discuss a variety of potential RSD interventions. Many of these have been implemented experimentally, and the course will include evaluations of those experiments. The importance of experimental evaluations in early phases of RSD will be discussed. Methods for implementing interventions will also be discussed, including implementation of experiments aimed at evaluating new interventions. Strategies for implementing these interventions with both interviewer-mediated and self-administered (e.g., web and mail) surveys will be discussed. Methods for the evaluation of the results of the interventions (experimental and otherwise) will be considered. These evaluations will include measures of both costs and errors.   

RSD has financial support available to those who qualify.  

Not for academic credit.



Date: July 20, 2021

Time: 9:00 a.m.-1:00 p.m.

This first part of a two-part webinar series on data quality indicators will provide an overview of statistical approaches to evaluating data quality. The response rate has been shown to be a poor indicator for data quality with respect to nonresponse bias. Several alternatives have been proposed – the fraction of missing information (FMI), R-Indicators, subgroup response rates, etc. This webinar will explore the use of these indicators as guides for data collection when working within an RSD framework. We also explore optimization techniques that may be useful when designing a survey to maximize these alternative indicators. The consequences of optimizing a survey to other indicators will be explored. We will also consider how the response rate fits into this approach. We will end with a brief discussion of methods for post data collection evaluation of data quality.  

RSD has financial support available to those who qualify.

Not for academic credit.


Instructors: Barry SchoutenNatalie Shlomo

Date: July 22, 2021

Time: 9:00 a.m.-1:00 p.m.

Prerequisite: RSD Webinar: Data Quality Indicators-Lecture

This second part of a two-part webinar series on data quality indicators will give participants a chance to work through hands-on examples of computing and interpreting the data quality indicators introduced in the first part of the series. Example code will be provided and discussed in detail as students are applying it to real production data. 

RSD has financial support available to those who qualify.

Not for academic credit.


Instructor: Brad Edwards and Victoria Vignare

Date: July 14, 2021

Time: 9:00 a.m.-1:00 p.m.

This second webinar in a two-part webinar series on data visualization for production monitoring will demonstrate concepts from the first webinar using examples from actual dashboards. We will briefly explore methods for modeling incoming paradata in order to detect outliers. We will then consider practical issues associated with the development of dashboards, including software alternatives. Finally, we will demonstrate how to update dashboards using data reflecting the results of ongoing fieldwork. Participants will be provided with template spreadsheet dashboards for their own applications.   

Prerequisite: RSD Webinar: Data Visualization for Active Monitoring-Part 1

RSD has financial support available to those who qualify.  

Not for academic credit.


Instructor: Brad Edwards and Victoria Vignare

Date: July 12, 2021

Time: 9:00 a.m.-1:00 p.m.

This first webinar in a two-part webinar series on data visualization for production monitoring will cover basic concepts for the design and use of “dashboards” for monitoring survey data collection. We will begin with a detailed discussion of how to design dashboards from an RSD perspective. This will include concrete discussions of how relevant data may be collected and summarized across a variety of production environments. We will also discuss how these dashboards can be used to implement RSD interventions on an ongoing basis.

RSD has financial support available to those who qualify.  

Not for academic credit.



Instructors:  Scott CrawfordJoe MurphyStephanie Coffey

Date: July 1, 2021

Time: 9:00 a.m.-1:00 p.m.

Web surveys can be an inexpensive method for collecting data. This is especially true for designs that repeat measurement over several time periods. However, these relatively low-cost data collections may result in reduced data quality if the problem of nonresponse is ignored. This webinar will examine methods for using RSD to effectively deploy scarce resources in order to minimize the risk of nonresponse bias.

Recent experiences with the University of Michigan Campus Climate Survey (UM-CCS), the National Survey of College Graduates (NSCG), and the Residential Energy Consumption Survey (RECS) are used to illustrate this point. These surveys are all defined by phased designs and multiple modes or methods of contact. This approach improves survey outcomes, including response rates, representativeness, and cost by using alternative contact methods in later phases to recruit sample members from subgroups that were less likely to respond in earlier phases. These surveys demonstrate the benefit of RSD in web surveys across a variety of different sample sizes, and both small and large budgets and management teams. As a result, lessons from these experiences can be directly applied in many similar settings.

