Engage is an AI-powered interview and analysis tool that enhances and simplifies quantitative, qualitative, and mixed-methods research. With Engage you can collect hundreds of respondent interviews with in-depth analyses in hours, giving you powerful insights into your research questions and saving significant time and resources.
What you should expect from Engage:
- In-Depth Participant Insights: Use Engage’s AI capabilities to delve deeper into participant experiences, motivations, perceptions, and emotional responses, offering valuable insights for mixed-method social psychological research.
- Customizable Engagement Models: Create dynamic, responsive surveys that adapt to participant answers, enabling you to explore complex participant experiences and social perceptions with greater accuracy.
- Thematic Analysis: Benefit from advanced theme analysis of qualitative data to interpret patterns and trends within your research, helping you to attain more nuanced and complete findings of theoretical and practical importance.
Discover the next generation of research tools with special access to Engage, specifically designed to improve and transform social psychological research practices. Engage blends sophisticated AI-driven conversations with deep psychological insights, facilitating innovative studies into human behavior, attitudes, judgments, emotion, and perception. Secure your spot now to start using this transformative platform for your social psychological research initiatives.
- Expert Interviews at Scale: Engage uses text or speech to interview, probe, and follow up, helping you obtain rich responses and more thorough responses from participants.
- Enhanced Engagement: Designed to eliminate participant burnout, Engage makes research an enjoyable experience, leading to more meaningful conversations and enriched data.
- Data Reliability: Advanced AI tools detect inattention, improper usage of ChatGPT, and prevent fraud, ensuring reliable data.
- Coding and Analysis: Utilize Engage for transcribing, coding, and identifying themes within your qualitative data using AI driven insights.
- Expanded Reporting: Save hours of time effortlessly exploring your transcripts using Engage advanced research query tools.
- Flexible Integration: Easily incorporate Engage into your existing research practices and survey applications with several familiar question types (multiple choice, open-ended, images, instructions) allowing for quant and qual question types.
Perceptions of Sexual Harassment: A Comparison of Human and AI Analyses
Stefanie Simon 1, Jennifer L. Mezzapelle 2, Anna-Kaisa Reiman 3, Leib Litman 4, & Jonathan Robinson 4
1 Siena College, 2 George Mason University, 3 University at Albany, State University of New York, 4 CloudResearch
Background & Purpose
Qualitative research provides rich insights but is often limited by the time-intensive nature of data collection and analysis. This study demonstrates how AI-powered platforms can make large-scale qualitative research more feasible while maintaining rigor. We replicated Mezzapelle and Reiman’s (2022) findings on how people perceive sexual harassment differently based on the target’s identity using Engage, the AI-driven survey platform from CloudResearch.
Method
- 290 participants recruited through the CloudResearch Connect platform
- Participants read about workplace sexual harassment targeting either a transgender woman, lesbian woman, or cisgender/straight woman
- Engage (AI platform) conducted interviews and analyzed open-ended responses using same coding scheme as original human coders
- AI interviewer provided adaptive follow-up questions to probe initial responses (see below)
Key Findings
The AI analysis successfully replicated key findings from the original study:
- Type of Harassment: Transgender women were more likely to be perceived as targets of gender harassment, while lesbian and straight cisgender women were more likely to be perceived as targets of unwanted sexual attention (i.e., prototypical sexual harassment).
- Perceived Motivation: For transgender targets, prejudice was seen as a stronger driver; for cisgender targets, attraction was seen as the primary motivation.
The Engage Interface for Running Custom AI Tag Analysis
Example AI Tag Analysis on Type of Behavior for Cisgender/Straight Condition
Implications
- Demonstrates AI’s potential for reliable qualitative analysis
- Enables larger sample sizes for qualitative research
- Allows for adaptive interviewing at scale
Conclusion
AI-powered platforms like Engage could help resolve the traditional trade-off between rich qualitative insights and large sample sizes in psychological research. This approach shows promise for expanding the scope of qualitative methods in psychology and beyond.
Please address correspondence regarding this research to Stefanie Simon at ssimon@siena.edu