Framework for Data Collection and Analysis

About this course: This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products.

Created by:  University of Maryland, College Park

• Taught by:  Frauke Kreuter, Ph.D., Professor, Joint Program in Survey Methodology

  Adjunct Research Professor, Institute for Social Research

• Taught by:  Mariel Leonard, Lecturer

  Joint Program in Survey Methodology

Basic Info Course 1 of 7 in the Survey Data Collection and Analytics Specialization Level Intermediate Commitment 4 weeks of study, 1-2 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.2 stars Average User Rating 4.2See 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


Research Designs and Data Sources



The first course in the specialization provides an overview of the topics to come. This module walks you through the process of data collection and analysis. Starting with a research question and a review of existing data sources, we cover survey data collection techniques, highlight the importance of data curation, and some basic features that can affect your data analysis when dealing with sample data. Issues of data access and resources for access are introduced in this module.


9 videos, 5 readings expand

1. Reading: Course Overview
2. Reading: Readings and Resources List
3. Video: Research Question Design
4. Discussion Prompt: Discussion Prompt: Your own experience
5. Video: Types of Data
6. Video: Examples of Found Data
7. Video: Visualizing the Data Generation Process
8. Video: Data Curation
9. Video: Data Analysis
10. Video: Access Issues
11. Video: Access Resources
12. Video: Summary
13. Discussion Prompt: Discussion Prompt: Privacy
14. Reading: Handouts
15. Reading: AAPOR (2015)
16. Reading: Couper (2013)

Graded: Quiz for Week 1

WEEK 2


Measurements and Analysis Plan



In this module we will emphasize the importance of having a well specified research question and analysis plan. We will provide an overview over the various data collection strategies, a variety of available modes for data collection and some thinking on how to choose the right mode.


6 videos, 2 readings expand

1. Video: Issues with Inductive Reasoning
2. Video: Planning on What You Want to Observe
3. Video: Planning on How to Collect Data
4. Video: New Modes
5. Video: Web and Google
6. Video: Choosing a Mode
7. Reading: Handouts
8. Reading: Jäckle et al. (2015)

Graded: Quiz for Week 2

WEEK 3


Quality Framework



In this module you will be introduced to a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source.


8 videos, 3 readings expand

1. Video: Quality of Data
2. Video: Inference
3. Video: Survey Life Cycle from a Design Perspective - Measurement
4. Video: Survey Life Cycle from a Design Perspective - Representation
5. Video: Survey Lifecycle from a Process Perspective
6. Video: Survey Lifeycle from a Quality Perspective
7. Video: Survey Lifecycle from a Quality Perspective (II) - Metrics
8. Video: Survey Lifecycle from a Quality Perspective (III) - Coverage and Sampling
9. Reading: Handouts
10. Reading: Groves (2011)
11. Reading: Groves & Lyberg (2010)

Graded: Quiz for Week 3

WEEK 4


Application of TSE Framework to Existing Surveys



In this module we introduce a few surveys across a variety of topics. For each we highlight data collection features. The surveys span a variety of topics. We challenge you to think about alternative data sources that can be used to gather the same information or insights.


8 videos, 2 readings expand

1. Video: NCVS
2. Video: NSDUH
3. Video: SCA
4. Video: NAEP
5. Video: BRFSS
6. Video: CES
7. Discussion Prompt: Discussion Prompt: Alternative Data Sources
8. Video: SHARE
9. Video: ESS
10. Discussion Prompt: Discussion prompt: In your country
11. Reading: Handouts
12. Reading: Davidov (2008)

Graded: Quiz for Week 4