78th Annual Conference Short Courses

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The 2023 Conference will include eight short courses to enhance your learning experience. These in-depth, half-day courses are taught by well-known experts in the survey research field and cover topics that affect our ever-changing industry. You will have the opportunity to register for the Short Courses during the conference registration process.

Virtual Short Courses:

Using Cell Phone Tracking for Predictive Polling

Virtual  |  May 1, 2023  |  10:00 am – 1:30 pm ET

Presenter(s): Thomas Young, Youngceltic

The course covers the entire process of using cell phone tracking data to model political polling. The initial steps use R and Python to extract large data sets and transform them into useful structures for modeling at Census Block Groups. We will also cover visualization and modeling of political affiliation.

Introduction to Data Integration for Combining Probability and Non-Probability Samples

Virtual  |  May 1, 2023 |  2:00 pm – 5:30 pm ET

Presenter(s): Sixia Chen, University of Oklahoma Health Sciences Center

Non-probability samples have been used frequently in practice including education, medical study, and public opinion research. Due to selection bias, naïve estimates without adjustments by using non-probability samples may lead to misleading results. In this course, we will include the following topics: 1. Introduction to probability samples, non-probability samples, and their applications in practice; 2. Calibration weighting approach; 3. Propensity score weighting approaches; 4. Mass imputation approaches; 5. Hybrid approaches by combining both propensity score weighting and mass imputation approaches. For each of the previous topics, we will provide hands on exercises by using some real data applications including National Health Nutrition and Examination Survey, the Behavioral Risk Factor Surveillance System, and National Health Interview Survey by using SAS/R computational codes. Computational codes will be made publicly available for audience to use.

Open-ended Questions to Measure Opinions, Preferences, and Values

Virtual  |  May 2, 2023  |  10:00-1:30 ET

Presenter(s): Eric Plutzer, Penn State University

Web survey technology makes it easy to ask open-ended questions and their use has risen sharply in the last decade.  This course provides a hands-on overview of the ways that open-ended questions can be effectively utilized to provide valid and reliable measures of opinions, preferences, and values.

The course will begin with a review the four main ways that survey methodologists have used open-ended questions: a) Probing to improve question wording and questionnaire design. b) Elaboration of answers to prior forced-choice questions. c) Exploratory opinion research allowing respondents to answer “in their own words” rather than in terms posed by the investigator. d) Measurement of system-1 (thinking fast) considerations and associations through “top-of-head” responses.

In the middle third of the course, attendees will work through practical issues and challenges concerning question design, eliciting thoughtful responses, qualitative data analysis, quantitative content analysis, and computational approaches to analysis (e.g., topic models).

The final third of the class will focus on research ethics, effective reporting, and how researchers can apply AAPOR and professional norms of transparency and replicability to open-ended question data.

Demystifying Nonprobability Samples from Online Panels

Virtual  |  May 3, 2023  |  2:00-5:30 ET

Presenter(s):  Mansour Fahimi, Marketing Systems Group

Conducting credible survey research in the 21st century is subject to evolving challenges that require thinking outside of the traditional survey sampling toolbox. While the statistical machinery developed by Neyman (1934) made it possible to produce measurable inferences based on samples of modest size; however, that paradigm relies on requirements that are becoming exceedingly difficult to fulfill. Specifically, samples must be selected from complete frames with known selection probabilities and surveys need to secure near-perfect rates of response. Since most surveys conducted these days struggle to satisfy these requirements, there is a growing interest in hybrid sampling alternatives that rely on inexpensive samples from online panels. Have we come upon a panacea?

This empirical webinar will demystify the type of respondents one can secure quickly from online panels comprised of individuals ready to take surveys 24/7, oftentimes with inadequate due diligence. Results are based on several recent surveys conducted using a common questionnaire with dozens of questions for which independent benchmarks were available for external validity comparisons. Moreover, the author will offer sampling and calibration suggestions that can help improve the representation of respondents from such panels, without any promise of alchemy that may attempt to convert copper to gold.

In Person Short Courses:

You will be prompted to register for in-person short courses during the conference registration process.

Fresh, Organic Data for All: A Comprehensive Overview of Big Data Collection Methods

In Person  |  May 9, 2023  |  2:00 pm to 5:30 pm ET

Presenter(s): Trent Buskirk, Bowling Green State University, Sarah Kelley, Child Trend, & Claire Kelley, Child Trend

The abundance of information in our data-centric world provides survey researchers and social scientists with unprecedented new ways to understand public opinion.  While some quantitative social scientists rely almost exclusively on these so-called organic or big data, current practice in survey research suggests their use is still in the early phases with some survey researchers combining traditional survey data with these alternate sources.    But unlike survey data where the data collection processes are well established and defined in practice, access to big data sources is rather heterogeneous.  Some researchers access or generate big data through the use of third-party sensors while others may rely on application programming interfaces (APIs) and yet others may source data using web scraping methods.

