Combining and Analyzing Complex Data

About 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.

Created by:  University of Maryland, College Park

• Taught by:  Richard Valliant, Ph.D., Research Professor

  Joint Program in Survey Methodology

Basic Info Course 6 of 7 in the Survey Data Collection and Analytics Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.6 stars Average User Rating 4.6See what learners said Coursework

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University of 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.

Syllabus


WEEK 1


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.


7 videos, 6 readings expand

1. Video: Overview
2. Reading: Slides
3. Video: Basic R examples
4. Video: Basic R examples (continued)
5. Reading: Slides
6. Video: Degrees of Freedom
7. Reading: Slides
8. Video: Estimating Means
9. Video: Multistage samples
10. Reading: Slides
11. Reading: Slides (continued)
12. Video: Quantile estimation in R
13. Reading: Slides

Graded: Course 6 Module 1

WEEK 2


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.


8 videos, 8 readings expand

1. Video: Introduction
2. Reading: Slides
3. Video: Estimation Method
4. Reading: Slides
5. Video: Linear Models
6. Reading: Slides
7. Video: Diagnostics in R
8. Reading: Slides
9. Video: Linear Models in Stata
10. Reading: Slides
11. Video: Logistic Models in R
12. Reading: Slides
13. Video: Odds Ratios
14. Reading: Slides
15. Video: Logistic Regression in Stata
16. Reading: Slides

Graded: Course 6 Module 2

WEEK 3


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.


4 videos, 12 readings expand

1. Reading: Improving Federal Statistics Using Multiple Data Sources
2. Reading: Longitudinal Employer-Household Dynamics (LEHD)
3. Reading: Impact of Research on Innovation, Competition and Science
4. Discussion Prompt: Country specific examples
5. Video: Why we link records
6. Reading: Slides
7. Video: Gentle Introduction
8. Reading: Slides - Introduction
9. Reading: Technical Overview - Software
10. Video: Challenges
11. Reading: Slides: Challenges
12. Video: Key Techniques
13. Reading: Slides
14. Reading: Record Linkage (Herzog/Scheuren/Winkler 2010)
15. Reading: Febrl - A Freely Available Record Linkage System (Christen)
16. Reading: Machine Learning and Record Linkage (Winkler 2011)
17. Reading: Privacy Preserving Record Linkage (Schnell et al. 2009)

Graded: Quiz 3 - Record Linkage

WEEK 4


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.


5 videos, 3 readings expand

1. Video: Privacy and Confidentiality
2. Video: Linkage Consent and Consent Bias
3. Video: Correlates of Consent
4. Video: Bias in Administrative Estimates
5. Video: Optimizing Linkage Consent
6. Reading: Slides
7. Reading: Assessing the Magnitude of Non-Consent Biases (Sakshaug & Kreuter 2012)
8. Reading: Placement, Wording and Interviewers (Sakshaug et al.)

Graded: Quiz - Linkage Consent