In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.
Este curso forma parte de Programa Especializado - Survey Data Collection and Analytics
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Programa Especializado - Survey Data Collection and Analytics Universidad de Maryland en College ParkAcerca de este Curso
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Resultados profesionales del estudiante
17%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
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100 % en línea
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Fechas límite flexibles
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Aprox. 10 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Resultados profesionales del estudiante
17%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 10 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Maryland en College Park
The University of Maryland is the state's flagship university and one of the nation's preeminent public research universities. A global leader in research, entrepreneurship and innovation, the university is home to more than 37,000 students, 9,000 faculty and staff, and 250 academic programs. Its faculty includes three Nobel laureates, three Pulitzer Prize winners, 47 members of the national academies and scores of Fulbright scholars. The institution has a $1.8 billion operating budget, secures $500 million annually in external research funding and recently completed a $1 billion fundraising campaign.
Programa - Qué aprenderás en este curso
3 horas para completar
Basic Estimation
After completing Modules 1 and 2 of this course you will understand how to estimate descriptive statistics, overall and for subgroups, when you deal with survey data. We will review software for estimation (R, Stata, SAS) with examples for how to estimate things like means, proportions, and totals. You will also learn how to estimate parameters in linear, logistic, and other models and learn software options with emphasis on R. Module 3 and 4 discuss how you can add additional data to your analysis. This requires knowing about record linkage techniques, and what it takes to get permission to link data.
3 horas para completar
7 videos (Total 69 minutos), 6 lecturas, 1 cuestionario
7 videos
Overview7m
Basic R examples15m
Basic R examples (continued)13m
Degrees of Freedom7m
Estimating Means7m
Multistage samples5m
Quantile estimation in R10m
6 lecturas
Slides10m
Slides10m
Slides10m
Slides10m
Slides (continued)10m
Slides10m
1 ejercicio de práctica
Course 6 Module 130m
3 horas para completar
Models
Module 2 covers how to estimate linear and logistic model parameters using survey data. After completing this module, you will understand how the methods used differ from the ones for non-survey data. We also cover the features of survey data sets that need to be accounted for when estimating standard errors of estimated model parameters.
3 horas para completar
8 videos (Total 50 minutos), 8 lecturas, 1 cuestionario
8 videos
Estimation Method3m
Linear Models5m
Diagnostics in R8m
Linear Models in Stata5m
Logistic Models in R4m
Odds Ratios8m
Logistic Regression in Stata6m
8 lecturas
Slides10m
Slides10m
Slides10m
Slides10m
Slides10m
Slides10m
Slides10m
Slides10m
1 ejercicio de práctica
Course 6 Module 230m
3 horas para completar
Record Linkage
Module starts with the current debate on using more (linked) administrative records in the U.S. Federal Statistical System, and a general motivation for linking records. Several examples will be given on why it is useful to link data. Challenges of record linkage will be discussed. A brief overview over key linkage techniques is included as well.
3 horas para completar
4 videos (Total 38 minutos), 12 lecturas, 1 cuestionario
12 lecturas
Improving Federal Statistics Using Multiple Data Sources10m
Longitudinal Employer-Household Dynamics (LEHD)10m
Impact of Research on Innovation, Competition and Science10m
Slides10m
Slides - Introduction10m
Technical Overview - Software10m
Slides: Challenges10m
Slides10m
Record Linkage (Herzog/Scheuren/Winkler 2010)10m
Febrl - A Freely Available Record Linkage System (Christen)10m
Machine Learning and Record Linkage (Winkler 2011)10m
Privacy Preserving Record Linkage (Schnell et al. 2009)10m
1 ejercicio de práctica
Quiz 3 - Record Linkage30m
1 hora para completar
Ethics
This module will discuss key issues in obtaining consent to record linkage. Failure to consent can lead to bias estimates. Current research examples will be given as well as practical suggestions on how to obtain linkage consent.
1 hora para completar
5 videos (Total 23 minutos), 3 lecturas, 1 cuestionario
5 videos
Linkage Consent and Consent Bias8m
Correlates of Consent3m
Bias in Administrative Estimates2m
Optimizing Linkage Consent6m
3 lecturas
Slides10m
Assessing the Magnitude of Non-Consent Biases (Sakshaug & Kreuter 2012)10m
Placement, Wording and Interviewers (Sakshaug et al.)10m
1 ejercicio de práctica
Quiz - Linkage Consent30m
Acerca de Programa especializado: 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.
Preguntas Frecuentes
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