stop_if_missing <- function(x, name) {
if (is.na(x)) stop(sprintf("Missing required input: %s", name), call. = FALSE)
}
# Exact small-sample correction factor used by metafor (.cmicalc).
hedges_correction <- function(df) {
ifelse(df <= 1, NA_real_, exp(lgamma(df / 2) - log(sqrt(df / 2)) - lgamma((df - 1) / 2)))
}
pooled_sd <- function(n_high, n_low, sd_high, sd_low) {
sqrt(((n_high - 1) * sd_high^2 + (n_low - 1) * sd_low^2) / (n_high + n_low - 2))
}
d_from_groups_independent <- function(n_high, n_low, mean_high, mean_low, sd_high, sd_low) {
s <- pooled_sd(n_high, n_low, sd_high, sd_low)
(mean_low - mean_high) / s
}
var_d_independent <- function(d, n_high, n_low) {
n <- n_high + n_low
(n / (n_high * n_low)) + (d^2 / (2 * (n - 2)))
}
# Project metric for within-subject effects: standardize by pooled SD across occasions.
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)
}
# Recover within-person correlation r from paired t + means/SDs.
r_from_paired_t <- function(mean_low, mean_high, sd_low, sd_high, t_value, n_total) {
mean_diff <- mean_low - mean_high
sd_diff <- abs(mean_diff) * sqrt(n_total) / abs(t_value)
(sd_low^2 + sd_high^2 - sd_diff^2) / (2 * 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)
}
# The paper's reported paired-samples d values match the dz convention t / sqrt(n),
# reported as unsigned magnitudes.
paper_dz_signed <- function(mean_low, mean_high, t_value, n_total) {
sign(mean_low - mean_high) * abs(t_value) / sqrt(n_total)
}
compute_effect_size_between_groups <- function(
paper_key,
study_id,
effect_id,
n_low,
n_high,
mean_low,
mean_high,
sd_low,
sd_high,
sign_convention = "d = mean(low) - mean(high)",
notes_on_assumptions = ""
) {
stop_if_missing(n_low, "n_low")
stop_if_missing(n_high, "n_high")
stop_if_missing(mean_low, "mean_low")
stop_if_missing(mean_high, "mean_high")
stop_if_missing(sd_low, "sd_low")
stop_if_missing(sd_high, "sd_high")
d <- d_from_groups_independent(
n_high = n_high,
n_low = n_low,
mean_high = mean_high,
mean_low = mean_low,
sd_high = sd_high,
sd_low = sd_low
)
v <- var_d_independent(d = d, n_high = n_high, n_low = n_low)
df_used <- n_high + n_low - 2
J <- hedges_correction(df_used)
g <- J * d
v_g <- (J^2) * v
inputs_used <- sprintf(
"method=between_groups, sign_convention=%s, n_low=%s, n_high=%s, mean_low=%s, mean_high=%s, sd_low=%s, sd_high=%s",
sign_convention, n_low, n_high, mean_low, mean_high, sd_low, sd_high
)
audit <- data.frame(
analysis_variant = "first_block_primary",
paper_key = paper_key,
study_id = study_id,
effect_id = effect_id,
design = "Between-Subjects",
method_used = "between_groups",
computed_from_suggested = "groups",
inputs_used = inputs_used,
d = d,
v = v,
g = g,
v_g = v_g,
notes_on_assumptions = notes_on_assumptions,
stringsAsFactors = FALSE
)
yaml_snippet <- sprintf(
"effect_size:\\n metric: SMD\\n d: %.12f\\n v: %.12f\\n computed_from: groups\\n needs_review: false\\n notes: \"%s\"\\n",
d, v, gsub(pattern = "\"", replacement = "'", x = inputs_used)
)
list(audit = audit, yaml_snippet = yaml_snippet)
}
compute_effect_size_within <- function(
paper_key,
study_id,
effect_id,
n_total,
df,
mean_low,
