Load task and task outcome data
# outcomes %>% select(adid, ftldcdr_global)tr
outcomes = read_excel(path = "C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/mobile_mApp_baseline_validation_manuscript_10_02_2023_flanker_os_jt_dist_scored_cy_nb_fix_outliers.xlsx") %>%
mutate(first_login = ymd(first_login)) %>%
filter(!(prim_language %in% c("Spanish", "Russian", "Other")))
#filter(baseline_flag_phase3 == 1)
#################################################################
# # # # # #
# outcomes = outcomes %>%
# filter(new_cohort==0 | new_cohort==1) # so can adjust based on if want new cohort or not.
# #
# # outcomes = outcomes %>%
# filter(new_cohort==0)
# # #
# outcomes = outcomes %>%
# filter(new_cohort==1) # so can adjust based on if want new cohort or not.
# #
# outcomes = outcomes %>%
# filter(ftldcdr_global=="0") # so can adjust based on if want new cohort or not.
#
# outcomes = outcomes %>%
# filter(os=="Android")
#
#
# outcomes = outcomes %>%
# filter(os=="iOS")
# # # so can adjust based on if want new cohort or not.
# #
# # # #
# # outcomes = outcomes %>%
# # filter(ftldcdr_global>0) # so can adjust based on if want new cohort or not.
# # #
# #
# #
outcome.group<-"full" # "full", "validation" "original" "FTLDCDR0" "FTLDCDRgreater0.5" "Android" "iOS"
#
####################################################################
outcomes_stroop = outcomes %>%
select(adid, visit_chapter, participant_id, chapter_adjusted, stroop_total_correct_wo_to, stroop_speed_accuracy) %>%
filter(!is.na(stroop_total_correct_wo_to)) %>%
arrange(adid, chapter_adjusted) %>%
group_by(adid) %>%
mutate(task_iteration = row_number()) %>%
select(adid, visit_chapter, participant_id, task_iteration, stroop_total_correct_wo_to, stroop_speed_accuracy)
outcomes_stroop_first_itrn = outcomes_stroop %>%
filter(task_iteration == 1) %>%
ungroup() %>%
select(participant_id, visit_chapter)
outcomes_flanker = outcomes %>%
select(adid, visit_chapter , participant_id,
chapter_adjusted, flanker_total_time, flanker_speed_accuracy) %>%
filter(!is.na(flanker_total_time)) %>%
arrange(adid, chapter_adjusted) %>%
group_by(adid) %>%
mutate(task_iteration = row_number()) %>%
select(adid, visit_chapter, participant_id, task_iteration, flanker_total_time, flanker_speed_accuracy)
outcomes_flanker_first_itrn = outcomes_flanker %>% filter(task_iteration == 1) %>%
ungroup() %>%
select(participant_id, visit_chapter)
outcomes_gonogo = outcomes %>%
select(adid, visit_chapter, participant_id,
chapter_adjusted, gonogo_cor_hits_min_inc_rej) %>%
filter(!is.na(gonogo_cor_hits_min_inc_rej)) %>%
arrange(adid, chapter_adjusted) %>%
group_by(adid) %>%
mutate(task_iteration = row_number()) %>%
select(adid, visit_chapter, participant_id, task_iteration, gonogo_cor_hits_min_inc_rej)
outcomes_gonogo_first_itrn = outcomes_gonogo %>% filter(task_iteration == 1) %>%
ungroup() %>%
select(participant_id, visit_chapter)
outcomes_humi = outcomes %>%
select(adid, visit_chapter, participant_id,
chapter_adjusted, humi_corr_by_chap) %>%
filter(!is.na(humi_corr_by_chap)) %>%
arrange(adid, chapter_adjusted) %>%
group_by(adid) %>%
mutate(task_iteration = row_number()) %>%
select(adid, visit_chapter, participant_id, task_iteration, humi_corr_by_chap)
