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analysis_script.R
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# load libraries
library(brms)
library(tidyverse)
library(ggplot2)
library(bayesplot)
library(tidybayes)
library(patchwork)
library(broom)
library(dotwhisker)
library(skimr)
library(broom.mixed)
library(lavaan)
library(psych)
library(patchwork)
library(ggdist)
library(gt)
library(corrplot)
# load data
long_data <- read_csv(here::here('cleaned_numeric_data_long.csv'), col_types = cols(article_type = col_factor(),
Field = col_factor(),
Match = col_factor(),
Order = col_factor(),
keyword_batch_comp = col_factor())) %>%
mutate(question = case_when(article_type == 'RR' ~ str_sub(question, 3),
article_type == 'nonRR' ~ str_sub(question, 4))) %>%
mutate(article_type = fct_relevel(article_type, c('nonRR', 'RR'))) %>%
mutate(guessed_right_first = case_when(Order == 'RRFirst' & BelieveFirstRR == 1 ~ 1,
Order == 'RRFirst' & (BelieveFirstRR == 2 | BelieveFirstRR == 3) ~ 0,
Order == 'RRSecond' & (BelieveFirstRR == 1 | BelieveFirstRR == 2) ~ 0,
Order == 'RRSecond' & BelieveFirstRR == 3 ~ 1),
guessed_right_second = case_when(Order == 'RRSecond' & BelieveSecondRR == 1 ~ 1,
Order == 'RRSecond' & (BelieveSecondRR == 2 | BelieveSecondRR == 3) ~ 0,
Order == 'RRFirst' & (BelieveSecondRR == 1 | BelieveSecondRR == 2) ~ 0,
Order == 'RRFirst' & BelieveSecondRR == 3 ~ 1)) %>%
mutate(behavior_familiar = rowMeans(across(c(RRFamiliar,PreregFamiliar)), na.rm = T),
believe_improve = rowMeans(across(c(BelieveRigor,BelieveQuality)), na.rm = T))
wide_data <- read_csv(here::here('cleaned_numeric_data_wide.csv'), col_types = cols(Field = col_factor(),
Match = col_factor(),
Order = col_factor(),
keyword_batch_comp = col_factor())) %>%
mutate(behavior_familiar = rowMeans(across(c(RRFamiliar,PreregFamiliar)), na.rm = T),
believe_improve = rowMeans(across(c(BelieveRigor,BelieveQuality)), na.rm = T)) %>%
# add categorical predictors for improve and familiar variables
mutate(improve_6L = as.factor(case_when(believe_improve < 0 ~ 'negative',
believe_improve == 0 ~ 'neutral',
believe_improve > 0 & believe_improve <= 1 ~ 'slightly_more',
believe_improve > 1 & believe_improve <= 2 ~ 'moderately_more',
believe_improve > 2 & believe_improve <= 3 ~ 'much_more',
believe_improve > 3 & believe_improve <= 4 ~ 'substantially_more')),
familiar_5L = as.factor(floor(behavior_familiar)),
improve_6L = fct_relevel(improve_6L, c('negative', 'neutral', 'slightly_more', 'moderately_more', 'much_more', 'substantially_more')),
familiar_5L = fct_relevel(familiar_5L, c('1', '2', '3', '4', '5'))) %>%
mutate(guessed_RR_right = case_when(Order == 'RRFirst' & BelieveFirstRR == 1 ~ 1,
Order == 'RRFirst' & (BelieveFirstRR == 2 | BelieveFirstRR == 3) ~ 0,
Order == 'RRSecond' & BelieveSecondRR == 1 ~ 0,
Order == 'RRSecond' & (BelieveSecondRR == 3 | BelieveSecondRR == 2) ~ 1),
guessed_nonRR_right = case_when(Order == 'RRSecond' & BelieveFirstRR == 3 ~ 1,
Order == 'RRSecond' & (BelieveFirstRR == 2 | BelieveFirstRR == 1) ~ 0,
Order == 'RRFirst' & (BelieveSecondRR == 1 | BelieveSecondRR == 2) ~ 0,
Order == 'RRFirst' & (BelieveSecondRR == 3) ~ 1),
guessed_right = case_when(guessed_RR_right == 1 & guessed_nonRR_right == 1 ~ 'both',
guessed_RR_right == 0 & guessed_nonRR_right == 0 ~ 'neither',
TRUE ~ 'half'),
guessed_right = as.factor(guessed_right),
guessed_right = fct_relevel(guessed_right, c('neither', 'half', 'both')))
# set up contrasts codes
contrasts(wide_data$Field) <- contr.sum(3)
contrasts(wide_data$keyword_batch_comp) <- contr.sum(2)
contrasts(wide_data$Order) <- contr.sum(2)
contrasts(wide_data$Match) <- contr.sum(2)
contrasts(wide_data$improve_6L) <- contr.treatment(6)
contrasts(wide_data$familiar_5L) <- contr.treatment(5)
contrasts(wide_data$guessed_right) <- contr.treatment(3)
contrasts(long_data$article_type) <- contr.treatment(2)
contrasts(long_data$Field) <- contr.sum(3)
contrasts(long_data$keyword_batch_comp) <- contr.sum(2)
contrasts(long_data$Order) <- contr.sum(2)
contrasts(long_data$Match) <- contr.sum(2)
# check means & SDs of each question by article type
long_data %>%
group_by(question, article_type) %>%
summarize(mean = mean(response, na.rm = T), sd = sd(response, na.rm = T))
# bayesian models for single DV
priors <- c(set_prior("normal(0,2", "Intercept"),
set_prior("normal(0,2)", "b"),
set_prior("normal(0, 2.5)", "sd"),
set_prior("normal(0, 2)", "sigma"))
#### demographics and sample descriptives ####
for_descriptive <- wide_data %>%
select(-starts_with('diff'), -starts_with('Alt'), -c(StartDate, EndDate)) %>%