mean_high,
sd_low,
sd_high,
t_value,
reported_d_paper,
sign_convention = "d = mean(low) - mean(high)",
notes_on_assumptions = ""
) {
stop_if_missing(n_total, "n_total")
stop_if_missing(df, "df")
stop_if_missing(mean_low, "mean_low")
stop_if_missing(mean_high, "mean_high")
stop_if_missing(sd_low, "sd_low")
stop_if_missing(sd_high, "sd_high")
stop_if_missing(t_value, "t_value")
if (df != (n_total - 1)) {
stop(sprintf("For paired t tests, expected df = n_total - 1 but got df=%s and n_total=%s.", df, n_total), call. = FALSE)
}
r_est <- r_from_paired_t(
mean_low = mean_low,
mean_high = mean_high,
sd_low = sd_low,
sd_high = sd_high,
t_value = t_value,
n_total = n_total
)
if (abs(r_est) > 1) {
stop(sprintf("Recovered r=%.4f outside [-1,1]; check inputs.", r_est), call. = FALSE)
}
d <- d_within_smcrp(
mean_low = mean_low,
mean_high = mean_high,
sd_low = sd_low,
sd_high = sd_high
)
v <- var_d_within_smcrp(d = d, r = r_est, n_total = n_total)
df_used <- 2 * (n_total - 1) / (1 + r_est^2)
J <- hedges_correction(df_used)
g <- J * d
v_g <- (2 * (1 - r_est) / n_total) + (g^2) * (1 + r_est^2) / (4 * n_total)
paper_d_signed <- paper_dz_signed(
mean_low = mean_low,
mean_high = mean_high,
t_value = t_value,
n_total = n_total
)
inputs_used <- sprintf(
"method=within_smcrp_t, sign_convention=%s, n_total=%s, df=%s, mean_low=%s, mean_high=%s, sd_low=%s, sd_high=%s, t=%s",
sign_convention, n_total, df, mean_low, mean_high, sd_low, sd_high, t_value
)
audit <- data.frame(
analysis_variant = "all_responses_within_subject",
paper_key = paper_key,
study_id = study_id,
effect_id = effect_id,
design = "Within-Subjects",
method_used = "within_smcrp_t",
computed_from_suggested = "groups",
inputs_used = inputs_used,
r_est = r_est,
d = d,
v = v,
g = g,
v_g = v_g,
paper_d_signed = paper_d_signed,
paper_d_reported = reported_d_paper,
paper_d_abs_diff = abs(abs(paper_d_signed) - reported_d_paper),
notes_on_assumptions = notes_on_assumptions,
stringsAsFactors = FALSE
)
list(audit = audit)
}Effect size computation: grindrodetalndthirdpersonknowledge
Computes standardized mean differences (d) and sampling variances (v) for the extraction YAML papers/grindrodetalndthirdpersonknowledge/grindrodetalndthirdpersonknowledge.yaml. For negative-polarity knowledge-denial targets, raw truth-rating contrasts are also reported, but the YAML stores reverse-coded d_for_yaml values so that positive effects consistently indicate stronger low-context knowledge attribution.
Data sources
- Experiment 1 primary first-block results:
papers/grindrodetalndthirdpersonknowledge/out/tables/camelot_stream_p15_t3.csv(Table 7). - Experiment 2 primary first-block results:
papers/grindrodetalndthirdpersonknowledge/out/tables/camelot_stream_p19_t4.csv(Table 8,When presented first). - Experiment 1 supplemental all-response results:
papers/grindrodetalndthirdpersonknowledge/out/tables/camelot_stream_p12_t2.csv(Table 6). - Experiment 2 supplemental all-response results:
papers/grindrodetalndthirdpersonknowledge/out/tables/camelot_stream_p19_t4.csv(Table 8, Overall). - Within-subject paired
ttests:papers/grindrodetalndthirdpersonknowledge/out/fulltext.md.