outcomes_humi_first_itrn = outcomes_humi %>% filter(task_iteration == 1) %>%
ungroup() %>%
select(participant_id, visit_chapter)
outcomes_nback = outcomes %>%
select(adid, visit_chapter, participant_id,
chapter_adjusted, nback_dprime) %>%
filter(!is.na(nback_dprime)) %>%
arrange(adid, chapter_adjusted) %>%
group_by(adid) %>%
mutate(task_iteration = row_number()) %>%
select(adid, visit_chapter, participant_id, task_iteration, nback_dprime)
outcomes_nback_first_itrn = outcomes_nback %>% filter(task_iteration == 1) %>%
ungroup() %>%
select(participant_id, visit_chapter)
outcomes_card = outcomes %>%
select(adid, visit_chapter, participant_id,
chapter_adjusted, card_total_rounds) %>%
filter(!is.na(card_total_rounds)) %>%
arrange(adid, chapter_adjusted) %>%
group_by(adid) %>%
mutate(task_iteration = row_number()) %>%
select(adid, visit_chapter, participant_id, task_iteration, card_total_rounds)
outcomes_card_first_itrn = outcomes_card %>% filter(task_iteration == 1) %>%
ungroup() %>%
select(participant_id, visit_chapter)
outcomes_task_itrn = outcomes %>% select(adid) %>% unique()
outcomes_task_itrn =
outcomes_task_itrn %>%
full_join(outcomes_stroop %>% select(-visit_chapter, -participant_id), by = "adid") %>%
mutate(task_iteration = as.numeric(task_iteration),
task_iteration = if_else(is.na(task_iteration), 1, task_iteration)) %>%
full_join(outcomes_flanker %>% select(-visit_chapter, -participant_id), by = c("adid", "task_iteration")) %>%
full_join(outcomes_gonogo %>% select(-visit_chapter, -participant_id), by = c("adid", "task_iteration")) %>%
full_join(outcomes_humi %>% select(-visit_chapter, -participant_id), by = c("adid", "task_iteration")) %>%
full_join(outcomes_nback %>% select(-visit_chapter, -participant_id), by = c("adid", "task_iteration")) %>%
full_join(outcomes_card %>% select(-visit_chapter, -participant_id), by = c("adid", "task_iteration")) %>%
arrange(adid, task_iteration)
## full task datasets
outcomes_stroop_first_itrn
## # A tibble: 277 × 2
## participant_id visit_chapter
## <dbl> <dbl>
## 1 765 0.1
## 2 3375 0.1
## 3 783 0.1
## 4 782 0.1
## 5 785 0.1
## 6 815 0.1
## 7 818 0.1
## 8 819 0.1
## 9 848 0.1
## 10 849 0.1
## # ℹ 267 more rows
stroop = read_csv(file = "stroop_raw_combined_202309.csv") %>%
janitor::clean_names() %>%
select(-folder) %>% unique() %>%
inner_join(outcomes_stroop_first_itrn, by = c("participant_id", "visit_chapter"))
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
flanker = read_csv(file = "flanker_raw_combined_202309.csv") %>% janitor::clean_names()%>%
select(-folder) %>% unique()%>%
inner_join(outcomes_flanker_first_itrn, by = c("participant_id", "visit_chapter"))
gonogo = read_csv(file = "gonogo_raw_combined_202309.csv") %>%
janitor::clean_names()%>%
select(-folder) %>% unique()%>%
inner_join(outcomes_gonogo_first_itrn, by = c("participant_id", "visit_chapter"))
humi = read_csv(file = "humi_raw_combined_202309.csv") %>%
janitor::clean_names()%>%
select(-folder) %>% unique()%>%
inner_join(outcomes_humi_first_itrn, by = c("participant_id", "visit_chapter"))
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
nback = read_csv(file = "nback_raw_combined_202309.csv") %>%
janitor::clean_names()%>%
select(-folder) %>% unique() %>%
inner_join(outcomes_nback_first_itrn, by = c("participant_id", "visit_chapter"))
