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Combining and Analyzing Complex Data

Combining and Analyzing Complex Data

This course is part of Survey Data Collection and Analytics Specialization

Instructor: Richard Valliant, Ph.D.

9,574 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

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 videosTotal 69 minutes

Overview7 minutes
Basic R examples16 minutes
Basic R examples (continued)14 minutes
Degrees of Freedom8 minutes
Estimating Means8 minutes
Multistage samples6 minutes
Quantile estimation in R11 minutes

6 readingsTotal 60 minutes

Slides10 minutes
Slides10 minutes
Slides10 minutes
Slides10 minutes
Slides (continued)10 minutes
Slides10 minutes

1 assignmentTotal 30 minutes

Course 6 Module 130 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 videosTotal 50 minutes

Introduction5 minutes
Estimation Method4 minutes
Linear Models6 minutes
Diagnostics in R9 minutes
Linear Models in Stata6 minutes
Logistic Models in R5 minutes
Odds Ratios9 minutes
Logistic Regression in Stata7 minutes

8 readingsTotal 80 minutes

Slides10 minutes
Slides10 minutes
Slides10 minutes
Slides10 minutes
Slides10 minutes
Slides10 minutes
Slides10 minutes
Slides10 minutes

1 assignmentTotal 30 minutes

Course 6 Module 230 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 videosTotal 38 minutes

Why we link records8 minutes
Gentle Introduction10 minutes
Challenges8 minutes
Key Techniques12 minutes

12 readingsTotal 120 minutes

Improving Federal Statistics Using Multiple Data Sources10 minutes
Longitudinal Employer-Household Dynamics (LEHD)10 minutes
Impact of Research on Innovation, Competition and Science10 minutes
Slides10 minutes
Slides - Introduction10 minutes
Technical Overview - Software10 minutes
Slides: Challenges10 minutes
Slides10 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 assignmentTotal 30 minutes

Quiz 3 - Record Linkage30 minutes

1 discussion promptTotal 10 minutes

Country specific examples10 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 videosTotal 23 minutes

Privacy and Confidentiality3 minutes
Linkage Consent and Consent Bias8 minutes
Correlates of Consent4 minutes
Bias in Administrative Estimates2 minutes
Optimizing Linkage Consent6 minutes

3 readingsTotal 30 minutes

Slides10 minutes
Assessing the Magnitude of Non-Consent Biases (Sakshaug & Kreuter 2012)10 minutes
Placement, Wording and Interviewers (Sakshaug et al.)10 minutes