paper_key <- "francis2019stakesscalesskepticism"
sign_convention <- "d = mean(low) - mean(high)"
if (!requireNamespace("esc", quietly = TRUE)) {
stop("Package 'esc' is required for this template.", call. = FALSE)
}
suppressPackageStartupMessages(library(esc))
candidate_paper_dirs <- c(
file.path("papers", paper_key),
"..",
"."
)
paper_dir <- candidate_paper_dirs[
vapply(
candidate_paper_dirs,
function(x) file.exists(file.path(x, "out", "external")),
logical(1)
)
][1]
if (is.na(paper_dir)) stop("Could not locate paper directory.", call. = FALSE)
paper_dir <- normalizePath(paper_dir, mustWork = TRUE)
external_dir <- file.path(paper_dir, "out", "external")
analysis_dir <- file.path(paper_dir, "analysis")
audit_csv <- file.path(analysis_dir, "effect_sizes_raw_data.csv")Effect size computation: francis2019stakesscalesskepticism
This document is the source of truth for raw-data effect-size recovery in papers/francis2019stakesscalesskepticism/francis2019stakesscalesskepticism.yaml.
It follows the project template in docs/quarto_effect_template.qmd:
- Sign convention:
d = mean(low) - mean(high). - Within-subject scalar studies use the template’s
within_smcrp_rmethod. - The registered replication uses the template’s
between_groupsmethod. - Study 1 negative-polarity evidence-fixed prompts are reverse-coded to knowledge-attribution direction (
8 - raw agreement with "doesn't know") before computing the low-high contrast. - For evidence-seeking outcomes,
never, blank, and non-positive values are excluded from the continuous contrast. Extreme values are removed using the authors’ documented log-MAD rule as closely as the data documentation permits. - The audit retains the raw low-minus-high
dfor YAML. Downstream meta-analysis code reverses evidence-seeking effects programmatically.
The final audit table is written to analysis/effect_sizes_raw_data.csv and is used to update the YAML.
Setup
Template Methods
The functions below are copied from the project effect-size template, with a thin wrapper for batch computation from raw CSV files.
hedges_correction <- function(df) {
ifelse(df <= 1, NA_real_, exp(lgamma(df / 2) - log(sqrt(df / 2)) - lgamma((df - 1) / 2)))
}
sd_pooled_within <- function(sd_low, sd_high) {
sqrt((sd_low^2 + sd_high^2) / 2)
}
d_within_smcrp <- function(mean_low, mean_high, sd_low, sd_high) {
(mean_low - mean_high) / sd_pooled_within(sd_low, sd_high)
}
var_d_within_smcrp <- function(d, r, n_total) {
(2 * (1 - r) / n_total) + (d^2) * (1 + r^2) / (4 * n_total)
}
extract_esc <- function(x) {
list(d = as.numeric(x$es), v = as.numeric(x$var))
}
compute_with_esc <- function(fun, ...) {
d_obj <- fun(..., es.type = "d")
g_obj <- fun(..., es.type = "g")
d_out <- extract_esc(d_obj)
g_out <- extract_esc(g_obj)
list(d = d_out$d, v = d_out$v, g = g_out$d, v_g = g_out$v)
}
compute_within_smcrp_r <- function(mean_low, mean_high, sd_low, sd_high, n_total, r_within) {
d <- d_within_smcrp(mean_low, mean_high, sd_low, sd_high)
v <- var_d_within_smcrp(d = d, r = r_within, n_total = n_total)
df_used <- 2 * (n_total - 1) / (1 + r_within^2)
J <- hedges_correction(df_used)
g <- J * d
v_g <- (2 * (1 - r_within) / n_total) + (g^2) * (1 + r_within^2) / (4 * n_total)
list(d = d, v = v, g = g, v_g = v_g)
}
compute_between_groups <- function(mean_low, mean_high, sd_low, sd_high, n_low, n_high) {
res <- compute_with_esc(
esc::esc_mean_sd,
grp1m = mean_low,
grp1sd = sd_low,
grp1n = n_low,
grp2m = mean_high,
grp2sd = sd_high,
grp2n = n_high
)
list(d = res$d, v = res$v, g = res$g, v_g = res$v_g)
}Data Helpers
trim_names <- function(x) {
out <- trimws(x)
make.unique(out, sep = ".")
}
read_qualtrics_csv <- function(path, skip = 0) {
dat <- read.csv(
path,
check.names = FALSE,
stringsAsFactors = FALSE,
fill = TRUE,
skip = skip
)
names(dat) <- trim_names(names(dat))
finished_col <- names(dat)[tolower(gsub(" ", "", names(dat))) == "finished"]
if (length(finished_col) > 0) {
dat <- dat[as.character(dat[[finished_col[1]]]) == "TRUE", , drop = FALSE]
}
dat
}
read_replication_csv <- function(path) {
raw <- read.csv(
path,
header = FALSE,
check.names = FALSE,
stringsAsFactors = FALSE,
fill = TRUE
)
condition <- trimws(as.character(raw[1, ]))
variable <- trimws(as.character(raw[2, ]))
active_condition <- ""
for (i in seq_along(condition)) {
if (!is.na(condition[i]) && condition[i] != "") {
active_condition <- condition[i]
} else {
condition[i] <- active_condition
}
}
names_out <- ifelse(
condition %in% c("Basic Low", "Basic High", "Implicit Low", "Explicit High", "Ignorant Low", "Ignorant High"),
paste(condition, variable, sep = "__"),
ifelse(variable != "", variable, condition)
)
names_out[names_out == ""] <- paste0("unnamed_", which(names_out == ""))
dat <- raw[-c(1, 2), , drop = FALSE]
names(dat) <- trim_names(names_out)
finished_col <- names(dat)[tolower(gsub(" ", "", names(dat))) == "finished"]
if (length(finished_col) > 0) {
dat <- dat[as.character(dat[[finished_col[1]]]) == "TRUE", , drop = FALSE]
}
dat
}
parse_numeric_response <- function(x) {
y <- trimws(as.character(x))
y[y == ""] <- NA_character_
y[grepl("never", y, ignore.case = TRUE)] <- NA_character_
match <- regexpr("-?[0-9]+(\\.[0-9]+)?", y, perl = TRUE)
out <- rep(NA_real_, length(y))
hit <- !is.na(y) & match > 0
out[hit] <- as.numeric(regmatches(y[hit], regexpr("-?[0-9]+(\\.[0-9]+)?", y[hit], perl = TRUE)))
out
}
log_mad_bounds <- function(values) {
values <- values[!is.na(values) & values > 0]
if (length(values) < 3) return(c(lower = -Inf, upper = Inf))
logged <- log(values)
center <- median(logged, na.rm = TRUE)
mad_value <- mad(logged, center = center, constant = 1, na.rm = TRUE)
if (is.na(mad_value) || mad_value == 0) return(c(lower = -Inf, upper = Inf))
c(lower = center - 2.5 * mad_value, upper = center + 2.5 * mad_value)
}
apply_log_mad <- function(values, bounds) {
out <- values
bad <- !is.na(out) & out > 0 & (log(out) < bounds[["lower"]] | log(out) > bounds[["upper"]])
out[bad] <- NA_real_
out
}
summarise_within_pair <- function(
dat,
low_col,
high_col,
all_stake_cols,
outcome_type,
score_transform = identity,
transform_note = NULL
) {
low <- score_transform(parse_numeric_response(dat[[low_col]]))
high <- score_transform(parse_numeric_response(dat[[high_col]]))
n_finished <- nrow(dat)
n_pair_raw <- sum(!is.na(low) & !is.na(high))
cleaning <- "complete low-high pairs from post-removal CSV"
if (!is.null(transform_note)) {
cleaning <- paste(cleaning, transform_note, sep = "; ")
}
if (outcome_type == "evidence_seeking") {
low[low <= 0] <- NA_real_
high[high <= 0] <- NA_real_
all_values <- unlist(lapply(all_stake_cols, function(col) parse_numeric_response(dat[[col]])), use.names = FALSE)
all_values[all_values <= 0] <- NA_real_
bounds <- log_mad_bounds(all_values)
low <- apply_log_mad(low, bounds)
high <- apply_log_mad(high, bounds)
cleaning <- paste(
"complete low-high pairs; 'never'/blank/non-positive excluded;",
"log-MAD outlier removal over the scenario's four stakes cells"
)
}
keep <- !is.na(low) & !is.na(high)
low <- low[keep]
high <- high[keep]
n_total <- length(low)
if (n_total < 3) stop(sprintf("Too few complete pairs for %s vs %s", low_col, high_col), call. = FALSE)
mean_low <- mean(low)
mean_high <- mean(high)
sd_low <- sd(low)
sd_high <- sd(high)
r_within <- suppressWarnings(cor(low, high))
res <- compute_within_smcrp_r(
mean_low = mean_low,
mean_high = mean_high,
sd_low = sd_low,
sd_high = sd_high,
n_total = n_total,
r_within = r_within
)
data.frame(
design = "Within-Subjects",
method_used = "within_smcrp_r",
computed_from_suggested = "groups",
n_finished = n_finished,
n_pair_raw = n_pair_raw,
n_low = n_total,
n_high = n_total,
n_total = n_total,
mean_low = mean_low,
mean_high = mean_high,
sd_low = sd_low,
sd_high = sd_high,
r_within = r_within,
d = res$d,
v = res$v,
g = res$g,
v_g = res$v_g,
cleaning = cleaning,
stringsAsFactors = FALSE
)
}
summarise_between_groups <- function(dat, low_col, high_col, transform = identity) {
low <- transform(parse_numeric_response(dat[[low_col]]))
high <- transform(parse_numeric_response(dat[[high_col]]))
low <- low[!is.na(low)]
high <- high[!is.na(high)]
n_low <- length(low)
n_high <- length(high)
if (n_low < 3 || n_high < 3) stop(sprintf("Too few between-group observations for %s vs %s", low_col, high_col), call. = FALSE)
mean_low <- mean(low)
mean_high <- mean(high)
sd_low <- sd(low)
sd_high <- sd(high)
res <- compute_between_groups(
mean_low = mean_low,
mean_high = mean_high,
sd_low = sd_low,
sd_high = sd_high,
n_low = n_low,
n_high = n_high
)
data.frame(
design = "Between-Subjects",
method_used = "between_groups",
computed_from_suggested = "groups",
n_finished = nrow(dat),
n_pair_raw = NA_integer_,
n_low = n_low,
n_high = n_high,
n_total = n_low + n_high,
mean_low = mean_low,
mean_high = mean_high,
sd_low = sd_low,
sd_high = sd_high,
r_within = NA_real_,
d = res$d,
v = res$v,
g = res$g,
v_g = res$v_g,
cleaning = "available low and high between-participant responses from post-removal CSV",
stringsAsFactors = FALSE
)
}Scalar Experiments
scenario_labels <- c(
paramedic = "Paramedic",
vaccine = "Vaccine",
mountaineering = "Mountaineering",
game_show = "Game show",
introduction = "Introduction",
possessions = "Possessions/Arson"
)
full_map <- list(
paramedic = c(low = "Paramedic Low", s2 = "Paramedic 1", s3 = "Paramedic 2", high = "Paramedic 3"),
vaccine = c(low = "Vaccine Low", s2 = "Vaccine 1", s3 = "Vaccine 2", high = "Vaccine 3"),
mountaineering = c(low = "Mountaineering Low", s2 = "Mountaineering 1", s3 = "Mountaineering 2", high = "Mountaineering 3"),
game_show = c(low = "GameShow low", s2 = "GameShow 1", s3 = "GameShow 2", high = "GameShow 3"),
introduction = c(low = "Intro Low", s2 = "Intro 1", s3 = "Intro 2", high = "Intro 3"),
possessions = c(low = "Personal Val Low", s2 = "Personal Val 1", s3 = "Personal Val 2", high = "Personal Val 3")
)
exp2_neg_map <- list(
paramedic = c(low = "Paramedic Low", s2 = "Paramedic 1", s3 = "Paramedic 2", high = "Paramedic 3"),
vaccine = c(low = "Vaccine Low", s2 = "Vaccine 1", s3 = "Vaccine 2", high = "Vaccine 3"),
mountaineering = c(low = "Mountaineering Low", s2 = "Mountaineering 1", s3 = "Mountaineering 2", high = "Mountaineering 3"),
game_show = c(low = "Game Show Low", s2 = "Game Show 1", s3 = "Game Show 2", high = "Game Show 3"),
introduction = c(low = "Introductions Low", s2 = "Intro 1", s3 = "Intro 2", high = "Intro 3"),
possessions = c(low = "Pvalue Low", s2 = "Pvalue 1", s3 = "Pvalue 2", high = "Pvalue 3")
)
abbr_map <- list(
paramedic = c(low = "Para Low", s2 = "Para 1", s3 = "Para 2", high = "Para 3"),
vaccine = c(low = "Vacc Low", s2 = "Vacc 1", s3 = "Vacc 2", high = "Vacc 3"),
mountaineering = c(low = "Mount Low", s2 = "Mount 1", s3 = "Mount 2", high = "Mount 3"),
game_show = c(low = "Game Low", s2 = "Game 1", s3 = "Game 2", high = "Game 3"),
introduction = c(low = "Intro Low", s2 = "Intro 1", s3 = "Intro 2", high = "Intro 3"),
possessions = c(low = "Pval Low", s2 = "Pval 1", s3 = "Pval 2", high = "Pval 3")
)
scalar_sets <- list(
list(
study_id = 1,
experiment = "Experiment 1: Evidence-fixed design",
outcome_type = "evidence_fixed",
pos_file = file.path(external_dir, "Exp1_Evidence_Fixed_Design", "x1_EF_Pos_data_post_removal.csv"),
neg_file = file.path(external_dir, "Exp1_Evidence_Fixed_Design", "x1_EF_Neg_data_post_removal.csv"),
pos_map = full_map,
neg_map = full_map
),
list(
study_id = 3,
experiment = "Experiment 2: Evidence-seeking design",
outcome_type = "evidence_seeking",
pos_file = file.path(external_dir, "Exp2_Evidence_Seeking_Design", "x2_ES_Pos_data_post_removal.csv"),
neg_file = file.path(external_dir, "Exp2_Evidence_Seeking_Design", "x2_ES_Neg_data_post_removal.csv"),
pos_map = full_map,
neg_map = exp2_neg_map
),
list(
study_id = 4,
experiment = "Appendix IV: Symmetrical evidence-seeking experiment",
outcome_type = "evidence_seeking",
pos_file = file.path(external_dir, "Exp2.1_Symmetrical_Experiment", "x2.1_Sym_Pos_data_post_removal.csv"),
neg_file = file.path(external_dir, "Exp2.1_Symmetrical_Experiment", "x2.1_Sym_Neg_data_post_removal.csv"),
