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.
Ce cours fait partie de la Spécialisation Survey Data Collection and Analytics
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Spécialisation Survey Data Collection and Analytics Université du Maryland, College ParkÀ propos de ce cours
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Cours 6 sur 7 dans le
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Approx. 10 heures pour terminer
Anglais
Sous-titres : Anglais
Résultats de carrière des étudiants
17%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 6 sur 7 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Approx. 10 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université du Maryland, 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.
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
7 vidéos (Total 69 min), 6 lectures, 1 quiz
7 vidéos
Overview7 min
Basic R examples15 min
Basic R examples (continued)13 min
Degrees of Freedom7 min
Estimating Means7 min
Multistage samples5 min
Quantile estimation in R10 min
6 lectures
Slides10 min
Slides10 min
Slides10 min
Slides10 min
Slides (continued)10 min
Slides10 min
1 exercice pour s'entraîner
Course 6 Module 130 min
3 heures pour terminer
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 heures pour terminer
8 vidéos (Total 50 min), 8 lectures, 1 quiz
8 vidéos
Introduction4 min
Estimation Method3 min
Linear Models5 min
Diagnostics in R8 min
Linear Models in Stata5 min
Logistic Models in R4 min
Odds Ratios8 min
Logistic Regression in Stata6 min
8 lectures
Slides10 min
Slides10 min
Slides10 min
Slides10 min
Slides10 min
Slides10 min
Slides10 min
Slides10 min
1 exercice pour s'entraîner
Course 6 Module 230 min
3 heures pour terminer
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 heures pour terminer
4 vidéos (Total 38 min), 12 lectures, 1 quiz
12 lectures
Improving Federal Statistics Using Multiple Data Sources10 min
Longitudinal Employer-Household Dynamics (LEHD)10 min
Impact of Research on Innovation, Competition and Science10 min
Slides10 min
Slides - Introduction10 min
Technical Overview - Software10 min
Slides: Challenges10 min
Slides10 min
Record Linkage (Herzog/Scheuren/Winkler 2010)10 min
Febrl - A Freely Available Record Linkage System (Christen)10 min
Machine Learning and Record Linkage (Winkler 2011)10 min
Privacy Preserving Record Linkage (Schnell et al. 2009)10 min
1 exercice pour s'entraîner
Quiz 3 - Record Linkage30 min
1 heure pour terminer
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 heure pour terminer
5 vidéos (Total 23 min), 3 lectures, 1 quiz
5 vidéos
Linkage Consent and Consent Bias8 min
Correlates of Consent3 min
Bias in Administrative Estimates2 min
Optimizing Linkage Consent6 min
3 lectures
Slides10 min
Assessing the Magnitude of Non-Consent Biases (Sakshaug & Kreuter 2012)10 min
Placement, Wording and Interviewers (Sakshaug et al.)10 min
1 exercice pour s'entraîner
Quiz - Linkage Consent30 min
À propos du Spécialisation 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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