paper_key <- "turrindepistemiccontextualismidle"
sign_convention <- "d = mean(low) - mean(high)"
effects <- list(
list(
study_id = 1,
effect_id = "s1_e1",
method_used = "between_groups",
n_low = 102,
n_high = 99,
mean_low = 5.02,
sd_low = 1.48,
mean_high = 4.36,
sd_high = 1.81,
reported_d = 0.41,
notes_on_assumptions = "Experiment 1 (bank): split Ns recovered from one-sample tests, Low t(101), High t(98)."
),
list(
study_id = 2,
effect_id = "s2_e1",
method_used = "between_groups",
n_low = 52,
n_high = 49,
mean_low = 4.44,
sd_low = 1.55,
mean_high = 5.41,
sd_high = 1.61,
reported_d = 0.61,
notes_on_assumptions = "Conceptual replication of Experiment 1 (flight): split Ns recovered from one-sample tests, Low t(51), High t(48)."
),
list(
study_id = 3,
effect_id = "s3_e1",
method_used = "between_groups",
n_low = 98,
n_high = 101,
mean_low = 4.58,
sd_low = 1.67,
mean_high = 4.49,
sd_high = 1.78,
reported_d = 0.06,
notes_on_assumptions = "Experiment 2 (bank; deferral): stakes held constant; map low=Yes ('I do know'), high=No ('I don't know'). Split Ns recovered from one-sample tests, Yes t(97), No t(100)."
),
list(
study_id = 4,
effect_id = "s4_e1",
method_used = "between_groups",
n_low = 50,
n_high = 51,
mean_low = 4.54,
sd_low = 1.73,
mean_high = 4.61,
sd_high = 1.98,
reported_d = 0.04,
notes_on_assumptions = "Conceptual replication of Experiment 2 (flight; deferral): stakes held constant; map low=Yes ('I do know'), high=No ('I don't know'). Split Ns recovered from one-sample tests, Yes t(49), No t(50)."
),
list(
study_id = 5,
effect_id = "s5_e1",
method_used = "between_groups",
n_low = 102,
n_high = 100,
mean_low = 4.79,
sd_low = 1.72,
mean_high = 4.64,
sd_high = 1.68,
reported_d = 0.09,
notes_on_assumptions = "Experiment 3 (bank): split Ns recovered from one-sample tests, Low t(101), High t(99)."
),
list(
study_id = 6,
effect_id = "s6_e1",
method_used = "between_groups",
n_low = 50,
n_high = 51,
mean_low = 4.90,
sd_low = 1.61,
mean_high = 4.76,
sd_high = 1.72,
reported_d = 0.08,
notes_on_assumptions = "Conceptual replication of Experiment 3 (flight): split Ns recovered from one-sample tests, Low t(49), High t(50)."
),
list(
study_id = 7,
effect_id = "s7_e1",
method_used = "between_groups",
n_low = 99 / 2,
n_high = 99 / 2,
mean_low = 8.31,
sd_low = 1.86,
mean_high = 8.62,
sd_high = 1.98,
t_value = -0.81,
df = 97,
imputed_flag = TRUE,
needs_sensitivity = TRUE,
notes_on_assumptions = "Experiment 4 (bank; evidence strength required): split Ns not reported; equal-N assumption authorized by analyst, n_low=n_high=99/2."
),
list(
study_id = 8,
effect_id = "s8_e1",
method_used = "between_reported_d_t_df",
reported_d = 0.34,
t_value = 1.69,
df = 98,
mean_low = 8.59,
mean_high = 9.08,
notes_on_assumptions = "Conceptual replication of Experiment 4 (flight): reported Cohen's d is unsigned; sign set from means using d = mean(low) - mean(high)."
