SourceMonitoring: Performance in a Source-Monitoring Experiment

In psychotools: Psychometric Modeling Infrastructure

Performance in a Source-Monitoring Experiment

Description

Response frequencies of 128 participants who took part in a source-monitoring experiment with two sources.

Usage

data("SourceMonitoring")

Format

A data frame containing 128 observations on four components.

sources

Factor. Sources A and B.

age

Integer. Age of the respondents in years.

gender

Factor coding gender.

y

Matrix containing the response frequencies. The column names indicate the nine response categories:

a.a	Number of source A items judged to be of source A.
a.b	Number of source A items judged to be of source B.
a.n	Number of source A items judged to be new.
b.a	Number of source B items judged to be of source A.
b.b	Number of source B items judged to be of source B.
b.n	Number of source B items judged to be new.
n.a	Number of new items judged to be of source A.
n.b	Number of new items judged to be of source B.
n.n	Number of new items judged to be new.

Details

In a source-monitoring experiment with two sources, participants study items from two different sources, A and B. The final memory test consists of A and B items along with new distractor items, N. Participants are required to classify each item as A, B, or N.

In an experiment at the Department of Psychology, University of Tuebingen (Wickelmaier & Zeileis, 2013, 2018), two source conditions were used in the study phase: Half of the subjects had to read items either quietly (source A = think) or aloud (source B = say). The other half had to write items down (source A = write) or read them aloud (source B = say).

The data were analyzed using the multinomial processing tree model of source monitoring (Batchelder & Riefer, 1990).

Source

Wickelmaier F, Zeileis A (2013). A First Implementation of Recursive Partitioning for Multinomial Processing Tree Models. Presented at the Psychoco 2013 International Workshop on Psychometric Computing, February 14–15, Zurich, Switzerland.

References

Batchelder WH, Riefer DM (1990). Multinomial Processing Tree Models of Source Monitoring. Psychological Review, 97, 548–564.

Wickelmaier F, Zeileis A (2018). Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models. Behavior Research Methods, 50(3), 1217–1233. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-017-0937-z")}

Examples

data("SourceMonitoring", package = "psychotools")
xtabs(~ gender + I(age >= 30) + sources, SourceMonitoring)

psychotools documentation built on May 29, 2024, 8:12 a.m.