select(Field, SPSubfield, Keyword, Gender, Education, ProfTitle, FirstFamiliar, FirstQualified,
SecondFamiliar, SecondQualified, RRFamiliar, EverRR, PreregFamiliar, EverPrereg,
BelieveRigor, BelieveQuality, BelieveFirstRR, BelieveSecondRR, RR, Order, Match)
## Overall descriptives
#### means & sds for Familiarity and Rigor questions
for_descriptive %>%
select(RRFamiliar, PreregFamiliar, BelieveRigor, BelieveQuality) %>%
summarise(across(everything(), list(mean = mean, sd = sd)))
#### cor for rigor and quality beliefs
cor(for_descriptive$BelieveRigor, for_descriptive$BelieveQuality)
cor(for_descriptive$RRFamiliar, for_descriptive$PreregFamiliar)
### every RR or Pre-reg tallys
for_descriptive %>%
group_by(EverRR) %>%
tally()
for_descriptive %>%
group_by(EverPrereg) %>%
tally()
for_descriptive %>%
select(RRFamiliar) %>%
group_by(RRFamiliar) %>%
tally() %>%
mutate(RRFamiliar = as.factor(RRFamiliar),
RRFamiliar = fct_recode(RRFamiliar, `Not at all familiar` = "1", `Slightly familiary` = "2", `Somewhat familiary` = "3",
`Moderately familiary` = "4", `Substantially familiar` = "5"))
for_descriptive %>%
select(PreregFamiliar) %>%
group_by(PreregFamiliar) %>%
tally() %>%
mutate(PreregFamiliar = as.factor(PreregFamiliar),
PreregFamiliar = fct_recode(PreregFamiliar, `Not at all familiar` = "1", `Slightly familiary` = "2", `Somewhat familiary` = "3",
`Moderately familiary` = "4", `Substantially familiar` = "5"))
for_descriptive %>%
select(BelieveRigor) %>%
group_by(BelieveRigor) %>%
tally() %>%
mutate(BelieveRigor = as.factor(BelieveRigor),
BelieveRigor = fct_recode(BelieveRigor,
`Subtantially less` = "-4", `Much less` = "-3", `Moderately less` = "-2", `Slightly less` = "-1", `Not change` = "0",
`Subtantially more` = "4", `Much more` = "3", `Moderately more` = "2", `Slightly more` = "1" ))
for_descriptive %>%
select(BelieveQuality) %>%
group_by(BelieveQuality) %>%
tally() %>%
mutate(BelieveQuality = as.factor(BelieveQuality),
BelieveQuality = fct_recode(BelieveQuality,
`Subtantially less` = "-4", `Much less` = "-3", `Moderately less` = "-2", `Slightly less` = "-1", `Not change` = "0",
`Subtantially more` = "4", `Much more` = "3", `Moderately more` = "2", `Slightly more` = "1" ))
#### RR guesses
## RR guesses by order
for_descriptive %>%
mutate(RR_isRR = case_when(Order == 'RRFirst' ~ BelieveFirstRR,
Order == 'RRSecond' ~ BelieveSecondRR),
Alt_isRR = case_when(Order == 'RRFirst' ~ BelieveSecondRR,
Order == 'RRSecond' ~ BelieveFirstRR)) %>%
summarize(RR_yesRR = sum(RR_isRR == 1), RR_notRR = sum(RR_isRR == 3), RR_unsure = sum(RR_isRR == 2),
Alt_yesRR = sum(Alt_isRR == 1), Alt_notRR = sum(Alt_isRR == 3), Alt_unsure = sum(Alt_isRR == 2))
# RR guesses by article & order
for_descriptive %>%
mutate(RR_isRR = case_when(Order == 'RRFirst' ~ BelieveFirstRR,
Order == 'RRSecond' ~ BelieveSecondRR),
Alt_isRR = case_when(Order == 'RRFirst' ~ BelieveSecondRR,
Order == 'RRSecond' ~ BelieveFirstRR)) %>%
group_by(RR) %>%
summarize(RR_yesRR = sum(RR_isRR == 1), RR_notRR = sum(RR_isRR == 3), RR_unsure = sum(RR_isRR == 2),
Alt_yesRR = sum(Alt_isRR == 1), Alt_notRR = sum(Alt_isRR == 3), Alt_unsure = sum(Alt_isRR == 2))
#### Article review qualifications
# by order
for_descriptive %>%
mutate(RR_qualified = case_when(Order == 'RRFirst' ~ FirstQualified,
Order == 'RRSecond' ~ SecondQualified),
Alt_qualified = case_when(Order == 'RRFirst' ~ SecondQualified,
Order == 'RRSecond' ~ FirstQualified)) %>%
select(Order, RR_qualified, Alt_qualified) %>%
summarize(RR_qualified_mean = mean(RR_qualified), RR_qualified_sd = sd(RR_qualified),
Alt_qualified_mean = mean(Alt_qualified), Alt_qualified_sd = sd(Alt_qualified))
# by order & article
for_descriptive %>%
mutate(RR_qualified = case_when(Order == 'RRFirst' ~ FirstQualified,
Order == 'RRSecond' ~ SecondQualified),
Alt_qualified = case_when(Order == 'RRFirst' ~ SecondQualified,
Order == 'RRSecond' ~ FirstQualified)) %>%
select(RR, RR_qualified, Alt_qualified) %>%
group_by(RR) %>%
summarize(RR_qualified_mean = mean(RR_qualified), RR_qualified_sd = sd(RR_qualified),
Alt_qualified_mean = mean(Alt_qualified), Alt_qualified_sd = sd(Alt_qualified))
#### RR/Prereg behavior
for_descriptive %>%
group_by(EverPrereg) %>%
tally() %>%
mutate(EverPrereg = as.factor(EverPrereg),
EverPrereg = fct_recode(EverPrereg,
No = '1', `Yes, once` = '2', `Yes, a few times` = '3', `Yes, many times` = '4'))
for_descriptive %>%
group_by(EverRR) %>%
tally() %>%
mutate(EverRR = as.factor(EverRR),
EverRR = fct_recode(EverRR,
No = '2', Yes = '1'))
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
for_descriptive %>%
select(BelieveQuality, BelieveRigor, PreregFamiliar, RRFamiliar, FirstQualified, SecondQualified, Order) %>%