)
)
legacy_effect_sizes <- data.frame(
study_id = c(1, 2, 3, 4, 5, 6, 7, 8),
effect_id = c("s1_e1", "s2_e1", "s3_e1", "s4_e1", "s5_e1", "s6_e1", "s7_e1", "s8_e1"),
previous_d = c(
0.399811605602,
-0.614166764737,
0.052122137662,
-0.037624978833,
0.088218811866,
0.084010249876,
NA_real_,
-0.34
),
previous_v = c(
0.020306563127,
0.041543987117,
0.020111966953,
0.039614992829,
0.019823377966,
0.039643488198,
NA_real_,
0.041064569211
)
)Effect size computations: turrindepistemiccontextualismidle
Computes standardized mean differences (d) and sampling variances (v) for the extraction YAML papers/turrindepistemiccontextualismidle/turrindepistemiccontextualismidle.yaml.
This file was rebuilt to follow the current project template:
- Between-group effects with condition means/SDs and split Ns use
esc::esc_mean_sd. - Effects with reported
dbut no split Ns use the project fallbackbetween_reported_d_t_df. - Study 7 (Experiment 4, bank; evidence strength required) uses an analyst-authorized equal-N assumption because the paper reports total
N = 99but not split condition Ns. The implemented assumption isn_low = n_high = 99 / 2.
Inputs and methods
Computation
compute_effect <- function(effect_inputs) {
study_id <- effect_inputs$study_id
effect_id <- effect_inputs$effect_id
method_used <- effect_inputs$method_used
n_high <- default_or(effect_inputs$n_high, NA_real_)
n_low <- default_or(effect_inputs$n_low, NA_real_)
mean_high <- default_or(effect_inputs$mean_high, NA_real_)
mean_low <- default_or(effect_inputs$mean_low, NA_real_)
sd_high <- default_or(effect_inputs$sd_high, NA_real_)
sd_low <- default_or(effect_inputs$sd_low, NA_real_)
reported_d <- default_or(effect_inputs$reported_d, NA_real_)
t_value <- default_or(effect_inputs$t_value, NA_real_)
df <- default_or(effect_inputs$df, NA_real_)
sign_d <- default_or(effect_inputs$sign_d, NA_real_)
notes_on_assumptions <- default_or(effect_inputs$notes_on_assumptions, "")
imputed_flag <- default_or(effect_inputs$imputed_flag, FALSE)
needs_sensitivity <- default_or(effect_inputs$needs_sensitivity, FALSE)
d <- NA_real_
v <- NA_real_
g <- NA_real_
v_g <- NA_real_
computed_from_suggested <- NA_character_
if (method_used == "between_groups") {
computed_from_suggested <- "groups"
stop_if_missing(n_high, "n_high")
stop_if_missing(n_low, "n_low")
stop_if_missing(mean_high, "mean_high")
stop_if_missing(mean_low, "mean_low")
stop_if_missing(sd_high, "sd_high")
stop_if_missing(sd_low, "sd_low")