pos_map = abbr_map,
neg_map = abbr_map
),
list(
study_id = 5,
experiment = "Appendix IV: Matched evidence-seeking experiment",
outcome_type = "evidence_seeking",
pos_file = file.path(external_dir, "Exp2.2_Matched_Experiment", "x2.2_Matc_Pos_data_post_removal.csv"),
neg_file = file.path(external_dir, "Exp2.2_Matched_Experiment", "x2.2_Matc_Neg_data_post_removal.csv"),
pos_map = abbr_map,
neg_map = abbr_map
)
)
compute_scalar_set <- function(spec) {
out <- list()
effect_index <- 1
for (polarity in c("pos", "neg")) {
path <- if (polarity == "pos") spec$pos_file else spec$neg_file
col_map <- if (polarity == "pos") spec$pos_map else spec$neg_map
dat <- read_qualtrics_csv(path)
polarity_label <- if (polarity == "pos") "positive polarity" else "negative polarity"
score_transform <- identity
transform_note <- NULL
if (spec$outcome_type == "evidence_fixed" && polarity == "neg") {
score_transform <- function(x) 8 - x
transform_note <- "negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction"
}
for (scenario_code in names(scenario_labels)) {
cols <- col_map[[scenario_code]]
stats <- summarise_within_pair(
dat = dat,
low_col = unname(cols[["low"]]),
high_col = unname(cols[["high"]]),
all_stake_cols = unname(cols),
outcome_type = spec$outcome_type,
score_transform = score_transform,
transform_note = transform_note
)
effect_id <- sprintf("s%d_e%d", spec$study_id, effect_index)
subgroup <- sprintf("%s -- %s -- %s", spec$experiment, scenario_labels[[scenario_code]], polarity_label)
out[[length(out) + 1]] <- cbind(
paper_key = paper_key,
study_id = spec$study_id,
effect_id = effect_id,
experiment = spec$experiment,
subgroup = subgroup,
scenario_code = scenario_code,
scenario_label = scenario_labels[[scenario_code]],
polarity = polarity,
polarity_label = polarity_label,
outcome_type = spec$outcome_type,
source_file = path,
source_file_short = file.path(basename(dirname(path)), basename(path)),
low_col = unname(cols[["low"]]),
high_col = unname(cols[["high"]]),
all_stake_cols = paste(unname(cols), collapse = " | "),
stats,
stringsAsFactors = FALSE
)
effect_index <- effect_index + 1
}
}
do.call(rbind, out)
}
scalar_audit <- do.call(rbind, lapply(scalar_sets, compute_scalar_set))
scalar_audit paper_key study_id effect_id
1 francis2019stakesscalesskepticism 1 s1_e1
2 francis2019stakesscalesskepticism 1 s1_e2
3 francis2019stakesscalesskepticism 1 s1_e3
4 francis2019stakesscalesskepticism 1 s1_e4
5 francis2019stakesscalesskepticism 1 s1_e5
6 francis2019stakesscalesskepticism 1 s1_e6
7 francis2019stakesscalesskepticism 1 s1_e7
8 francis2019stakesscalesskepticism 1 s1_e8
9 francis2019stakesscalesskepticism 1 s1_e9
10 francis2019stakesscalesskepticism 1 s1_e10
11 francis2019stakesscalesskepticism 1 s1_e11
12 francis2019stakesscalesskepticism 1 s1_e12
13 francis2019stakesscalesskepticism 3 s3_e1
14 francis2019stakesscalesskepticism 3 s3_e2
15 francis2019stakesscalesskepticism 3 s3_e3
16 francis2019stakesscalesskepticism 3 s3_e4
17 francis2019stakesscalesskepticism 3 s3_e5
18 francis2019stakesscalesskepticism 3 s3_e6
19 francis2019stakesscalesskepticism 3 s3_e7
20 francis2019stakesscalesskepticism 3 s3_e8
21 francis2019stakesscalesskepticism 3 s3_e9
22 francis2019stakesscalesskepticism 3 s3_e10
23 francis2019stakesscalesskepticism 3 s3_e11
24 francis2019stakesscalesskepticism 3 s3_e12
25 francis2019stakesscalesskepticism 4 s4_e1
26 francis2019stakesscalesskepticism 4 s4_e2
27 francis2019stakesscalesskepticism 4 s4_e3
28 francis2019stakesscalesskepticism 4 s4_e4
29 francis2019stakesscalesskepticism 4 s4_e5
30 francis2019stakesscalesskepticism 4 s4_e6
31 francis2019stakesscalesskepticism 4 s4_e7
32 francis2019stakesscalesskepticism 4 s4_e8
33 francis2019stakesscalesskepticism 4 s4_e9
34 francis2019stakesscalesskepticism 4 s4_e10
35 francis2019stakesscalesskepticism 4 s4_e11
36 francis2019stakesscalesskepticism 4 s4_e12
37 francis2019stakesscalesskepticism 5 s5_e1
38 francis2019stakesscalesskepticism 5 s5_e2
39 francis2019stakesscalesskepticism 5 s5_e3
40 francis2019stakesscalesskepticism 5 s5_e4
41 francis2019stakesscalesskepticism 5 s5_e5
42 francis2019stakesscalesskepticism 5 s5_e6
43 francis2019stakesscalesskepticism 5 s5_e7
44 francis2019stakesscalesskepticism 5 s5_e8
45 francis2019stakesscalesskepticism 5 s5_e9
46 francis2019stakesscalesskepticism 5 s5_e10
47 francis2019stakesscalesskepticism 5 s5_e11
48 francis2019stakesscalesskepticism 5 s5_e12
experiment
1 Experiment 1: Evidence-fixed design
2 Experiment 1: Evidence-fixed design
3 Experiment 1: Evidence-fixed design
4 Experiment 1: Evidence-fixed design
5 Experiment 1: Evidence-fixed design
6 Experiment 1: Evidence-fixed design
7 Experiment 1: Evidence-fixed design
8 Experiment 1: Evidence-fixed design
9 Experiment 1: Evidence-fixed design
10 Experiment 1: Evidence-fixed design
11 Experiment 1: Evidence-fixed design
12 Experiment 1: Evidence-fixed design
13 Experiment 2: Evidence-seeking design
14 Experiment 2: Evidence-seeking design
15 Experiment 2: Evidence-seeking design
16 Experiment 2: Evidence-seeking design
17 Experiment 2: Evidence-seeking design
18 Experiment 2: Evidence-seeking design
19 Experiment 2: Evidence-seeking design
20 Experiment 2: Evidence-seeking design
21 Experiment 2: Evidence-seeking design
22 Experiment 2: Evidence-seeking design
23 Experiment 2: Evidence-seeking design
24 Experiment 2: Evidence-seeking design
25 Appendix IV: Symmetrical evidence-seeking experiment
26 Appendix IV: Symmetrical evidence-seeking experiment
27 Appendix IV: Symmetrical evidence-seeking experiment
28 Appendix IV: Symmetrical evidence-seeking experiment
29 Appendix IV: Symmetrical evidence-seeking experiment
30 Appendix IV: Symmetrical evidence-seeking experiment
31 Appendix IV: Symmetrical evidence-seeking experiment
32 Appendix IV: Symmetrical evidence-seeking experiment
33 Appendix IV: Symmetrical evidence-seeking experiment
34 Appendix IV: Symmetrical evidence-seeking experiment
35 Appendix IV: Symmetrical evidence-seeking experiment
36 Appendix IV: Symmetrical evidence-seeking experiment
37 Appendix IV: Matched evidence-seeking experiment
38 Appendix IV: Matched evidence-seeking experiment
39 Appendix IV: Matched evidence-seeking experiment
40 Appendix IV: Matched evidence-seeking experiment
41 Appendix IV: Matched evidence-seeking experiment
42 Appendix IV: Matched evidence-seeking experiment
43 Appendix IV: Matched evidence-seeking experiment
44 Appendix IV: Matched evidence-seeking experiment
45 Appendix IV: Matched evidence-seeking experiment
46 Appendix IV: Matched evidence-seeking experiment
47 Appendix IV: Matched evidence-seeking experiment
48 Appendix IV: Matched evidence-seeking experiment
subgroup
1 Experiment 1: Evidence-fixed design -- Paramedic -- positive polarity
2 Experiment 1: Evidence-fixed design -- Vaccine -- positive polarity
3 Experiment 1: Evidence-fixed design -- Mountaineering -- positive polarity
4 Experiment 1: Evidence-fixed design -- Game show -- positive polarity
5 Experiment 1: Evidence-fixed design -- Introduction -- positive polarity
6 Experiment 1: Evidence-fixed design -- Possessions/Arson -- positive polarity
7 Experiment 1: Evidence-fixed design -- Paramedic -- negative polarity
8 Experiment 1: Evidence-fixed design -- Vaccine -- negative polarity
9 Experiment 1: Evidence-fixed design -- Mountaineering -- negative polarity
10 Experiment 1: Evidence-fixed design -- Game show -- negative polarity
11 Experiment 1: Evidence-fixed design -- Introduction -- negative polarity
12 Experiment 1: Evidence-fixed design -- Possessions/Arson -- negative polarity
13 Experiment 2: Evidence-seeking design -- Paramedic -- positive polarity
14 Experiment 2: Evidence-seeking design -- Vaccine -- positive polarity
15 Experiment 2: Evidence-seeking design -- Mountaineering -- positive polarity
16 Experiment 2: Evidence-seeking design -- Game show -- positive polarity
17 Experiment 2: Evidence-seeking design -- Introduction -- positive polarity
18 Experiment 2: Evidence-seeking design -- Possessions/Arson -- positive polarity
19 Experiment 2: Evidence-seeking design -- Paramedic -- negative polarity
20 Experiment 2: Evidence-seeking design -- Vaccine -- negative polarity
21 Experiment 2: Evidence-seeking design -- Mountaineering -- negative polarity
22 Experiment 2: Evidence-seeking design -- Game show -- negative polarity
23 Experiment 2: Evidence-seeking design -- Introduction -- negative polarity
24 Experiment 2: Evidence-seeking design -- Possessions/Arson -- negative polarity
25 Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- positive polarity
26 Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- positive polarity
27 Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- positive polarity
28 Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- positive polarity
29 Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- positive polarity
30 Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- positive polarity
31 Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- negative polarity
32 Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- negative polarity
33 Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- negative polarity
34 Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- negative polarity
35 Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- negative polarity
36 Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- negative polarity
37 Appendix IV: Matched evidence-seeking experiment -- Paramedic -- positive polarity
38 Appendix IV: Matched evidence-seeking experiment -- Vaccine -- positive polarity
39 Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- positive polarity
40 Appendix IV: Matched evidence-seeking experiment -- Game show -- positive polarity
41 Appendix IV: Matched evidence-seeking experiment -- Introduction -- positive polarity
42 Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- positive polarity
43 Appendix IV: Matched evidence-seeking experiment -- Paramedic -- negative polarity
44 Appendix IV: Matched evidence-seeking experiment -- Vaccine -- negative polarity
45 Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- negative polarity
46 Appendix IV: Matched evidence-seeking experiment -- Game show -- negative polarity
47 Appendix IV: Matched evidence-seeking experiment -- Introduction -- negative polarity
48 Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- negative polarity
scenario_code scenario_label polarity polarity_label outcome_type
1 paramedic Paramedic pos positive polarity evidence_fixed
2 vaccine Vaccine pos positive polarity evidence_fixed
3 mountaineering Mountaineering pos positive polarity evidence_fixed
4 game_show Game show pos positive polarity evidence_fixed
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35 Intro Low Intro 3
36 Pval Low Pval 3
37 Para Low Para 3
38 Vacc Low Vacc 3
39 Mount Low Mount 3
40 Game Low Game 3
41 Intro Low Intro 3
42 Pval Low Pval 3
43 Para Low Para 3
44 Vacc Low Vacc 3
45 Mount Low Mount 3
46 Game Low Game 3
47 Intro Low Intro 3
48 Pval Low Pval 3
all_stake_cols
1 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
2 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
3 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
4 GameShow low | GameShow 1 | GameShow 2 | GameShow 3
5 Intro Low | Intro 1 | Intro 2 | Intro 3
6 Personal Val Low | Personal Val 1 | Personal Val 2 | Personal Val 3
7 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
8 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
9 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
10 GameShow low | GameShow 1 | GameShow 2 | GameShow 3
11 Intro Low | Intro 1 | Intro 2 | Intro 3
12 Personal Val Low | Personal Val 1 | Personal Val 2 | Personal Val 3
13 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
14 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
15 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
16 GameShow low | GameShow 1 | GameShow 2 | GameShow 3
