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Creating Implicit Measure Stimulus Sets Using a Multi-Step Piloting Method.

Daniel J Phipps1,2,3, Kyra Hamilton1,2,4

  • 1School of Applied Psychology, Griffith University, 176 Messines Ridge Road, Mt Gravatt, QLD 4122, Australia.

Methods and Protocols
|May 23, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a data-driven method for selecting stimuli for implicit measures, improving the link between implicit attitudes and behavior. Empirically chosen items better reflect target concepts than expected, emphasizing informed stimulus selection.

Keywords:
implicit association testimplicit attitudeimplicit measures

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Area of Science:

  • Psychology
  • Behavioral Science
  • Cognitive Science

Background:

  • Arbitrary stimulus selection poses challenges for implicit measures.
  • Implicit measures assess automatic associations, crucial for understanding behavior.
  • Developing robust stimulus sets is vital for reliable implicit measure research.

Purpose of the Study:

  • To introduce and evaluate a data-driven, multi-step procedure for creating stimulus items for implicit measures.
  • To assess the effectiveness of empirically selected stimuli in capturing implicit associations.
  • To compare the performance of data-driven stimulus sets against traditional methods.

Main Methods:

  • A combination of free-recall and survey data was used to generate stimulus items.
  • Six sets of stimuli were created for healthy food and high-sugar items across children, adolescents, and adults.
  • Stimulus items were selected based on representativeness, usage frequency, and length.

Main Results:

  • The data-driven procedure yielded stimulus items highly representative of target concepts and in frequent use.
  • Piloted items demonstrated slightly enhanced implicit measure-behavior relations compared to a previous measure.
  • Associated items often differed from expected associations based on guidelines or consumption patterns.

Conclusions:

  • Empirically based stimulus selection offers a valuable improvement for implicit measures.
  • Data-driven methods are crucial for informed stimulus selection in implicit association research.
  • This procedure enhances the validity and reliability of implicit measures in psychological research.