Course Workload Expectations

The Summer Institute courses are taught in a supportive, virtual format designed for learners at every level.

With the exception of the Health and Retirement Study Workshops, all Summer Institute course are taught at the graduate level at an accelerated pace. You should keep this in mind when making selecting courses to ensure you are allowing enough time to meet each class requirement.

Choosing the Right Courses

Participants new to survey research or with little statistical background find Introduction to Survey Methodology an appropriate course to begin with in their studies. The Summer Institute courses are offered for the general topics of Data Collection Methods and Study Design, Instrument Design, Sampling, Data Analysis and Research Data Series. 

Data Collection Methods and Study Design

• Introduction to Survey Methodology: This course introduces the fundamentals of designing and conducting high-quality surveys, using the Total Survey Error framework to examine sampling, data collection modes, questionnaire design, and respondent cognition. It also covers pretesting and post-collection processes to equip students with practical skills for producing reliable survey data.
• Introduction to Qualitative Research Methods: introduces qualitative research and core data collection methods.
• Qualitative Methods: Overview and Semi-Structured Interviewing: introduces semi-structured interviewing as a qualitative method, covering its goals, design, strengths, limitations, and practical techniques for conducting effective in-depth interviews.
• Integrating Qualitative Methods into Survey Research: introduces integrating qualitative methods into quantitative studies, showing how mixed-methods designs can strengthen research questions, execution, and interpretation.
• Data Collection Using Wearables, Sensors, and Apps in the Social, Behavioral, and Health Sciences: explores how wearables, sensors, and mobile apps can be integrated into surveys to collect high-quality, real-time behavioral and health data while addressing data quality, cost, and privacy concerns.
• Basic Concepts in Responsive Survey Design: introduces the core ideas of Responsive Survey Design, showing how paradata and targeted interventions can be used to improve survey effectiveness across all major survey modes.
• Interventions in a Responsive Survey Design Framework: explore how to implement and experimentally evaluate Responsive Survey Design interventions, with a focus on practical strategies for both interviewer-mediated and self-administered surveys.

Instrument Design

• Designing and Writing Questions For Surveys: Guidelines and Recommendations: distills research about survey questions to principles that can be applied to write survey questions that are clear and obtain reliable answers. Provides participants with tools to use in diagnosing problems in survey questions and in writing their own survey questions.
• Going Deeper into Questionnaire Design with Alternative Methods and Tools: building on prior experience in questionnaire design by exploring how linguistic and cognitive factors affect question comprehension, memory, and attitude measurement, including tools and methods for improving data quality and handling multicultural and translation issues.

Sampling

• Sampling in Practice: designed for applied practitioners who want to master real-world sampling techniques through active learning and practical programming.

Data Analysis

• Natural Language Processing with R: learn how to analyze and extract meaning from text data.

Research Data Series

• Introduction to the Health and Retirement Study (HRS) Workshop: introduction to HRS that will enable course participants to get started using the data for research.