mutate(RR_qualified = case_when(Order == 'RRFirst' ~ FirstQualified,
Order == 'RRSecond' ~ SecondQualified),
Alt_qualified = case_when(Order == 'RRFirst' ~ SecondQualified,
Order == 'RRSecond' ~ FirstQualified)) %>%
select(-c("Order", "FirstQualified", "SecondQualified")) %>%
cor(use = 'everything') %>%
corrplot(method="color", col=col(200),
type="lower", order="hclust",
addCoef.col = "black", # Add coefficient of correlation
tl.col="black", tl.srt=45, #Text label color and rotation
# hide correlation coefficient on the principal diagonal
diag=FALSE
)
#### within model with DVs as ML component ####
mlm_dvs_data <- wide_data %>%
select(starts_with('diff'), participant_id, RR, Field, keyword_batch_comp, Order, Match, improve_6L, familiar_5L, guessed_right) %>%
pivot_longer(cols = starts_with('diff'), names_to = 'questions', values_to = 'response') %>%
mutate(questions = as.factor(questions))
within_alldvs <- brm(response ~ Field + keyword_batch_comp +
Order + Match + Order*Match +
(1|RR) + (1|participant_id) + (1|questions),
data = mlm_dvs_data,
prior = priors,
family = 'gaussian',
chains = 4,
iter = 4000,
seed = 25,
control = list(adapt_delta = 0.95, max_treedepth = 15))
# effect across all DVs
within_alldvs %>%
spread_draws(b_Intercept) %>%
mean_qi(mean = b_Intercept)
# effect across DVs from different paper parts
within_alldvs %>%
spread_draws(b_Intercept, r_questions[dv,]) %>%
mean_qi(mean = b_Intercept + r_questions) %>%
mutate(paper_part = case_when(dv == 'diff_aligned' | dv == 'diff_will_learn' | dv == 'diff_intro_importance' |
dv == 'diff_method_rigor' | dv == 'diff_method_quality' | dv == 'diff_question_quality' |
dv == 'diff_method_creative' | dv == 'diff_question_novel' ~ 'intro_method',
dv == "diff_analysis_rigor" | dv == "diff_overall_import" | dv == "diff_did_learn" |
dv == "diff_discussion_quality" | dv == "diff_justificed" | dv == 'diff_result_innovative' |
dv == 'diff_result_quality' ~ 'result',
dv == 'diff_inspire' | dv == 'diff_abstract_aligned' | dv == 'diff_field_importance' |
dv == "diff_overall_quality" ~ 'overall')) %>%
group_by(paper_part) %>%
summarize(mean_dv = mean(mean), min = min(mean), max = max(mean))
# main figure for RR vs. non-RR difference across DVs with partial pooling
with_alldvs_graph_nums <- within_alldvs %>%
spread_draws(b_Intercept, r_questions[dv,]) %>%
mutate(dv_estimates = b_Intercept + r_questions) %>%
left_join(within_alldvs %>%
spread_draws(b_Intercept, r_questions[dv,]) %>%
mean_qi(dv_mean = b_Intercept + r_questions) %>%
select(dv, dv_mean), by = 'dv') %>%
ungroup()
## setup function to create base graph for each section
main_graph_creation <- function(data) {
data %>%
mutate(dv = as.factor(dv),
dv = fct_reorder(dv, dv_mean)) %>%
ggplot(aes(y = dv, x = dv_estimates, fill = stat(x <= 0))) +
stat_halfeye(point_interval = mean_qi, .width = c(.95, .8),
interval_size_range = c(0.5, 1),
fatten_point = 1.5) +
scale_x_continuous(breaks=seq(-.5, 1.5, .5),
limits = c(-.75, 1.8),
name = 'Difference between RR and non-RR articles') +
geom_vline(xintercept = 0, linetype = "dashed") +
theme_minimal() +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_text(size = 7),
axis.text = element_text(size = 7),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank())
}
## graph for intro questions
intro_qs <- with_alldvs_graph_nums %>%
filter(grepl('question', dv) |
grepl('method', dv) |
dv == 'diff_aligned' |
dv == 'diff_will_learn' |
dv == 'diff_intro_importance') %>%
mutate(dv = case_when(dv == 'diff_aligned' ~ 'Question & methods aligned',
dv == 'diff_will_learn' ~ 'Amount will learn',
dv == 'diff_intro_importance' ~ 'Important research',
dv == 'diff_method_rigor' ~ 'Methods rigor',
dv == 'diff_method_quality' ~ 'Quality of methods',
dv == 'diff_question_quality' ~ 'Quality of question',
dv == 'diff_method_creative' ~ 'Creativity of methods',
dv == 'diff_question_novel' ~ 'Novelty of question')) %>%
main_graph_creation() +
scale_fill_manual(values = c("#fbb4ae", "gray80")) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank()) +
ggtitle('Evaluation before knowing study outcomes') +
theme(plot.title = element_text(face = 'bold', size = 7))
## graph for results questions
results_qs <- with_alldvs_graph_nums %>%
filter(grepl('result', dv) |
dv == "diff_analysis_rigor" |
dv == "diff_overall_import" |
dv == "diff_did_learn" |
dv == "diff_discussion_quality" |
dv == "diff_justificed") %>%
mutate(dv = case_when(dv == 'diff_analysis_rigor' ~ 'Analysis rigor',
dv == 'diff_overall_import' ~ 'Important findings',
dv == 'diff_justificed' ~ 'Conclusions justified',
