Moving Behavioral Experimentation Online
A Tutorial and Some Recommendations for Drift Diffusion Modeling
Bibliographic Data
| ID | 3755745 |
|---|---|
| Authors | Xuanjun Gong (0000-0001-9642-8885, University of California, Davis), Richard Huskey (0000-0002-4559-2439, University of California, Davis, corresponding author) |
| Year | 2025 |
| Volume | 69 |
| Issue | 10 |
| Pages | 1271-1288 |
| Publication date | 2025-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Behavioral Scientist (JOURNAL) |
| Journal identifiers | ISSN: 0002-7642 • E-ISSN: 1552-3381 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/00027642231207073 |
| OpenAlex | W4388462064 |
| Language | EN |
| Citations received | 2 |
| References cited | 45 |
Behavioral science demands skillful experimentation and high-quality data that are typically gathered in person. However, the COVID-19 pandemic forced many behavioral research laboratories to close. Thankfully, new tools for conducting online experiments allow researchers to elicit psychological responses and gather behavioral data with unprecedented precision. It is now possible to quickly conduct large-scale high-quality behavioral experiments online, even for studies designed to generate data necessary for complex computational models. However, these techniques require new skills that might be unfamiliar to behavioral researchers who are more familiar with laboratory-based experimentation. We present a detailed tutorial introducing an end-to-end build of an online experimental pipeline and corresponding data analysis. We provide an example study investigating people's media preferences using drift-diffusion modeling (DDM), paying particular attention to potential issues that come with online behavioral experimentation. This tutorial includes sample data and code for conducting and analyzing DDM data gathered in an online experiment, thereby mitigating the extent to which researchers must reinvent the wheel
Behavioral analysis · Behavioral modeling · Behavioural sciences · Data science · Human–computer interaction · Pipeline (software) · Quality (philosophy) · Scale (ratio) · Applied Psychology · Artificial Intelligence · Behavioral Health and Interventions · Computer Science · Environmental Education and Sustainability · Mental Health Research Topics · Psychology
Choice and Preference in Media Use
The timing mega-study
Human Research and Data Collection via the Internet
Diffusion Decision Model
The Diffusion Decision Model
HDDM
Gorilla in our midst
Validating vignette and conjoint survey experiments against real-world behavior
PsychoPy2
Using mouse cursor tracking to investigate online cognition
Thinking more or thinking differently? Using drift-diffusion modeling to illuminate why accuracy prompts decrease misinformation sharing
Media Choice
Standards for Internet-Based Experimenting
A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use
Is There a Cost to Convenience? An Experimental Comparison of Data Quality in Laboratory and Online Studies
Time Will Tell
Process components of the Implicit Association Test
Methods for dealing with reaction time outliers
Mood Management Through Communication Choices
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |