This specialization in Survey Data Collection and Analytics covers the fundamentals of surveys used in various domains such as market research, social science, and government statistics. The six-course program provides an in-depth understanding of questionnaire design, data collection methods, sampling design, dealing with missing values, and analyzing survey data. The final Capstone Project involves applying the acquired skills to analyze and compare multiple data sources.
The faculty for this specialization is drawn from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, offering expertise and practical insights into survey research. The program is suitable for beginners and individuals familiar with specific data sources, providing a general framework for evaluating data products and employing various large-scale data collection efforts as examples.
Upon completion, learners will have the knowledge and skills to collect quality data, conduct insightful data analysis, and effectively communicate the results.
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Survey Data Collection and Analytics covers a range of topics, including data collection methods, questionnaire design, sampling techniques, and dealing with missing data. The specialization culminates in a capstone project, allowing learners to apply their skills to analyze and compare multiple data sources.
This course provides an overview of existing data products, the data collection landscape, and a general framework for successful data collection and analysis. Learners will gain the skills to identify suitable data sources, create an analysis plan, and understand metrics for evaluating data quality.
Learners will explore and compare the pros and cons of self-administered modes and interviews, emerging data sources such as mobile web surveys, and key concepts related to survey data collection methods.
This module focuses on understanding the basic elements of designing and evaluating questionnaires for social surveys, providing essential skills in questionnaire design and evaluation.
Learners will understand the value and risks of sampling and randomization methods, differentiate sampling methods, and explain the principles and techniques of probability sampling.
This course covers the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using external data for calibration, and techniques for imputing values for missing items.
Learners will learn to use survey weights to estimate descriptive statistics and model parameters, understand record linkage and statistical matching, and review ethical issues related to combining datasets.
The capstone project involves analyzing and comparing multiple data sources on a specific topic, developing research questions, and evaluating data source quality using the Total Survey Error approach.
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