dv == 'diff_result_quality' ~ 'Quality of results',
dv == 'diff_discussion_quality' ~ 'Qualtiy of discussion',
dv == 'diff_did_learn' ~ 'Amount learned',
dv == 'diff_result_innovative' ~ 'Innovative results')) %>%
main_graph_creation() +
scale_fill_manual(values = c("#b3cde3", "gray80")) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank()) +
ggtitle('Evaluation after knowing study outcomes') +
theme(plot.title = element_text(face = 'bold', size = 7))
## graph fo abstract questions
abstract_qs <- with_alldvs_graph_nums %>%
filter(dv == 'diff_inspire' |
dv == 'diff_abstract_aligned' |
dv == 'diff_field_importance' |
dv == "diff_overall_quality") %>%
mutate(dv = case_when(dv == 'diff_overall_quality' ~ 'Overall quality of paper',
dv == 'diff_field_importance' ~ 'Important discoveries',
dv == 'diff_abstract_aligned' ~ 'Abstract & findings aligned',
dv == 'diff_inspire' ~ 'Inspire new research')) %>%
main_graph_creation() +
scale_fill_manual(values = c("#ccebc5", "gray80"))+
ggtitle('Evaluation after finishing the paper') +
theme(plot.title = element_text(face = 'bold', size = 7),
axis.title.x = element_text(hjust = .15, vjust = 0))
## combine graphs
combined_plot <- intro_qs / results_qs / abstract_qs + plot_layout(heights = c(8, 7, 4))
combined_plot
ggsave(file = 'main_fig.pdf', combined_plot, width = 18, height = 18.5, units = 'cm', dpi = 300)
# calculate cohen's dz-esq metric for each DV in within-subj mlm
# sd difference per question
ysd_diff <- mlm_dvs_data %>%
group_by(questions) %>%
summarise(mean = mean(response, na.rm = T), sd = sd(response, na.rm = T))
# effect across DVs in standardized metric
within_alldvs %>%
spread_draws(b_Intercept, r_questions[dv,]) %>%
left_join(ysd_diff, by = c('dv' = 'questions')) %>%
mean_qi(mean = (b_Intercept + r_questions)/sd)
#### Btw subjects model with DV as ML ####
between_model_mlm <- brm(response ~ Field + keyword_batch_comp + article_type + Match + article_type*Match +
(1|RR) + (1|question) + (1|participant_id),
data = long_data %>% filter((Order == 'RRFirst' & article_type == 'RR') | (Order == 'RRSecond' & article_type == 'nonRR')),
prior = priors,
family = 'gaussian',
seed = 53,
chains = 4,
iter = 8000,
control = list(adapt_delta = 0.99, max_treedepth = 15))
between_model_mlm_slopes <- brm(response ~ Field + keyword_batch_comp + article_type + Match + article_type*Match +
(article_type|RR) + (article_type|question) + (1|participant_id),
data = long_data %>% filter((Order == 'RRFirst' & article_type == 'RR') | (Order == 'RRSecond' & article_type == 'nonRR')),
prior = priors,
family = 'gaussian',
seed = 54,
chains = 4,
iter = 8000,
control = list(adapt_delta = 0.99, max_treedepth = 15))
# get posteriors by DV
posteriors_btw_mlm <- between_model_mlm_slopes %>%
spread_draws(b_article_type2, r_question[DV, article_type]) %>%
filter(article_type == 'article_type2') %>%
mean_qi(article_effect = b_article_type2 + r_question, .width = c(.95, .80))
# get overall, min and max across DVs
posteriors_btw_mlm %>%
ungroup() %>%
filter(.width == 0.95) %>%
summarize(mean = round(mean(article_effect),2), min = round(min(article_effect),2), max = round(max(article_effect),2))
#### Exploratory Models (improve, familiar, guessing) ####
# partial pooling across all DVs and improve
within_alldvs_improve <- brm(response ~ Field + keyword_batch_comp +
Order + Match + Order*Match +
(1|RR) + (1|participant_id) + (1|questions) + (1|improve_6L),
data = mlm_dvs_data,
prior = priors,
family = 'gaussian',
chains = 4,
iter = 3000,
seed = 28,
control = list(adapt_delta = 0.99, max_treedepth = 15))
summary(within_alldvs_improve)
pp_check(within_alldvs_improve)
WAIC(within_alldvs_improve)
loo(within_alldvs_improve)
# parameters by improvement level
within_alldvs_improve %>%
spread_draws(b_Intercept, r_improve_6L[improve_level,]) %>%
mean_qi(cond_mean = b_Intercept + r_improve_6L, .width = c(.95)) %>%
mutate_if(is.numeric, round, 2)
### graph collapsed across DVs
within_alldvs_improve %>%
spread_draws(b_Intercept, r_improve_6L[improve_level,]) %>%
median_qi(cond_mean = b_Intercept + r_improve_6L, .width = c(.95, .90)) %>%
ungroup() %>%
mutate(improve_level = as.factor(improve_level),
improve_level = fct_relevel(improve_level, c('negative', 'neutral', 'slightly_more', 'moderately_more', 'much_more', 'substantially_more'))) %>%
ggplot(aes(y = improve_level, x = cond_mean, xmin = .lower, xmax = .upper)) +
geom_pointinterval(position=position_dodge(width=1)) +
geom_vline(xintercept = 0) +
theme_classic()
# partial pooling across all DVs and familiar
within_alldvs_familiar <- brm(response ~ Field + keyword_batch_comp +