In this course, we provide a comprehensive overview of established and new methods for sourcing big data and evaluating the sourced data for overall quality.  We contrast emerging sampling methods with APIs with web scraping and discuss various third-party software solutions that also automate the process and provide a critical assessment of various aspects of these tools that may impact representation and reproducibility. Extensive examples illustrating how to gather data from Twitter, Zillow, Census, and TikTok among others, will be presented.

All Ifs and Buts and Nots About Qualitative Research Methods: Data & Analysis

In Person  |  May 9, 2023  |  2:00 pm to 5:30 pm ET

Presenter(s):  Ellie Abdi, Internationa Academic Enterprises & Angela Randal-West, Internationa Academic Enterprises

In this course, the audience will be introduced to the ideas behind qualitative research in social sciences. We focus on the idea once conducting qualitative research which is at its core, by asking open-ended question(s) such as ‘how-what’ and ‘why’. Due to the open-ended nature of the research question(s), qualitative research design is linear toward descriptive, in the form of interviews, observations, surveys, and documents to withdraw data analysis inductively using a particular set of facts to form a general principle. Since the research and the question(s) are not quantitative, we will examine that ideas are based on understanding views and perceptions with a deep investigation of principal findings. Social scientists often want to understand how individuals think, feel, or behave in particular situations, or in relations with others. Therefore, this course speaks on qualitative methods [In-depth interviews, observations, surveys, and related documents] gather for data use. For example in human performance: why and how humans interpret specific situations such as describing actions using words, arts, drama, and music.

The course emphasizes how the data is taken and analyzed to investigate the problem and answer the research question(s), the next claim is built, and further knowledge around the specific topic or problem is constructed which the course will explore briefly. The course offers practical knowledge in qualitative study which emphasizes on a holistic approach and final outcomes; accordingly, to the sources of data such as real-world situations, natural, non-manipulated settings will be discussed. Together with receiving information on data collection (survey, interview, observation, and documents) followed by analyzing and interpreting the collected data, audience can hypothesize how research will form to its final steps.

At the end, the course will seek to offer familiarity with best practices to address ethical (and IRB and CITI-related) concerns, quality criteria, and good practices (validity, legitimacy, and credibility), as well to consider how best to balance transparency with confidentiality.

This short course is sponsored by:


Respondent-Driven Sampling: Overview and Practical Tools

In Person  |  May 9, 2023  |  2:00 pm to 5:30 pm ET

Presenter(s): Sunghee Lee, University of Michigan

Respondent-driven sampling (RDS), proposed as an alternative for hard-to-sample populations, has been increasingly applied to numerous studies, including the National HIV Behavioral Surveillance (NHBS) by the U.S. Centers for Disease Control and Prevention (CDC) for its component targeting people who inject drugs. As of 2011, the U.S. National Institutes of Health spent $100 million for funding RDS-related studies, and the list has grown since.

It should be noted that the theoretical underpinnings of RDS are not grounded on statistics but on a sociological concept of collective action and incentive systems within. The clearest distinction between RDS and traditional sampling is who is in control for locating and sampling participants: It is the researchers who control the sampling process in traditional sampling, while it is mostly participants themselves who control this process in RDS. This introduces uncertainty for the fieldwork, which also has direct implications for the inference.

This course will 1) introduce the concept of RDS, 2) illustrate empirical applications of RDS, 3) provide practical tools to implement RDS studies, and 4) discuss options for RDS data analysis.

Collecting Digital Trace Data in Surveys

In Person  |  May 9, 2023  |  2:00 pm to 5:30 pm ET

Presenter(s): Bella Struminskaya, Utretcht University & Florian Keusch, University of Mannheim

Humans leave digital traces when posting on social media, using smartphones and wearable devices, browsing websites, and searching online. These digital traces allow us to observe individuals unobtrusively, in their natural environment, offering ample opportunities for studying social reality. In combination with surveys, digital traces allow the study of human behavior and social interactions in a more holistic way than was possible before.

In practice, when collecting digital trace data as part of a survey, challenges arise regarding sampling, recruitment, and participation, as well as data quality. In this course, participants will tackle such challenges both from a conceptual and a practical level. We will review different state-of-the-art methods of implementing digital trace data collection in surveys, for example, via online meters, smartphone apps, and data donation tools. We will also discuss how to assess the quality of the resulting data and what research questions are best suited to be addressed by these data. While this course will not show how to program apps or plug-ins for digital trace data, we will provide ample resources for conducting digital trace data collection.