card = read_csv(file = "card_raw_combined_202309.csv") %>%
janitor::clean_names()%>%
select(-folder) %>% unique() %>%
inner_join(outcomes_card_first_itrn, by = c("participant_id", "visit_chapter"))
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
stroop %>% group_by(participant_id) %>% count() %>% arrange(desc(n))
## # A tibble: 277 × 2
## # Groups: participant_id [277]
## participant_id n
## <dbl> <int>
## 1 9218 181
## 2 5393 135
## 3 12146 126
## 4 9971 123
## 5 7843 120
## 6 9476 118
## 7 7730 112
## 8 9018 112
## 9 10282 112
## 10 6466 111
## # ℹ 267 more rows
stroop %>% group_by(participant_id, file_name) %>% count() %>% arrange(desc(n))
## # A tibble: 278 × 3
## # Groups: participant_id, file_name [278]
## participant_id file_name n
## <dbl> <chr> <int>
## 1 5393 participant5393_2021-08-06 16꞉52꞉52.csv 135
## 2 12146 participant12146_2023-07-25 19꞉26꞉16.csv 126
## 3 9971 participant9971_2022-10-15 16꞉49꞉47.csv 123
## 4 7843 participant7843_2022-01-22 15꞉39꞉37.csv 120
## 5 9476 participant9476_2022-08-10 13꞉50꞉35.csv 118
## 6 7730 participant7730_2022-01-07 06꞉06꞉02.csv 112
## 7 9018 participant9018_2022-06-08 16꞉49꞉00.csv 112
## 8 10282 participant10282_2022-11-29 19꞉47꞉44.csv 112
## 9 6466 participant6466_2021-09-13 15꞉31꞉08.csv 111
## 10 8860 participant8860_2022-05-13 00꞉45꞉19.csv 110
## # ℹ 268 more rows
flanker %>% group_by(participant_id) %>% count() %>% arrange(desc(n))
## # A tibble: 364 × 2
## # Groups: participant_id [364]
## participant_id n
## <dbl> <int>
## 1 9582 220
## 2 765 110
## 3 782 110
## 4 783 110
## 5 785 110
## 6 815 110
## 7 818 110
## 8 819 110
## 9 848 110
## 10 849 110
## # ℹ 354 more rows
flanker %>% group_by(participant_id, file_name) %>% count() %>% arrange(desc(n))
## # A tibble: 365 × 3
## # Groups: participant_id, file_name [365]
## participant_id file_name n
## <dbl> <chr> <int>
## 1 765 participant765.csv 110
## 2 782 participant782.csv 110
## 3 783 participant783.csv 110
## 4 785 participant785.csv 110
## 5 815 participant815.csv 110
## 6 818 participant818.csv 110
## 7 819 participant819.csv 110
## 8 848 participant848.csv 110
## 9 849 participant849.csv 110
## 10 867 participant867.csv 110
## # ℹ 355 more rows
humi %>% group_by(participant_id) %>% count() %>% arrange(desc(n))
## # A tibble: 358 × 2
## # Groups: participant_id [358]
## participant_id n
## <dbl> <int>
## 1 8376 78
## 2 8410 78
## 3 9273 78
## 4 10670 77
## 5 7287 76
## 6 9038 76
## 7 11245 76
## 8 868 75
## 9 2040 75
## 10 4567 74
## # ℹ 348 more rows
humi %>% group_by(participant_id, file_name) %>% count() %>% arrange(desc(n))
## # A tibble: 358 × 3
## # Groups: participant_id, file_name [358]
## participant_id file_name n
## <dbl> <chr> <int>
## 1 8376 participant8376_2022-03-20 22꞉08꞉42.csv 78
## 2 8410 participant8410_2022-03-30 22꞉03꞉43.csv 78
## 3 9273 participant9273_2022-07-13 20꞉10꞉11.csv 78
## 4 10670 participant10670_2023-01-20 14꞉53꞉33.csv 77
## 5 7287 participant7287_2021-11-16 21꞉10꞉34.csv 76
## 6 9038 participant9038_2022-05-29 02꞉59꞉47.csv 76
## 7 11245 participant11245_2023-02-23 02꞉40꞉16.csv 76
## 8 868 participant868.csv 75
## 9 2040 participant2040_2020-07-28 02꞉05꞉07.csv 75
## 10 4567 participant4567_2021-06-04 01꞉02꞉52.csv 74
## # ℹ 348 more rows
gonogo %>% group_by(participant_id) %>% count() %>% arrange(desc(n))