Order + Match + Order*Match +
(1|RR) + (1|participant_id) + (1|questions) + (1|familiar_5L),
data = mlm_dvs_data,
prior = priors,
family = 'gaussian',
chains = 4,
iter = 4000,
seed = 27,
control = list(adapt_delta = .99, max_treedepth = 15))
# get table of estimates for familiarity levels across all DVs
within_alldvs_familiar %>%
spread_draws(b_Intercept,r_familiar_5L[familiar_level,]) %>%
mean_qi(fam_level_mean = b_Intercept + r_familiar_5L, .width = c(.95)) %>%
mutate_if(is.numeric, round, 2)
### graph collapsed across DVs
within_alldvs_familiar %>%
spread_draws(b_Intercept, r_familiar_5L[familiar_level,]) %>%
median_qi(cond_mean = b_Intercept + r_familiar_5L, .width = c(.95, .90)) %>%
ungroup() %>%
mutate(familiar_level = as.factor(familiar_level),
familiar_level = fct_relevel(familiar_level, c('1', '2', '3', '4', '5'))) %>%
ggplot(aes(y = familiar_level, x = cond_mean, xmin = .lower, xmax = .upper)) +
geom_pointinterval(position=position_dodge(width=1)) +
geom_vline(xintercept = 0) +
theme_classic()
## model for guessing (random slope for guessing by question) ##
within_diff_pooled_guessed_model_slopes <- brm(response ~ Field + keyword_batch_comp + guessed_right +
Order + Match + Order*Match +
(1|RR) + (1|participant_id) + (guessed_right|questions),
data = mlm_dvs_data,
prior = priors,
family = 'gaussian',
chains = 4,
iter = 4000,
seed = 31,
control = list(adapt_delta = .99, max_treedepth = 15))
# get posteriors for guessing across DVs
posteriors_by_guessing <- within_diff_pooled_guessed_model_slopes %>%
spread_draws(b_Intercept, b_guessed_right2, b_guessed_right3, r_questions[DV,guessed]) %>%
pivot_longer(cols = b_Intercept:b_guessed_right3,
names_to = "fixed_guessed",
values_to = 'fixed_value') %>%
mutate(fixed_guessed = case_when(fixed_guessed == 'b_Intercept' ~ 'Intercept',
fixed_guessed == 'b_guessed_right2' ~ 'guessed_right2',
fixed_guessed == 'b_guessed_right3' ~ 'guessed_right3')) %>%
filter(guessed == fixed_guessed) %>%
mean_qi(mean = fixed_value + r_questions, .width = c(.95, .80)) %>%
mutate(guessed = case_when(guessed == 'Intercept' ~ 'None',
guessed == 'guessed_right2' ~ 'Half',
guessed == 'guessed_right3' ~ 'Both'))
# calculate number in the same direction as main model [where all are positive]
posteriors_by_guessing %>%
select(-c(.point, .interval)) %>%
filter(.width == 0.95) %>%
group_by(guessed) %>%
summarize(num_same_direction = sum(mean > 0),
mean_effect = mean(mean),
min = min(mean),
max = max(mean))
##### Supplemental Analyses #####
#### Correlations between DVs ####
jpeg(height = 18.3, width = 18.3, units = 'cm', res = 300, file = 'dv_corrs.jpeg')
wide_data %>%
select(starts_with('diff')) %>%
cor(use = "pairwise.complete.obs") %>%
corrplot(type = 'upper', order="hclust", method = 'circle')
dev.off()
#### Model fit and summary results from within-subjects ML pooled DV model ####
waic(within_alldvs)
loo(within_alldvs)
# table of results
within_alldvs_model_results <- summary(within_alldvs)
within_alldvs_model_table <- rbind(within_alldvs_model_results$fixed %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor"),
within_alldvs_model_results$random$participant_id %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Participant_id'),
within_alldvs_model_results$random$questions %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Question'),
within_alldvs_model_results$random$RR %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): RR'),
within_alldvs_model_results$spec_pars %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor")) %>%
mutate(effect_type = case_when(grepl('sd', Predictor) | grepl('sigma', Predictor) ~ 'Random Effects',
TRUE ~ 'Fixed Effects')) %>%
mutate_if(is.numeric, round, 2) %>%
select(-c(Bulk_ESS, Tail_ESS, Rhat)) %>%
rename(SE = 'Est.Error',
lower_ci = "l-95% CI",
upper_ci = "u-95% CI") %>%
gt(groupname_col = 'effect_type') %>%
tab_stubhead("label") %>%
cols_merge(columns = vars(lower_ci,upper_ci),
hide_columns = vars(upper_ci),
pattern = "[{1}, {2}]") %>%
cols_label(lower_ci = '95% CrI',
Predictor = '') %>%
cols_align(align = 'center',
columns = 2:4) %>%
cols_align(align = 'left',
columns = 1) %>%
tab_style(
style = cell_text(color = "black", weight = "bold"),
locations = list(
cells_row_groups(),
cells_column_labels(everything())
)
) %>%
tab_options(
table.font.size = px(7),
row_group.border.top.width = px(3),
row_group.border.top.color = "black",
row_group.border.bottom.color = "black",
table_body.hlines.color = "white",
table.border.top.color = "white",
table.border.top.width = px(3),
table.border.bottom.color = "white",
table.border.bottom.width = px(3),