17 Intro Low | Intro 1 | Intro 2 | Intro 3
18 Personal Val Low | Personal Val 1 | Personal Val 2 | Personal Val 3
19 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
20 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
21 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
22 Game Show Low | Game Show 1 | Game Show 2 | Game Show 3
23 Introductions Low | Intro 1 | Intro 2 | Intro 3
24 Pvalue Low | Pvalue 1 | Pvalue 2 | Pvalue 3
25 Para Low | Para 1 | Para 2 | Para 3
26 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
27 Mount Low | Mount 1 | Mount 2 | Mount 3
28 Game Low | Game 1 | Game 2 | Game 3
29 Intro Low | Intro 1 | Intro 2 | Intro 3
30 Pval Low | Pval 1 | Pval 2 | Pval 3
31 Para Low | Para 1 | Para 2 | Para 3
32 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
33 Mount Low | Mount 1 | Mount 2 | Mount 3
34 Game Low | Game 1 | Game 2 | Game 3
35 Intro Low | Intro 1 | Intro 2 | Intro 3
36 Pval Low | Pval 1 | Pval 2 | Pval 3
37 Para Low | Para 1 | Para 2 | Para 3
38 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
39 Mount Low | Mount 1 | Mount 2 | Mount 3
40 Game Low | Game 1 | Game 2 | Game 3
41 Intro Low | Intro 1 | Intro 2 | Intro 3
42 Pval Low | Pval 1 | Pval 2 | Pval 3
43 Para Low | Para 1 | Para 2 | Para 3
44 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
45 Mount Low | Mount 1 | Mount 2 | Mount 3
46 Game Low | Game 1 | Game 2 | Game 3
47 Intro Low | Intro 1 | Intro 2 | Intro 3
48 Pval Low | Pval 1 | Pval 2 | Pval 3
design method_used computed_from_suggested n_finished n_pair_raw
1 Within-Subjects within_smcrp_r groups 57 55
2 Within-Subjects within_smcrp_r groups 57 55
3 Within-Subjects within_smcrp_r groups 57 55
4 Within-Subjects within_smcrp_r groups 57 55
5 Within-Subjects within_smcrp_r groups 57 55
6 Within-Subjects within_smcrp_r groups 57 55
7 Within-Subjects within_smcrp_r groups 42 42
8 Within-Subjects within_smcrp_r groups 42 42
9 Within-Subjects within_smcrp_r groups 42 42
10 Within-Subjects within_smcrp_r groups 42 42
11 Within-Subjects within_smcrp_r groups 42 42
12 Within-Subjects within_smcrp_r groups 42 42
13 Within-Subjects within_smcrp_r groups 78 57
14 Within-Subjects within_smcrp_r groups 78 58
15 Within-Subjects within_smcrp_r groups 78 54
16 Within-Subjects within_smcrp_r groups 78 57
17 Within-Subjects within_smcrp_r groups 78 58
18 Within-Subjects within_smcrp_r groups 78 54
19 Within-Subjects within_smcrp_r groups 368 36
20 Within-Subjects within_smcrp_r groups 368 40
21 Within-Subjects within_smcrp_r groups 368 39
22 Within-Subjects within_smcrp_r groups 368 38
23 Within-Subjects within_smcrp_r groups 368 40
24 Within-Subjects within_smcrp_r groups 368 30
25 Within-Subjects within_smcrp_r groups 196 58
26 Within-Subjects within_smcrp_r groups 196 56
27 Within-Subjects within_smcrp_r groups 196 57
28 Within-Subjects within_smcrp_r groups 196 56
29 Within-Subjects within_smcrp_r groups 196 57
30 Within-Subjects within_smcrp_r groups 196 57
31 Within-Subjects within_smcrp_r groups 183 36
32 Within-Subjects within_smcrp_r groups 183 35
33 Within-Subjects within_smcrp_r groups 183 42
34 Within-Subjects within_smcrp_r groups 183 36
35 Within-Subjects within_smcrp_r groups 183 38
36 Within-Subjects within_smcrp_r groups 183 33
37 Within-Subjects within_smcrp_r groups 65 43
38 Within-Subjects within_smcrp_r groups 65 44
39 Within-Subjects within_smcrp_r groups 65 43
40 Within-Subjects within_smcrp_r groups 65 44
41 Within-Subjects within_smcrp_r groups 65 45
42 Within-Subjects within_smcrp_r groups 65 41
43 Within-Subjects within_smcrp_r groups 102 35
44 Within-Subjects within_smcrp_r groups 102 33
45 Within-Subjects within_smcrp_r groups 102 31
46 Within-Subjects within_smcrp_r groups 102 36
47 Within-Subjects within_smcrp_r groups 102 38
48 Within-Subjects within_smcrp_r groups 102 31
n_low n_high n_total mean_low mean_high sd_low sd_high r_within
1 55 55 55 5.509091 5.672727 1.3453999 1.347900 0.6858534
2 55 55 55 5.981818 6.072727 1.1465069 1.119764 0.5780320
3 55 55 55 5.836364 5.963636 1.2135598 1.035725 0.5992440
4 55 55 55 5.272727 5.218182 1.4587481 1.370197 0.8220532
5 55 55 55 5.981818 6.127273 1.2246074 1.019342 0.6546312
6 55 55 55 5.909091 5.890909 1.0050378 1.196797 0.5612483
7 42 42 42 5.428571 5.642857 1.5482934 1.303306 0.5611795
8 42 42 42 5.619048 5.547619 1.4305445 1.517417 0.7838412
9 42 42 42 5.404762 5.333333 1.7951089 1.720276 0.7371638
10 42 42 42 5.333333 5.119048 1.6028430 1.655772 0.7842300
11 42 42 42 5.976190 5.738095 1.1993514 1.623900 0.6103502
12 42 42 42 5.785714 5.809524 1.3710547 1.418314 0.5429172
13 39 39 39 1.948718 2.769231 1.1227016 1.346761 0.5315066
14 48 48 48 2.479167 4.229167 1.5843595 2.486043 0.5711296
15 44 44 44 2.000000 2.886364 1.2007749 1.384565 0.6154735
16 27 27 27 2.666667 5.481481 2.4494897 3.856766 0.1072096
17 50 50 50 1.740000 2.400000 0.8283251 1.124858 0.5081516
18 47 47 47 1.531915 2.829787 0.9053240 2.067546 0.3746143
19 35 35 35 2.257143 2.142857 2.0628772 1.833397 0.3399496
20 34 34 34 2.705882 3.794118 1.9467052 3.641340 0.4357870
21 36 36 36 1.861111 2.472222 1.2683573 1.889612 0.2427276
22 33 33 33 8.151515 8.000000 10.5478707 8.158584 0.5327219
23 32 32 32 1.656250 2.093750 0.9708452 1.444888 0.2996679
24 26 26 26 2.153846 3.115385 1.6172151 2.673229 0.2733027
25 47 47 47 2.234043 3.000000 1.5909085 1.944893 0.6112523
26 44 44 44 2.863636 4.704545 2.2577094 3.573673 0.6088336
27 35 35 35 2.828571 3.600000 1.2001400 1.769014 0.6178637
28 41 41 41 4.000000 6.609756 4.2836900 5.919789 0.6674280
29 45 45 45 2.222222 2.466667 1.1849221 1.179368 0.5095828
30 46 46 46 1.913043 3.347826 1.1704906 2.433264 0.3853726
31 30 30 30 1.833333 2.033333 0.9128709 1.098065 0.3153377
32 30 30 30 3.366667 3.433333 2.9300269 2.737773 0.4910501
33 39 39 39 2.153846 2.846154 1.9130915 2.390094 0.7132107
34 23 23 23 3.608696 4.956522 2.5179200 3.067102 0.3567320
35 34 34 34 1.970588 2.294118 1.1930428 1.732565 0.8399442
36 30 30 30 1.566667 2.200000 0.7279320 1.882771 0.2163775
37 43 43 43 2.023256 3.906977 1.5351186 2.670954 0.4708968
38 34 34 34 3.500000 5.735294 2.2730303 3.048187 0.4701622
39 26 26 26 2.653846 3.923077 1.0175385 1.598076 0.4995424
40 40 40 40 2.900000 10.250000 3.0025630 9.142042 0.4315609
41 41 41 41 2.048780 2.512195 1.1169427 1.227232 0.5102255
42 41 41 41 1.951220 2.902439 1.6725911 2.165697 0.1573911
43 18 18 18 3.333333 3.555556 1.6087993 1.423427 0.1455599
44 28 28 28 2.678571 3.857143 2.0376743 2.915022 0.5157511
45 18 18 18 3.055556 3.444444 0.9983647 1.338226 0.2446019
46 26 26 26 2.576923 4.153846 2.0817892 2.824072 0.2632522
47 36 36 36 2.583333 2.583333 1.8727367 1.645340 0.2851312
48 27 27 27 2.851852 2.296296 2.3155611 2.015609 0.2734692
d v g v_g
1 -0.12151360 0.01152220 -0.12026786 0.011520187
2 -0.08022232 0.01538332 -0.07947639 0.015382597
3 -0.11281521 0.01465157 -0.11174652 0.014650087
4 0.03854351 0.00648211 0.03809293 0.006481847
5 -0.12910256 0.01266709 -0.12781684 0.012664948
6 0.01645287 0.01595623 0.01630209 0.015956197
7 -0.14974001 0.02107171 -0.14793066 0.021067492
8 0.04843860 0.01031582 0.04771923 0.010315159
9 0.04062849 0.01253117 0.04005179 0.012530747
10 0.13150217 0.01044100 0.12954846 0.010436099
11 0.16679215 0.01878203 0.16468803 0.018776335
12 -0.01706917 0.02176809 -0.01686610 0.021768041
13 -0.66180892 0.02762609 -0.65339145 0.027535077
14 -0.83951429 0.02273771 -0.83059443 0.022634813
15 -0.68395845 0.02114328 -0.67569554 0.021055263
16 -0.87127507 0.07324230 -0.85849160 0.073035207
17 -0.66816328 0.02248254 -0.66170520 0.022428514
18 -0.81320920 0.03062341 -0.80562199 0.030548905
19 0.05856262 0.03774449 0.05783859 0.037743820
20 -0.37272464 0.03440449 -0.36765811 0.034371671
21 -0.37974924 0.04313115 -0.37542168 0.043107116
22 0.01606870 0.02832240 0.01582553 0.028322322
23 -0.35543026 0.04484635 -0.35072044 0.044818029
24 -0.43523371 0.05785727 -0.42817327 0.057794277
25 -0.43110311 0.01790037 -0.42625447 0.017870001
26 -0.61589204 0.02073444 -0.60849552 0.020663907
27 -0.51034586 0.02440695 -0.50252182 0.024328735
28 -0.50509037 0.01847156 -0.49820952 0.018410718
29 -0.20677984 0.02209555 -0.20455057 0.022089132
30 -0.75147213 0.03024780 -0.74425296 0.030180404
31 -0.19807416 0.04600361 -0.19524250 0.045993402
32 -0.02351118 0.03393571 -0.02313148 0.033935530
33 -0.31980639 0.01569625 -0.31501731 0.015666852
34 -0.48034077 0.05876340 -0.47104168 0.058654999
35 -0.21750316 0.01000831 -0.21325575 0.009985362
36 -0.44370963 0.05395897 -0.43767157 0.053912542
37 -0.86473896 0.02992101 -0.85526581 0.029805267
38 -0.83136915 0.03737252 -0.81977079 0.037200583
39 -0.94744694 0.04928192 -0.92955835 0.048878501
40 -1.08022664 0.03707331 -1.06785044 0.036876209
41 -0.39493871 0.02509011 -0.39025072 0.025061824
42 -0.49160749 0.04261302 -0.48686678 0.042584037
43 -0.14630051 0.09524136 -0.14297577 0.095227723
44 -0.46863501 0.03707168 -0.46033808 0.036984561
45 -0.32940238 0.08553032 -0.32163070 0.085455839
46 -0.63563829 0.06082710 -0.62537892 0.060694084
47 0.00000000 0.03971493 0.00000000 0.039714933
48 0.25592535 0.05446891 0.25193399 0.054448738
cleaning
1 complete low-high pairs from post-removal CSV
2 complete low-high pairs from post-removal CSV
3 complete low-high pairs from post-removal CSV
4 complete low-high pairs from post-removal CSV
5 complete low-high pairs from post-removal CSV
6 complete low-high pairs from post-removal CSV
7 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
8 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
9 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
10 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
11 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
12 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
13 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
14 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
15 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
16 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
17 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
18 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
19 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
20 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
21 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
22 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
23 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
24 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
25 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
26 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
27 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
28 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
29 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
30 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
31 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
32 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
33 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
34 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
35 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
36 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
37 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
38 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
39 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
40 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
41 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
42 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
43 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
44 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
45 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
46 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
47 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
48 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cellsRegistered Replication
For the knowledge prompt in the replication, the raw scale is 1 = strongly agree and 7 = strongly disagree; the QMD reverse-codes it to agreement (8 - raw) before computing d = mean(low) - mean(high).
replication_path <- file.path(external_dir, "Replication_Experiment", "xReplic_data_post_removal.csv")
replication_dat <- read_replication_csv(replication_path)
agreement_transform <- function(x) 8 - x
rep_specs <- data.frame(
study_id = 2L,
effect_id = paste0("s2_e", 1:6),
experiment = "Appendix II: Registered replication of Sripada & Stanley (2012)",
scenario_code = "peanuts",
scenario_label = c(
"Basic -- Evidence strength",
"Basic -- Knowledge attribution",
"Implicit/Explicit -- Evidence strength",
"Implicit/Explicit -- Knowledge attribution",
"Ignorant -- Evidence strength",
"Ignorant -- Knowledge attribution"
),
polarity = NA_character_,
polarity_label = NA_character_,
outcome_type = c("evidence_strength", "knowledge_attribution", "evidence_strength", "knowledge_attribution", "evidence_strength", "knowledge_attribution"),
low_col = c(
"Basic Low__Strength_Evidence",
"Basic Low__Know_Prompt",
"Implicit Low__Strength_Evidence",
"Implicit Low__Know_Prompt",
"Ignorant Low__Strength_Evidence",
"Ignorant Low__Know_Prompt"
),
high_col = c(
"Basic High__Strength_Evidence",
"Basic High__Know_Prompt",
"Explicit High__Strength_Evidence",
"Explicit High__Know_Prompt",
"Ignorant High__Strength_Evidence",
"Ignorant High__Know_Prompt"
),
stringsAsFactors = FALSE
)
rep_out <- lapply(seq_len(nrow(rep_specs)), function(i) {
row <- rep_specs[i, ]
transform <- if (row$outcome_type == "knowledge_attribution") agreement_transform else identity
stats <- summarise_between_groups(replication_dat, row$low_col, row$high_col, transform = transform)
subgroup <- sprintf("%s -- %s", row$experiment, row$scenario_label)
cbind(
paper_key = paper_key,
study_id = row$study_id,
effect_id = row$effect_id,
experiment = row$experiment,
subgroup = subgroup,
scenario_code = row$scenario_code,
scenario_label = row$scenario_label,
polarity = row$polarity,
polarity_label = row$polarity_label,
outcome_type = row$outcome_type,
source_file = replication_path,
source_file_short = file.path(basename(dirname(replication_path)), basename(replication_path)),
low_col = row$low_col,
high_col = row$high_col,
all_stake_cols = paste(row$low_col, row$high_col, sep = " | "),
stats,
stringsAsFactors = FALSE
)
})
replication_audit <- do.call(rbind, rep_out)
replication_audit paper_key study_id effect_id
1 francis2019stakesscalesskepticism 2 s2_e1
2 francis2019stakesscalesskepticism 2 s2_e2
3 francis2019stakesscalesskepticism 2 s2_e3
4 francis2019stakesscalesskepticism 2 s2_e4
5 francis2019stakesscalesskepticism 2 s2_e5
6 francis2019stakesscalesskepticism 2 s2_e6
experiment
1 Appendix II: Registered replication of Sripada & Stanley (2012)
2 Appendix II: Registered replication of Sripada & Stanley (2012)
3 Appendix II: Registered replication of Sripada & Stanley (2012)
4 Appendix II: Registered replication of Sripada & Stanley (2012)
5 Appendix II: Registered replication of Sripada & Stanley (2012)
6 Appendix II: Registered replication of Sripada & Stanley (2012)
subgroup
1 Appendix II: Registered replication of Sripada & Stanley (2012) -- Basic -- Evidence strength
2 Appendix II: Registered replication of Sripada & Stanley (2012) -- Basic -- Knowledge attribution
3 Appendix II: Registered replication of Sripada & Stanley (2012) -- Implicit/Explicit -- Evidence strength
4 Appendix II: Registered replication of Sripada & Stanley (2012) -- Implicit/Explicit -- Knowledge attribution
5 Appendix II: Registered replication of Sripada & Stanley (2012) -- Ignorant -- Evidence strength
6 Appendix II: Registered replication of Sripada & Stanley (2012) -- Ignorant -- Knowledge attribution
scenario_code scenario_label polarity
1 peanuts Basic -- Evidence strength <NA>
2 peanuts Basic -- Knowledge attribution <NA>
3 peanuts Implicit/Explicit -- Evidence strength <NA>
4 peanuts Implicit/Explicit -- Knowledge attribution <NA>
5 peanuts Ignorant -- Evidence strength <NA>
6 peanuts Ignorant -- Knowledge attribution <NA>
polarity_label outcome_type
1 <NA> evidence_strength
2 <NA> knowledge_attribution
3 <NA> evidence_strength
4 <NA> knowledge_attribution
5 <NA> evidence_strength
6 <NA> knowledge_attribution
source_file
1 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Replication_Experiment/xReplic_data_post_removal.csv
2 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Replication_Experiment/xReplic_data_post_removal.csv
3 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Replication_Experiment/xReplic_data_post_removal.csv
4 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Replication_Experiment/xReplic_data_post_removal.csv
5 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Replication_Experiment/xReplic_data_post_removal.csv
6 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Replication_Experiment/xReplic_data_post_removal.csv
source_file_short
1 Replication_Experiment/xReplic_data_post_removal.csv
2 Replication_Experiment/xReplic_data_post_removal.csv
3 Replication_Experiment/xReplic_data_post_removal.csv
4 Replication_Experiment/xReplic_data_post_removal.csv
5 Replication_Experiment/xReplic_data_post_removal.csv
6 Replication_Experiment/xReplic_data_post_removal.csv
low_col high_col
1 Basic Low__Strength_Evidence Basic High__Strength_Evidence
2 Basic Low__Know_Prompt Basic High__Know_Prompt
3 Implicit Low__Strength_Evidence Explicit High__Strength_Evidence
4 Implicit Low__Know_Prompt Explicit High__Know_Prompt
5 Ignorant Low__Strength_Evidence Ignorant High__Strength_Evidence
6 Ignorant Low__Know_Prompt Ignorant High__Know_Prompt
all_stake_cols
1 Basic Low__Strength_Evidence | Basic High__Strength_Evidence
2 Basic Low__Know_Prompt | Basic High__Know_Prompt
3 Implicit Low__Strength_Evidence | Explicit High__Strength_Evidence
4 Implicit Low__Know_Prompt | Explicit High__Know_Prompt
5 Ignorant Low__Strength_Evidence | Ignorant High__Strength_Evidence
6 Ignorant Low__Know_Prompt | Ignorant High__Know_Prompt
design method_used computed_from_suggested n_finished n_pair_raw
1 Between-Subjects between_groups groups 367 NA
2 Between-Subjects between_groups groups 367 NA
3 Between-Subjects between_groups groups 367 NA
4 Between-Subjects between_groups groups 367 NA
5 Between-Subjects between_groups groups 367 NA
6 Between-Subjects between_groups groups 367 NA
n_low n_high n_total mean_low mean_high sd_low sd_high r_within d
1 58 68 126 3.931034 2.897059 1.824416 1.829605 NA 0.56587320
2 58 68 126 3.724138 2.911765 1.842892 1.708061 NA 0.45862734
3 58 61 119 3.620690 3.426230 1.945103 1.961794 NA 0.09953532
4 58 61 119 3.793103 3.278689 1.926351 1.871997 NA 0.27093414
5 62 60 122 4.258065 4.033333 1.881188 1.886317 NA 0.11930233
6 62 60 122 4.080645 3.716667 2.010614 2.091886 NA 0.17746603
v g v_g
1 0.03321795 0.56244366 0.03321795
2 0.03278194 0.45584778 0.03278194
3 0.03367645 0.09889591 0.03367645
4 0.03394325 0.26919367 0.03394325
5 0.03285403 0.11855514 0.03285403
6 0.03292477 0.17635455 0.03292477
cleaning
1 available low and high between-participant responses from post-removal CSV
2 available low and high between-participant responses from post-removal CSV
3 available low and high between-participant responses from post-removal CSV
4 available low and high between-participant responses from post-removal CSV
5 available low and high between-participant responses from post-removal CSV
6 available low and high between-participant responses from post-removal CSVCombined Audit
audit <- rbind(scalar_audit, replication_audit)
audit$sign_convention <- sign_convention
audit$inputs_used <- paste(
sprintf("method=%s", audit$method_used),
sprintf("sign_convention=%s", audit$sign_convention),
sprintf("n_low=%s", audit$n_low),
sprintf("n_high=%s", audit$n_high),
sprintf("n_total=%s", audit$n_total),
sprintf("mean_low=%s", signif(audit$mean_low, 12)),
sprintf("mean_high=%s", signif(audit$mean_high, 12)),
sprintf("sd_low=%s", signif(audit$sd_low, 12)),
sprintf("sd_high=%s", signif(audit$sd_high, 12)),
ifelse(is.na(audit$r_within), "", sprintf(", r_within=%s", signif(audit$r_within, 12))),
sep = ", "
)
audit$notes_on_assumptions <- paste(
audit$cleaning,
"Computed from the open University of Reading dataset.",
sep = "; "
)
audit$imputed_flag <- FALSE
audit$needs_sensitivity <- audit$outcome_type == "evidence_seeking"
audit$yaml_sign_multiplier <- 1
audit$d_for_yaml <- audit$yaml_sign_multiplier * audit$d
audit$yaml_sign_note <- ifelse(
audit$outcome_type == "evidence_seeking",
"YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.",
"YAML uses the raw low-minus-high d."
)
write.csv(audit, audit_csv, row.names = FALSE)
audit paper_key study_id effect_id
1 francis2019stakesscalesskepticism 1 s1_e1
2 francis2019stakesscalesskepticism 1 s1_e2
3 francis2019stakesscalesskepticism 1 s1_e3
4 francis2019stakesscalesskepticism 1 s1_e4
5 francis2019stakesscalesskepticism 1 s1_e5
6 francis2019stakesscalesskepticism 1 s1_e6
7 francis2019stakesscalesskepticism 1 s1_e7
8 francis2019stakesscalesskepticism 1 s1_e8
9 francis2019stakesscalesskepticism 1 s1_e9
10 francis2019stakesscalesskepticism 1 s1_e10
11 francis2019stakesscalesskepticism 1 s1_e11
12 francis2019stakesscalesskepticism 1 s1_e12
13 francis2019stakesscalesskepticism 3 s3_e1
14 francis2019stakesscalesskepticism 3 s3_e2
15 francis2019stakesscalesskepticism 3 s3_e3
16 francis2019stakesscalesskepticism 3 s3_e4
17 francis2019stakesscalesskepticism 3 s3_e5
18 francis2019stakesscalesskepticism 3 s3_e6
19 francis2019stakesscalesskepticism 3 s3_e7
20 francis2019stakesscalesskepticism 3 s3_e8
21 francis2019stakesscalesskepticism 3 s3_e9
22 francis2019stakesscalesskepticism 3 s3_e10
23 francis2019stakesscalesskepticism 3 s3_e11
24 francis2019stakesscalesskepticism 3 s3_e12
25 francis2019stakesscalesskepticism 4 s4_e1
26 francis2019stakesscalesskepticism 4 s4_e2
27 francis2019stakesscalesskepticism 4 s4_e3
28 francis2019stakesscalesskepticism 4 s4_e4
29 francis2019stakesscalesskepticism 4 s4_e5
30 francis2019stakesscalesskepticism 4 s4_e6
31 francis2019stakesscalesskepticism 4 s4_e7
32 francis2019stakesscalesskepticism 4 s4_e8
33 francis2019stakesscalesskepticism 4 s4_e9
34 francis2019stakesscalesskepticism 4 s4_e10
35 francis2019stakesscalesskepticism 4 s4_e11
36 francis2019stakesscalesskepticism 4 s4_e12
37 francis2019stakesscalesskepticism 5 s5_e1
38 francis2019stakesscalesskepticism 5 s5_e2
39 francis2019stakesscalesskepticism 5 s5_e3
40 francis2019stakesscalesskepticism 5 s5_e4
41 francis2019stakesscalesskepticism 5 s5_e5
42 francis2019stakesscalesskepticism 5 s5_e6
43 francis2019stakesscalesskepticism 5 s5_e7
44 francis2019stakesscalesskepticism 5 s5_e8
45 francis2019stakesscalesskepticism 5 s5_e9
46 francis2019stakesscalesskepticism 5 s5_e10
47 francis2019stakesscalesskepticism 5 s5_e11
48 francis2019stakesscalesskepticism 5 s5_e12
49 francis2019stakesscalesskepticism 2 s2_e1
50 francis2019stakesscalesskepticism 2 s2_e2
51 francis2019stakesscalesskepticism 2 s2_e3
52 francis2019stakesscalesskepticism 2 s2_e4
53 francis2019stakesscalesskepticism 2 s2_e5
54 francis2019stakesscalesskepticism 2 s2_e6
experiment
1 Experiment 1: Evidence-fixed design
2 Experiment 1: Evidence-fixed design
3 Experiment 1: Evidence-fixed design
4 Experiment 1: Evidence-fixed design
5 Experiment 1: Evidence-fixed design
6 Experiment 1: Evidence-fixed design
7 Experiment 1: Evidence-fixed design
8 Experiment 1: Evidence-fixed design
9 Experiment 1: Evidence-fixed design
10 Experiment 1: Evidence-fixed design
11 Experiment 1: Evidence-fixed design
12 Experiment 1: Evidence-fixed design
13 Experiment 2: Evidence-seeking design
14 Experiment 2: Evidence-seeking design
15 Experiment 2: Evidence-seeking design
16 Experiment 2: Evidence-seeking design
17 Experiment 2: Evidence-seeking design
18 Experiment 2: Evidence-seeking design
19 Experiment 2: Evidence-seeking design
20 Experiment 2: Evidence-seeking design
21 Experiment 2: Evidence-seeking design
22 Experiment 2: Evidence-seeking design
23 Experiment 2: Evidence-seeking design
24 Experiment 2: Evidence-seeking design
25 Appendix IV: Symmetrical evidence-seeking experiment
26 Appendix IV: Symmetrical evidence-seeking experiment
27 Appendix IV: Symmetrical evidence-seeking experiment
28 Appendix IV: Symmetrical evidence-seeking experiment
29 Appendix IV: Symmetrical evidence-seeking experiment
30 Appendix IV: Symmetrical evidence-seeking experiment
31 Appendix IV: Symmetrical evidence-seeking experiment
32 Appendix IV: Symmetrical evidence-seeking experiment
33 Appendix IV: Symmetrical evidence-seeking experiment
34 Appendix IV: Symmetrical evidence-seeking experiment
35 Appendix IV: Symmetrical evidence-seeking experiment
36 Appendix IV: Symmetrical evidence-seeking experiment
37 Appendix IV: Matched evidence-seeking experiment
38 Appendix IV: Matched evidence-seeking experiment
39 Appendix IV: Matched evidence-seeking experiment
40 Appendix IV: Matched evidence-seeking experiment
41 Appendix IV: Matched evidence-seeking experiment
42 Appendix IV: Matched evidence-seeking experiment
43 Appendix IV: Matched evidence-seeking experiment
44 Appendix IV: Matched evidence-seeking experiment
45 Appendix IV: Matched evidence-seeking experiment
46 Appendix IV: Matched evidence-seeking experiment
47 Appendix IV: Matched evidence-seeking experiment
48 Appendix IV: Matched evidence-seeking experiment
49 Appendix II: Registered replication of Sripada & Stanley (2012)
50 Appendix II: Registered replication of Sripada & Stanley (2012)
51 Appendix II: Registered replication of Sripada & Stanley (2012)
52 Appendix II: Registered replication of Sripada & Stanley (2012)
53 Appendix II: Registered replication of Sripada & Stanley (2012)
54 Appendix II: Registered replication of Sripada & Stanley (2012)
subgroup
1 Experiment 1: Evidence-fixed design -- Paramedic -- positive polarity
2 Experiment 1: Evidence-fixed design -- Vaccine -- positive polarity
3 Experiment 1: Evidence-fixed design -- Mountaineering -- positive polarity
4 Experiment 1: Evidence-fixed design -- Game show -- positive polarity
5 Experiment 1: Evidence-fixed design -- Introduction -- positive polarity
6 Experiment 1: Evidence-fixed design -- Possessions/Arson -- positive polarity
7 Experiment 1: Evidence-fixed design -- Paramedic -- negative polarity
8 Experiment 1: Evidence-fixed design -- Vaccine -- negative polarity
9 Experiment 1: Evidence-fixed design -- Mountaineering -- negative polarity
10 Experiment 1: Evidence-fixed design -- Game show -- negative polarity
11 Experiment 1: Evidence-fixed design -- Introduction -- negative polarity
12 Experiment 1: Evidence-fixed design -- Possessions/Arson -- negative polarity
13 Experiment 2: Evidence-seeking design -- Paramedic -- positive polarity
14 Experiment 2: Evidence-seeking design -- Vaccine -- positive polarity
15 Experiment 2: Evidence-seeking design -- Mountaineering -- positive polarity
16 Experiment 2: Evidence-seeking design -- Game show -- positive polarity
17 Experiment 2: Evidence-seeking design -- Introduction -- positive polarity
18 Experiment 2: Evidence-seeking design -- Possessions/Arson -- positive polarity
19 Experiment 2: Evidence-seeking design -- Paramedic -- negative polarity
20 Experiment 2: Evidence-seeking design -- Vaccine -- negative polarity
21 Experiment 2: Evidence-seeking design -- Mountaineering -- negative polarity
22 Experiment 2: Evidence-seeking design -- Game show -- negative polarity
23 Experiment 2: Evidence-seeking design -- Introduction -- negative polarity
24 Experiment 2: Evidence-seeking design -- Possessions/Arson -- negative polarity
25 Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- positive polarity
26 Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- positive polarity
27 Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- positive polarity
28 Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- positive polarity
29 Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- positive polarity
30 Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- positive polarity
31 Appendix IV: Symmetrical evidence-seeking experiment -- Paramedic -- negative polarity
32 Appendix IV: Symmetrical evidence-seeking experiment -- Vaccine -- negative polarity