## # A tibble: 334 × 2
## # Groups: participant_id [334]
## participant_id n
## <dbl> <int>
## 1 9575 198
## 2 1018 101
## 3 2245 101
## 4 8907 101
## 5 2157 100
## 6 2304 100
## 7 4729 100
## 8 5265 100
## 9 5463 100
## 10 6506 100
## # ℹ 324 more rows
gonogo %>% group_by(participant_id, file_name) %>% count() %>% arrange(desc(n))
## # A tibble: 335 × 3
## # Groups: participant_id, file_name [335]
## participant_id file_name n
## <dbl> <chr> <int>
## 1 1018 participant1018.csv 101
## 2 2245 participant2245_2020-09-14 22꞉26꞉33.csv 101
## 3 8907 participant8907_2022-05-26 16꞉55꞉27.csv 101
## 4 2157 participant2157_2020-08-20 18꞉34꞉00.csv 100
## 5 2304 participant2304_2020-09-16 21꞉43꞉13.csv 100
## 6 4729 participant4729_2021-06-11 01꞉17꞉48.csv 100
## 7 5265 participant5265_2021-07-23 18꞉36꞉58.csv 100
## 8 5463 participant5463_2022-08-17 21꞉10꞉02.csv 100
## 9 6506 participant6506_2021-09-23 01꞉30꞉46.csv 100
## 10 7420 participant7420_2021-12-02 02꞉04꞉33.csv 100
## # ℹ 325 more rows
card %>% group_by(participant_id) %>% count() %>% arrange(desc(n))
## # A tibble: 281 × 2
## # Groups: participant_id [281]
## participant_id n
## <dbl> <int>
## 1 2037 48
## 2 2130 48
## 3 2131 48
## 4 2245 48
## 5 2448 48
## 6 2520 48
## 7 2616 48
## 8 2670 48
## 9 2728 48
## 10 2921 48
## # ℹ 271 more rows
card %>% group_by(participant_id, file_name) %>% count() %>% arrange(desc(n))
## # A tibble: 281 × 3
## # Groups: participant_id, file_name [281]
## participant_id file_name n
## <dbl> <chr> <int>
## 1 2037 participant2037_2020-07-27 21꞉36꞉19.csv 48
## 2 2130 participant2130_2020-08-16 03꞉23꞉46.csv 48
## 3 2131 participant2131_2020-08-15 19꞉17꞉17.csv 48
## 4 2245 participant2245_2020-09-12 12꞉24꞉17.csv 48
## 5 2448 participant2448_2020-10-07 22꞉02꞉07.csv 48
## 6 2520 participant2520_2020-10-12 17꞉14꞉33.csv 48
## 7 2616 participant2616_2020-10-21 19꞉22꞉31.csv 48
## 8 2670 participant2670_2020-10-27 00꞉08꞉11.csv 48
## 9 2728 participant2728_2020-11-05 21꞉34꞉10.csv 48
## 10 2921 participant2921_2020-11-16 23꞉00꞉18.csv 48
## # ℹ 271 more rows
nback %>% group_by(participant_id) %>% count() %>% arrange(desc(n))
## # A tibble: 269 × 2
## # Groups: participant_id [269]
## participant_id n
## <dbl> <int>
## 1 8928 114
## 2 785 113
## 3 2040 113
## 4 3155 113
## 5 4465 113
## 6 4478 113
## 7 4855 113
## 8 6944 113
## 9 8373 113
## 10 8383 113
## # ℹ 259 more rows
nback %>% group_by(participant_id, file_name) %>% count() %>% arrange(desc(n))
## # A tibble: 269 × 3
## # Groups: participant_id, file_name [269]
## participant_id file_name n
## <dbl> <chr> <int>
## 1 8928 participant8928_2022-05-21 01꞉39꞉30.csv 114
## 2 785 participant785.csv 113
## 3 2040 participant2040_2020-07-28 02꞉12꞉23.csv 113
## 4 3155 participant3155_2020-12-26 22꞉23꞉39.csv 113
## 5 4465 participant4465_2021-05-11 20꞉51꞉23.csv 113
## 6 4478 participant4478_2021-05-13 15꞉35꞉48.csv 113
## 7 4855 participant4855_2021-07-16 18꞉05꞉49.csv 113
## 8 6944 participant6944_2021-12-01 14꞉53꞉55.csv 113
## 9 8373 participant8373_2022-03-19 22꞉25꞉58.csv 113
## 10 8383 participant8383_2022-03-23 05꞉57꞉27.csv 113
## # ℹ 259 more rows
stroop_dedupe =
stroop %>% select(participant_id, start_time) %>% unique() %>%
