suppressPackageStartupMessages({
library(dplyr)
library(esc)
})Effect size computations: porteretalndpuzzleaboutknowledge
Computes split stakes effects by evidence condition (weak vs strong) from raw OSF data for the extraction YAML papers/porteretalndpuzzleaboutknowledge/porteretalndpuzzleaboutknowledge.yaml.
- Evidence-fixed (binary knowledge attribution): exact 2x2 counts with
esc::esc_2x2. - Evidence-seeking (numeric checks required): group means/SDs with
esc::esc_mean_sd.
Sign convention throughout:
d = mean(low stakes) - mean(high stakes)
Data source and filtering
paper_key <- "porteretalndpuzzleaboutknowledge"
raw_candidates <- c(
"../data/GPP_study3_nlp.csv",
"../data/GPP study 3 nlp.csv",
"/tmp/GPP_study3_nlp.csv"
)
raw_path <- raw_candidates[file.exists(raw_candidates)][1]
if (is.na(raw_path) || !nzchar(raw_path)) {
raw_path <- tempfile(fileext = ".csv")
download.file("https://osf.io/download/59qd6/", destfile = raw_path, mode = "wb", quiet = TRUE)
}
raw <- read.csv(raw_path, check.names = FALSE)
sample_order <- c(
"China",
"Russia",
"Slovakia",
"Ecuador - Spanish",
"India - Hindi",
"India - Meitei",
"Japan",
"Morocco",
"Peru - Shipibo",
"Peru - Spanish",
"South Africa - Afrikaans",
"South Africa - Sepedi",
"South Africa - isiZulu",
"South Korea",
"United States"
)
population_map <- c(
"China" = "china",
"Russia" = "russia",
"Slovakia" = "europe_slovak",
"Ecuador - Spanish" = "ecuador_spanish",
"India - Hindi" = "india_hindi",
"India - Meitei" = "india_meitei",
"Japan" = "japan",
"Morocco" = "morocco",
"Peru - Shipibo" = "peru_shipibo",
"Peru - Spanish" = "peru_spanish",
"South Africa - Afrikaans" = "safrica_afrikaans",
"South Africa - Sepedi" = "safrica_sepedi",
"South Africa - isiZulu" = "safrica_isizulu",
"South Korea" = "korea",
"United States" = "usa_mturk"
)
# Paper filtering logic (as implemented in OSF analysis):
# q1_importance < 3, merge Russia sub-sites, age >= 18, and pass comprehension check via stakes==importance.
base <- raw %>%
filter(q1_importance < 3) %>%
mutate(
q1_importance = ifelse(q1_importance == 1, 1, ifelse(q1_importance == 2, 0, q1_importance)),
q2_knowledge = ifelse(q2_knowledge == 1, 1, ifelse(q2_knowledge == 2, 0, q2_knowledge)),
importance = ifelse(q1_importance == 1, "H", ifelse(q1_importance == 0, "L", NA_character_)),
population = case_when(
population == "europe_russian_syktyvkar" ~ "russia",
population == "europe_russian_moscow" ~ "russia",
population == "europe_russian_stpbg" ~ "russia",
TRUE ~ population
),
age_numeric = suppressWarnings(as.numeric(as.character(age))),
evidence_strength = case_when(
num_checks == "O" ~ "weak", # checked once
num_checks == "F" ~ "strong", # checked several times
TRUE ~ NA_character_
)
) %>%
filter(age_numeric >= 18, stakes == importance, !is.na(evidence_strength))
base %>%
count(population, evidence_strength, stakes) %>%
arrange(population, evidence_strength, stakes) population evidence_strength stakes n
1 china strong H 91
2 china strong L 44
3 china weak H 88
4 china weak L 38
5 ecuador_spanish strong H 66
6 ecuador_spanish strong L 42
7 ecuador_spanish weak H 62
8 ecuador_spanish weak L 38
9 europe_slovak strong H 101
10 europe_slovak strong L 48
11 europe_slovak weak H 98
12 europe_slovak weak L 43
13 india_hindi strong H 38
14 india_hindi strong L 12
15 india_hindi weak H 40
16 india_hindi weak L 10
17 india_meitei strong H 20
18 india_meitei strong L 2
19 india_meitei weak H 20
20 japan strong H 132
21 japan strong L 85
22 japan weak H 132
23 japan weak L 96
24 korea strong H 58
25 korea strong L 38
26 korea weak H 65
27 korea weak L 32
28 morocco strong H 115
29 morocco strong L 49
30 morocco weak H 117
31 morocco weak L 42
32 peru_shipibo strong H 46
33 peru_shipibo strong L 5
34 peru_shipibo weak H 43
35 peru_shipibo weak L 8
36 peru_spanish strong H 68
37 peru_spanish strong L 39
38 peru_spanish weak H 68
39 peru_spanish weak L 41
40 russia strong H 159
41 russia strong L 118
42 russia weak H 177
43 russia weak L 104
44 safrica_afrikaans strong H 72
45 safrica_afrikaans strong L 20
46 safrica_afrikaans weak H 61
47 safrica_afrikaans weak L 25
48 safrica_isizulu strong H 65
49 safrica_isizulu strong L 4
50 safrica_isizulu weak H 57
51 safrica_isizulu weak L 4
52 safrica_sepedi strong H 53
53 safrica_sepedi strong L 4
54 safrica_sepedi weak H 56
55 safrica_sepedi weak L 9
56 usa_mturk strong H 94
57 usa_mturk strong L 81
58 usa_mturk weak H 97
59 usa_mturk weak L 76Evidence-fixed split effects (2x2 counts -> d)
compute_fixed_split <- function(pop, ev) {
s <- base %>% filter(population == pop, evidence_strength == ev, !is.na(q2_knowledge))
low_yes <- sum(s$stakes == "L" & s$q2_knowledge == 1)
low_no <- sum(s$stakes == "L" & s$q2_knowledge == 0)
high_yes <- sum(s$stakes == "H" & s$q2_knowledge == 1)
high_no <- sum(s$stakes == "H" & s$q2_knowledge == 0)
n_low <- low_yes + low_no
n_high <- high_yes + high_no
out <- list(
n_low = n_low,
n_high = n_high,
mean_low = if (n_low > 0) low_yes / n_low else NA_real_,
mean_high = if (n_high > 0) high_yes / n_high else NA_real_,
sd_low = if (n_low > 1) sd(c(rep(1, low_yes), rep(0, low_no))) else NA_real_,
sd_high = if (n_high > 1) sd(c(rep(1, high_yes), rep(0, high_no))) else NA_real_,
low_yes = low_yes,
low_no = low_no,
high_yes = high_yes,
high_no = high_no,
cc_applied = FALSE,
d = NA_real_,
v = NA_real_,
can_compute = FALSE,
note = NA_character_
)
if (n_low == 0 || n_high == 0) {
out$note <- "Missing one stakes group in this evidence stratum."
return(out)
}
# Use continuity correction only when needed for zero cells.
cc <- ifelse(any(c(low_yes, low_no, high_yes, high_no) == 0), 0.5, 0)
out$cc_applied <- cc > 0
fit <- tryCatch(
esc::esc_2x2(
grp1yes = low_yes + cc,
grp1no = low_no + cc,
grp2yes = high_yes + cc,
grp2no = high_no + cc,
es.type = "d"
),
error = function(e) e
)
if (inherits(fit, "error")) {
out$note <- paste("esc_2x2 failed:", fit$message)
return(out)
}
out$d <- as.numeric(fit$es)
out$v <- as.numeric(fit$var)
out$can_compute <- is.finite(out$d) && is.finite(out$v)
out$note <- if (out$cc_applied) {
"Computed with esc::esc_2x2 with 0.5 continuity correction."
} else {
"Computed with esc::esc_2x2 from exact 2x2 counts."
}
out
}
fixed_rows <- list()
for (sid in seq_along(sample_order)) {
sample <- sample_order[sid]
pop <- population_map[[sample]]
for (ev in c("weak", "strong")) {
tmp <- compute_fixed_split(pop, ev)
fixed_rows[[length(fixed_rows) + 1]] <- data.frame(
paper_key = paper_key,
study_id = sid,
sample = sample,
effect_id = if (ev == "weak") sprintf("s%d_e1", sid) else sprintf("s%d_e2", sid),
domain = "evidence_fixed",
evidence_strength = ev,
n_low = tmp$n_low,
n_high = tmp$n_high,
mean_low = tmp$mean_low,
mean_high = tmp$mean_high,
sd_low = tmp$sd_low,
sd_high = tmp$sd_high,
low_yes = tmp$low_yes,
low_no = tmp$low_no,
high_yes = tmp$high_yes,
high_no = tmp$high_no,
cc_applied = tmp$cc_applied,
d = tmp$d,
v = tmp$v,
can_compute = tmp$can_compute,
note = tmp$note,
stringsAsFactors = FALSE
)
}
}
fixed_results <- bind_rows(fixed_rows)
fixed_results paper_key study_id sample effect_id
1 porteretalndpuzzleaboutknowledge 1 China s1_e1
2 porteretalndpuzzleaboutknowledge 1 China s1_e2
3 porteretalndpuzzleaboutknowledge 2 Russia s2_e1
4 porteretalndpuzzleaboutknowledge 2 Russia s2_e2
5 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e1
6 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e2
7 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e1
8 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e2
9 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e1
10 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e2
11 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e1
12 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e2
13 porteretalndpuzzleaboutknowledge 7 Japan s7_e1
14 porteretalndpuzzleaboutknowledge 7 Japan s7_e2
15 porteretalndpuzzleaboutknowledge 8 Morocco s8_e1
16 porteretalndpuzzleaboutknowledge 8 Morocco s8_e2
17 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e1
18 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e2
19 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e1
20 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e2
21 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e1
22 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e2
23 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e1
24 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e2
25 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e1
26 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e2
27 porteretalndpuzzleaboutknowledge 14 South Korea s14_e1
28 porteretalndpuzzleaboutknowledge 14 South Korea s14_e2
29 porteretalndpuzzleaboutknowledge 15 United States s15_e1
30 porteretalndpuzzleaboutknowledge 15 United States s15_e2
domain evidence_strength n_low n_high mean_low mean_high sd_low
1 evidence_fixed weak 38 88 0.2368421 0.1022727 0.4308515
2 evidence_fixed strong 44 91 0.1590909 0.2527473 0.3699894
3 evidence_fixed weak 104 177 0.3076923 0.2937853 0.4637735
4 evidence_fixed strong 118 159 0.4067797 0.3081761 0.4933279
5 evidence_fixed weak 43 98 0.4418605 0.1938776 0.5024855
6 evidence_fixed strong 48 101 0.5416667 0.3168317 0.5035336
7 evidence_fixed weak 38 62 0.2105263 0.3387097 0.4131550
8 evidence_fixed strong 42 66 0.4285714 0.3484848 0.5008703
9 evidence_fixed weak 10 40 0.4000000 0.4000000 0.5163978
10 evidence_fixed strong 12 38 0.5833333 0.4473684 0.5149287
11 evidence_fixed weak 0 20 NA 0.8500000 NA
12 evidence_fixed strong 2 20 0.5000000 0.7000000 0.7071068
13 evidence_fixed weak 96 132 0.1875000 0.1212121 0.3923613
14 evidence_fixed strong 85 132 0.3176471 0.1893939 0.4683244
15 evidence_fixed weak 42 117 0.5952381 0.3504274 0.4967958
16 evidence_fixed strong 49 115 0.2857143 0.4173913 0.4564355
17 evidence_fixed weak 8 43 0.5000000 0.8372093 0.5345225
18 evidence_fixed strong 5 46 0.0000000 0.8043478 0.0000000
19 evidence_fixed weak 41 68 0.3170732 0.2500000 0.4711170
20 evidence_fixed strong 39 68 0.2307692 0.2205882 0.4268328
21 evidence_fixed weak 25 61 0.6000000 0.4918033 0.5000000
22 evidence_fixed strong 20 72 0.5500000 0.5833333 0.5104178
23 evidence_fixed weak 9 56 0.6666667 0.7142857 0.5000000
24 evidence_fixed strong 4 53 0.2500000 0.6037736 0.5000000
25 evidence_fixed weak 4 57 0.7500000 0.6666667 0.5000000
26 evidence_fixed strong 4 65 0.5000000 0.6769231 0.5773503
27 evidence_fixed weak 32 65 0.2187500 0.2923077 0.4200134
28 evidence_fixed strong 38 58 0.1842105 0.3275862 0.3928595
29 evidence_fixed weak 76 97 0.3947368 0.2783505 0.4920419
30 evidence_fixed strong 81 94 0.4074074 0.3936170 0.4944132
sd_high low_yes low_no high_yes high_no cc_applied d v
1 0.3047431 9 29 9 79 FALSE 0.55251537 0.08187660
2 0.4369950 7 37 23 68 FALSE -0.32031893 0.06932445
3 0.4567870 32 72 52 125 FALSE 0.03646478 0.02199774
4 0.4631986 48 70 49 110 FALSE 0.23782445 0.01964155
5 0.3973667 19 24 19 79 FALSE 0.65684995 0.04850895
6 0.4675616 26 22 32 69 FALSE 0.51572666 0.03941156
7 0.4771345 8 30 21 41 FALSE -0.35985579 0.07001576
8 0.4801418 18 24 23 43 FALSE 0.18636230 0.04983674
9 0.4961389 4 6 16 24 FALSE 0.00000000 0.15831435
10 0.5038966 7 5 17 21 FALSE 0.30200768 0.13657074
11 0.3663475 0 0 17 3 FALSE NA NA
12 0.4701623 1 1 14 6 FALSE -0.46713979 0.68029938
13 0.3276170 18 78 16 116 FALSE 0.28374936 0.04240193
14 0.3933139 27 58 25 107 FALSE 0.38005685 0.03149798
15 0.4791559 25 17 41 76 FALSE 0.55288571 0.04145202
16 0.4952867 14 35 48 67 FALSE -0.32131400 0.04126570
17 0.3735437 4 4 36 7 FALSE -0.90286104 0.20384857
18 0.4010855 0 5 37 9 TRUE -2.07903061 0.70329506
19 0.4362322 13 28 17 51 FALSE 0.18268676 0.05807793
20 0.4177262 9 30 15 53 FALSE 0.03212533 0.06990524
21 0.5040817 15 10 30 31 FALSE 0.24162261 0.07059799
22 0.4964664 11 9 42 30 FALSE -0.07487131 0.07877612
23 0.4558423 6 3 40 16 FALSE -0.12302549 0.17857859
24 0.4937931 1 3 32 21 FALSE -0.83792385 0.42925805
25 0.4755949 3 1 38 19 FALSE 0.22354463 0.42928186
26 0.4712912 2 2 44 21 FALSE -0.40779990 0.32534627
27 0.4583625 7 25 19 46 FALSE -0.21433642 0.07818789
28 0.4734321 7 31 19 39 FALSE -0.42394677 0.07702066
29 0.4505152 30 46 27 70 FALSE 0.28956585 0.03234027
30 0.4911712 33 48 37 57 FALSE 0.03166828 0.02909152
can_compute note
1 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
2 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
3 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
4 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
5 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
6 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
7 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
8 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
9 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
10 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
11 FALSE Missing one stakes group in this evidence stratum.
12 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
13 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
14 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
15 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
16 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
17 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
18 TRUE Computed with esc::esc_2x2 with 0.5 continuity correction.
19 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
20 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
21 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
22 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
23 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
24 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
25 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
26 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
27 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
28 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
29 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.
30 TRUE Computed with esc::esc_2x2 from exact 2x2 counts.Evidence-seeking split effects (means/SDs -> d)
compute_seeking_split <- function(pop, ev) {
s <- base %>% filter(population == pop, evidence_strength == ev, !is.na(nlp), is.finite(nlp))
low <- s %>% filter(stakes == "L") %>% pull(nlp)
high <- s %>% filter(stakes == "H") %>% pull(nlp)
n_low <- length(low)
n_high <- length(high)
mean_low <- if (n_low > 0) mean(low) else NA_real_
mean_high <- if (n_high > 0) mean(high) else NA_real_
sd_low <- if (n_low > 1) sd(low) else NA_real_
sd_high <- if (n_high > 1) sd(high) else NA_real_
out <- list(
n_low = n_low,
n_high = n_high,
mean_low = mean_low,
mean_high = mean_high,
sd_low = sd_low,
sd_high = sd_high,
d = NA_real_,
v = NA_real_,
can_compute = FALSE,
note = NA_character_
)
if (n_low < 2 || n_high < 2 || !is.finite(sd_low) || !is.finite(sd_high)) {
out$note <- "Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups)."
return(out)
}
fit <- tryCatch(
esc::esc_mean_sd(
grp1m = mean_low,
grp1sd = sd_low,
grp1n = n_low,
grp2m = mean_high,
grp2sd = sd_high,
grp2n = n_high,
es.type = "d"
),
error = function(e) e
)
if (inherits(fit, "error")) {
out$note <- paste("esc_mean_sd failed:", fit$message)
return(out)
}
out$d <- as.numeric(fit$es)
out$v <- as.numeric(fit$var)
out$can_compute <- is.finite(out$d) && is.finite(out$v)
out$note <- "Computed with esc::esc_mean_sd from raw group means/SDs."