33 Appendix IV: Symmetrical evidence-seeking experiment -- Mountaineering -- negative polarity
34 Appendix IV: Symmetrical evidence-seeking experiment -- Game show -- negative polarity
35 Appendix IV: Symmetrical evidence-seeking experiment -- Introduction -- negative polarity
36 Appendix IV: Symmetrical evidence-seeking experiment -- Possessions/Arson -- negative polarity
37 Appendix IV: Matched evidence-seeking experiment -- Paramedic -- positive polarity
38 Appendix IV: Matched evidence-seeking experiment -- Vaccine -- positive polarity
39 Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- positive polarity
40 Appendix IV: Matched evidence-seeking experiment -- Game show -- positive polarity
41 Appendix IV: Matched evidence-seeking experiment -- Introduction -- positive polarity
42 Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- positive polarity
43 Appendix IV: Matched evidence-seeking experiment -- Paramedic -- negative polarity
44 Appendix IV: Matched evidence-seeking experiment -- Vaccine -- negative polarity
45 Appendix IV: Matched evidence-seeking experiment -- Mountaineering -- negative polarity
46 Appendix IV: Matched evidence-seeking experiment -- Game show -- negative polarity
47 Appendix IV: Matched evidence-seeking experiment -- Introduction -- negative polarity
48 Appendix IV: Matched evidence-seeking experiment -- Possessions/Arson -- negative polarity
49 Appendix II: Registered replication of Sripada & Stanley (2012) -- Basic -- Evidence strength
50 Appendix II: Registered replication of Sripada & Stanley (2012) -- Basic -- Knowledge attribution
51 Appendix II: Registered replication of Sripada & Stanley (2012) -- Implicit/Explicit -- Evidence strength
52 Appendix II: Registered replication of Sripada & Stanley (2012) -- Implicit/Explicit -- Knowledge attribution
53 Appendix II: Registered replication of Sripada & Stanley (2012) -- Ignorant -- Evidence strength
54 Appendix II: Registered replication of Sripada & Stanley (2012) -- Ignorant -- Knowledge attribution
scenario_code scenario_label polarity
1 paramedic Paramedic pos
2 vaccine Vaccine pos
3 mountaineering Mountaineering pos
4 game_show Game show pos
5 introduction Introduction pos
6 possessions Possessions/Arson pos
7 paramedic Paramedic neg
8 vaccine Vaccine neg
9 mountaineering Mountaineering neg
10 game_show Game show neg
11 introduction Introduction neg
12 possessions Possessions/Arson neg
13 paramedic Paramedic pos
14 vaccine Vaccine pos
15 mountaineering Mountaineering pos
16 game_show Game show pos
17 introduction Introduction pos
18 possessions Possessions/Arson pos
19 paramedic Paramedic neg
20 vaccine Vaccine neg
21 mountaineering Mountaineering neg
22 game_show Game show neg
23 introduction Introduction neg
24 possessions Possessions/Arson neg
25 paramedic Paramedic pos
26 vaccine Vaccine pos
27 mountaineering Mountaineering pos
28 game_show Game show pos
29 introduction Introduction pos
30 possessions Possessions/Arson pos
31 paramedic Paramedic neg
32 vaccine Vaccine neg
33 mountaineering Mountaineering neg
34 game_show Game show neg
35 introduction Introduction neg
36 possessions Possessions/Arson neg
37 paramedic Paramedic pos
38 vaccine Vaccine pos
39 mountaineering Mountaineering pos
40 game_show Game show pos
41 introduction Introduction pos
42 possessions Possessions/Arson pos
43 paramedic Paramedic neg
44 vaccine Vaccine neg
45 mountaineering Mountaineering neg
46 game_show Game show neg
47 introduction Introduction neg
48 possessions Possessions/Arson neg
49 peanuts Basic -- Evidence strength <NA>
50 peanuts Basic -- Knowledge attribution <NA>
51 peanuts Implicit/Explicit -- Evidence strength <NA>
52 peanuts Implicit/Explicit -- Knowledge attribution <NA>
53 peanuts Ignorant -- Evidence strength <NA>
54 peanuts Ignorant -- Knowledge attribution <NA>
polarity_label outcome_type
1 positive polarity evidence_fixed
2 positive polarity evidence_fixed
3 positive polarity evidence_fixed
4 positive polarity evidence_fixed
5 positive polarity evidence_fixed
6 positive polarity evidence_fixed
7 negative polarity evidence_fixed
8 negative polarity evidence_fixed
9 negative polarity evidence_fixed
10 negative polarity evidence_fixed
11 negative polarity evidence_fixed
12 negative polarity evidence_fixed
13 positive polarity evidence_seeking
14 positive polarity evidence_seeking
15 positive polarity evidence_seeking
16 positive polarity evidence_seeking
17 positive polarity evidence_seeking
18 positive polarity evidence_seeking
19 negative polarity evidence_seeking
20 negative polarity evidence_seeking
21 negative polarity evidence_seeking
22 negative polarity evidence_seeking
23 negative polarity evidence_seeking
24 negative polarity evidence_seeking
25 positive polarity evidence_seeking
26 positive polarity evidence_seeking
27 positive polarity evidence_seeking
28 positive polarity evidence_seeking
29 positive polarity evidence_seeking
30 positive polarity evidence_seeking
31 negative polarity evidence_seeking
32 negative polarity evidence_seeking
33 negative polarity evidence_seeking
34 negative polarity evidence_seeking
35 negative polarity evidence_seeking
36 negative polarity evidence_seeking
37 positive polarity evidence_seeking
38 positive polarity evidence_seeking
39 positive polarity evidence_seeking
40 positive polarity evidence_seeking
41 positive polarity evidence_seeking
42 positive polarity evidence_seeking
43 negative polarity evidence_seeking
44 negative polarity evidence_seeking
45 negative polarity evidence_seeking
46 negative polarity evidence_seeking
47 negative polarity evidence_seeking
48 negative polarity evidence_seeking
49 <NA> evidence_strength
50 <NA> knowledge_attribution
51 <NA> evidence_strength
52 <NA> knowledge_attribution
53 <NA> evidence_strength
54 <NA> knowledge_attribution
source_file
1 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
2 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
3 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
4 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
5 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
6 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
7 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
8 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
9 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
10 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
11 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
12 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
13 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
14 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
15 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
16 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
17 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
18 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
19 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
20 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
21 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
22 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
23 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
24 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
25 /home/bartosz/Insync/b.mackiewicz@uw.edu.pl/Google Drive/Meta-analizy w filozofii eksperymentalnej/Metaanaliza - stakes, context-sensitivity/Epistemology: Anti-intellectualism and contextualism/ANALYSIS/papers/francis2019stakesscalesskepticism/out/external/Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv
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source_file_short
1 Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
2 Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
3 Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
4 Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
5 Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
6 Exp1_Evidence_Fixed_Design/x1_EF_Pos_data_post_removal.csv
7 Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
8 Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
9 Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
10 Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
11 Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
12 Exp1_Evidence_Fixed_Design/x1_EF_Neg_data_post_removal.csv
13 Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
14 Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
15 Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
16 Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
17 Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
18 Exp2_Evidence_Seeking_Design/x2_ES_Pos_data_post_removal.csv
19 Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
20 Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
21 Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
22 Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
23 Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
24 Exp2_Evidence_Seeking_Design/x2_ES_Neg_data_post_removal.csv
25 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv
26 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv
27 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv
28 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv
29 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv
30 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Pos_data_post_removal.csv
31 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv
32 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv
33 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv
34 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv
35 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv
36 Exp2.1_Symmetrical_Experiment/x2.1_Sym_Neg_data_post_removal.csv
37 Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv
38 Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv
39 Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv
40 Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv
41 Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv
42 Exp2.2_Matched_Experiment/x2.2_Matc_Pos_data_post_removal.csv
43 Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv
44 Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv
45 Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv
46 Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv
47 Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv
48 Exp2.2_Matched_Experiment/x2.2_Matc_Neg_data_post_removal.csv
49 Replication_Experiment/xReplic_data_post_removal.csv
50 Replication_Experiment/xReplic_data_post_removal.csv
51 Replication_Experiment/xReplic_data_post_removal.csv
52 Replication_Experiment/xReplic_data_post_removal.csv
53 Replication_Experiment/xReplic_data_post_removal.csv
54 Replication_Experiment/xReplic_data_post_removal.csv
low_col high_col
1 Paramedic Low Paramedic 3
2 Vaccine Low Vaccine 3
3 Mountaineering Low Mountaineering 3
4 GameShow low GameShow 3
5 Intro Low Intro 3
6 Personal Val Low Personal Val 3
7 Paramedic Low Paramedic 3
8 Vaccine Low Vaccine 3
9 Mountaineering Low Mountaineering 3
10 GameShow low GameShow 3
11 Intro Low Intro 3
12 Personal Val Low Personal Val 3
13 Paramedic Low Paramedic 3
14 Vaccine Low Vaccine 3
15 Mountaineering Low Mountaineering 3
16 GameShow low GameShow 3
17 Intro Low Intro 3
18 Personal Val Low Personal Val 3
19 Paramedic Low Paramedic 3
20 Vaccine Low Vaccine 3
21 Mountaineering Low Mountaineering 3
22 Game Show Low Game Show 3
23 Introductions Low Intro 3
24 Pvalue Low Pvalue 3
25 Para Low Para 3
26 Vacc Low Vacc 3
27 Mount Low Mount 3
28 Game Low Game 3
29 Intro Low Intro 3
30 Pval Low Pval 3
31 Para Low Para 3
32 Vacc Low Vacc 3
33 Mount Low Mount 3
34 Game Low Game 3
35 Intro Low Intro 3
36 Pval Low Pval 3
37 Para Low Para 3
38 Vacc Low Vacc 3
39 Mount Low Mount 3
40 Game Low Game 3
41 Intro Low Intro 3
42 Pval Low Pval 3
43 Para Low Para 3
44 Vacc Low Vacc 3
45 Mount Low Mount 3
46 Game Low Game 3
47 Intro Low Intro 3
48 Pval Low Pval 3
49 Basic Low__Strength_Evidence Basic High__Strength_Evidence
50 Basic Low__Know_Prompt Basic High__Know_Prompt
51 Implicit Low__Strength_Evidence Explicit High__Strength_Evidence
52 Implicit Low__Know_Prompt Explicit High__Know_Prompt
53 Ignorant Low__Strength_Evidence Ignorant High__Strength_Evidence
54 Ignorant Low__Know_Prompt Ignorant High__Know_Prompt
all_stake_cols
1 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
2 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
3 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
4 GameShow low | GameShow 1 | GameShow 2 | GameShow 3
5 Intro Low | Intro 1 | Intro 2 | Intro 3
6 Personal Val Low | Personal Val 1 | Personal Val 2 | Personal Val 3
7 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
8 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
9 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
10 GameShow low | GameShow 1 | GameShow 2 | GameShow 3
11 Intro Low | Intro 1 | Intro 2 | Intro 3
12 Personal Val Low | Personal Val 1 | Personal Val 2 | Personal Val 3
13 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
14 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
15 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
16 GameShow low | GameShow 1 | GameShow 2 | GameShow 3
17 Intro Low | Intro 1 | Intro 2 | Intro 3
18 Personal Val Low | Personal Val 1 | Personal Val 2 | Personal Val 3
19 Paramedic Low | Paramedic 1 | Paramedic 2 | Paramedic 3
20 Vaccine Low | Vaccine 1 | Vaccine 2 | Vaccine 3
21 Mountaineering Low | Mountaineering 1 | Mountaineering 2 | Mountaineering 3
22 Game Show Low | Game Show 1 | Game Show 2 | Game Show 3
23 Introductions Low | Intro 1 | Intro 2 | Intro 3
24 Pvalue Low | Pvalue 1 | Pvalue 2 | Pvalue 3
25 Para Low | Para 1 | Para 2 | Para 3
26 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
27 Mount Low | Mount 1 | Mount 2 | Mount 3
28 Game Low | Game 1 | Game 2 | Game 3
29 Intro Low | Intro 1 | Intro 2 | Intro 3
30 Pval Low | Pval 1 | Pval 2 | Pval 3
31 Para Low | Para 1 | Para 2 | Para 3
32 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
33 Mount Low | Mount 1 | Mount 2 | Mount 3
34 Game Low | Game 1 | Game 2 | Game 3
35 Intro Low | Intro 1 | Intro 2 | Intro 3
36 Pval Low | Pval 1 | Pval 2 | Pval 3
37 Para Low | Para 1 | Para 2 | Para 3
38 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
39 Mount Low | Mount 1 | Mount 2 | Mount 3
40 Game Low | Game 1 | Game 2 | Game 3
41 Intro Low | Intro 1 | Intro 2 | Intro 3
42 Pval Low | Pval 1 | Pval 2 | Pval 3
43 Para Low | Para 1 | Para 2 | Para 3
44 Vacc Low | Vacc 1 | Vacc 2 | Vacc 3
45 Mount Low | Mount 1 | Mount 2 | Mount 3
46 Game Low | Game 1 | Game 2 | Game 3
47 Intro Low | Intro 1 | Intro 2 | Intro 3
48 Pval Low | Pval 1 | Pval 2 | Pval 3
49 Basic Low__Strength_Evidence | Basic High__Strength_Evidence
50 Basic Low__Know_Prompt | Basic High__Know_Prompt
51 Implicit Low__Strength_Evidence | Explicit High__Strength_Evidence
52 Implicit Low__Know_Prompt | Explicit High__Know_Prompt
53 Ignorant Low__Strength_Evidence | Ignorant High__Strength_Evidence
54 Ignorant Low__Know_Prompt | Ignorant High__Know_Prompt
design method_used computed_from_suggested n_finished
1 Within-Subjects within_smcrp_r groups 57
2 Within-Subjects within_smcrp_r groups 57
3 Within-Subjects within_smcrp_r groups 57
4 Within-Subjects within_smcrp_r groups 57
5 Within-Subjects within_smcrp_r groups 57
6 Within-Subjects within_smcrp_r groups 57
7 Within-Subjects within_smcrp_r groups 42
8 Within-Subjects within_smcrp_r groups 42
9 Within-Subjects within_smcrp_r groups 42
10 Within-Subjects within_smcrp_r groups 42
11 Within-Subjects within_smcrp_r groups 42
12 Within-Subjects within_smcrp_r groups 42
13 Within-Subjects within_smcrp_r groups 78
14 Within-Subjects within_smcrp_r groups 78
15 Within-Subjects within_smcrp_r groups 78
16 Within-Subjects within_smcrp_r groups 78
17 Within-Subjects within_smcrp_r groups 78
18 Within-Subjects within_smcrp_r groups 78
19 Within-Subjects within_smcrp_r groups 368
20 Within-Subjects within_smcrp_r groups 368
21 Within-Subjects within_smcrp_r groups 368
22 Within-Subjects within_smcrp_r groups 368
23 Within-Subjects within_smcrp_r groups 368
24 Within-Subjects within_smcrp_r groups 368
25 Within-Subjects within_smcrp_r groups 196