RSD has financial support available to those who qualify.  

Not for academic credit.


Instructors: Brady T. WestWilliam G. AxinnBarry Schouten 

Date: June 30, 2021

Time: 9:00 a.m.-1:00 p.m.

This webinar will explore several well-developed examples of RSD. Dr. West will serve as a moderator of the webinar, and also introduce a case study from the National Survey of Family Growth (NSFG). The instructors will then provide independent examples of the implementation of RSD in different international surveys using face-to-face and telephone modes of data collection. All case studies will be supplemented with discussions of issues regarding the development and implementation of RSD. Case studies will include the NSFG, the Relationship Dynamics and Social Life (RDSL) survey, and the Netherlands Survey of Consumer Satisfaction, among others. This variety of case studies will reflect a diversity of survey conditions. The NSFG (West) is a cross-sectional survey that is run on a continuous basis with in-person interviewing. The RDSL (Axinn) is a small-scale panel survey that employed a mixed-mode approach to collecting weekly journal data from a panel of young women. The Netherlands Survey of Consumer Satisfaction (Schouten) is a mixed-mode survey combining web and mail survey data collection with telephone interviewing. The focus of the course will be on practical tools for implementing RSD in a variety of conditions, including small-scale surveys.  

RSD has financial support available to those who qualify.  

Not for academic credit.


Instructors: Frauke KreuterAndy PeytchevJames Wagner

Date: June 28, 2021

Time: 9:00 a.m.-1:00 p.m.

This course will provide participants with an overview of the primary concepts underlying RSD. This will include discussion of the uncertainty in survey design, the role of paradata, or data describing the data collection process, in informing decisions, and potential RSD interventions. These interventions include timing and sequence of modes, techniques for efficiently deploying incentives, and combining two-phase sampling with other design changes. Interventions appropriate for face-to-face, telephone, web, mail and mixed-mode surveys will be discussed. Using the Total Survey Error (TSE) framework, the main concepts behind these designs will be explained with a focus on how these principles are designed to simultaneously control survey errors and survey costs. Examples of RSD in both large and small studies will be provided as motivation.  Small group exercises will help participants to think through some of the common questions that need to be answered when employing RSD.  

RSD has financial support available to those who qualify.  

Not for academic credit.


Instructor: Brady T. West

Date: June 25, 2021

Time: 9:00 a.m-1:00 p.m.

This four-hour webinar will focus on the survey methodology topics most important for understanding the objectives of responsive survey design and its applications. One set of tools will focus on maximizing participation and minimizing attrition of survey participants.  Core survey methodology tools for encouraging participation will be featured.  These tools include incentives, tailoring refusal conversion, switching modes, and tracking strategies. A second set of tools will focus on measurement construction. These tools include mode options, questionnaire design issues, and special instruments (such as life history calendars) to minimize reporting error.  Each portion of the course will feature examples applying each specific tool to various real studies.  

RSD has financial support available to those who qualify.

Not for academic credit.



Date: June 21, 2021

Time: 9:00 a.m.-1:30 p.m.

This is the first of two webinars that will introduce participants to a general framework for evaluating and maximizing data quality when working with data from a variety of different study designs. In this first webinar, we will introduce a general framework for evaluating total data quality (TDQ), considering concepts related to sampling, nonresponse, measurement, processing, and data analysis. We will then discuss how to apply this framework to different types of data sources, including designed data (such as surveys) and found / organic data (which arise following some organic process, e.g., consumer transactions), focusing on various metrics for evaluating total data quality.  

RSD has financial support available to those who qualify.  

Not for academic credit workshop.


Date: June 23, 2021

Time: 9:00 a.m.-1:30 p.m.

This is the second of two webinars on the total data quality framework. In this webinar, we will continue our discussion on measuring total data quality. The focus will then turn to tools and techniques for maximizing total data quality (such as responsive and adaptive survey design for designed survey data, weighting approaches, and tools for repairing linkage error). We will present a series of examples considering data from real studies, where the concepts introduced will be applied to vet the total quality of the data sets analyzed. Small-group exercises will be used to give participants hands-on experience with applying some of the concepts discussed to assess data quality.   

Prerequisite: RSD Webinar: Total Data Quality-Part 1

RSD has financial support available to those who qualify.  

Not for academic credit.