out
}
seeking_rows <- list()
for (sid in seq_along(sample_order)) {
sample <- sample_order[sid]
pop <- population_map[[sample]]
for (ev in c("weak", "strong")) {
tmp <- compute_seeking_split(pop, ev)
seeking_rows[[length(seeking_rows) + 1]] <- data.frame(
paper_key = paper_key,
study_id = sid,
sample = sample,
effect_id = if (ev == "weak") sprintf("s%d_e3", sid) else sprintf("s%d_e4", sid),
domain = "evidence_seeking",
evidence_strength = ev,
n_low = tmp$n_low,
n_high = tmp$n_high,
mean_low = tmp$mean_low,
mean_high = tmp$mean_high,
sd_low = tmp$sd_low,
sd_high = tmp$sd_high,
d = tmp$d,
v = tmp$v,
can_compute = tmp$can_compute,
note = tmp$note,
stringsAsFactors = FALSE
)
}
}
seeking_results <- bind_rows(seeking_rows)
seeking_results paper_key study_id sample effect_id
1 porteretalndpuzzleaboutknowledge 1 China s1_e3
2 porteretalndpuzzleaboutknowledge 1 China s1_e4
3 porteretalndpuzzleaboutknowledge 2 Russia s2_e3
4 porteretalndpuzzleaboutknowledge 2 Russia s2_e4
5 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e3
6 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e4
7 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e3
8 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e4
9 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e3
10 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e4
11 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e3
12 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e4
13 porteretalndpuzzleaboutknowledge 7 Japan s7_e3
14 porteretalndpuzzleaboutknowledge 7 Japan s7_e4
15 porteretalndpuzzleaboutknowledge 8 Morocco s8_e3
16 porteretalndpuzzleaboutknowledge 8 Morocco s8_e4
17 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e3
18 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e4
19 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e3
20 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e4
21 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e3
22 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e4
23 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e3
24 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e4
25 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e3
26 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e4
27 porteretalndpuzzleaboutknowledge 14 South Korea s14_e3
28 porteretalndpuzzleaboutknowledge 14 South Korea s14_e4
29 porteretalndpuzzleaboutknowledge 15 United States s15_e3
30 porteretalndpuzzleaboutknowledge 15 United States s15_e4
domain evidence_strength n_low n_high mean_low mean_high
1 evidence_seeking weak 31 57 3.483871 4.789474
2 evidence_seeking strong 36 58 4.611111 4.724138
3 evidence_seeking weak 103 167 2.174757 8.185629
4 evidence_seeking strong 115 145 4.469565 10.565517
5 evidence_seeking weak 35 85 3.571429 11.541176
6 evidence_seeking strong 41 82 2.463415 11.731707
7 evidence_seeking weak 38 53 2.710526 6.207547
8 evidence_seeking strong 36 45 3.055556 9.977778
9 evidence_seeking weak 10 34 2.600000 4.970588
10 evidence_seeking strong 12 28 3.083333 7.285714
11 evidence_seeking weak 0 18 NA 7.611111
12 evidence_seeking strong 2 17 5.000000 7.705882
13 evidence_seeking weak 88 110 4.295455 9.490909
14 evidence_seeking strong 71 111 8.169014 17.558559
15 evidence_seeking weak 41 108 1.585366 3.500000
16 evidence_seeking strong 47 105 5.170213 7.961905
17 evidence_seeking weak 0 1 NA 5.000000
18 evidence_seeking strong 0 2 NA 4.500000
19 evidence_seeking weak 27 35 2.222222 3.771429
20 evidence_seeking strong 23 41 7.086957 6.682927
21 evidence_seeking weak 24 48 1.750000 2.562500
22 evidence_seeking strong 15 45 10.200000 5.311111
23 evidence_seeking weak 1 10 3.000000 4.100000
24 evidence_seeking strong 1 10 2.000000 6.300000
25 evidence_seeking weak 0 7 NA 8.714286
26 evidence_seeking strong 1 4 5.000000 5.500000
27 evidence_seeking weak 32 65 7.218750 12.923077
28 evidence_seeking strong 38 58 6.894737 19.224138
29 evidence_seeking weak 72 91 2.652778 8.626374
30 evidence_seeking strong 80 83 4.775000 7.590361
sd_low sd_high d v can_compute
1 2.4613136 3.1494718 -0.44592622 0.05093175 TRUE
2 4.7285019 5.0115414 -0.02303948 0.04502198 TRUE
3 2.6324788 20.1014364 -0.37796130 0.01596131 TRUE
4 9.8491444 22.6868212 -0.33550360 0.01580867 TRUE
5 11.0700596 22.8663576 -0.39479231 0.04098556 TRUE
6 2.4809027 25.3796111 -0.44528976 0.03739139 TRUE
7 2.3122225 13.4455576 -0.33673697 0.04580675 TRUE
8 2.5628605 19.8705914 -0.46373234 0.05132745 TRUE
9 2.9888682 2.7796369 -0.83891563 0.13740926 TRUE
10 3.5021638 3.2530002 -1.26309671 0.13899029 TRUE
11 NA 3.1462188 NA NA FALSE
12 0.0000000 3.7875570 -0.73640071 0.57309421 TRUE
13 12.1675648 20.3581444 -0.30187611 0.02068467 TRUE
14 19.6301415 29.6091769 -0.35859418 0.02344678 TRUE
15 1.9745330 2.7598591 -0.74498197 0.03551191 TRUE
16 4.4100633 13.3113051 -0.24596971 0.03099942 TRUE
17 NA NA NA NA FALSE
18 NA 0.7071068 NA NA FALSE
19 1.0860420 2.0012601 -0.92904614 0.07256917 TRUE
20 20.2931091 15.2846966 0.02345010 0.06787280 TRUE
21 0.7939992 1.5561306 -0.60011973 0.06500100 TRUE
22 25.0718966 3.2947770 0.38654133 0.09013401 TRUE
23 NA 2.2827858 NA NA FALSE
24 NA 3.4334951 NA NA FALSE
25 NA 11.1162686 NA NA FALSE
26 NA 1.9148542 NA NA FALSE
27 18.9630105 25.8658243 -0.23933475 0.04692988 TRUE
28 9.9588056 31.5077507 -0.48697534 0.04479230 TRUE
29 3.9722975 20.1993665 -0.38964036 0.02534360 TRUE
30 3.0769663 11.0266154 -0.34505615 0.02491342 TRUE
note
1 Computed with esc::esc_mean_sd from raw group means/SDs.
2 Computed with esc::esc_mean_sd from raw group means/SDs.
3 Computed with esc::esc_mean_sd from raw group means/SDs.
4 Computed with esc::esc_mean_sd from raw group means/SDs.
5 Computed with esc::esc_mean_sd from raw group means/SDs.
6 Computed with esc::esc_mean_sd from raw group means/SDs.
7 Computed with esc::esc_mean_sd from raw group means/SDs.
8 Computed with esc::esc_mean_sd from raw group means/SDs.
9 Computed with esc::esc_mean_sd from raw group means/SDs.
10 Computed with esc::esc_mean_sd from raw group means/SDs.
11 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
12 Computed with esc::esc_mean_sd from raw group means/SDs.
13 Computed with esc::esc_mean_sd from raw group means/SDs.
14 Computed with esc::esc_mean_sd from raw group means/SDs.
15 Computed with esc::esc_mean_sd from raw group means/SDs.
16 Computed with esc::esc_mean_sd from raw group means/SDs.
17 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
18 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
19 Computed with esc::esc_mean_sd from raw group means/SDs.
20 Computed with esc::esc_mean_sd from raw group means/SDs.
21 Computed with esc::esc_mean_sd from raw group means/SDs.
22 Computed with esc::esc_mean_sd from raw group means/SDs.
23 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
24 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
25 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
26 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
27 Computed with esc::esc_mean_sd from raw group means/SDs.
28 Computed with esc::esc_mean_sd from raw group means/SDs.
29 Computed with esc::esc_mean_sd from raw group means/SDs.
30 Computed with esc::esc_mean_sd from raw group means/SDs.Combined split effects
all_split <- bind_rows(fixed_results, seeking_results) %>%
arrange(study_id, effect_id)
all_split %>%
group_by(domain) %>%
summarise(k = n(), computable = sum(can_compute), .groups = "drop")# A tibble: 2 × 3