# mutate(file_name2 = file_name) %>%
# separate(file_name2, into = c("a", "b"), sep = "_") %>%
# separate(b, into = "b", sep = ".csv", extra = "drop") %>%
mutate(date = ymd_hms(start_time)) %>%
# select(-a, -b) %>%
arrange(participant_id, date) %>%
group_by(participant_id) %>%
mutate(index = row_number())
stroop =
stroop %>%
left_join(stroop_dedupe,
by = c("participant_id", "start_time")) %>%
filter(index == 1)
flanker_dedupe =
flanker %>% select(participant_id, start_time) %>% unique() %>%
# mutate(file_name2 = file_name) %>%
# separate(file_name2, into = c("a", "b"), sep = "_") %>%
# separate(b, into = "b", sep = ".csv", extra = "drop") %>%
mutate(date = ymd_hms(start_time)) %>%
# select(-a, -b) %>%
arrange(participant_id, date) %>%
group_by(participant_id) %>%
mutate(index = row_number())
flanker =
flanker %>%
left_join(flanker_dedupe, by = c("participant_id", "start_time")) %>%
filter(index == 1)
gonogo_dedupe =
gonogo %>% select(participant_id, start_time) %>% unique() %>%
# mutate(file_name2 = file_name) %>%
# separate(file_name2, into = c("a", "b"), sep = "_") %>%
# separate(b, into = "b", sep = ".csv", extra = "drop") %>%
mutate(date = ymd_hms(start_time)) %>%
# select(-a, -b) %>%
arrange(participant_id, date) %>%
group_by(participant_id) %>%
mutate(index = row_number())
gonogo =
gonogo %>%
left_join(gonogo_dedupe, by = c("participant_id", "start_time")) %>%
filter(index == 1)
humi_dedupe =
humi %>% select(participant_id, start_time) %>% unique() %>%
# mutate(file_name2 = file_name) %>%
# separate(file_name2, into = c("a", "b"), sep = "_") %>%
# separate(b, into = "b", sep = ".csv", extra = "drop") %>%
mutate(date = ymd_hms(start_time)) %>%
# select(-a, -b) %>%
arrange(participant_id, date) %>%
group_by(participant_id) %>%
mutate(index = row_number())
humi =
humi %>%
left_join(humi_dedupe, by = c("participant_id", "start_time")) %>%
filter(index == 1)
nback_dedupe =
nback %>% select(participant_id, start_time) %>% unique() %>%
# mutate(file_name2 = file_name) %>%
# separate(file_name2, into = c("a", "b"), sep = "_") %>%
# separate(b, into = "b", sep = ".csv", extra = "drop") %>%
mutate(date = ymd_hms(start_time)) %>%
# select(-a, -b) %>%
arrange(participant_id, date) %>%
group_by(participant_id) %>%
mutate(index = row_number())
nback =
nback %>%
left_join(nback_dedupe, by = c("participant_id", "start_time")) %>%
filter(index == 1)
card_dedupe =
card %>% select(participant_id, start_time) %>% unique() %>%
# mutate(file_name2 = file_name) %>%
# separate(file_name2, into = c("a", "b"), sep = "_") %>%
# separate(b, into = "b", sep = ".csv", extra = "drop") %>%
mutate(date = ymd_hms(start_time)) %>%
# select(-a, -b) %>%
arrange(participant_id, date) %>%
group_by(participant_id) %>%
mutate(index = row_number())
card =
card %>%
left_join(card_dedupe, by = c("participant_id", "start_time")) %>%
filter(index == 1)
stroop_cronbach_cong =
stroop %>%
filter(data_rounds_on_practice_mode == "FALSE",
data_rounds_trial_type == "CONGRUENT") %>%
mutate(rxntime = (data_rounds_end - data_rounds_start)/100) %>%
select(participant_id, data_rounds_trial_number, rxntime) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "rxntime", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
stroop_cronbach_incong =
