truth_trajectory_data: Data from the Longitudinal Illusory Truth Study
In truthiness: Illusory Truth Longitudinal Study

Description Usage Format Details References Examples

A collection of four data frames representing the anonymized longitudinal data in tidy format from \insertCiteHenderson_Simons_Barr_2021;textualtruthiness.

sessions

phases

cjudgments

ratings

An object of class tbl_df (inherits from tbl, data.frame) with 631 rows and 17 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 2282 rows and 8 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 39406 rows and 3 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 72215 rows and 4 columns.

Each data frame contains a subset of the following variables:

ID: Participant identifier.
list_id: Stimulus list identifier.
phase_id: Phase number (1-4).
stim_id: Stimulus identifier.
Age: Age of participant in years.
Gender: Gender of participant.
Nationality: Nationality of participant.
NativeLang: Native language of participant.
duration_secs: Duration of the phase in seconds.
category: Category the participant selected for this statement.
trating: Truth rating on a seven-point scale, 1=Definitely False, 7=Definitely True.
excl_phase: Phase in which participant was excluded (NA if never excluded).
excl_reason: Reason for participant exclusion.
p_excl_reason: Reason for phase exclusion.
chk_anydata: Whether there is ratings data for at least one phase for this participant after phase-level exclusions.
chk_consent_all: Whether participant gave consent for all phases.
chk_consent: Whether participant gave consent for this phase.
chk_dur_all: Whether all phase durations for this participant were within an acceptable range.
chk_finished: Whether participant completed the rating task for this phase.
chk_native: Whether participant is a native speaker of English.
chk_nocheat: Whether participant never looked up answers.
chk_noduplicates: Whether there were no duplicated sessions.
chk_noflatline: Whether the participant did not produce 'flatline' responses.
chk_notmanex: Whether the participant (or phase) is not manually excluded.
keep: Logical value, whether to keep (TRUE) or exclude (FALSE) participant (or phase data); this is a boolean "and" of all of the exclusion criteria (chk_* variables) for that participant (or phase).

The sessions data frame contains information about the 631 participants who were recruited to the study. The chk_* variables are logical variables representing exclusion criteria. The variable keep is a boolean "AND" of these criteria, and thus has a value of TRUE for participants who are to be included and FALSE for those who are to be excluded.

The phases data frame contains data from the 2,282 phases that were initiated by participants. Each participant who was not excluded during data collection had the opportunity to complete up to four phases of data collection taking place (1) immediately after the exposure phase; (2) one day after exposure; (3) one week after exposure; and (4) one month after exposure. The chk_* variables in this data frame represent exclusion criteria, and keep is a boolean "AND" of those criteria along with the keep variable from the sessions table. In other words, to apply the full set of participant-level and phase-level exclusion criteria for the study, simply include those rows in phases where keep is set to TRUE, and join this table to the others in the set; see the example below.

The cjudgments table contains 39,406 category judgments that were produced in the exposure phase (phase 1) of the study.

The ratings data frame contains 72,215 truth ratings of the stimulus statements used in the study. Ratings were on a 1-7 scale (1 = definitely false; 7 = definitely true).

\insertAllCited

library(dplyr)

## apply exclusions and merge with ratings data
ratings_incl <- phases %>%
  filter(keep) %>%                                        # apply exclusions
  inner_join(sessions %>% select(ID, list_id), "ID") %>%  # get list ID
  inner_join(ratings, c("ID", "phase_id"))

## look up conditions and calculate cell means
ratings_incl %>%
  inner_join(stimulus_conditions, c("list_id", "stim_id")) %>% # lookup condition
  group_by(repetition, interval) %>%
  summarize(rating_mean = mean(trating),
            rating_sd = sd(trating),
            N = n()) %>%
  ungroup()