Big Data in Survey Research
Aapor Task Force Report
Bibliographic Data
| ID | 6369652 |
|---|---|
| Authors | Lilli Japec, Frauke Kreuter (0000-0002-7339-2645), Marcus Berg (0000-0001-9611-2450), Paul P Biemer (0000-0003-2214-2707), Paul Biemer, Paul T Decker, Paul Decker, Cliff Lampe, Julia Lane (0000-0002-8139-2960), Cathy O’neil, Abe Usher |
| Year | 2015 |
| Volume | 79 |
| Issue | 4 |
| Pages | 839-880 |
| Publication date | 2015-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Public Opinion Quarterly (JOURNAL) |
| Journal identifiers | ISSN: 0033-362X • E-ISSN: 1537-5331 |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1093/poq/nfv039 |
| OpenAlex | W2193451006 |
| Language | EN |
| Citations received | 36 |
| References cited | 36 |
Recent years have seen an increase in the amount of statistics describing different phenomena based on “Big Data.” This term includes data characterized not only by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, and the way in which they are collected and disseminated is fundamental. This change constitutes a paradigm shift for survey research. There is great potential in Big Data, but there are some fundamental challenges that have to be resolved before its full potential can be realized. This report provides examples of different types of Big Data and their potential for survey research; it also describes the Big Data process, discusses its main challenges, and considers solutions and research needs
Big data · Data mining · Data science · Survey research · Complex Network Analysis Techniques · Computer Science · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis · Psychology · Applied Psychology
The Pushshift Reddit Dataset
Social media analytics – Challenges in topic discovery, data collection, and data preparation
Data Mining for Community Resilience
Capturing country images
No magic bullet
Inquéritos domiciliares nacionais de base populacional em saúde
Assessing Data Quality in the Age of Digital Social Research
Dictionary-based and machine learning classification approaches
Beyond the Walls
What Counts as “People” in Digital Social Research? Subject Rethinking and Its Ethical Consequences
Artificial intelligence and big data-driven evaluation research and practices
The value of online surveys
Capture–Recapture Techniques for Transport Survey Estimate Adjustment Using Permanently Installed Highway-Sensors
How Much Data Should I Request? Balancing Richness and Compliance in Digital Trace Data Donations
Integrating Survey Data and Digital Trace Data
Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel
Big Data Meets Survey Science”
Editors' Overview of Special Section on Big Data and Public Policy
Big data and prediction
Data Quality of Digital Process Data
Not Even Our Own Facts
The demise of the survey? A research note on trends in the use of survey data in the social sciences, 1939 to 2015
Multilevel Calibration Weighting for Survey Data
Using Data from Reddit, Public Deliberation, and Surveys to Measure Public Opinion about Autonomous Vehicles
New Data in Social and Behavioral Research
Social Media Analyses for Social Measurement
Using Administrative Records and Survey Data to Construct Samples of Tweeters and Tweets
Research Synthesis
The Stability of Economic Correlations over Time
A Total Error Framework for Digital Traces of Human Behavior on Online Platforms
Computational Social Science and the Study of Political Communication
The Internet in China
Social impacts of algorithmic decision-making
Disclosure Standards for Social Media and Generative Artificial Intelligence Research
Vectors into the Future of Mass and Interpersonal Communication Research
Social Media and Twitter Data Quality for New Social Indicators
The Parable of Google Flu
The Unreasonable Effectiveness of Data
When Google got flu wrong
Selection and the Evolution of Industry
Big Data’s End Run around Anonymity and Consent
Forecasting Using Principal Components From a Large Number of Predictors
The Psychology of Survey Response
Economic Contributions to the Understanding of Crime
Tests of Alternative Theories of Firm Growth
A Contextual Approach to Privacy Online
Mapping the data shadows of Hurricane Sandy
Total Survey Error
Three Eras of Survey Research
Why the 1936 Literary Digest Poll Failed
Big Data
Avoiding Disclosure of Individually Identifiable Health Information
| Unique citing works | 36 |
|---|---|
| Citations per year | 3,6 |
| Citation span | 2016 - 2026 (11) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 36 |