1 Stimulus example

Download stimuli [ZIP folder containing PNG files]

2 Demo Video

4 Data

4.1 Pilot Study

4.1.1 Data set information

  • sID: subject ID
  • Order: Counterbalancing factor (whether subjects first gave a strength rating for the multiple or the single-effect cause)
  • Process: Factor coding whether the causes were generative or preventive.
  • Valence: Factor coding whether the effects were positive or negative.
  • Multiple_Effects: Counterbalancing factor coding whether the multiple-effects common cause was red or blue
  • Target: Counterbalacning factor (whether the target effect was E1, E2, or E3)
  • multiple_strength_rating: DV (subjects’ ratings for the multiple-effects cause)
  • single_strength_rating: DV (subjects’ ratings for the single-effect cause)
  • Desktop_Confirmation: Check query (subjects had to indicate that they work on a Desktop PC [1])
  • Attention_Confirmation: Chech query (subjectes had to indicate that they will pay attention [1])
  • Effect_Valence: Control query (effect valence check) asking subjects to say whether the effects were postive or negative (only subjects who answered correctly were included)
  • Age: Subjects’ age in years
  • Sex: Subjects’ sex (1 = male, 2 = female, 3 = non-binary)
  • Technical_issues: Open query (subjects could report any experienced technical issues)
  • Duration (sek): The time subjects needed to complete the study (in seconds)

4.2 Main Study

4.2.1 Data set information

  • sID: subject ID
  • Order: Counterbalancing factor (whether subjects first gave a strength rating for the multiple or the single-effect cause)
  • Process: Factor coding whether the causes were generative or preventive.
  • Valence: Factor coding whether the effects were positive or negative.
  • Multiple_Effects: Counterbalancing factor coding whether the multiple-effects common cause was red or blue
  • Target: Counterbalacning factor (whether the target effect was E1, E2, or E3)
  • multiple_strength_rating: DV (subjects’ ratings for the multiple-effects cause)
  • single_strength_rating: DV (subjects’ ratings for the single-effect cause)
  • Desktop_Confirmation: Check query (subjects had to indicate that they work on a Desktop PC [1])
  • Attention_Confirmation: Chech query (subjectes had to indicate that they will pay attention [1])
  • Effect_Valence: Control query (effect valence check) asking subjects to say whether the effects were postive or negative (only subjects who answered correctly were included)
  • Age: Subjects’ age in years
  • Sex: Subjects’ sex (1 = male, 2 = female, 3 = non-binary)
  • Technical_issues: Open query (subjects could report any experienced technical issues)
  • Duration (sek): The time subjects needed to complete the study (in seconds)

5 Analysis Scripts

2022 Simon Stephan.