column_labels.border.bottom.color = "black",
column_labels.border.bottom.width = px(2)
)
gtsave(within_alldvs_model_table, 'within_alldvs_model_table.rtf')
jpeg(file = 'test.jpeg', width = 8.9, height = 8.9, units = 'cm', res = 300)
within_alldvs_model_table
dev.off()
# table of point estimates & CrI by DV
within_alldvs_dv_posteriors <- within_alldvs %>%
spread_draws(b_Intercept, r_questions[dv,]) %>%
mean_qi(dv_mean = b_Intercept + r_questions) %>%
mutate_if(is.numeric, round, 2) %>%
select(dv, dv_mean, .lower, .upper) %>%
gt() %>%
cols_merge(columns = vars(.lower, .upper),
hide_columns = vars(.upper),
pattern = "[{1}, {2}]") %>%
cols_label(dv = 'DV',
dv_mean = 'Posterior Mean',
.lower = '95% CrI') %>%
cols_align(align = 'center',
columns = 2:3) %>%
cols_align(align = 'left',
columns = 1) %>%
tab_style(
style = cell_text(color = "black", weight = "bold"),
locations = list(
cells_column_labels(everything())
)
) %>%
tab_options(
table_body.hlines.color = "white",
table.border.top.color = "white",
table.border.top.width = px(3),
table.border.bottom.color = "white",
table.border.bottom.width = px(3),
column_labels.border.bottom.color = "black",
column_labels.border.bottom.width = px(2)
)
gtsave(within_alldvs_dv_posteriors, 'within_alldvs_dv_posteriors.rtf')
#### Familiarity variable exploratory analysis supplement ####
waic(within_alldvs_familiar)
loo(within_alldvs_familiar)
within_familiar_model_results <- summary(within_alldvs_familiar)
# model results table
within_familiar_model_table <- rbind(within_familiar_model_results$fixed %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor"),
within_familiar_model_results$random$familiar_5L %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Familiarity'),
within_familiar_model_results$random$participant_id %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Participant_id'),
within_familiar_model_results$random$questions %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Question'),
within_familiar_model_results$random$RR %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): RR'),
within_familiar_model_results$spec_pars %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor")) %>%
mutate(effect_type = case_when(grepl('sd', Predictor) | grepl('sigma', Predictor) ~ 'Random Effects',
TRUE ~ 'Fixed Effects')) %>%
mutate_if(is.numeric, round, 2) %>%
select(-c(Bulk_ESS, Tail_ESS, Rhat)) %>%
rename(SE = 'Est.Error',
lower_ci = "l-95% CI",
upper_ci = "u-95% CI") %>%
gt(groupname_col = 'effect_type') %>%
tab_stubhead("label") %>%
cols_merge(columns = vars(lower_ci,upper_ci),
hide_columns = vars(upper_ci),
pattern = "[{1}, {2}]") %>%
cols_label(lower_ci = '95% CrI',
Predictor = '') %>%
cols_align(align = 'center',
columns = 2:4) %>%
cols_align(align = 'left',
columns = 1) %>%
tab_style(
style = cell_text(color = "black", weight = "bold"),
locations = list(
cells_row_groups(),
cells_column_labels(everything())
)
) %>%
tab_options(
row_group.border.top.width = px(3),
row_group.border.top.color = "black",
row_group.border.bottom.color = "black",
table_body.hlines.color = "white",
table.border.top.color = "white",
table.border.top.width = px(3),
table.border.bottom.color = "white",
table.border.bottom.width = px(3),
column_labels.border.bottom.color = "black",
column_labels.border.bottom.width = px(2)
)
gtsave(within_familiar_model_table, 'within_familiar_model_table.rtf')
pp_check(within_alldvs_familiar)
loo(within_alldvs_familiar)
# graph of familiarity by DV
familiarity_by_dv_graph <- within_alldvs_familiar %>%
spread_draws(b_Intercept, r_familiar_5L[familiar_level,], r_questions[DV,]) %>%
mean_qi(cond_mean = b_Intercept + r_familiar_5L + r_questions, .width = c(.95, .80)) %>%
ungroup() %>%
mutate(familiar_level = as.factor(familiar_level),
familiar_level = fct_relevel(familiar_level, c('1', '2', '3', '4', '5')),
familiar_level = fct_recode(familiar_level, `Not familiar` = '1',
`Slightly familiar` = '2',
`Moderately familiar` = '3',
`Very familiar` = '4',
`Substantially familiar` = '5')) %>%
mutate(DV = case_when(DV == 'diff_abstract_aligned' ~ 'Abstract Aligned',
DV == 'diff_aligned' ~ 'Methods Aligned',
DV == 'diff_analysis_rigor' ~ 'Analysis Rigor',
DV == 'diff_did_learn' ~ 'Amt Learned',
DV == 'diff_discussion_quality' ~ 'Disc Quality',
DV == 'diff_field_importance' ~ 'Impt Discovery',
DV == 'diff_inspire' ~ 'Inspire Research',
DV == 'diff_intro_importance' ~ 'Impt Research',
DV == 'diff_method_rigor' ~ 'Methdos Rigor',
DV == 'diff_will_learn' ~ 'Amt will Learn',
DV == 'diff_method_quality' ~ 'Methdos Quality',
DV == 'diff_question_quality' ~ 'Quest Quality',