domain k computable
<chr> <int> <int>
1 evidence_fixed 30 29
2 evidence_seeking 30 23all_split paper_key study_id sample effect_id
1 porteretalndpuzzleaboutknowledge 1 China s1_e1
2 porteretalndpuzzleaboutknowledge 1 China s1_e2
3 porteretalndpuzzleaboutknowledge 1 China s1_e3
4 porteretalndpuzzleaboutknowledge 1 China s1_e4
5 porteretalndpuzzleaboutknowledge 2 Russia s2_e1
6 porteretalndpuzzleaboutknowledge 2 Russia s2_e2
7 porteretalndpuzzleaboutknowledge 2 Russia s2_e3
8 porteretalndpuzzleaboutknowledge 2 Russia s2_e4
9 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e1
10 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e2
11 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e3
12 porteretalndpuzzleaboutknowledge 3 Slovakia s3_e4
13 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e1
14 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e2
15 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e3
16 porteretalndpuzzleaboutknowledge 4 Ecuador - Spanish s4_e4
17 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e1
18 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e2
19 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e3
20 porteretalndpuzzleaboutknowledge 5 India - Hindi s5_e4
21 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e1
22 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e2
23 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e3
24 porteretalndpuzzleaboutknowledge 6 India - Meitei s6_e4
25 porteretalndpuzzleaboutknowledge 7 Japan s7_e1
26 porteretalndpuzzleaboutknowledge 7 Japan s7_e2
27 porteretalndpuzzleaboutknowledge 7 Japan s7_e3
28 porteretalndpuzzleaboutknowledge 7 Japan s7_e4
29 porteretalndpuzzleaboutknowledge 8 Morocco s8_e1
30 porteretalndpuzzleaboutknowledge 8 Morocco s8_e2
31 porteretalndpuzzleaboutknowledge 8 Morocco s8_e3
32 porteretalndpuzzleaboutknowledge 8 Morocco s8_e4
33 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e1
34 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e2
35 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e3
36 porteretalndpuzzleaboutknowledge 9 Peru - Shipibo s9_e4
37 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e1
38 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e2
39 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e3
40 porteretalndpuzzleaboutknowledge 10 Peru - Spanish s10_e4
41 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e1
42 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e2
43 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e3
44 porteretalndpuzzleaboutknowledge 11 South Africa - Afrikaans s11_e4
45 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e1
46 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e2
47 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e3
48 porteretalndpuzzleaboutknowledge 12 South Africa - Sepedi s12_e4
49 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e1
50 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e2
51 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e3
52 porteretalndpuzzleaboutknowledge 13 South Africa - isiZulu s13_e4
53 porteretalndpuzzleaboutknowledge 14 South Korea s14_e1
54 porteretalndpuzzleaboutknowledge 14 South Korea s14_e2
55 porteretalndpuzzleaboutknowledge 14 South Korea s14_e3
56 porteretalndpuzzleaboutknowledge 14 South Korea s14_e4
57 porteretalndpuzzleaboutknowledge 15 United States s15_e1
58 porteretalndpuzzleaboutknowledge 15 United States s15_e2
59 porteretalndpuzzleaboutknowledge 15 United States s15_e3
60 porteretalndpuzzleaboutknowledge 15 United States s15_e4
domain evidence_strength n_low n_high mean_low mean_high
1 evidence_fixed weak 38 88 0.2368421 0.1022727
2 evidence_fixed strong 44 91 0.1590909 0.2527473
3 evidence_seeking weak 31 57 3.4838710 4.7894737
4 evidence_seeking strong 36 58 4.6111111 4.7241379
5 evidence_fixed weak 104 177 0.3076923 0.2937853
6 evidence_fixed strong 118 159 0.4067797 0.3081761
7 evidence_seeking weak 103 167 2.1747573 8.1856287
8 evidence_seeking strong 115 145 4.4695652 10.5655172
9 evidence_fixed weak 43 98 0.4418605 0.1938776
10 evidence_fixed strong 48 101 0.5416667 0.3168317
11 evidence_seeking weak 35 85 3.5714286 11.5411765
12 evidence_seeking strong 41 82 2.4634146 11.7317073
13 evidence_fixed weak 38 62 0.2105263 0.3387097
14 evidence_fixed strong 42 66 0.4285714 0.3484848
15 evidence_seeking weak 38 53 2.7105263 6.2075472
16 evidence_seeking strong 36 45 3.0555556 9.9777778
17 evidence_fixed weak 10 40 0.4000000 0.4000000