stroop %>%
filter(data_rounds_on_practice_mode == "FALSE",
data_rounds_trial_type == "INCONGRUENT") %>%
mutate(rxntime = (data_rounds_end - data_rounds_start)/100) %>%
select(participant_id, data_rounds_trial_number, rxntime) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "rxntime", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
flanker_cronbach_cong =
flanker %>%
filter(data_rounds_on_practice_mode == "FALSE",
data_rounds_trial_type == "congruent") %>%
mutate(rxntime = as.numeric(data_rounds_end) - as.numeric(data_rounds_start)) %>%
select(participant_id, data_rounds_trial_number, rxntime) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "rxntime", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
flanker_cronbach_incong =
flanker %>%
filter(data_rounds_on_practice_mode == "FALSE",
data_rounds_trial_type == "incongruent") %>%
mutate(rxntime = as.numeric(data_rounds_end) - as.numeric(data_rounds_start)) %>%
select(participant_id, data_rounds_trial_number, rxntime) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "rxntime", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
gonogo_cronbach_go =
gonogo %>%
filter(data_rounds_on_practice_mode == "FALSE",
data_rounds_trial_type == "Go") %>%
mutate(rxntime = data_rounds_end - data_rounds_start) %>%
select(participant_id, data_rounds_trial_number, rxntime) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "rxntime", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
gonogo_cronbach_nogo =
gonogo %>%
filter(data_rounds_on_practice_mode == "FALSE",
data_rounds_trial_type == "No-go") %>%
mutate(rxntime = data_rounds_end - data_rounds_start) %>%
select(participant_id, data_rounds_trial_number, rxntime) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "rxntime", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
humi_cronbach =
humi %>%
filter(data_rounds_on_practice_mode == "FALSE") %>%
mutate(data_round_correct = if_else(data_rounds_is_correct == "TRUE", 1, 0)) %>%
select(participant_id, data_rounds_order_delivery_time, data_round_correct) %>%
arrange(participant_id, data_rounds_order_delivery_time) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_order_delivery_time) %>%
pivot_wider(names_from = "trial_number_index", values_from = "data_round_correct", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
card_cronbach =
card %>%
mutate(data_round_correct = if_else(data_rounds_correct == "TRUE", 1, 0)) %>%
select(participant_id, data_rounds_trial_number, data_round_correct) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "data_round_correct", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
nback_cronbach =
nback %>%
filter(data_on_practice_mode == "FALSE") %>%
mutate(data_rounds_correct = if_else(data_rounds_correct == "TRUE", 1, 0)) %>%
select(participant_id, data_rounds_trial_number, data_rounds_correct) %>%
filter(!is.na(data_rounds_correct)) %>%
arrange(participant_id, data_rounds_trial_number) %>%
group_by(participant_id) %>%
mutate(trial_number_index = row_number()) %>%
select(-data_rounds_trial_number) %>%
pivot_wider(names_from = "trial_number_index", values_from = "data_rounds_correct", names_prefix = "trial_") %>%
ungroup() %>%
select(-participant_id, -trial_1)
if (outcome.group=="full") {
library(ltm)