Primary YAML computations: first-block between-subject estimates
paper_key <- "grindrodetalndthirdpersonknowledge"
negative_polarity_effects <- c("s1_e2", "s1_e4", "s1_e6", "s1_e8", "s2_e2", "s2_e4")
add_yaml_direction <- function(audit) {
audit$d_raw <- audit$d
audit$reverse_negative_polarity <- audit$effect_id %in% negative_polarity_effects
audit$d_for_yaml <- ifelse(audit$reverse_negative_polarity, -audit$d_raw, audit$d_raw)
audit
}
effects_first_block <- data.frame(
study_id = c(1, 1, 1, 1, 2, 2),
effect_id = c("s1_e1", "s1_e2", "s1_e3", "s1_e4", "s2_e1", "s2_e2"),
n_low = c(105, 121, 121, 105, 107, 102),
mean_low = c(71.90, 48.36, 72.67, 43.27, 49.77, 52.91),
sd_low = c(33.69, 41.17, 34.15, 40.94, 42.48, 41.29),
n_high = c(107, 98, 98, 107, 96, 97),
mean_high = c(36.83, 78.73, 63.23, 54.86, 41.66, 75.07),
sd_high = c(38.49, 31.29, 35.56, 36.82, 40.22, 31.30),
notes_on_assumptions = c(
"Experiment 1, first-block between-subjects emulation: Knowledge-Neighbor, positive sentence (Table 7).",
"Experiment 1, first-block between-subjects emulation: Knowledge-Neighbor, negative sentence (Table 7).",
"Experiment 1, first-block between-subjects emulation: Knowledge-Sunshine, positive sentence (Table 7).",
"Experiment 1, first-block between-subjects emulation: Knowledge-Sunshine, negative sentence (Table 7).",
"Experiment 2, between-subjects emulation: Revised Neighbor, positive sentence (Table 8, 'When presented first').",
"Experiment 2, between-subjects emulation: Revised Neighbor, negative sentence (Table 8, 'When presented first')."
),
stringsAsFactors = FALSE
)
effects_first_block study_id effect_id n_low mean_low sd_low n_high mean_high sd_high
1 1 s1_e1 105 71.90 33.69 107 36.83 38.49
2 1 s1_e2 121 48.36 41.17 98 78.73 31.29
3 1 s1_e3 121 72.67 34.15 98 63.23 35.56
4 1 s1_e4 105 43.27 40.94 107 54.86 36.82
5 2 s2_e1 107 49.77 42.48 96 41.66 40.22
6 2 s2_e2 102 52.91 41.29 97 75.07 31.30
notes_on_assumptions
1 Experiment 1, first-block between-subjects emulation: Knowledge-Neighbor, positive sentence (Table 7).
2 Experiment 1, first-block between-subjects emulation: Knowledge-Neighbor, negative sentence (Table 7).
3 Experiment 1, first-block between-subjects emulation: Knowledge-Sunshine, positive sentence (Table 7).
4 Experiment 1, first-block between-subjects emulation: Knowledge-Sunshine, negative sentence (Table 7).
5 Experiment 2, between-subjects emulation: Revised Neighbor, positive sentence (Table 8, 'When presented first').
6 Experiment 2, between-subjects emulation: Revised Neighbor, negative sentence (Table 8, 'When presented first').results_first_block <- lapply(seq_len(nrow(effects_first_block)), function(i) {
row <- effects_first_block[i, ]
compute_effect_size_between_groups(
paper_key = paper_key,
study_id = row$study_id,
effect_id = row$effect_id,
n_low = row$n_low,
n_high = row$n_high,
mean_low = row$mean_low,
mean_high = row$mean_high,
sd_low = row$sd_low,
sd_high = row$sd_high,
notes_on_assumptions = row$notes_on_assumptions
)
})
audit_first_block <- do.call(rbind, lapply(results_first_block, `[[`, "audit"))
audit_first_block <- add_yaml_direction(audit_first_block)
audit_first_block analysis_variant paper_key study_id effect_id
1 first_block_primary grindrodetalndthirdpersonknowledge 1 s1_e1
2 first_block_primary grindrodetalndthirdpersonknowledge 1 s1_e2
3 first_block_primary grindrodetalndthirdpersonknowledge 1 s1_e3
4 first_block_primary grindrodetalndthirdpersonknowledge 1 s1_e4
5 first_block_primary grindrodetalndthirdpersonknowledge 2 s2_e1
6 first_block_primary grindrodetalndthirdpersonknowledge 2 s2_e2
design method_used computed_from_suggested
1 Between-Subjects between_groups groups
2 Between-Subjects between_groups groups
3 Between-Subjects between_groups groups
4 Between-Subjects between_groups groups
5 Between-Subjects between_groups groups
6 Between-Subjects between_groups groups
inputs_used
1 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=105, n_high=107, mean_low=71.9, mean_high=36.83, sd_low=33.69, sd_high=38.49
2 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=121, n_high=98, mean_low=48.36, mean_high=78.73, sd_low=41.17, sd_high=31.29
3 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=121, n_high=98, mean_low=72.67, mean_high=63.23, sd_low=34.15, sd_high=35.56
4 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=105, n_high=107, mean_low=43.27, mean_high=54.86, sd_low=40.94, sd_high=36.82
5 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=107, n_high=96, mean_low=49.77, mean_high=41.66, sd_low=42.48, sd_high=40.22
6 method=between_groups, sign_convention=d = mean(low) - mean(high), n_low=102, n_high=97, mean_low=52.91, mean_high=75.07, sd_low=41.29, sd_high=31.3