18 evidence_fixed strong 12 38 0.5833333 0.4473684
19 evidence_seeking weak 10 34 2.6000000 4.9705882
20 evidence_seeking strong 12 28 3.0833333 7.2857143
21 evidence_fixed weak 0 20 NA 0.8500000
22 evidence_fixed strong 2 20 0.5000000 0.7000000
23 evidence_seeking weak 0 18 NA 7.6111111
24 evidence_seeking strong 2 17 5.0000000 7.7058824
25 evidence_fixed weak 96 132 0.1875000 0.1212121
26 evidence_fixed strong 85 132 0.3176471 0.1893939
27 evidence_seeking weak 88 110 4.2954545 9.4909091
28 evidence_seeking strong 71 111 8.1690141 17.5585586
29 evidence_fixed weak 42 117 0.5952381 0.3504274
30 evidence_fixed strong 49 115 0.2857143 0.4173913
31 evidence_seeking weak 41 108 1.5853659 3.5000000
32 evidence_seeking strong 47 105 5.1702128 7.9619048
33 evidence_fixed weak 8 43 0.5000000 0.8372093
34 evidence_fixed strong 5 46 0.0000000 0.8043478
35 evidence_seeking weak 0 1 NA 5.0000000
36 evidence_seeking strong 0 2 NA 4.5000000
37 evidence_fixed weak 41 68 0.3170732 0.2500000
38 evidence_fixed strong 39 68 0.2307692 0.2205882
39 evidence_seeking weak 27 35 2.2222222 3.7714286
40 evidence_seeking strong 23 41 7.0869565 6.6829268
41 evidence_fixed weak 25 61 0.6000000 0.4918033
42 evidence_fixed strong 20 72 0.5500000 0.5833333
43 evidence_seeking weak 24 48 1.7500000 2.5625000
44 evidence_seeking strong 15 45 10.2000000 5.3111111
45 evidence_fixed weak 9 56 0.6666667 0.7142857
46 evidence_fixed strong 4 53 0.2500000 0.6037736
47 evidence_seeking weak 1 10 3.0000000 4.1000000
48 evidence_seeking strong 1 10 2.0000000 6.3000000
49 evidence_fixed weak 4 57 0.7500000 0.6666667
50 evidence_fixed strong 4 65 0.5000000 0.6769231
51 evidence_seeking weak 0 7 NA 8.7142857
52 evidence_seeking strong 1 4 5.0000000 5.5000000
53 evidence_fixed weak 32 65 0.2187500 0.2923077
54 evidence_fixed strong 38 58 0.1842105 0.3275862
55 evidence_seeking weak 32 65 7.2187500 12.9230769
56 evidence_seeking strong 38 58 6.8947368 19.2241379
57 evidence_fixed weak 76 97 0.3947368 0.2783505
58 evidence_fixed strong 81 94 0.4074074 0.3936170
59 evidence_seeking weak 72 91 2.6527778 8.6263736
60 evidence_seeking strong 80 83 4.7750000 7.5903614
sd_low sd_high low_yes low_no high_yes high_no cc_applied d
1 0.4308515 0.3047431 9 29 9 79 FALSE 0.55251537
2 0.3699894 0.4369950 7 37 23 68 FALSE -0.32031893
3 2.4613136 3.1494718 NA NA NA NA NA -0.44592622
4 4.7285019 5.0115414 NA NA NA NA NA -0.02303948
5 0.4637735 0.4567870 32 72 52 125 FALSE 0.03646478
6 0.4933279 0.4631986 48 70 49 110 FALSE 0.23782445
7 2.6324788 20.1014364 NA NA NA NA NA -0.37796130
8 9.8491444 22.6868212 NA NA NA NA NA -0.33550360
9 0.5024855 0.3973667 19 24 19 79 FALSE 0.65684995
10 0.5035336 0.4675616 26 22 32 69 FALSE 0.51572666
11 11.0700596 22.8663576 NA NA NA NA NA -0.39479231
12 2.4809027 25.3796111 NA NA NA NA NA -0.44528976
13 0.4131550 0.4771345 8 30 21 41 FALSE -0.35985579
14 0.5008703 0.4801418 18 24 23 43 FALSE 0.18636230
15 2.3122225 13.4455576 NA NA NA NA NA -0.33673697
16 2.5628605 19.8705914 NA NA NA NA NA -0.46373234
17 0.5163978 0.4961389 4 6 16 24 FALSE 0.00000000
18 0.5149287 0.5038966 7 5 17 21 FALSE 0.30200768
19 2.9888682 2.7796369 NA NA NA NA NA -0.83891563
20 3.5021638 3.2530002 NA NA NA NA NA -1.26309671
21 NA 0.3663475 0 0 17 3 FALSE NA
22 0.7071068 0.4701623 1 1 14 6 FALSE -0.46713979
23 NA 3.1462188 NA NA NA NA NA NA
24 0.0000000 3.7875570 NA NA NA NA NA -0.73640071
25 0.3923613 0.3276170 18 78 16 116 FALSE 0.28374936
26 0.4683244 0.3933139 27 58 25 107 FALSE 0.38005685
27 12.1675648 20.3581444 NA NA NA NA NA -0.30187611
28 19.6301415 29.6091769 NA NA NA NA NA -0.35859418
29 0.4967958 0.4791559 25 17 41 76 FALSE 0.55288571
30 0.4564355 0.4952867 14 35 48 67 FALSE -0.32131400
31 1.9745330 2.7598591 NA NA NA NA NA -0.74498197
32 4.4100633 13.3113051 NA NA NA NA NA -0.24596971
33 0.5345225 0.3735437 4 4 36 7 FALSE -0.90286104
34 0.0000000 0.4010855 0 5 37 9 TRUE -2.07903061
35 NA NA NA NA NA NA NA NA
36 NA 0.7071068 NA NA NA NA NA NA
37 0.4711170 0.4362322 13 28 17 51 FALSE 0.18268676
38 0.4268328 0.4177262 9 30 15 53 FALSE 0.03212533
39 1.0860420 2.0012601 NA NA NA NA NA -0.92904614
40 20.2931091 15.2846966 NA NA NA NA NA 0.02345010
41 0.5000000 0.5040817 15 10 30 31 FALSE 0.24162261
42 0.5104178 0.4964664 11 9 42 30 FALSE -0.07487131
43 0.7939992 1.5561306 NA NA NA NA NA -0.60011973
44 25.0718966 3.2947770 NA NA NA NA NA 0.38654133
45 0.5000000 0.4558423 6 3 40 16 FALSE -0.12302549
46 0.5000000 0.4937931 1 3 32 21 FALSE -0.83792385