#
#
# df.list<-list(stroop_cronbach_incong,stroop_cronbach_cong
# ) #datasets
#
# # for (ncol.i in 1:ncol(stroop_cronbach_cong)) {
# # if(ncol.i==1){
# # x<-as.data.frame(sum(is.na(stroop_cronbach_cong[,ncol.i])))
# # } else {
# # y<-as.data.frame(sum(is.na(stroop_cronbach_cong[,ncol.i])))
# # x<-rbind(x,y)
# #
# # }
# # }
# # plot(x$`sum(is.na(stroop_cronbach_cong[, ncol.i]))`)
# #
# df.list.string<-c("stroop_cronbach_incong","stroop_cronbach_cong")
df.list<-list(stroop_cronbach_cong,stroop_cronbach_incong,
flanker_cronbach_cong,flanker_cronbach_incong,
gonogo_cronbach_go, gonogo_cronbach_nogo,
humi_cronbach,
nback_cronbach,
card_cronbach) #datasets
df.list.string<-c("stroop_cronbach_cong","stroop_cronbach_incong",
"flanker_cronbach_cong","flanker_cronbach_incong",
"gonogo_cronbach_go", "gonogo_cronbach_nogo",
"humi_cronbach",
"nback_cronbach",
"card_cronbach") #names need to match names of datasets, in same order
min.part.percent<-.95
bootstrap.n<-5
###do not change below###
df.list.string.count<-1
df.i=df.list[1]
for (df.i in df.list) {
df.cba<-as.data.frame(df.i)
for (co.i in colnames(df.cba)) {
data.sum<-sum(is.na(df.cba[co.i]))
cnt<-nrow(df.cba)-data.sum
if (cnt/nrow(df.cba)<min.part.percent) {
#if (cnt/nrow(df.cba)<min.part.percent) {
df.cba[co.i]<-NULL
}
}#co.i
cba.names<-colnames(df.cba)[2:length(colnames(df.cba))]
cba.name.start<-colnames(df.cba)[1]
for (col.it.i in 1:length(cba.names)) {
#
# tempdf<-as.data.frame(df.cba[cba.names[col.it.i]])
# temodf.2<-as.data.frame(df.cba[cba.name.start[1]])
# df.cba$start<-temodf.2[,1]
# df.cba$end<-tempdf[,1]
count=1
while(TRUE){
cba.temp <- try(cronbach.alpha(df.cba %>% dplyr::select(cba.name.start[1]: cba.names[col.it.i]),
standardized = FALSE, CI = TRUE, B = bootstrap.n, na.rm = TRUE),
silent=TRUE)
count=count+1
if(!is(cba.temp, 'try-error')) break
}
# cba.temp<-cronbach.alpha(df.cba %>% dplyr::select(cba.name.start[1]: cba.names[col.it.i]), standardized = FALSE, CI = TRUE, B = bootstrap.n, na.rm = TRUE)
mat.CBA<-matrix(nrow=1, ncol=6, dimnames = list(c( df.list.string[df.list.string.count]), c("[items]","[alpha]","[LCI]","[UCI]","[CI width]","[Final trial sample Size]")))
if (col.it.i==1) {
mat.CBA[1,"[items]"]<-round(as.numeric(cba.temp[3]),3)
mat.CBA[1,"[alpha]"]<-round(as.numeric(cba.temp[1]),3)
mat.CBA[1,"[LCI]"]<-round(as.numeric(cba.temp$ci[1]),3)
mat.CBA[1,"[UCI]"]<-round(as.numeric(cba.temp$ci[2]),3)
mat.CBA[1,"[CI width]"]<- mat.CBA[1,"[UCI]"]- mat.CBA[1,"[LCI]"]
mat.CBA[1,"[Final trial sample Size]"]<-nrow(df.cba)-sum(is.na(df.cba[,cba.names[col.it.i]]))
mat.CBA.int<-mat.CBA
} else{
mat.CBA[1,"[items]"]<-round(as.numeric(cba.temp[3]),3)
mat.CBA[1,"[alpha]"]<-round(as.numeric(cba.temp[1]),3)
mat.CBA[1,"[LCI]"]<-round(as.numeric(cba.temp$ci[1]),3)
mat.CBA[1,"[UCI]"]<-round(as.numeric(cba.temp$ci[2]),3)
mat.CBA[1,"[CI width]"]<- mat.CBA[1,"[UCI]"]- mat.CBA[1,"[LCI]"]
mat.CBA[1,"[Final trial sample Size]"]<-nrow(df.cba)-sum(is.na(df.cba[,cba.names[col.it.i]]))
mat.CBA.int<-rbind(mat.CBA.int,mat.CBA)
} #ifelse
} #col.it.i
save.name<-paste(df.list.string[df.list.string.count],outcome.group,"Cronbachalpha", sep="_")
gnu.save(file = mat.CBA.int, filename = save.name)
df.list.string.count<-df.list.string.count+1
print("#######----------------------------------------------------#######")