# esc_mean_sd computes grp1 - grp2; use grp1=low and grp2=high to match sign convention.
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
)
d <- res$d
v <- res$v
g <- res$g
v_g <- res$v_g
} else if (method_used == "between_reported_d_t_df") {
computed_from_suggested <- "reported_d"
stop_if_missing(reported_d, "reported_d")
stop_if_missing(t_value, "t_value")
stop_if_missing(df, "df")
sign_used <- infer_sign(mean_low, mean_high, sign_d)
d <- sign_used * abs(reported_d)
v <- var_d_between_from_d_t_df(d = d, t_value = abs(t_value), df = df)
J <- hedges_correction(df)
g <- J * d
v_g <- (J^2) * v
} else {
stop(sprintf("Unknown method_used: %s", method_used), call. = FALSE)
}
inputs_used <- paste(
c(
sprintf("method=%s", method_used),
sprintf("sign_convention=%s", sign_convention),
if (!is.na(n_low)) sprintf("n_low=%s", n_low) else NULL,
if (!is.na(n_high)) sprintf("n_high=%s", n_high) else NULL,
if (!is.na(mean_low)) sprintf("mean_low=%s", mean_low) else NULL,
if (!is.na(mean_high)) sprintf("mean_high=%s", mean_high) else NULL,
if (!is.na(sd_low)) sprintf("sd_low=%s", sd_low) else NULL,
if (!is.na(sd_high)) sprintf("sd_high=%s", sd_high) else NULL,
if (!is.na(df)) sprintf("df=%s", df) else NULL,
if (!is.na(t_value)) sprintf("t=%s", t_value) else NULL,
if (!is.na(reported_d)) sprintf("reported_d=%s", reported_d) else NULL,
if (!is.na(sign_d)) sprintf("sign_d=%s", sign_d) else NULL,
if (isTRUE(imputed_flag)) "imputed_equal_n=TRUE" else NULL
),
collapse = ", "
)
data.frame(
paper_key = paper_key,
study_id = study_id,
effect_id = effect_id,
design = "Between-Subjects",
method_used = method_used,
computed_from_suggested = computed_from_suggested,
inputs_used = inputs_used,
d = d,
v = v,
g = g,
v_g = v_g,
notes_on_assumptions = notes_on_assumptions,
imputed_flag = imputed_flag,
needs_sensitivity = needs_sensitivity,
stringsAsFactors = FALSE
)
}
audit <- do.call(rbind, lapply(effects, compute_effect))
audit paper_key study_id effect_id design
1 turrindepistemiccontextualismidle 1 s1_e1 Between-Subjects
2 turrindepistemiccontextualismidle 2 s2_e1 Between-Subjects
3 turrindepistemiccontextualismidle 3 s3_e1 Between-Subjects
4 turrindepistemiccontextualismidle 4 s4_e1 Between-Subjects
5 turrindepistemiccontextualismidle 5 s5_e1 Between-Subjects
6 turrindepistemiccontextualismidle 6 s6_e1 Between-Subjects
7 turrindepistemiccontextualismidle 7 s7_e1 Between-Subjects
8 turrindepistemiccontextualismidle 8 s8_e1 Between-Subjects
method_used computed_from_suggested
1 between_groups groups
2 between_groups groups
3 between_groups groups
4 between_groups groups
5 between_groups groups
6 between_groups groups
7 between_groups groups
8 between_reported_d_t_df reported_d
inputs_used
1 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=102, n_high=99, mean_low=5.02, mean_high=4.36, sd_low=1.48, sd_high=1.81, reported_d=0.41
2 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=52, n_high=49, mean_low=4.44, mean_high=5.41, sd_low=1.55, sd_high=1.61, reported_d=0.61
3 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=98, n_high=101, mean_low=4.58, mean_high=4.49, sd_low=1.67, sd_high=1.78, reported_d=0.06
4 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=50, n_high=51, mean_low=4.54, mean_high=4.61, sd_low=1.73, sd_high=1.98, reported_d=0.04
5 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=102, n_high=100, mean_low=4.79, mean_high=4.64, sd_low=1.72, sd_high=1.68, reported_d=0.09
6 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=50, n_high=51, mean_low=4.9, mean_high=4.76, sd_low=1.61, sd_high=1.72, reported_d=0.08
7 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=49.5, n_high=49.5, mean_low=8.31, mean_high=8.62, sd_low=1.86, sd_high=1.98, df=97, t=-0.81, imputed_equal_n=TRUE
8 method=between_reported_d_t_df, sign_convention=d = mean(low) - mean(high), mean_low=8.59, mean_high=9.08, df=98, t=1.69, reported_d=0.34
d v g v_g
1 0.39981161 0.02030257 0.39830288 0.02030257
2 -0.61416676 0.04150626 -0.60950221 0.04150626
3 0.05212214 0.02011190 0.05192345 0.02011190
4 -0.03762498 0.03961485 -0.03733922 0.03961485
5 0.08821881 0.01982319 0.08788758 0.01982319
6 0.08401025 0.03964278 0.08337220 0.03964278
7 -0.16137955 0.04053557 -0.16012855 0.04053557
8 -0.34000000 0.04106457 -0.33739019 0.04043657
notes_on_assumptions
1 Experiment 1 (bank): split Ns recovered from one-sample tests, Low t(101), High t(98).
2 Conceptual replication of Experiment 1 (flight): split Ns recovered from one-sample tests, Low t(51), High t(48).
3 Experiment 2 (bank; deferral): stakes held constant; map low=Yes ('I do know'), high=No ('I don't know'). Split Ns recovered from one-sample tests, Yes t(97), No t(100).
