Experimental evidence shows that how information is presented in a laboratory study can change conclusions about candidate evaluations.

📚 How Information Was Presented

The experiment manipulated two dimensions of campaign information to create four experimental conditions: low-information vs. high-information campaigns and static vs. dynamic presentation formats. Hypothetical candidates were described to subjects, with the candidate's gender used as the focal attribute for treatment.

🧪 What the Design Compared

• Two levels of information: low-information campaign, high-information campaign
• Two presentation modes: static presentation, dynamic presentation
• Resulting in four combinations (low/static, low/dynamic, high/static, high/dynamic)
• Candidate gender served as the experimental manipulation; multiple candidate evaluation measures were collected

📈 Key Findings

• Study design affects conclusions: the same gender manipulation produced markedly different results depending on information conditions.
• In low-information conditions, candidate gender had significant effects across a variety of candidate-evaluation measures.
• In high-information conditions, gender produced almost no significant effects on those evaluation measures.
• Subjects in high-information settings sought out more information in dynamic environments than in static ones, but their final candidate evaluations did not differ between dynamic and static presentations.

🔎 Why It Matters

These results show that presentation choices in laboratory experiments—how much information is provided and whether it is static or interactive—can meaningfully alter observed treatment effects. The findings caution that conclusions about attribute effects (here, gender) depend on experimental information environments and suggest careful matching of study design to real-world inference.

🗂️ Next Steps and Recommendations

Implications and recommendations for future avenues of study are discussed, including attention to information levels and interactivity when designing experiments that aim to generalize to real-world political information environments.