26 Within-Subjects within_smcrp_r groups 196
27 Within-Subjects within_smcrp_r groups 196
28 Within-Subjects within_smcrp_r groups 196
29 Within-Subjects within_smcrp_r groups 196
30 Within-Subjects within_smcrp_r groups 196
31 Within-Subjects within_smcrp_r groups 183
32 Within-Subjects within_smcrp_r groups 183
33 Within-Subjects within_smcrp_r groups 183
34 Within-Subjects within_smcrp_r groups 183
35 Within-Subjects within_smcrp_r groups 183
36 Within-Subjects within_smcrp_r groups 183
37 Within-Subjects within_smcrp_r groups 65
38 Within-Subjects within_smcrp_r groups 65
39 Within-Subjects within_smcrp_r groups 65
40 Within-Subjects within_smcrp_r groups 65
41 Within-Subjects within_smcrp_r groups 65
42 Within-Subjects within_smcrp_r groups 65
43 Within-Subjects within_smcrp_r groups 102
44 Within-Subjects within_smcrp_r groups 102
45 Within-Subjects within_smcrp_r groups 102
46 Within-Subjects within_smcrp_r groups 102
47 Within-Subjects within_smcrp_r groups 102
48 Within-Subjects within_smcrp_r groups 102
49 Between-Subjects between_groups groups 367
50 Between-Subjects between_groups groups 367
51 Between-Subjects between_groups groups 367
52 Between-Subjects between_groups groups 367
53 Between-Subjects between_groups groups 367
54 Between-Subjects between_groups groups 367
n_pair_raw n_low n_high n_total mean_low mean_high sd_low sd_high
1 55 55 55 55 5.509091 5.672727 1.3453999 1.347900
2 55 55 55 55 5.981818 6.072727 1.1465069 1.119764
3 55 55 55 55 5.836364 5.963636 1.2135598 1.035725
4 55 55 55 55 5.272727 5.218182 1.4587481 1.370197
5 55 55 55 55 5.981818 6.127273 1.2246074 1.019342
6 55 55 55 55 5.909091 5.890909 1.0050378 1.196797
7 42 42 42 42 5.428571 5.642857 1.5482934 1.303306
8 42 42 42 42 5.619048 5.547619 1.4305445 1.517417
9 42 42 42 42 5.404762 5.333333 1.7951089 1.720276
10 42 42 42 42 5.333333 5.119048 1.6028430 1.655772
11 42 42 42 42 5.976190 5.738095 1.1993514 1.623900
12 42 42 42 42 5.785714 5.809524 1.3710547 1.418314
13 57 39 39 39 1.948718 2.769231 1.1227016 1.346761
14 58 48 48 48 2.479167 4.229167 1.5843595 2.486043
15 54 44 44 44 2.000000 2.886364 1.2007749 1.384565
16 57 27 27 27 2.666667 5.481481 2.4494897 3.856766
17 58 50 50 50 1.740000 2.400000 0.8283251 1.124858
18 54 47 47 47 1.531915 2.829787 0.9053240 2.067546
19 36 35 35 35 2.257143 2.142857 2.0628772 1.833397
20 40 34 34 34 2.705882 3.794118 1.9467052 3.641340
21 39 36 36 36 1.861111 2.472222 1.2683573 1.889612
22 38 33 33 33 8.151515 8.000000 10.5478707 8.158584
23 40 32 32 32 1.656250 2.093750 0.9708452 1.444888
24 30 26 26 26 2.153846 3.115385 1.6172151 2.673229
25 58 47 47 47 2.234043 3.000000 1.5909085 1.944893
26 56 44 44 44 2.863636 4.704545 2.2577094 3.573673
27 57 35 35 35 2.828571 3.600000 1.2001400 1.769014
28 56 41 41 41 4.000000 6.609756 4.2836900 5.919789
29 57 45 45 45 2.222222 2.466667 1.1849221 1.179368
30 57 46 46 46 1.913043 3.347826 1.1704906 2.433264
31 36 30 30 30 1.833333 2.033333 0.9128709 1.098065
32 35 30 30 30 3.366667 3.433333 2.9300269 2.737773
33 42 39 39 39 2.153846 2.846154 1.9130915 2.390094
34 36 23 23 23 3.608696 4.956522 2.5179200 3.067102
35 38 34 34 34 1.970588 2.294118 1.1930428 1.732565
36 33 30 30 30 1.566667 2.200000 0.7279320 1.882771
37 43 43 43 43 2.023256 3.906977 1.5351186 2.670954
38 44 34 34 34 3.500000 5.735294 2.2730303 3.048187
39 43 26 26 26 2.653846 3.923077 1.0175385 1.598076
40 44 40 40 40 2.900000 10.250000 3.0025630 9.142042
41 45 41 41 41 2.048780 2.512195 1.1169427 1.227232
42 41 41 41 41 1.951220 2.902439 1.6725911 2.165697
43 35 18 18 18 3.333333 3.555556 1.6087993 1.423427
44 33 28 28 28 2.678571 3.857143 2.0376743 2.915022
45 31 18 18 18 3.055556 3.444444 0.9983647 1.338226
46 36 26 26 26 2.576923 4.153846 2.0817892 2.824072
47 38 36 36 36 2.583333 2.583333 1.8727367 1.645340
48 31 27 27 27 2.851852 2.296296 2.3155611 2.015609
49 NA 58 68 126 3.931034 2.897059 1.8244160 1.829605
50 NA 58 68 126 3.724138 2.911765 1.8428915 1.708061
51 NA 58 61 119 3.620690 3.426230 1.9451026 1.961794
52 NA 58 61 119 3.793103 3.278689 1.9263512 1.871997
53 NA 62 60 122 4.258065 4.033333 1.8811880 1.886317
54 NA 62 60 122 4.080645 3.716667 2.0106144 2.091886
r_within d v g v_g
1 0.6858534 -0.12151360 0.01152220 -0.12026786 0.011520187
2 0.5780320 -0.08022232 0.01538332 -0.07947639 0.015382597
3 0.5992440 -0.11281521 0.01465157 -0.11174652 0.014650087
4 0.8220532 0.03854351 0.00648211 0.03809293 0.006481847
5 0.6546312 -0.12910256 0.01266709 -0.12781684 0.012664948
6 0.5612483 0.01645287 0.01595623 0.01630209 0.015956197
7 0.5611795 -0.14974001 0.02107171 -0.14793066 0.021067492
8 0.7838412 0.04843860 0.01031582 0.04771923 0.010315159
9 0.7371638 0.04062849 0.01253117 0.04005179 0.012530747
10 0.7842300 0.13150217 0.01044100 0.12954846 0.010436099
11 0.6103502 0.16679215 0.01878203 0.16468803 0.018776335
12 0.5429172 -0.01706917 0.02176809 -0.01686610 0.021768041
13 0.5315066 -0.66180892 0.02762609 -0.65339145 0.027535077
14 0.5711296 -0.83951429 0.02273771 -0.83059443 0.022634813
15 0.6154735 -0.68395845 0.02114328 -0.67569554 0.021055263
16 0.1072096 -0.87127507 0.07324230 -0.85849160 0.073035207
17 0.5081516 -0.66816328 0.02248254 -0.66170520 0.022428514
18 0.3746143 -0.81320920 0.03062341 -0.80562199 0.030548905
19 0.3399496 0.05856262 0.03774449 0.05783859 0.037743820
20 0.4357870 -0.37272464 0.03440449 -0.36765811 0.034371671
21 0.2427276 -0.37974924 0.04313115 -0.37542168 0.043107116
22 0.5327219 0.01606870 0.02832240 0.01582553 0.028322322
23 0.2996679 -0.35543026 0.04484635 -0.35072044 0.044818029
24 0.2733027 -0.43523371 0.05785727 -0.42817327 0.057794277
25 0.6112523 -0.43110311 0.01790037 -0.42625447 0.017870001
26 0.6088336 -0.61589204 0.02073444 -0.60849552 0.020663907
27 0.6178637 -0.51034586 0.02440695 -0.50252182 0.024328735
28 0.6674280 -0.50509037 0.01847156 -0.49820952 0.018410718
29 0.5095828 -0.20677984 0.02209555 -0.20455057 0.022089132
30 0.3853726 -0.75147213 0.03024780 -0.74425296 0.030180404
31 0.3153377 -0.19807416 0.04600361 -0.19524250 0.045993402
32 0.4910501 -0.02351118 0.03393571 -0.02313148 0.033935530
33 0.7132107 -0.31980639 0.01569625 -0.31501731 0.015666852
34 0.3567320 -0.48034077 0.05876340 -0.47104168 0.058654999
35 0.8399442 -0.21750316 0.01000831 -0.21325575 0.009985362
36 0.2163775 -0.44370963 0.05395897 -0.43767157 0.053912542
37 0.4708968 -0.86473896 0.02992101 -0.85526581 0.029805267
38 0.4701622 -0.83136915 0.03737252 -0.81977079 0.037200583
39 0.4995424 -0.94744694 0.04928192 -0.92955835 0.048878501
40 0.4315609 -1.08022664 0.03707331 -1.06785044 0.036876209
41 0.5102255 -0.39493871 0.02509011 -0.39025072 0.025061824
42 0.1573911 -0.49160749 0.04261302 -0.48686678 0.042584037
43 0.1455599 -0.14630051 0.09524136 -0.14297577 0.095227723
44 0.5157511 -0.46863501 0.03707168 -0.46033808 0.036984561
45 0.2446019 -0.32940238 0.08553032 -0.32163070 0.085455839
46 0.2632522 -0.63563829 0.06082710 -0.62537892 0.060694084
47 0.2851312 0.00000000 0.03971493 0.00000000 0.039714933
48 0.2734692 0.25592535 0.05446891 0.25193399 0.054448738
49 NA 0.56587320 0.03321795 0.56244366 0.033217946
50 NA 0.45862734 0.03278194 0.45584778 0.032781940
51 NA 0.09953532 0.03367645 0.09889591 0.033676449
52 NA 0.27093414 0.03394325 0.26919367 0.033943248
53 NA 0.11930233 0.03285403 0.11855514 0.032854031
54 NA 0.17746603 0.03292477 0.17635455 0.032924773
cleaning
1 complete low-high pairs from post-removal CSV
2 complete low-high pairs from post-removal CSV
3 complete low-high pairs from post-removal CSV
4 complete low-high pairs from post-removal CSV
5 complete low-high pairs from post-removal CSV
6 complete low-high pairs from post-removal CSV
7 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
8 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
9 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
10 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
11 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
12 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction
13 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
14 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
15 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
16 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
17 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
18 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
19 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
20 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
21 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
22 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
23 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
24 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
25 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
26 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
27 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
28 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
29 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
30 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
31 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
32 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
33 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
34 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
35 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
36 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
37 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
38 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
39 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
40 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
41 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
42 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
43 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
44 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
45 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
46 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
47 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
48 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells
49 available low and high between-participant responses from post-removal CSV
50 available low and high between-participant responses from post-removal CSV
51 available low and high between-participant responses from post-removal CSV
52 available low and high between-participant responses from post-removal CSV
53 available low and high between-participant responses from post-removal CSV
54 available low and high between-participant responses from post-removal CSV
sign_convention
1 d = mean(low) - mean(high)
2 d = mean(low) - mean(high)
3 d = mean(low) - mean(high)
4 d = mean(low) - mean(high)
5 d = mean(low) - mean(high)
6 d = mean(low) - mean(high)
7 d = mean(low) - mean(high)
8 d = mean(low) - mean(high)
9 d = mean(low) - mean(high)
10 d = mean(low) - mean(high)
11 d = mean(low) - mean(high)
12 d = mean(low) - mean(high)
13 d = mean(low) - mean(high)
14 d = mean(low) - mean(high)
15 d = mean(low) - mean(high)
16 d = mean(low) - mean(high)
17 d = mean(low) - mean(high)
18 d = mean(low) - mean(high)
19 d = mean(low) - mean(high)
20 d = mean(low) - mean(high)
21 d = mean(low) - mean(high)
22 d = mean(low) - mean(high)
23 d = mean(low) - mean(high)
24 d = mean(low) - mean(high)
25 d = mean(low) - mean(high)
26 d = mean(low) - mean(high)
27 d = mean(low) - mean(high)
28 d = mean(low) - mean(high)
29 d = mean(low) - mean(high)
30 d = mean(low) - mean(high)
31 d = mean(low) - mean(high)
32 d = mean(low) - mean(high)
33 d = mean(low) - mean(high)
34 d = mean(low) - mean(high)
35 d = mean(low) - mean(high)
36 d = mean(low) - mean(high)
37 d = mean(low) - mean(high)
38 d = mean(low) - mean(high)
39 d = mean(low) - mean(high)
40 d = mean(low) - mean(high)
41 d = mean(low) - mean(high)
42 d = mean(low) - mean(high)
43 d = mean(low) - mean(high)
44 d = mean(low) - mean(high)
45 d = mean(low) - mean(high)
46 d = mean(low) - mean(high)
47 d = mean(low) - mean(high)
48 d = mean(low) - mean(high)
49 d = mean(low) - mean(high)
50 d = mean(low) - mean(high)
51 d = mean(low) - mean(high)
52 d = mean(low) - mean(high)
53 d = mean(low) - mean(high)
54 d = mean(low) - mean(high)
inputs_used
1 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.50909090909, mean_high=5.67272727273, sd_low=1.34539994429, sd_high=1.3479002251, , r_within=0.685853391572
2 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.98181818182, mean_high=6.07272727273, sd_low=1.14650691864, sd_high=1.11976428496, , r_within=0.578031957126
3 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.83636363636, mean_high=5.96363636364, sd_low=1.21355975243, sd_high=1.03572548135, , r_within=0.599244020297
4 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.27272727273, mean_high=5.21818181818, sd_low=1.45874813726, sd_high=1.37019745929, , r_within=0.82205315469
5 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.98181818182, mean_high=6.12727272727, sd_low=1.22460740634, sd_high=1.01934157134, , r_within=0.654631213559
6 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.90909090909, mean_high=5.89090909091, sd_low=1.00503781526, sd_high=1.19679707232, , r_within=0.561248258983
7 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.42857142857, mean_high=5.64285714286, sd_low=1.54829342941, sd_high=1.30330590108, , r_within=0.561179544014
8 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.61904761905, mean_high=5.54761904762, sd_low=1.43054451431, sd_high=1.51741726905, , r_within=0.783841177752
9 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.40476190476, mean_high=5.33333333333, sd_low=1.79510885341, sd_high=1.72027602247, , r_within=0.737163791234
10 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.33333333333, mean_high=5.11904761905, sd_low=1.60284300262, sd_high=1.65577159012, , r_within=0.784229981593
11 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.97619047619, mean_high=5.7380952381, sd_low=1.19935135392, sd_high=1.62389960956, , r_within=0.610350196065
12 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.78571428571, mean_high=5.80952380952, sd_low=1.3710546819, sd_high=1.41831392923, , r_within=0.542917183602
13 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=39, n_high=39, n_total=39, mean_low=1.94871794872, mean_high=2.76923076923, sd_low=1.12270158074, sd_high=1.34676099668, , r_within=0.531506638657
14 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=48, n_high=48, n_total=48, mean_low=2.47916666667, mean_high=4.22916666667, sd_low=1.58435950323, sd_high=2.48604259847, , r_within=0.571129577788
15 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=44, n_high=44, n_total=44, mean_low=2, mean_high=2.88636363636, sd_low=1.20077494357, sd_high=1.38456456241, , r_within=0.615473547653
16 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=27, n_high=27, n_total=27, mean_low=2.66666666667, mean_high=5.48148148148, sd_low=2.44948974278, sd_high=3.8567659865, , r_within=0.10720957516
17 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=50, n_high=50, n_total=50, mean_low=1.74, mean_high=2.4, sd_low=0.828325086533, sd_high=1.12485826772, , r_within=0.508151586222
18 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=47, n_high=47, n_total=47, mean_low=1.53191489362, mean_high=2.82978723404, sd_low=0.905323959067, sd_high=2.06754579294, , r_within=0.374614307699
19 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=35, n_high=35, n_total=35, mean_low=2.25714285714, mean_high=2.14285714286, sd_low=2.06287716185, sd_high=1.83339699406, , r_within=0.33994964082
20 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=34, n_high=34, n_total=34, mean_low=2.70588235294, mean_high=3.79411764706, sd_low=1.9467052471, sd_high=3.64134017757, , r_within=0.435786978245
21 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=36, n_high=36, n_total=36, mean_low=1.86111111111, mean_high=2.47222222222, sd_low=1.26835726778, sd_high=1.88961237312, , r_within=0.24272756755
22 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=33, n_high=33, n_total=33, mean_low=8.15151515152, mean_high=8, sd_low=10.5478706741, sd_high=8.15858443604, , r_within=0.532721870308
