This course presents research conducted to increase our understanding of how data collection decisions affect survey errors. This is not a “how–to-do-it” course on data collection, but instead reviews the literature on survey design decisions and data quality in order to sensitize learners to how alternative survey designs might impact the data obtained from those surveys.
Informações sobre o curso
Universidade de Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
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Principais avaliações do DATA COLLECTION: ONLINE, TELEPHONE AND FACE-TO-FACE
- the difference between mobile survey and web survey\n\ndifference between online and face to face interviews\n\ndifference between telephone interviews and computer interviws
There is one word for this Course "Amazing". No matter if you are a undergrad student or a seasoned researcher, you'll feel that you have learned a lot from this course.
Very good summary of the advantages and disadvantages of different approaches to survey data collection, including some useful context about hybrid approaches.
The course material presents the traditional methods and latest developments in data collection for survey research. Excellent overall.
Sobre Programa de cursos integrados Survey Data Collection and Analytics
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. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.
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