47 NA 2.2827858 NA NA NA NA NA NA
48 NA 3.4334951 NA NA NA NA NA NA
49 0.5000000 0.4755949 3 1 38 19 FALSE 0.22354463
50 0.5773503 0.4712912 2 2 44 21 FALSE -0.40779990
51 NA 11.1162686 NA NA NA NA NA NA
52 NA 1.9148542 NA NA NA NA NA NA
53 0.4200134 0.4583625 7 25 19 46 FALSE -0.21433642
54 0.3928595 0.4734321 7 31 19 39 FALSE -0.42394677
55 18.9630105 25.8658243 NA NA NA NA NA -0.23933475
56 9.9588056 31.5077507 NA NA NA NA NA -0.48697534
57 0.4920419 0.4505152 30 46 27 70 FALSE 0.28956585
58 0.4944132 0.4911712 33 48 37 57 FALSE 0.03166828
59 3.9722975 20.1993665 NA NA NA NA NA -0.38964036
60 3.0769663 11.0266154 NA NA NA NA NA -0.34505615
v can_compute
1 0.08187660 TRUE
2 0.06932445 TRUE
3 0.05093175 TRUE
4 0.04502198 TRUE
5 0.02199774 TRUE
6 0.01964155 TRUE
7 0.01596131 TRUE
8 0.01580867 TRUE
9 0.04850895 TRUE
10 0.03941156 TRUE
11 0.04098556 TRUE
12 0.03739139 TRUE
13 0.07001576 TRUE
14 0.04983674 TRUE
15 0.04580675 TRUE
16 0.05132745 TRUE
17 0.15831435 TRUE
18 0.13657074 TRUE
19 0.13740926 TRUE
20 0.13899029 TRUE
21 NA FALSE
22 0.68029938 TRUE
23 NA FALSE
24 0.57309421 TRUE
25 0.04240193 TRUE
26 0.03149798 TRUE
27 0.02068467 TRUE
28 0.02344678 TRUE
29 0.04145202 TRUE
30 0.04126570 TRUE
31 0.03551191 TRUE
32 0.03099942 TRUE
33 0.20384857 TRUE
34 0.70329506 TRUE
35 NA FALSE
36 NA FALSE
37 0.05807793 TRUE
38 0.06990524 TRUE
39 0.07256917 TRUE
40 0.06787280 TRUE
41 0.07059799 TRUE
42 0.07877612 TRUE
43 0.06500100 TRUE
44 0.09013401 TRUE
45 0.17857859 TRUE
46 0.42925805 TRUE
47 NA FALSE
48 NA FALSE
49 0.42928186 TRUE
50 0.32534627 TRUE
51 NA FALSE
52 NA FALSE
53 0.07818789 TRUE
54 0.07702066 TRUE
55 0.04692988 TRUE
56 0.04479230 TRUE
57 0.03234027 TRUE
58 0.02909152 TRUE
59 0.02534360 TRUE
60 0.02491342 TRUE
note
1 Computed with esc::esc_2x2 from exact 2x2 counts.
2 Computed with esc::esc_2x2 from exact 2x2 counts.
3 Computed with esc::esc_mean_sd from raw group means/SDs.
4 Computed with esc::esc_mean_sd from raw group means/SDs.
5 Computed with esc::esc_2x2 from exact 2x2 counts.
6 Computed with esc::esc_2x2 from exact 2x2 counts.
7 Computed with esc::esc_mean_sd from raw group means/SDs.
8 Computed with esc::esc_mean_sd from raw group means/SDs.
9 Computed with esc::esc_2x2 from exact 2x2 counts.
10 Computed with esc::esc_2x2 from exact 2x2 counts.
11 Computed with esc::esc_mean_sd from raw group means/SDs.
12 Computed with esc::esc_mean_sd from raw group means/SDs.
13 Computed with esc::esc_2x2 from exact 2x2 counts.
14 Computed with esc::esc_2x2 from exact 2x2 counts.
15 Computed with esc::esc_mean_sd from raw group means/SDs.
16 Computed with esc::esc_mean_sd from raw group means/SDs.
17 Computed with esc::esc_2x2 from exact 2x2 counts.
18 Computed with esc::esc_2x2 from exact 2x2 counts.
19 Computed with esc::esc_mean_sd from raw group means/SDs.
20 Computed with esc::esc_mean_sd from raw group means/SDs.
21 Missing one stakes group in this evidence stratum.
22 Computed with esc::esc_2x2 from exact 2x2 counts.
23 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
24 Computed with esc::esc_mean_sd from raw group means/SDs.
25 Computed with esc::esc_2x2 from exact 2x2 counts.
26 Computed with esc::esc_2x2 from exact 2x2 counts.
27 Computed with esc::esc_mean_sd from raw group means/SDs.
28 Computed with esc::esc_mean_sd from raw group means/SDs.
29 Computed with esc::esc_2x2 from exact 2x2 counts.
30 Computed with esc::esc_2x2 from exact 2x2 counts.
31 Computed with esc::esc_mean_sd from raw group means/SDs.
32 Computed with esc::esc_mean_sd from raw group means/SDs.
33 Computed with esc::esc_2x2 from exact 2x2 counts.
34 Computed with esc::esc_2x2 with 0.5 continuity correction.
35 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
36 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
37 Computed with esc::esc_2x2 from exact 2x2 counts.
38 Computed with esc::esc_2x2 from exact 2x2 counts.
39 Computed with esc::esc_mean_sd from raw group means/SDs.
40 Computed with esc::esc_mean_sd from raw group means/SDs.
41 Computed with esc::esc_2x2 from exact 2x2 counts.
42 Computed with esc::esc_2x2 from exact 2x2 counts.
43 Computed with esc::esc_mean_sd from raw group means/SDs.
44 Computed with esc::esc_mean_sd from raw group means/SDs.
45 Computed with esc::esc_2x2 from exact 2x2 counts.
46 Computed with esc::esc_2x2 from exact 2x2 counts.
47 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
48 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