DV == 'diff_question_novel' ~ 'Quest Novelty',
DV == 'diff_method_creative' ~ 'Creative Methods',
DV == 'diff_overall_quality' ~ 'Overall Quality',
DV == 'diff_overall_import' ~ 'Impt Findings',
DV == 'diff_justificed' ~ 'Conclusion Justified',
DV == 'diff_result_quality' ~ 'Qualt Results',
DV == 'diff_result_innovative' ~ 'Innovative Result')) %>%
ggplot(aes(y = familiar_level, x = cond_mean, xmin = .lower, xmax = .upper)) +
geom_pointinterval(interval_size_range = c(0.5, 1),
fatten_point = 1.25,
position=position_dodge(width=1)) +
geom_vline(xintercept = 0) +
facet_wrap(~ DV) +
scale_x_continuous(breaks=seq(-1, 2, 1),
limits = c(-1, 2),
name = 'Difference between RR and non-RR articles') +
theme_minimal() +
theme(axis.title.y = element_blank(),
axis.title.x = element_text(size = 7),
axis.text = element_text(size = 7),
panel.grid.minor.y = element_blank(),
strip.text = element_text(size=7))
familiarity_by_dv_graph
ggsave('familiarity_by_dv_graph.jpeg', familiarity_by_dv_graph, device = 'jpeg', dpi = 300, width = 18.3, height = 18.3, units = 'cm')
#### Improve variable exploratory analysis supplement ####
waic(within_alldvs_improve)
loo(within_alldvs_improve)
within_improve_model_results <- summary(within_alldvs_improve)
# table for model results
within_improve_model_table <- rbind(within_improve_model_results$fixed %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor"),
within_improve_model_results$random$improve_6L %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Improve'),
within_improve_model_results$random$participant_id %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Participant_id'),
within_improve_model_results$random$questions %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): Question'),
within_improve_model_results$random$RR %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): RR'),
within_improve_model_results$spec_pars %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor")) %>%
mutate(effect_type = case_when(grepl('sd', Predictor) | grepl('sigma', Predictor) ~ 'Random Effects',
TRUE ~ 'Fixed Effects')) %>%
mutate_if(is.numeric, round, 2) %>%
select(-c(Bulk_ESS, Tail_ESS, Rhat)) %>%
rename(SE = 'Est.Error',
lower_ci = "l-95% CI",
upper_ci = "u-95% CI") %>%
gt(groupname_col = 'effect_type') %>%
tab_stubhead("label") %>%
cols_merge(columns = vars(lower_ci,upper_ci),
hide_columns = vars(upper_ci),
pattern = "[{1}, {2}]") %>%
cols_label(lower_ci = '95% CrI',
Predictor = '') %>%
cols_align(align = 'center',
columns = 2:4) %>%
cols_align(align = 'left',
columns = 1) %>%
tab_style(
style = cell_text(color = "black", weight = "bold"),
locations = list(
cells_row_groups(),
cells_column_labels(everything())
)
) %>%
tab_options(
row_group.border.top.width = px(3),
row_group.border.top.color = "black",
row_group.border.bottom.color = "black",
table_body.hlines.color = "white",
table.border.top.color = "white",
table.border.top.width = px(3),
table.border.bottom.color = "white",
table.border.bottom.width = px(3),
column_labels.border.bottom.color = "black",
column_labels.border.bottom.width = px(2)
)
gtsave(within_improve_model_table, 'within_improve_model_table.rtf')
# graph of improvement by DV
improve_by_dv_graph <- within_alldvs_improve %>%
spread_draws(b_Intercept, r_improve_6L[improve_level,], r_questions[DV,]) %>%
mean_qi(cond_mean = b_Intercept + r_improve_6L + r_questions, .width = c(.95, .8)) %>%
ungroup() %>%
mutate(improve_level = as.factor(improve_level),
improve_level = fct_relevel(improve_level, c('negative', 'neutral', 'slightly_more', 'moderately_more', 'much_more', 'substantially_more'))) %>%
mutate(DV = case_when(DV == 'diff_abstract_aligned' ~ 'Abstract Aligned',
DV == 'diff_aligned' ~ 'Methods Aligned',
DV == 'diff_analysis_rigor' ~ 'Analysis Rigor',
DV == 'diff_did_learn' ~ 'Amt Learned',
DV == 'diff_discussion_quality' ~ 'Disc Quality',
DV == 'diff_field_importance' ~ 'Impt Discovery',
DV == 'diff_inspire' ~ 'Inspire Research',
DV == 'diff_intro_importance' ~ 'Impt Research',
DV == 'diff_method_rigor' ~ 'Methdos Rigor',
DV == 'diff_will_learn' ~ 'Amt will Learn',
DV == 'diff_method_quality' ~ 'Methdos Quality',
DV == 'diff_question_quality' ~ 'Quest Quality',
DV == 'diff_question_novel' ~ 'Quest Novelty',
DV == 'diff_method_creative' ~ 'Creative Methods',
DV == 'diff_overall_quality' ~ 'Overall Quality',
DV == 'diff_overall_import' ~ 'Impt Findings',
DV == 'diff_justificed' ~ 'Conclusion Justified',
DV == 'diff_result_quality' ~ 'Qualt Results',
DV == 'diff_result_innovative' ~ 'Innovative Result')) %>%
ggplot(aes(y = improve_level, x = cond_mean, xmin = .lower, xmax = .upper)) +
