Replication data and code for: Determining optimal parameters of the Self Referent Encodin

Replication data and code for a study determining optimal parameters of the Self-Referent Encoding Task. The study analyzed three large samples: 572 college students, 293 adults from Amazon Mechanical Turk, and 408 adolescents from a school field study. The data was contributed by Justin Dainer-Best of The University of Texas at Austin.

Use Cases

• Modeling the relationship between SRET metrics and depression severity based on word endorsement and drift rate data.
• Assessing the reliability of self-referent cognition measures based on reported intra-task and test-retest metrics.
• Comparing behavioral responses across different demographic groups based on the college, MTurk, and adolescent samples.

Strengths

• Data originates from three distinct samples with a total of 1273 participants.
• Includes metrics with reported strong intra-task, split-half, and high 1-week test-retest reliability.
• Associated analysis code is provided, supporting reproducibility.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Justin Dainer-Best, The University of Texas at Austin

Collection Method

Collected via the Self-Referent Encoding Task administered to three participant samples.