49 Computed with esc::esc_2x2 from exact 2x2 counts.
50 Computed with esc::esc_2x2 from exact 2x2 counts.
51 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
52 Insufficient per-group data for esc_mean_sd (need n>=2 and finite SD in both stakes groups).
53 Computed with esc::esc_2x2 from exact 2x2 counts.
54 Computed with esc::esc_2x2 from exact 2x2 counts.
55 Computed with esc::esc_mean_sd from raw group means/SDs.
56 Computed with esc::esc_mean_sd from raw group means/SDs.
57 Computed with esc::esc_2x2 from exact 2x2 counts.
58 Computed with esc::esc_2x2 from exact 2x2 counts.
59 Computed with esc::esc_mean_sd from raw group means/SDs.
60 Computed with esc::esc_mean_sd from raw group means/SDs.Save machine-readable split results
out_csv <- "../scratch/split_effects_from_raw.csv"
write.csv(all_split, out_csv, row.names = FALSE)
out_csv[1] "../scratch/split_effects_from_raw.csv"YAML copy/paste lines (effect_size only)
ok <- all_split %>% filter(can_compute)
for (i in seq_len(nrow(ok))) {
cat(sprintf(
"%s (%s; %s): d=%.12f v=%.12f\n",
ok$effect_id[i],
ok$sample[i],
ok$evidence_strength[i],
ok$d[i],
ok$v[i]
))
}s1_e1 (China; weak): d=0.552515367599 v=0.081876597403
s1_e2 (China; strong): d=-0.320318931519 v=0.069324454306
s1_e3 (China; weak): d=-0.445926217288 v=0.050931754797
s1_e4 (China; strong): d=-0.023039481999 v=0.045021980587
s2_e1 (Russia; weak): d=0.036464784258 v=0.021997738261
s2_e2 (Russia; strong): d=0.237824450182 v=0.019641553202
s2_e3 (Russia; weak): d=-0.377961295062 v=0.015961307632
s2_e4 (Russia; strong): d=-0.335503596950 v=0.015808670559
s3_e1 (Slovakia; weak): d=0.656849950227 v=0.048508951096
s3_e2 (Slovakia; strong): d=0.515726659137 v=0.039411560668
s3_e3 (Slovakia; weak): d=-0.394792312364 v=0.040985555162
s3_e4 (Slovakia; strong): d=-0.445289756137 v=0.037391394174
s4_e1 (Ecuador - Spanish; weak): d=-0.359855794346 v=0.070015762181
s4_e2 (Ecuador - Spanish; strong): d=0.186362303339 v=0.049836738247
s4_e3 (Ecuador - Spanish; weak): d=-0.336736969824 v=0.045806745798
s4_e4 (Ecuador - Spanish; strong): d=-0.463732339102 v=0.051327454829
s5_e1 (India - Hindi; weak): d=0.000000000000 v=0.158314349441
s5_e2 (India - Hindi; strong): d=0.302007675761 v=0.136570738288
s5_e3 (India - Hindi; weak): d=-0.838915625664 v=0.137409258194
s5_e4 (India - Hindi; strong): d=-1.263096710093 v=0.138990285286
s6_e2 (India - Meitei; strong): d=-0.467139793461 v=0.680299375884
s6_e4 (India - Meitei; strong): d=-0.736400711010 v=0.573094213811
s7_e1 (Japan; weak): d=0.283749355205 v=0.042401929914
s7_e2 (Japan; strong): d=0.380056845671 v=0.031497983059
s7_e3 (Japan; weak): d=-0.301876109494 v=0.020684669660
s7_e4 (Japan; strong): d=-0.358594182612 v=0.023446784699
s8_e1 (Morocco; weak): d=0.552885712813 v=0.041452016467
s8_e2 (Morocco; strong): d=-0.321313996952 v=0.041265698487
s8_e3 (Morocco; weak): d=-0.744981969167 v=0.035511913009
s8_e4 (Morocco; strong): d=-0.245969711198 v=0.030999422041
s9_e1 (Peru - Shipibo; weak): d=-0.902861044993 v=0.203848571852
s9_e2 (Peru - Shipibo; strong): d=-2.079030614701 v=0.703295059969
s10_e1 (Peru - Spanish; weak): d=0.182686763756 v=0.058077931218
s10_e2 (Peru - Spanish; strong): d=0.032125332753 v=0.069905244312
s10_e3 (Peru - Spanish; weak): d=-0.929046137166 v=0.072569165003
s10_e4 (Peru - Spanish; strong): d=0.023450100457 v=0.067872800922
s11_e1 (South Africa - Afrikaans; weak): d=0.241622606983 v=0.070597986022
s11_e2 (South Africa - Afrikaans; strong): d=-0.074871313684 v=0.078776123732
s11_e3 (South Africa - Afrikaans; weak): d=-0.600119731518 v=0.065000997862
s11_e4 (South Africa - Afrikaans; strong): d=0.386541332590 v=0.090134007237
s12_e1 (South Africa - Sepedi; weak): d=-0.123025487667 v=0.178578586170
s12_e2 (South Africa - Sepedi; strong): d=-0.837923854045 v=0.429258050342
s13_e1 (South Africa - isiZulu; weak): d=0.223544630185 v=0.429281857011
s13_e2 (South Africa - isiZulu; strong): d=-0.407799898258 v=0.325346268254
s14_e1 (South Korea; weak): d=-0.214336417884 v=0.078187891364
s14_e2 (South Korea; strong): d=-0.423946773623 v=0.077020659085
s14_e3 (South Korea; weak): d=-0.239334748337 v=0.046929878899
s14_e4 (South Korea; strong): d=-0.486975335220 v=0.044792298873
s15_e1 (United States; weak): d=0.289565853630 v=0.032340267381
s15_e2 (United States; strong): d=0.031668279790 v=0.029091515654
s15_e3 (United States; weak): d=-0.389640360498 v=0.025343604205
s15_e4 (United States; strong): d=-0.345056147415 v=0.024913418982