d v g v_g
1 0.9689850 0.02110516 0.9655195 0.02095447
2 -0.8190311 0.02001419 -0.8161965 0.01987590
3 0.2713631 0.01863822 0.2704239 0.01850943
4 -0.2978291 0.01908080 -0.2967639 0.01894456
5 0.1957651 0.01985779 0.1950335 0.01970966
6 -0.6027885 0.02103542 -0.6004902 0.02087532
notes_on_assumptions
1 Experiment 1, first-block between-subjects emulation: Knowledge-Neighbor, positive sentence (Table 7).
2 Experiment 1, first-block between-subjects emulation: Knowledge-Neighbor, negative sentence (Table 7).
3 Experiment 1, first-block between-subjects emulation: Knowledge-Sunshine, positive sentence (Table 7).
4 Experiment 1, first-block between-subjects emulation: Knowledge-Sunshine, negative sentence (Table 7).
5 Experiment 2, between-subjects emulation: Revised Neighbor, positive sentence (Table 8, 'When presented first').
6 Experiment 2, between-subjects emulation: Revised Neighbor, negative sentence (Table 8, 'When presented first').
d_raw reverse_negative_polarity d_for_yaml
1 0.9689850 FALSE 0.9689850
2 -0.8190311 TRUE 0.8190311
3 0.2713631 FALSE 0.2713631
4 -0.2978291 TRUE 0.2978291
5 0.1957651 FALSE 0.1957651
6 -0.6027885 TRUE 0.6027885Supplemental computations: all-response within-subject estimates
effects_within <- data.frame(
study_id = c(1, 1, 1, 1, 2, 2),
effect_id = c("s1_e5", "s1_e6", "s1_e7", "s1_e8", "s2_e3", "s2_e4"),
n_total = c(431, 431, 431, 431, 402, 402),
df = c(430, 430, 430, 430, 401, 401),
mean_low = c(54.38, 61.75, 65.42, 47.61, 59.95, 58.03),
mean_high = c(37.45, 72.23, 54.37, 57.34, 50.88, 64.33),
sd_low = c(39.90, 38.97, 35.64, 39.98, 40.10, 40.33),
sd_high = c(38.22, 35.91, 37.31, 37.31, 41.71, 39.42),
t_value = c(9.63, 6.24, 5.69, 6.11, 5.73, 3.74),
reported_d_paper = c(0.46, 0.30, 0.27, 0.29, 0.29, 0.19),
notes_on_assumptions = c(
"Experiment 1, all responses within-subject: Knowledge-Neighbor, positive sentence (Table 6 + paired t test).",
"Experiment 1, all responses within-subject: Knowledge-Neighbor, negative sentence (Table 6 + paired t test).",
"Experiment 1, all responses within-subject: Knowledge-Sunshine, positive sentence (Table 6 + paired t test).",
"Experiment 1, all responses within-subject: Knowledge-Sunshine, negative sentence (Table 6 + paired t test).",
"Experiment 2, all responses within-subject: Revised Neighbor, positive sentence (Table 8 Overall + paired t test).",
"Experiment 2, all responses within-subject: Revised Neighbor, negative sentence (Table 8 Overall + paired t test)."
),
stringsAsFactors = FALSE
)
effects_within study_id effect_id n_total df mean_low mean_high sd_low sd_high t_value
