Combining and Analyzing Complex Data

University of Maryland, College Park via Coursera

Go to Course: https://www.coursera.org/learn/data-collection-analytics-project

Introduction

**Course Review: Combining and Analyzing Complex Data on Coursera** --- **Course Overview:** "Combining and Analyzing Complex Data" is a comprehensive course offered on Coursera that equips learners with essential skills to effectively handle complex survey data. In an era where data is prevalent, understanding how to leverage complex datasets becomes indispensable, and this course caters to that pressing need. Throughout the course, participants engage with fundamental concepts like survey weights and descriptive statistics while delving into more advanced topics, including model parameters for linear and logistic regressions. A significant emphasis is placed on utilizing R®, a powerful statistical software, although opportunities to explore other software such as Stata and SAS are also provided. Moreover, the course insightfully navigates the essentials of record linkage and statistical matching—techniques that are increasingly vital for synthesizing data from diverse sources. Given the ethical implications surrounding data combination, the course addresses these aspects candidly, ensuring participants are well-versed in responsible data practices. --- **Syllabus Breakdown:** 1. **Basic Estimation:** The initial modules of the course lay a solid foundation in estimating descriptive statistics from survey data. Learners are introduced to estimating means, proportions, and totals, with practical examples across different software platforms, particularly R. By the end of this segment, participants will confidently conduct statistical estimations for both overall datasets and subgroups, setting the stage for more complex analyses. 2. **Models:** Here, learners deepen their understanding of linear and logistic regression analysis specifically tailored for survey data. The module emphasizes the unique characteristics of survey data and instructs on the nuances of estimating model parameters, including the critical aspect of calculating standard errors. Mastery of this module is essential for those intending to build predictive models from survey data. 3. **Record Linkage:** This module addresses the growing trend of utilizing linked administrative records within the U.S. Federal Statistical System. Students are exposed to real-world scenarios that underline the utility of record linkage while also recognizing the inherent challenges involved. A brief exploration of key techniques offers a practical guide, making this area approachable for learners. 4. **Ethics:** Ethical considerations in data handling are paramount, and this module tackles the vital issue of consent in record linkage. Through a discussion of the potential biases stemming from non-consent, the course provides evidence-based research and actionable strategies for obtaining linkage consent. This section is crucial for any data professional looking to responsibly navigate complex ethical landscapes. --- **Recommendation:** I wholeheartedly recommend "Combining and Analyzing Complex Data" for professionals and students keen on deepening their statistical analysis skills, especially those with a focus on survey data. This course not only provides practical knowledge and hands-on experience with R®, but also prepares you to tackle real-world data challenges—be it through ethical data handling or sophisticated statistical modeling. Whether you are in academia, public health, social science, or any field that relies heavily on data, this course is a valuable investment in your professional development. The balance of theory and practical application coupled with a robust ethical framework equips learners to not only analyze data but to do so with integrity. In conclusion, if you're ready to elevate your analytical capabilities and navigate the complexities of modern data landscapes, embark on this learning journey on Coursera today!

Syllabus

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.

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.

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.

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.