geom_pointinterval(interval_size_range = c(0.5, 1),
fatten_point = 1.25,
position=position_dodge(width=1)) +
geom_vline(xintercept = 0) +
facet_wrap(~ DV) +
scale_x_continuous(breaks=seq(-1, 2, 1),
limits = c(-1, 2),
name = 'Difference between RR and non-RR articles') +
theme_minimal() +
theme(axis.title.y = element_blank(),
axis.title.x = element_text(size = 7),
axis.text = element_text(size = 7),
panel.grid.minor.y = element_blank(),
strip.text = element_text(size=7))
improve_by_dv_graph
ggsave('improve_by_dv_graph.jpeg', improve_by_dv_graph, device = 'jpeg', dpi = 300, width = 18.3, height = 18.3, units = 'cm')
#### supplementary info about guessing model ####
# exploration of guessing %>%
wide_data %>%
group_by(guessed_right) %>%
tally()
# full guessing model summary table
waic(within_diff_pooled_guessed_model_slopes)
loo(within_diff_pooled_guessed_model_slopes)
guessing_model_results <- summary(within_diff_pooled_guessed_model_slopes)
guessing_model_table <- rbind(guessing_model_results$fixed %>%
as.data.frame() %>%
rownames_to_column(var = 'Predictor'),
guessing_model_results$random$RR %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = case_when(Predictor == 'sd(Intercept)' ~ 'sd(Intercept): RR')),
guessing_model_results$random$question %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = case_when(Predictor == 'sd(Intercept)' ~ 'sd(Intercept): question',
Predictor == 'sd(guessed_right2)' ~ 'sd(guessed_right2): question',
Predictor == 'sd(guessed_right3)' ~ 'sd(guessed_right3): question',
Predictor == 'cor(Intercept,guessed_right2)' ~ 'cor(Intercept,guessed_right2): question',
Predictor == 'cor(Intercept,guessed_right3)' ~ 'cor(Intercept,guessed_right3): question',
Predictor == 'cor(guessed_right2,guessed_right3)' ~ 'cor(guessed_right2,guessed_right3): question')),
guessing_model_results$random$participant_id %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor") %>%
mutate(Predictor = 'sd(Intercept): participant_id'),
guessing_model_results$spec_pars %>%
as.data.frame() %>%
rownames_to_column(var = "Predictor")) %>%
mutate(effect_type = case_when(grepl('sd', Predictor) | grepl('sigma', Predictor) | grepl('cor', Predictor) ~ 'Random Effects',
TRUE ~ 'Fixed Effects')) %>%
mutate_if(is.numeric, round, 2) %>%
select(-c(Bulk_ESS, Tail_ESS, Rhat)) %>%
rename(SE = 'Est.Error',
lower_ci = "l-95% CI",
upper_ci = "u-95% CI") %>%
gt(groupname_col = 'effect_type') %>%
tab_stubhead("label") %>%
cols_merge(columns = vars(lower_ci,upper_ci),
hide_columns = vars(upper_ci),
pattern = "[{1}, {2}]") %>%
cols_label(lower_ci = '95% CrI',
Predictor = '') %>%
cols_align(align = 'center',
columns = 2:4) %>%
cols_align(align = 'left',
columns = 1) %>%
tab_style(
style = cell_text(color = "black", weight = "bold"),
locations = list(
cells_row_groups(),
cells_column_labels(everything())
)
) %>%
tab_options(
row_group.border.top.width = px(3),
row_group.border.top.color = "black",
row_group.border.bottom.color = "black",
table_body.hlines.color = "white",
table.border.top.color = "white",
table.border.top.width = px(3),
table.border.bottom.color = "white",
table.border.bottom.width = px(3),
column_labels.border.bottom.color = "black",
column_labels.border.bottom.width = px(2)
)
gtsave(guessing_model_table, 'guessing_model_table.rtf')
# graph for guessing by DV
guessed_by_dv_graph <- posteriors_by_guessing %>%
ungroup() %>%
mutate(DV = case_when(DV == 'diff_abstract_aligned' ~ 'Abstract Aligned',
DV == 'diff_aligned' ~ 'Methods Aligned',
DV == 'diff_analysis_rigor' ~ 'Analysis Rigor',
DV == 'diff_did_learn' ~ 'Amt Learned',
DV == 'diff_discussion_quality' ~ 'Disc Quality',
DV == 'diff_field_importance' ~ 'Impt Discovery',
DV == 'diff_inspire' ~ 'Inspire Research',
DV == 'diff_intro_importance' ~ 'Impt Research',
DV == 'diff_method_rigor' ~ 'Methdos Rigor',
DV == 'diff_will_learn' ~ 'Amt will Learn',
DV == 'diff_method_quality' ~ 'Methdos Quality',
DV == 'diff_question_quality' ~ 'Quest Quality',
DV == 'diff_question_novel' ~ 'Quest Novelty',
DV == 'diff_method_creative' ~ 'Creative Methods',
DV == 'diff_overall_quality' ~ 'Overall Quality',
DV == 'diff_overall_import' ~ 'Impt Findings',
DV == 'diff_justificed' ~ 'Conclusion Justified',
DV == 'diff_result_quality' ~ 'Qualt Results',
DV == 'diff_result_innovative' ~ 'Innovative Result')) %>%
ggplot(aes(y = guessed, x = mean, xmin = .lower, xmax = .upper)) +
geom_pointinterval(interval_size_range = c(0.5, 1),
fatten_point = 1.25,
position=position_dodge(width=1)) +
geom_vline(xintercept = 0) +
facet_wrap(~ DV) +
scale_x_continuous(breaks=seq(-1, 2, 1),
limits = c(-1, 2),