1 1 s1_e5 431 430 54.38 37.45 39.90 38.22 9.63
2 1 s1_e6 431 430 61.75 72.23 38.97 35.91 6.24
3 1 s1_e7 431 430 65.42 54.37 35.64 37.31 5.69
4 1 s1_e8 431 430 47.61 57.34 39.98 37.31 6.11
5 2 s2_e3 402 401 59.95 50.88 40.10 41.71 5.73
6 2 s2_e4 402 401 58.03 64.33 40.33 39.42 3.74
reported_d_paper
1 0.46
2 0.30
3 0.27
4 0.29
5 0.29
6 0.19
notes_on_assumptions
1 Experiment 1, all responses within-subject: Knowledge-Neighbor, positive sentence (Table 6 + paired t test).
2 Experiment 1, all responses within-subject: Knowledge-Neighbor, negative sentence (Table 6 + paired t test).
3 Experiment 1, all responses within-subject: Knowledge-Sunshine, positive sentence (Table 6 + paired t test).
4 Experiment 1, all responses within-subject: Knowledge-Sunshine, negative sentence (Table 6 + paired t test).
5 Experiment 2, all responses within-subject: Revised Neighbor, positive sentence (Table 8 Overall + paired t test).
6 Experiment 2, all responses within-subject: Revised Neighbor, negative sentence (Table 8 Overall + paired t test).results_within <- lapply(seq_len(nrow(effects_within)), function(i) {
row <- effects_within[i, ]
compute_effect_size_within(
paper_key = paper_key,
study_id = row$study_id,
effect_id = row$effect_id,
n_total = row$n_total,
df = row$df,
mean_low = row$mean_low,
mean_high = row$mean_high,
sd_low = row$sd_low,
sd_high = row$sd_high,
t_value = row$t_value,
reported_d_paper = row$reported_d_paper,
notes_on_assumptions = row$notes_on_assumptions
)
})
audit_within <- do.call(rbind, lapply(results_within, `[[`, "audit"))
audit_within <- add_yaml_direction(audit_within)
audit_within analysis_variant paper_key study_id
1 all_responses_within_subject grindrodetalndthirdpersonknowledge 1
2 all_responses_within_subject grindrodetalndthirdpersonknowledge 1
3 all_responses_within_subject grindrodetalndthirdpersonknowledge 1
4 all_responses_within_subject grindrodetalndthirdpersonknowledge 1
5 all_responses_within_subject grindrodetalndthirdpersonknowledge 2
6 all_responses_within_subject grindrodetalndthirdpersonknowledge 2
effect_id design method_used computed_from_suggested
1 s1_e5 Within-Subjects within_smcrp_t groups
2 s1_e6 Within-Subjects within_smcrp_t groups
3 s1_e7 Within-Subjects within_smcrp_t groups
4 s1_e8 Within-Subjects within_smcrp_t groups
5 s2_e3 Within-Subjects within_smcrp_t groups
6 s2_e4 Within-Subjects within_smcrp_t groups
inputs_used
1 method=within_smcrp_t, sign_convention=d = mean(low) - mean(high), n_total=431, df=430, mean_low=54.38, mean_high=37.45, sd_low=39.9, sd_high=38.22, t=9.63
2 method=within_smcrp_t, sign_convention=d = mean(low) - mean(high), n_total=431, df=430, mean_low=61.75, mean_high=72.23, sd_low=38.97, sd_high=35.91, t=6.24
3 method=within_smcrp_t, sign_convention=d = mean(low) - mean(high), n_total=431, df=430, mean_low=65.42, mean_high=54.37, sd_low=35.64, sd_high=37.31, t=5.69
4 method=within_smcrp_t, sign_convention=d = mean(low) - mean(high), n_total=431, df=430, mean_low=47.61, mean_high=57.34, sd_low=39.98, sd_high=37.31, t=6.11
5 method=within_smcrp_t, sign_convention=d = mean(low) - mean(high), n_total=402, df=401, mean_low=59.95, mean_high=50.88, sd_low=40.1, sd_high=41.71, t=5.73
6 method=within_smcrp_t, sign_convention=d = mean(low) - mean(high), n_total=402, df=401, mean_low=58.03, mean_high=64.33, sd_low=40.33, sd_high=39.42, t=3.74
