PROJECT:
Discovering Structure of Social Impressions

Social Perception ❖ Survey Research ❖ Dimensionality Reduction ❖ Latent Structure Modeling

People form impressions of others’ traits (friendly, clever, dependable, etc). Do these impressions reflect many unrelated judgements, or a simple underlying structure?

Using large-scale surveys and dimensionality reduction, I uncovered a reproducible and interpretable low-dimensional structure.

MY ROLE: In collaboration with social and behavioral scientists, I led:

  • research strategy

  • survey design

  • multi-platform online data collection

  • automated data preprocessing and quality control

  • dimensionality reduction (factor analysis)

  • visualization and interpretation

  • stakeholder communication

BEHAVIORAL INSIGHTS + UX relevance

Research question

Is impression of others’ traits governed by a low-dimensional structure? Such a structure can help design experiences where users interact with other users or computer agents, e.g., profile design or recommendation in social platforms.

Study design

I conducted online surveys on multiple platforms (Amazon Mechanical Turk, Prolific, university student pool; 500+ participants). Each dataset was analyzed separately and compared for reproducibility.

In the survey, participants rated 34 social traits (e.g., friendly, clever, dependable) for 24 occupations (e.g., nurse, lawyer, high school teacher). Occupations were tailored to the participant group to ensure familiarity. Each participant rated only a subset to reduce fatigue.

Finding

Trait ratings consistently reduced to three interpretable dimensions: Warmth/Trustworthiness, Competence, and Sociability. This structure was replicated across all datasets.

This finding demonstrates a reproducible, interpretable, and generalizable low-dimensional structure of social impressions.

MODELING + data science relevance

Challenge

I needed a principled approach to discover a low-dimensional structure that is comparable across datasets. Additionally, I needed to deal with variable data quality on online surveys.

Approach

I developed an automated data pipeline to remove those who failed attention checks or showed highly irregular responses.

For dimensionality reduction, I used factor analysis to account for covariance across traits by a few key factors. Since each factor is a linear combination of traits, it is easily interpretable. In addition, factor analysis provides a principled way to computationally determine the number of factor (parallel analysis), ensuring robustness and reproducibility. Another important feature of factor analysis is that, unlike highly related Principal Component Analysis (PCA), it remains agnostic on orthogonality between factors.

Results

Across all datasets, factor analysis revealed the identical three-factor structure. Each factor was easily interpretable as Warmth/Trustworthiness, Competence, and Sociability.

Factor analysis revealed three factors, each associated with traits related to warmth/trustworthiness, competence, and sociability, respectively.

This demonstrates that dimensionality reduction successfully uncovers interpretable latent structure in large, and noisy behavioral observations.