23 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=32, n_high=32, n_total=32, mean_low=1.65625, mean_high=2.09375, sd_low=0.970845158911, sd_high=1.44488809702, , r_within=0.299667883207
24 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=26, n_high=26, n_total=26, mean_low=2.15384615385, mean_high=3.11538461538, sd_low=1.61721508013, sd_high=2.67322910469, , r_within=0.273302686156
25 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=47, n_high=47, n_total=47, mean_low=2.23404255319, mean_high=3, sd_low=1.59090849021, sd_high=1.94489297794, , r_within=0.611252309578
26 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=44, n_high=44, n_total=44, mean_low=2.86363636364, mean_high=4.70454545455, sd_low=2.25770936695, sd_high=3.57367341108, , r_within=0.608833639666
27 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=35, n_high=35, n_total=35, mean_low=2.82857142857, mean_high=3.6, sd_low=1.20014004785, sd_high=1.76901434836, , r_within=0.617863667716
28 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=41, n_high=41, n_total=41, mean_low=4, mean_high=6.60975609756, sd_low=4.28368999812, sd_high=5.91978905359, , r_within=0.667427983862
29 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=45, n_high=45, n_total=45, mean_low=2.22222222222, mean_high=2.46666666667, sd_low=1.18492210885, sd_high=1.17936808966, , r_within=0.509582783511
30 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=46, n_high=46, n_total=46, mean_low=1.91304347826, mean_high=3.34782608696, sd_low=1.17049062755, sd_high=2.43326384654, , r_within=0.385372604054
31 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=30, n_high=30, n_total=30, mean_low=1.83333333333, mean_high=2.03333333333, sd_low=0.912870929175, sd_high=1.09806517404, , r_within=0.31533773688
32 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=30, n_high=30, n_total=30, mean_low=3.36666666667, mean_high=3.43333333333, sd_low=2.93002687211, sd_high=2.7377732373, , r_within=0.491050066968
33 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=39, n_high=39, n_total=39, mean_low=2.15384615385, mean_high=2.84615384615, sd_low=1.91309148457, sd_high=2.39009426745, , r_within=0.713210654922
34 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=23, n_high=23, n_total=23, mean_low=3.60869565217, mean_high=4.95652173913, sd_low=2.5179199647, sd_high=3.06710199121, , r_within=0.356732040173
35 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=34, n_high=34, n_total=34, mean_low=1.97058823529, mean_high=2.29411764706, sd_low=1.19304281509, sd_high=1.73256530359, , r_within=0.83994423388
36 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=30, n_high=30, n_total=30, mean_low=1.56666666667, mean_high=2.2, sd_low=0.727932041795, sd_high=1.88277125169, , r_within=0.216377480011
37 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=43, n_high=43, n_total=43, mean_low=2.02325581395, mean_high=3.90697674419, sd_low=1.53511861017, sd_high=2.67095447081, , r_within=0.470896843884
38 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=34, n_high=34, n_total=34, mean_low=3.5, mean_high=5.73529411765, sd_low=2.27303028283, sd_high=3.04818697758, , r_within=0.470162188533
39 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=26, n_high=26, n_total=26, mean_low=2.65384615385, mean_high=3.92307692308, sd_low=1.01753850806, sd_high=1.59807576599, , r_within=0.49954238892
40 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=40, n_high=40, n_total=40, mean_low=2.9, mean_high=10.25, sd_low=3.00256300773, sd_high=9.14204151582, , r_within=0.431560859795
41 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=41, n_high=41, n_total=41, mean_low=2.0487804878, mean_high=2.51219512195, sd_low=1.11694269128, sd_high=1.22723166557, , r_within=0.510225460808
42 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=41, n_high=41, n_total=41, mean_low=1.9512195122, mean_high=2.90243902439, sd_low=1.67259109636, sd_high=2.16569709388, , r_within=0.157391135451
43 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=18, n_high=18, n_total=18, mean_low=3.33333333333, mean_high=3.55555555556, sd_low=1.60879933308, sd_high=1.42342677748, , r_within=0.145559895866
44 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=28, n_high=28, n_total=28, mean_low=2.67857142857, mean_high=3.85714285714, sd_low=2.03767426301, sd_high=2.91502221215, , r_within=0.515751062183
45 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=18, n_high=18, n_total=18, mean_low=3.05555555556, mean_high=3.44444444444, sd_low=0.998364675929, sd_high=1.33822631614, , r_within=0.244601885858
46 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=26, n_high=26, n_total=26, mean_low=2.57692307692, mean_high=4.15384615385, sd_low=2.08178917133, sd_high=2.82407234599, , r_within=0.263252231231
47 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=36, n_high=36, n_total=36, mean_low=2.58333333333, mean_high=2.58333333333, sd_low=1.87273672927, sd_high=1.64533973912, , r_within=0.285131212175
48 method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=27, n_high=27, n_total=27, mean_low=2.85185185185, mean_high=2.2962962963, sd_low=2.31556113325, sd_high=2.0156086085, , r_within=0.273469213558
49 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=68, n_total=126, mean_low=3.93103448276, mean_high=2.89705882353, sd_low=1.82441597447, sd_high=1.82960484994,
50 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=68, n_total=126, mean_low=3.72413793103, mean_high=2.91176470588, sd_low=1.84289154423, sd_high=1.70806073258,
51 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=61, n_total=119, mean_low=3.62068965517, mean_high=3.4262295082, sd_low=1.94510258789, sd_high=1.96179353648,
52 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=61, n_total=119, mean_low=3.79310344828, mean_high=3.27868852459, sd_low=1.92635116117, sd_high=1.87199668394,
53 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=62, n_high=60, n_total=122, mean_low=4.25806451613, mean_high=4.03333333333, sd_low=1.88118800858, sd_high=1.88631707048,
54 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=62, n_high=60, n_total=122, mean_low=4.08064516129, mean_high=3.71666666667, sd_low=2.01061435107, sd_high=2.09188639762,
notes_on_assumptions
1 complete low-high pairs from post-removal CSV; Computed from the open University of Reading dataset.
2 complete low-high pairs from post-removal CSV; Computed from the open University of Reading dataset.
3 complete low-high pairs from post-removal CSV; Computed from the open University of Reading dataset.
4 complete low-high pairs from post-removal CSV; Computed from the open University of Reading dataset.
5 complete low-high pairs from post-removal CSV; Computed from the open University of Reading dataset.
6 complete low-high pairs from post-removal CSV; Computed from the open University of Reading dataset.
7 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction; Computed from the open University of Reading dataset.
8 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction; Computed from the open University of Reading dataset.
9 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction; Computed from the open University of Reading dataset.
10 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction; Computed from the open University of Reading dataset.
11 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction; Computed from the open University of Reading dataset.
12 complete low-high pairs from post-removal CSV; negative-polarity agreement-with-denial responses reverse-coded to knowledge-attribution direction; Computed from the open University of Reading dataset.
13 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
14 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
15 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
16 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
17 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
18 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
19 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
20 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
21 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
22 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
23 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
24 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
25 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
26 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
27 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
28 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
29 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
30 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
31 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
32 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
33 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
34 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
35 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
36 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
37 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
38 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
39 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
40 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
41 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
42 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
43 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
44 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
45 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
46 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
47 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
48 complete low-high pairs; 'never'/blank/non-positive excluded; log-MAD outlier removal over the scenario's four stakes cells; Computed from the open University of Reading dataset.
49 available low and high between-participant responses from post-removal CSV; Computed from the open University of Reading dataset.
50 available low and high between-participant responses from post-removal CSV; Computed from the open University of Reading dataset.
51 available low and high between-participant responses from post-removal CSV; Computed from the open University of Reading dataset.
52 available low and high between-participant responses from post-removal CSV; Computed from the open University of Reading dataset.
53 available low and high between-participant responses from post-removal CSV; Computed from the open University of Reading dataset.
54 available low and high between-participant responses from post-removal CSV; Computed from the open University of Reading dataset.
imputed_flag needs_sensitivity yaml_sign_multiplier d_for_yaml
1 FALSE FALSE 1 -0.12151360
2 FALSE FALSE 1 -0.08022232
3 FALSE FALSE 1 -0.11281521
4 FALSE FALSE 1 0.03854351
5 FALSE FALSE 1 -0.12910256
6 FALSE FALSE 1 0.01645287
7 FALSE FALSE 1 -0.14974001
8 FALSE FALSE 1 0.04843860
9 FALSE FALSE 1 0.04062849
10 FALSE FALSE 1 0.13150217
11 FALSE FALSE 1 0.16679215
12 FALSE FALSE 1 -0.01706917
13 FALSE TRUE 1 -0.66180892
14 FALSE TRUE 1 -0.83951429
15 FALSE TRUE 1 -0.68395845
16 FALSE TRUE 1 -0.87127507
17 FALSE TRUE 1 -0.66816328
18 FALSE TRUE 1 -0.81320920
19 FALSE TRUE 1 0.05856262
20 FALSE TRUE 1 -0.37272464
21 FALSE TRUE 1 -0.37974924
22 FALSE TRUE 1 0.01606870
23 FALSE TRUE 1 -0.35543026
24 FALSE TRUE 1 -0.43523371
25 FALSE TRUE 1 -0.43110311
26 FALSE TRUE 1 -0.61589204
27 FALSE TRUE 1 -0.51034586
28 FALSE TRUE 1 -0.50509037
29 FALSE TRUE 1 -0.20677984
30 FALSE TRUE 1 -0.75147213
31 FALSE TRUE 1 -0.19807416
32 FALSE TRUE 1 -0.02351118
33 FALSE TRUE 1 -0.31980639
34 FALSE TRUE 1 -0.48034077
35 FALSE TRUE 1 -0.21750316
36 FALSE TRUE 1 -0.44370963
37 FALSE TRUE 1 -0.86473896
38 FALSE TRUE 1 -0.83136915
39 FALSE TRUE 1 -0.94744694
40 FALSE TRUE 1 -1.08022664
41 FALSE TRUE 1 -0.39493871
42 FALSE TRUE 1 -0.49160749
43 FALSE TRUE 1 -0.14630051
44 FALSE TRUE 1 -0.46863501
45 FALSE TRUE 1 -0.32940238
46 FALSE TRUE 1 -0.63563829
47 FALSE TRUE 1 0.00000000
48 FALSE TRUE 1 0.25592535
49 FALSE FALSE 1 0.56587320
50 FALSE FALSE 1 0.45862734
51 FALSE FALSE 1 0.09953532
52 FALSE FALSE 1 0.27093414
53 FALSE FALSE 1 0.11930233
54 FALSE FALSE 1 0.17746603
yaml_sign_note
1 YAML uses the raw low-minus-high d.
2 YAML uses the raw low-minus-high d.
3 YAML uses the raw low-minus-high d.
4 YAML uses the raw low-minus-high d.
5 YAML uses the raw low-minus-high d.
6 YAML uses the raw low-minus-high d.
7 YAML uses the raw low-minus-high d.
8 YAML uses the raw low-minus-high d.
9 YAML uses the raw low-minus-high d.
10 YAML uses the raw low-minus-high d.
11 YAML uses the raw low-minus-high d.
12 YAML uses the raw low-minus-high d.
13 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
14 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
15 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
16 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
17 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
18 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
19 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
20 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
21 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
22 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
23 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
24 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
25 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
26 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
27 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
28 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
29 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
30 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
31 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
32 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
33 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
34 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
35 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
36 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
37 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
38 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
39 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
40 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
41 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
42 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
43 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
44 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
45 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
46 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
47 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
48 YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically.
49 YAML uses the raw low-minus-high d.
50 YAML uses the raw low-minus-high d.
51 YAML uses the raw low-minus-high d.
52 YAML uses the raw low-minus-high d.
53 YAML uses the raw low-minus-high d.
54 YAML uses the raw low-minus-high d.Paste-Ready YAML Snippets
for (i in seq_len(nrow(audit))) {
row <- audit[i, ]
cat(sprintf(
"\n# %s / %s\n",
row$study_id,
row$effect_id
))
cat(sprintf(
"effect_size:\n metric: SMD\n d: %.12f\n v: %.12f\n computed_from: %s\n needs_review: false\n notes: \"%s\"\n",
row$d_for_yaml,
row$v,
row$computed_from_suggested,
gsub('"', "'", paste(row$inputs_used, row$yaml_sign_note, sep = "; "))
))
}
# 1 / s1_e1
effect_size:
metric: SMD
d: -0.121513595094
v: 0.011522200282
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.50909090909, mean_high=5.67272727273, sd_low=1.34539994429, sd_high=1.3479002251, , r_within=0.685853391572; YAML uses the raw low-minus-high d."
# 1 / s1_e2
effect_size:
metric: SMD
d: -0.080222315877
v: 0.015383319265
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.98181818182, mean_high=6.07272727273, sd_low=1.14650691864, sd_high=1.11976428496, , r_within=0.578031957126; YAML uses the raw low-minus-high d."
# 1 / s1_e3
effect_size:
metric: SMD
d: -0.112815214964
v: 0.014651569954
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.83636363636, mean_high=5.96363636364, sd_low=1.21355975243, sd_high=1.03572548135, , r_within=0.599244020297; YAML uses the raw low-minus-high d."