4 Conceptual replication of Experiment 2 (flight; deferral): stakes held constant; map low=Yes ('I do know'), high=No ('I don't know'). Split Ns recovered from one-sample tests, Yes t(49), No t(50).
5 Experiment 3 (bank): split Ns recovered from one-sample tests, Low t(101), High t(99).
6 Conceptual replication of Experiment 3 (flight): split Ns recovered from one-sample tests, Low t(49), High t(50).
7 Experiment 4 (bank; evidence strength required): split Ns not reported; equal-N assumption authorized by analyst, n_low=n_high=99/2.
8 Conceptual replication of Experiment 4 (flight): reported Cohen's d is unsigned; sign set from means using d = mean(low) - mean(high).
imputed_flag needs_sensitivity
1 FALSE FALSE
2 FALSE FALSE
3 FALSE FALSE
4 FALSE FALSE
5 FALSE FALSE
6 FALSE FALSE
7 TRUE TRUE
8 FALSE FALSEComparison With Previous Stored Values
comparison <- merge(
audit[, c("study_id", "effect_id", "d", "v", "method_used", "imputed_flag")],
legacy_effect_sizes,
by = c("study_id", "effect_id"),
all.x = TRUE
)
comparison$delta_d <- comparison$d - comparison$previous_d
comparison$delta_v <- comparison$v - comparison$previous_v
comparison$abs_delta_d <- abs(comparison$delta_d)
comparison$abs_delta_v <- abs(comparison$delta_v)
comparison <- comparison[order(comparison$study_id), ]
comparison study_id effect_id d v method_used
1 1 s1_e1 0.39981161 0.02030257 between_groups
2 2 s2_e1 -0.61416676 0.04150626 between_groups
3 3 s3_e1 0.05212214 0.02011190 between_groups
4 4 s4_e1 -0.03762498 0.03961485 between_groups
5 5 s5_e1 0.08821881 0.01982319 between_groups
6 6 s6_e1 0.08401025 0.03964278 between_groups
7 7 s7_e1 -0.16137955 0.04053557 between_groups
8 8 s8_e1 -0.34000000 0.04106457 between_reported_d_t_df
imputed_flag previous_d previous_v delta_d delta_v abs_delta_d
1 FALSE 0.39981161 0.02030656 1.121325e-13 -3.996333e-06 1.121325e-13
2 FALSE -0.61416676 0.04154399 8.304468e-14 -3.772385e-05 8.304468e-14
3 FALSE 0.05212214 0.02011197 4.513750e-14 -6.929864e-08 4.513750e-14
4 FALSE -0.03762498 0.03961499 1.362799e-14 -1.415777e-07 1.362799e-14
5 FALSE 0.08821881 0.01982338 -4.686390e-13 -1.926380e-07 4.686390e-13
6 FALSE 0.08401025 0.03964349 -2.342571e-13 -7.058425e-07 2.342571e-13
7 TRUE NA NA NA NA NA
8 FALSE -0.34000000 0.04106457 0.000000e+00 -3.809730e-13 0.000000e+00
abs_delta_v
1 3.996333e-06
2 3.772385e-05
3 6.929864e-08
4 1.415777e-07
5 1.926380e-07
6 7.058425e-07
7 NA
8 3.809730e-13Study 7 Equal-N Sensitivity
The primary Study 7 estimate uses n_low = n_high = 99 / 2. The two possible integer splits differ only by one participant. This table checks both directions.