r_est d v g v_g paper_d_signed
1 0.5641632 0.4333355 0.002166033 0.4328371 0.002165703 0.4638608
2 0.5689805 -0.2796811 0.002060151 -0.2793581 0.002060013 -0.3005702
3 0.3898473 0.3028679 0.002892629 0.3025635 0.002892506 0.2740777
4 0.6360174 -0.2516289 0.001740598 -0.2513206 0.001740472 -0.2943083
5 0.6996707 0.2216904 0.001539702 0.2213814 0.001539575 0.2857864
6 0.6415128 -0.1579834 0.001805428 -0.1577748 0.001805370 -0.1865342
paper_d_reported paper_d_abs_diff
1 0.46 0.0038607780
2 0.30 0.0005702237
3 0.27 0.0040776559
4 0.29 0.0043083441
5 0.29 0.0042135752
6 0.19 0.0034657541
notes_on_assumptions
1 Experiment 1, all responses within-subject: Knowledge-Neighbor, positive sentence (Table 6 + paired t test).
2 Experiment 1, all responses within-subject: Knowledge-Neighbor, negative sentence (Table 6 + paired t test).
3 Experiment 1, all responses within-subject: Knowledge-Sunshine, positive sentence (Table 6 + paired t test).
4 Experiment 1, all responses within-subject: Knowledge-Sunshine, negative sentence (Table 6 + paired t test).
5 Experiment 2, all responses within-subject: Revised Neighbor, positive sentence (Table 8 Overall + paired t test).
6 Experiment 2, all responses within-subject: Revised Neighbor, negative sentence (Table 8 Overall + paired t test).
d_raw reverse_negative_polarity d_for_yaml
1 0.4333355 FALSE 0.4333355
2 -0.2796811 TRUE 0.2796811
3 0.3028679 FALSE 0.3028679
4 -0.2516289 TRUE 0.2516289
5 0.2216904 FALSE 0.2216904
6 -0.1579834 TRUE 0.1579834Comparison with paper-reported within-subject d
comparison <- audit_within[, c(
"effect_id",
"d_raw",
"d_for_yaml",
"reverse_negative_polarity",
"paper_d_signed",
"paper_d_reported",
"paper_d_abs_diff",
"r_est"
)]
comparison effect_id d_raw d_for_yaml reverse_negative_polarity paper_d_signed
1 s1_e5 0.4333355 0.4333355 FALSE 0.4638608
2 s1_e6 -0.2796811 0.2796811 TRUE -0.3005702
3 s1_e7 0.3028679 0.3028679 FALSE 0.2740777
4 s1_e8 -0.2516289 0.2516289 TRUE -0.2943083
5 s2_e3 0.2216904 0.2216904 FALSE 0.2857864
6 s2_e4 -0.1579834 0.1579834 TRUE -0.1865342
paper_d_reported paper_d_abs_diff r_est
1 0.46 0.0038607780 0.5641632
2 0.30 0.0005702237 0.5689805
3 0.27 0.0040776559 0.3898473
4 0.29 0.0043083441 0.6360174
5 0.29 0.0042135752 0.6996707
6 0.19 0.0034657541 0.6415128# Double-check that the paper's reported d values are recoverable from t/sqrt(n)
# up to normal reporting-rounding tolerance.
stopifnot(all(comparison$paper_d_abs_diff < 0.01))Paste-ready YAML snippets for the supplemental within-subject effects
for (row_idx in seq_len(nrow(audit_within))) {
row <- audit_within[row_idx, ]
direction_note <- if (isTRUE(row$reverse_negative_polarity)) {
sprintf(
" Raw truth-rating d was %.12f; YAML stores the reverse-coded value to align with knowledge-attribution direction.",
row$d_raw
)
} else {
""
}
cat(
sprintf(
"\n### %s\n\neffect_size:\n metric: SMD\n d: %.12f\n v: %.12f\n computed_from: groups\n needs_review: false\n notes: \"Computed in analysis/effect_sizes.qmd using within_smcrp_t (paired t used to recover within-person r);%s supplemental within-subject all-response effect retained alongside the primary first-block between-subject effect.\"\n",
row$effect_id,
row$d_for_yaml,
row$v,
direction_note
)
)
}
### s1_e5
effect_size:
metric: SMD
d: 0.433335546696
v: 0.002166032866
computed_from: groups
needs_review: false
notes: "Computed in analysis/effect_sizes.qmd using within_smcrp_t (paired t used to recover within-person r); supplemental within-subject all-response effect retained alongside the primary first-block between-subject effect."
### s1_e6
effect_size:
metric: SMD
d: 0.279681096460
v: 0.002060151215
computed_from: groups
needs_review: false
notes: "Computed in analysis/effect_sizes.qmd using within_smcrp_t (paired t used to recover within-person r); Raw truth-rating d was -0.279681096460; YAML stores the reverse-coded value to align with knowledge-attribution direction. supplemental within-subject all-response effect retained alongside the primary first-block between-subject effect."
