Combining and Analyzing Complex Data

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Combining and Analyzing Complex Data

This course is part of Survey Data Collection and Analytics Specialization

Instructor: Richard Valliant, Ph.D.

9,431 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

70 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

70 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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This course is part of the Survey Data Collection and Analytics Specialization

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There are 4 modules in this course

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.

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.

What's included

7 videos6 readings1 assignment

7 videos Total 69 minutes

Overview 7 minutes
Basic R examples 16 minutes
Basic R examples (continued) 14 minutes
Degrees of Freedom 8 minutes
Estimating Means 8 minutes
Multistage samples 6 minutes
Quantile estimation in R 11 minutes

6 readings Total 60 minutes

Slides 10 minutes
Slides 10 minutes
Slides 10 minutes
Slides 10 minutes
Slides (continued) 10 minutes
Slides 10 minutes

1 assignment Total 30 minutes

Course 6 Module 1 30 minutes

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.

What's included

8 videos8 readings1 assignment

8 videos Total 50 minutes

Introduction 5 minutes
Estimation Method 4 minutes
Linear Models 6 minutes
Diagnostics in R 9 minutes
Linear Models in Stata 6 minutes
Logistic Models in R 5 minutes
Odds Ratios 9 minutes
Logistic Regression in Stata 7 minutes

8 readings Total 80 minutes

Slides 10 minutes
Slides 10 minutes
Slides 10 minutes
Slides 10 minutes
Slides 10 minutes
Slides 10 minutes
Slides 10 minutes
Slides 10 minutes

1 assignment Total 30 minutes

Course 6 Module 2 30 minutes

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.

What's included

4 videos12 readings1 assignment1 discussion prompt

4 videos Total 38 minutes

Why we link records 8 minutes
Gentle Introduction 10 minutes
Challenges 8 minutes
Key Techniques 12 minutes

12 readings Total 120 minutes

Improving Federal Statistics Using Multiple Data Sources 10 minutes
Longitudinal Employer-Household Dynamics (LEHD) 10 minutes
Impact of Research on Innovation, Competition and Science 10 minutes
Slides 10 minutes
Slides - Introduction 10 minutes
Technical Overview - Software 10 minutes
Slides: Challenges 10 minutes
Slides 10 minutes
Record Linkage (Herzog/Scheuren/Winkler 2010) 10 minutes
Febrl - A Freely Available Record Linkage System (Christen) 10 minutes
Machine Learning and Record Linkage (Winkler 2011) 10 minutes
Privacy Preserving Record Linkage (Schnell et al. 2009) 10 minutes

1 assignment Total 30 minutes

Quiz 3 - Record Linkage 30 minutes

1 discussion prompt Total 10 minutes

Country specific examples 10 minutes

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.

What's included

5 videos3 readings1 assignment

5 videos Total 23 minutes

Privacy and Confidentiality 3 minutes
Linkage Consent and Consent Bias 8 minutes
Correlates of Consent 4 minutes
Bias in Administrative Estimates 2 minutes
Optimizing Linkage Consent 6 minutes

3 readings Total 30 minutes

Slides 10 minutes
Assessing the Magnitude of Non-Consent Biases (Sakshaug & Kreuter 2012) 10 minutes
Placement, Wording and Interviewers (Sakshaug et al.) 10 minutes

1 assignment Total 30 minutes

Quiz - Linkage Consent 30 minutes

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Instructor

Instructor ratings

(7 ratings)

Richard Valliant, Ph.D.

University of Maryland, College Park

5 Courses 17,561 learners

Offered by

University of Maryland, College Park

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Reviewed on Feb 11, 2021

Great course! Thanks, Professsor Valliant and Professor Frauke Kreuter.

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