study7_sensitivity_inputs <- list(
list(split = "equal_fractional_49.5_49.5", n_low = 99 / 2, n_high = 99 / 2),
list(split = "low49_high50", n_low = 49, n_high = 50),
list(split = "low50_high49", n_low = 50, n_high = 49)
)
study7_sensitivity <- do.call(rbind, lapply(study7_sensitivity_inputs, function(x) {
res <- compute_with_esc(
esc::esc_mean_sd,
grp1m = 8.31,
grp1sd = 1.86,
grp1n = x$n_low,
grp2m = 8.62,
grp2sd = 1.98,
grp2n = x$n_high
)
data.frame(
split = x$split,
n_low = x$n_low,
n_high = x$n_high,
d = res$d,
v = res$v,
stringsAsFactors = FALSE
)
}))
study7_sensitivity$delta_d_from_primary <- study7_sensitivity$d - study7_sensitivity$d[1]
study7_sensitivity$delta_v_from_primary <- study7_sensitivity$v - study7_sensitivity$v[1]
study7_sensitivity split n_low n_high d v
1 equal_fractional_49.5_49.5 49.5 49.5 -0.1613796 0.04053557
2 low49_high50 49.0 50.0 -0.1613276 0.04053961
3 low50_high49 50.0 49.0 -0.1614315 0.04053978
delta_d_from_primary delta_v_from_primary
1 0.000000e+00 0.000000e+00
2 5.191505e-05 4.038248e-06
3 -5.196520e-05 4.207583e-06Paste-ready YAML snippets
for (i in seq_len(nrow(audit))) {
row <- audit[i, ]
cat(sprintf("\n# %s (study_id=%s)\n", row$effect_id, row$study_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$d,
row$v,
row$computed_from_suggested,
gsub("\"", "'", row$inputs_used)
))
}
# s1_e1 (study_id=1)
effect_size:
metric: SMD
d: 0.399811605602
v: 0.020302566794
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=102, n_high=99, mean_low=5.02, mean_high=4.36, sd_low=1.48, sd_high=1.81, reported_d=0.41"
# s2_e1 (study_id=2)
effect_size:
metric: SMD
d: -0.614166764737
v: 0.041506263263
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=52, n_high=49, mean_low=4.44, mean_high=5.41, sd_low=1.55, sd_high=1.61, reported_d=0.61"
# s3_e1 (study_id=3)
effect_size:
metric: SMD
d: 0.052122137662
v: 0.020111897654
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=98, n_high=101, mean_low=4.58, mean_high=4.49, sd_low=1.67, sd_high=1.78, reported_d=0.06"
# s4_e1 (study_id=4)
effect_size:
metric: SMD
d: -0.037624978833
v: 0.039614851251
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=50, n_high=51, mean_low=4.54, mean_high=4.61, sd_low=1.73, sd_high=1.98, reported_d=0.04"
# s5_e1 (study_id=5)
effect_size:
metric: SMD
d: 0.088218811866
v: 0.019823185328
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=102, n_high=100, mean_low=4.79, mean_high=4.64, sd_low=1.72, sd_high=1.68, reported_d=0.09"
# s6_e1 (study_id=6)
effect_size:
metric: SMD
d: 0.084010249876
v: 0.039642782355
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=50, n_high=51, mean_low=4.9, mean_high=4.76, sd_low=1.61, sd_high=1.72, reported_d=0.08"
# s7_e1 (study_id=7)
effect_size:
metric: SMD
d: -0.161379553952
v: 0.040535572527
computed_from: groups
needs_review: false
notes: "method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=49.5, n_high=49.5, mean_low=8.31, mean_high=8.62, sd_low=1.86, sd_high=1.98, df=97, t=-0.81, imputed_equal_n=TRUE"
# s8_e1 (study_id=8)
effect_size:
metric: SMD
d: -0.340000000000
v: 0.041064569211
computed_from: reported_d
needs_review: false
notes: "method=between_reported_d_t_df, sign_convention=d = mean(low) - mean(high), mean_low=8.59, mean_high=9.08, df=98, t=1.69, reported_d=0.34"