### s1_e7
effect_size:
metric: SMD
d: 0.302867873808
v: 0.002892628747
computed_from: groups
needs_review: false
notes: "Computed in analysis/effect_sizes.qmd using within_smcrp_t (paired t used to recover within-person r); supplemental within-subject all-response effect retained alongside the primary first-block between-subject effect."
### s1_e8
effect_size:
metric: SMD
d: 0.251628915397
v: 0.001740597883
computed_from: groups
needs_review: false
notes: "Computed in analysis/effect_sizes.qmd using within_smcrp_t (paired t used to recover within-person r); Raw truth-rating d was -0.251628915397; YAML stores the reverse-coded value to align with knowledge-attribution direction. supplemental within-subject all-response effect retained alongside the primary first-block between-subject effect."
### s2_e3
effect_size:
metric: SMD
d: 0.221690359100
v: 0.001539701734
computed_from: groups
needs_review: false
notes: "Computed in analysis/effect_sizes.qmd using within_smcrp_t (paired t used to recover within-person r); supplemental within-subject all-response effect retained alongside the primary first-block between-subject effect."
### s2_e4
effect_size:
metric: SMD
d: 0.157983445773
v: 0.001805428012
computed_from: groups
needs_review: false
notes: "Computed in analysis/effect_sizes.qmd using within_smcrp_t (paired t used to recover within-person r); Raw truth-rating d was -0.157983445773; YAML stores the reverse-coded value to align with knowledge-attribution direction. supplemental within-subject all-response effect retained alongside the primary first-block between-subject effect."Paste-ready YAML snippets for the primary first-block effects
for (row_idx in seq_len(nrow(audit_first_block))) {
row <- audit_first_block[row_idx, ]
direction_note <- if (isTRUE(row$reverse_negative_polarity)) {
sprintf(
" Raw truth-rating d was %.12f; YAML stores the reverse-coded value to align with knowledge-attribution direction.",
row$d_raw
)
} else {
""
}
cat(
sprintf(
"\n### %s\n\neffect_size:\n metric: SMD\n d: %.12f\n v: %.12f\n computed_from: groups\n needs_review: false\n notes: \"Computed from Table 7/Table 8 first-block group stats in analysis/effect_sizes.qmd (method=between_groups).%s\"\n",
row$effect_id,
row$d_for_yaml,
row$v,
direction_note
)
)
}
### s1_e1
effect_size:
metric: SMD
d: 0.968984964361
v: 0.021105155967
computed_from: groups
needs_review: false
notes: "Computed from Table 7/Table 8 first-block group stats in analysis/effect_sizes.qmd (method=between_groups)."
### s1_e2
effect_size:
metric: SMD
d: 0.819031051268
v: 0.020014193896
computed_from: groups
needs_review: false
notes: "Computed from Table 7/Table 8 first-block group stats in analysis/effect_sizes.qmd (method=between_groups). Raw truth-rating d was -0.819031051268; YAML stores the reverse-coded value to align with knowledge-attribution direction."
### s1_e3
effect_size:
metric: SMD
d: 0.271363082188
v: 0.018638217075
computed_from: groups
needs_review: false
notes: "Computed from Table 7/Table 8 first-block group stats in analysis/effect_sizes.qmd (method=between_groups)."
### s1_e4
effect_size:
metric: SMD
d: 0.297829073010
v: 0.019080799528
computed_from: groups
needs_review: false
notes: "Computed from Table 7/Table 8 first-block group stats in analysis/effect_sizes.qmd (method=between_groups). Raw truth-rating d was -0.297829073010; YAML stores the reverse-coded value to align with knowledge-attribution direction."
### s2_e1
effect_size:
metric: SMD
d: 0.195765059327
v: 0.019857794289
computed_from: groups
needs_review: false
notes: "Computed from Table 7/Table 8 first-block group stats in analysis/effect_sizes.qmd (method=between_groups)."
### s2_e2
effect_size:
metric: SMD
d: 0.602788532859
v: 0.021035418232
computed_from: groups
needs_review: false
notes: "Computed from Table 7/Table 8 first-block group stats in analysis/effect_sizes.qmd (method=between_groups). Raw truth-rating d was -0.602788532859; YAML stores the reverse-coded value to align with knowledge-attribution direction."