# 1 / s1_e4
effect_size:
metric: SMD
d: 0.038543514297
v: 0.006482110421
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.27272727273, mean_high=5.21818181818, sd_low=1.45874813726, sd_high=1.37019745929, , r_within=0.82205315469; YAML uses the raw low-minus-high d."
# 1 / s1_e5
effect_size:
metric: SMD
d: -0.129102557916
v: 0.012667093062
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.98181818182, mean_high=6.12727272727, sd_low=1.22460740634, sd_high=1.01934157134, , r_within=0.654631213559; YAML uses the raw low-minus-high d."
# 1 / s1_e6
effect_size:
metric: SMD
d: 0.016452873454
v: 0.015956226794
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=55, n_high=55, n_total=55, mean_low=5.90909090909, mean_high=5.89090909091, sd_low=1.00503781526, sd_high=1.19679707232, , r_within=0.561248258983; YAML uses the raw low-minus-high d."
# 1 / s1_e7
effect_size:
metric: SMD
d: -0.149740006799
v: 0.021071707924
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.42857142857, mean_high=5.64285714286, sd_low=1.54829342941, sd_high=1.30330590108, , r_within=0.561179544014; YAML uses the raw low-minus-high d."
# 1 / s1_e8
effect_size:
metric: SMD
d: 0.048438604238
v: 0.010315824158
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.61904761905, mean_high=5.54761904762, sd_low=1.43054451431, sd_high=1.51741726905, , r_within=0.783841177752; YAML uses the raw low-minus-high d."
# 1 / s1_e9
effect_size:
metric: SMD
d: 0.040628491984
v: 0.012531174632
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.40476190476, mean_high=5.33333333333, sd_low=1.79510885341, sd_high=1.72027602247, , r_within=0.737163791234; YAML uses the raw low-minus-high d."
# 1 / s1_e10
effect_size:
metric: SMD
d: 0.131502174867
v: 0.010441002041
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.33333333333, mean_high=5.11904761905, sd_low=1.60284300262, sd_high=1.65577159012, , r_within=0.784229981593; YAML uses the raw low-minus-high d."
# 1 / s1_e11
effect_size:
metric: SMD
d: 0.166792146986
v: 0.018782033461
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.97619047619, mean_high=5.7380952381, sd_low=1.19935135392, sd_high=1.62389960956, , r_within=0.610350196065; YAML uses the raw low-minus-high d."
# 1 / s1_e12
effect_size:
metric: SMD
d: -0.017069172683
v: 0.021768093856
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=42, n_high=42, n_total=42, mean_low=5.78571428571, mean_high=5.80952380952, sd_low=1.3710546819, sd_high=1.41831392923, , r_within=0.542917183602; YAML uses the raw low-minus-high d."
# 3 / s3_e1
effect_size:
metric: SMD
d: -0.661808915227
v: 0.027626090361
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=39, n_high=39, n_total=39, mean_low=1.94871794872, mean_high=2.76923076923, sd_low=1.12270158074, sd_high=1.34676099668, , r_within=0.531506638657; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e2
effect_size:
metric: SMD
d: -0.839514293934
v: 0.022737710902
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=48, n_high=48, n_total=48, mean_low=2.47916666667, mean_high=4.22916666667, sd_low=1.58435950323, sd_high=2.48604259847, , r_within=0.571129577788; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e3
effect_size:
metric: SMD
d: -0.683958446104
v: 0.021143276656
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=44, n_high=44, n_total=44, mean_low=2, mean_high=2.88636363636, sd_low=1.20077494357, sd_high=1.38456456241, , r_within=0.615473547653; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e4
effect_size:
metric: SMD
d: -0.871275071557
v: 0.073242304592
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=27, n_high=27, n_total=27, mean_low=2.66666666667, mean_high=5.48148148148, sd_low=2.44948974278, sd_high=3.8567659865, , r_within=0.10720957516; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e5
effect_size:
metric: SMD
d: -0.668163278631
v: 0.022482544480
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=50, n_high=50, n_total=50, mean_low=1.74, mean_high=2.4, sd_low=0.828325086533, sd_high=1.12485826772, , r_within=0.508151586222; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e6
effect_size:
metric: SMD
d: -0.813209200727
v: 0.030623405060
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=47, n_high=47, n_total=47, mean_low=1.53191489362, mean_high=2.82978723404, sd_low=0.905323959067, sd_high=2.06754579294, , r_within=0.374614307699; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e7
effect_size:
metric: SMD
d: 0.058562617160
v: 0.037744491397
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=35, n_high=35, n_total=35, mean_low=2.25714285714, mean_high=2.14285714286, sd_low=2.06287716185, sd_high=1.83339699406, , r_within=0.33994964082; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e8
effect_size:
metric: SMD
d: -0.372724636385
v: 0.034404491619
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=34, n_high=34, n_total=34, mean_low=2.70588235294, mean_high=3.79411764706, sd_low=1.9467052471, sd_high=3.64134017757, , r_within=0.435786978245; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e9
effect_size:
metric: SMD
d: -0.379749242052
v: 0.043131147844
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=36, n_high=36, n_total=36, mean_low=1.86111111111, mean_high=2.47222222222, sd_low=1.26835726778, sd_high=1.88961237312, , r_within=0.24272756755; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e10
effect_size:
metric: SMD
d: 0.016068697196
v: 0.028322397854
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=33, n_high=33, n_total=33, mean_low=8.15151515152, mean_high=8, sd_low=10.5478706741, sd_high=8.15858443604, , r_within=0.532721870308; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e11
effect_size:
metric: SMD
d: -0.355430263187
v: 0.044846345366
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=32, n_high=32, n_total=32, mean_low=1.65625, mean_high=2.09375, sd_low=0.970845158911, sd_high=1.44488809702, , r_within=0.299667883207; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 3 / s3_e12
effect_size:
metric: SMD
d: -0.435233708389
v: 0.057857270414
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=26, n_high=26, n_total=26, mean_low=2.15384615385, mean_high=3.11538461538, sd_low=1.61721508013, sd_high=2.67322910469, , r_within=0.273302686156; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e1
effect_size:
metric: SMD
d: -0.431103113867
v: 0.017900374469
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=47, n_high=47, n_total=47, mean_low=2.23404255319, mean_high=3, sd_low=1.59090849021, sd_high=1.94489297794, , r_within=0.611252309578; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e2
effect_size:
metric: SMD
d: -0.615892037585
v: 0.020734435957
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=44, n_high=44, n_total=44, mean_low=2.86363636364, mean_high=4.70454545455, sd_low=2.25770936695, sd_high=3.57367341108, , r_within=0.608833639666; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e3
effect_size:
metric: SMD
d: -0.510345860764
v: 0.024406949179
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=35, n_high=35, n_total=35, mean_low=2.82857142857, mean_high=3.6, sd_low=1.20014004785, sd_high=1.76901434836, , r_within=0.617863667716; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e4
effect_size:
metric: SMD
d: -0.505090368385
v: 0.018471564247
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=41, n_high=41, n_total=41, mean_low=4, mean_high=6.60975609756, sd_low=4.28368999812, sd_high=5.91978905359, , r_within=0.667427983862; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e5
effect_size:
metric: SMD
d: -0.206779836189
v: 0.022095548744
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=45, n_high=45, n_total=45, mean_low=2.22222222222, mean_high=2.46666666667, sd_low=1.18492210885, sd_high=1.17936808966, , r_within=0.509582783511; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e6
effect_size:
metric: SMD
d: -0.751472126025
v: 0.030247803332
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=46, n_high=46, n_total=46, mean_low=1.91304347826, mean_high=3.34782608696, sd_low=1.17049062755, sd_high=2.43326384654, , r_within=0.385372604054; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e7
effect_size:
metric: SMD
d: -0.198074155086
v: 0.046003606329
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=30, n_high=30, n_total=30, mean_low=1.83333333333, mean_high=2.03333333333, sd_low=0.912870929175, sd_high=1.09806517404, , r_within=0.31533773688; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e8
effect_size:
metric: SMD
d: -0.023511184407
v: 0.033935712758
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=30, n_high=30, n_total=30, mean_low=3.36666666667, mean_high=3.43333333333, sd_low=2.93002687211, sd_high=2.7377732373, , r_within=0.491050066968; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e9
effect_size:
metric: SMD
d: -0.319806387682
v: 0.015696254010
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=39, n_high=39, n_total=39, mean_low=2.15384615385, mean_high=2.84615384615, sd_low=1.91309148457, sd_high=2.39009426745, , r_within=0.713210654922; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e10
effect_size:
metric: SMD
d: -0.480340771592
v: 0.058763399638
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=23, n_high=23, n_total=23, mean_low=3.60869565217, mean_high=4.95652173913, sd_low=2.5179199647, sd_high=3.06710199121, , r_within=0.356732040173; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e11
effect_size:
metric: SMD
d: -0.217503158496
v: 0.010008305738
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=34, n_high=34, n_total=34, mean_low=1.97058823529, mean_high=2.29411764706, sd_low=1.19304281509, sd_high=1.73256530359, , r_within=0.83994423388; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 4 / s4_e12
effect_size:
metric: SMD
d: -0.443709626053
v: 0.053958967302
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=30, n_high=30, n_total=30, mean_low=1.56666666667, mean_high=2.2, sd_low=0.727932041795, sd_high=1.88277125169, , r_within=0.216377480011; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e1
effect_size:
metric: SMD
d: -0.864738958369
v: 0.029921005076
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=43, n_high=43, n_total=43, mean_low=2.02325581395, mean_high=3.90697674419, sd_low=1.53511861017, sd_high=2.67095447081, , r_within=0.470896843884; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e2
effect_size:
metric: SMD
d: -0.831369146354
v: 0.037372522234
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=34, n_high=34, n_total=34, mean_low=3.5, mean_high=5.73529411765, sd_low=2.27303028283, sd_high=3.04818697758, , r_within=0.470162188533; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e3
effect_size:
metric: SMD
d: -0.947446942608
v: 0.049281922459
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=26, n_high=26, n_total=26, mean_low=2.65384615385, mean_high=3.92307692308, sd_low=1.01753850806, sd_high=1.59807576599, , r_within=0.49954238892; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e4
effect_size:
metric: SMD
d: -1.080226638456
v: 0.037073311263
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=40, n_high=40, n_total=40, mean_low=2.9, mean_high=10.25, sd_low=3.00256300773, sd_high=9.14204151582, , r_within=0.431560859795; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e5
effect_size:
metric: SMD
d: -0.394938706522
v: 0.025090111477
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=41, n_high=41, n_total=41, mean_low=2.0487804878, mean_high=2.51219512195, sd_low=1.11694269128, sd_high=1.22723166557, , r_within=0.510225460808; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e6
effect_size:
metric: SMD
d: -0.491607487397
v: 0.042613022416
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=41, n_high=41, n_total=41, mean_low=1.9512195122, mean_high=2.90243902439, sd_low=1.67259109636, sd_high=2.16569709388, , r_within=0.157391135451; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e7
effect_size:
metric: SMD
d: -0.146300512989
v: 0.095241363486
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=18, n_high=18, n_total=18, mean_low=3.33333333333, mean_high=3.55555555556, sd_low=1.60879933308, sd_high=1.42342677748, , r_within=0.145559895866; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e8
effect_size:
metric: SMD
d: -0.468635005912
v: 0.037071684633
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=28, n_high=28, n_total=28, mean_low=2.67857142857, mean_high=3.85714285714, sd_low=2.03767426301, sd_high=2.91502221215, , r_within=0.515751062183; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e9
effect_size:
metric: SMD
d: -0.329402378246
v: 0.085530316090
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=18, n_high=18, n_total=18, mean_low=3.05555555556, mean_high=3.44444444444, sd_low=0.998364675929, sd_high=1.33822631614, , r_within=0.244601885858; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e10
effect_size:
metric: SMD
d: -0.635638288618
v: 0.060827101761
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=26, n_high=26, n_total=26, mean_low=2.57692307692, mean_high=4.15384615385, sd_low=2.08178917133, sd_high=2.82407234599, , r_within=0.263252231231; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e11
effect_size:
metric: SMD
d: 0.000000000000
v: 0.039714932657
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=36, n_high=36, n_total=36, mean_low=2.58333333333, mean_high=2.58333333333, sd_low=1.87273672927, sd_high=1.64533973912, , r_within=0.285131212175; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 5 / s5_e12
effect_size:
metric: SMD
d: 0.255925346834
v: 0.054468910679
computed_from: groups
needs_review: false
notes: "method=within_smcrp_r, sign_convention=d = mean(low) - mean(high), n_low=27, n_high=27, n_total=27, mean_low=2.85185185185, mean_high=2.2962962963, sd_low=2.31556113325, sd_high=2.0156086085, , r_within=0.273469213558; YAML uses the raw low-minus-high d; downstream meta-analysis reverses evidence-seeking effects programmatically."
# 2 / s2_e1
effect_size:
metric: SMD
d: 0.565873199091
v: 0.033217946098
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=68, n_total=126, mean_low=3.93103448276, mean_high=2.89705882353, sd_low=1.82441597447, sd_high=1.82960484994, ; YAML uses the raw low-minus-high d."
# 2 / s2_e2
effect_size:
metric: SMD
d: 0.458627340663
v: 0.032781940384
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=68, n_total=126, mean_low=3.72413793103, mean_high=2.91176470588, sd_low=1.84289154423, sd_high=1.70806073258, ; YAML uses the raw low-minus-high d."
# 2 / s2_e3
effect_size:
metric: SMD
d: 0.099535318347
v: 0.033676449158
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=61, n_total=119, mean_low=3.62068965517, mean_high=3.4262295082, sd_low=1.94510258789, sd_high=1.96179353648, ; YAML uses the raw low-minus-high d."
# 2 / s2_e4
effect_size:
metric: SMD
d: 0.270934142758
v: 0.033943247604
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=58, n_high=61, n_total=119, mean_low=3.79310344828, mean_high=3.27868852459, sd_low=1.92635116117, sd_high=1.87199668394, ; YAML uses the raw low-minus-high d."
# 2 / s2_e5
effect_size:
metric: SMD
d: 0.119302333567
v: 0.032854031084
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=62, n_high=60, n_total=122, mean_low=4.25806451613, mean_high=4.03333333333, sd_low=1.88118800858, sd_high=1.88631707048, ; YAML uses the raw low-minus-high d."
# 2 / s2_e6
effect_size:
metric: SMD
d: 0.177466028998
v: 0.032924773480
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=62, n_high=60, n_total=122, mean_low=4.08064516129, mean_high=3.71666666667, sd_low=2.01061435107, sd_high=2.09188639762, ; YAML uses the raw low-minus-high d."