print("#######----------------------------------------------------#######")
print("#######----------------------------------------------------#######")
print(mat.CBA.int)
print(plot(mat.CBA.int[,1], mat.CBA.int[,2], main = df.list.string[df.list.string.count]))
print("#######----------------------------------------------------#######")
print("#######----------------------------------------------------#######")
print("#######----------------------------------------------------#######")
}
detach("package:ltm", unload = TRUE)
}
## Loading required package: MASS
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
## Loading required package: msm
## Loading required package: polycor
## [1] "Rows: 26"
## [1] "Cols: 6"
## File saved as: stroop_cronbach_cong_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width]
## stroop_cronbach_cong 2 0.659 0.643 0.747 0.104
## stroop_cronbach_cong 3 0.717 0.649 0.765 0.116
## stroop_cronbach_cong 4 0.734 0.653 0.757 0.104
## stroop_cronbach_cong 5 0.758 0.760 0.803 0.043
## stroop_cronbach_cong 6 0.777 0.743 0.802 0.059
## stroop_cronbach_cong 7 0.811 0.787 0.810 0.023
## stroop_cronbach_cong 8 0.824 0.803 0.848 0.045
## stroop_cronbach_cong 9 0.840 0.831 0.844 0.013
## stroop_cronbach_cong 10 0.844 0.828 0.850 0.022
## stroop_cronbach_cong 11 0.854 0.829 0.888 0.059
## stroop_cronbach_cong 12 0.865 0.849 0.875 0.026
## stroop_cronbach_cong 13 0.874 0.850 0.885 0.035
## stroop_cronbach_cong 14 0.883 0.863 0.893 0.030
## stroop_cronbach_cong 15 0.891 0.877 0.912 0.035
## stroop_cronbach_cong 16 0.897 0.886 0.915 0.029
## stroop_cronbach_cong 17 0.902 0.898 0.917 0.019
## stroop_cronbach_cong 18 0.906 0.900 0.916 0.016
## stroop_cronbach_cong 19 0.910 0.899 0.912 0.013
## stroop_cronbach_cong 20 0.914 0.906 0.916 0.010
## stroop_cronbach_cong 21 0.918 0.907 0.932 0.025
## stroop_cronbach_cong 22 0.917 0.902 0.934 0.032
## stroop_cronbach_cong 23 0.915 0.904 0.927 0.023
## stroop_cronbach_cong 24 0.917 0.895 0.922 0.027
## stroop_cronbach_cong 25 0.914 0.901 0.924 0.023
## stroop_cronbach_cong 26 0.911 0.898 0.918 0.020
## stroop_cronbach_cong 27 0.909 0.897 0.913 0.016
## [Final trial sample Size]
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 277
## stroop_cronbach_cong 275
## stroop_cronbach_cong 274
## stroop_cronbach_cong 273
## stroop_cronbach_cong 271
## stroop_cronbach_cong 267
## stroop_cronbach_cong 264
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 8"
## [1] "Cols: 6"
## File saved as: stroop_cronbach_incong_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width]
## stroop_cronbach_incong 2 0.585 0.419 0.643 0.224
## stroop_cronbach_incong 3 0.636 0.555 0.736 0.181
## stroop_cronbach_incong 4 0.692 0.661 0.741 0.080
## stroop_cronbach_incong 5 0.755 0.727 0.782 0.055
## stroop_cronbach_incong 6 0.791 0.760 0.834 0.074
## stroop_cronbach_incong 7 0.825 0.800 0.826 0.026
## stroop_cronbach_incong 8 0.831 0.798 0.846 0.048
## stroop_cronbach_incong 9 0.845 0.828 0.855 0.027
## [Final trial sample Size]
## stroop_cronbach_incong 277
## stroop_cronbach_incong 277
## stroop_cronbach_incong 277
## stroop_cronbach_incong 277
## stroop_cronbach_incong 277
## stroop_cronbach_incong 277
## stroop_cronbach_incong 272
## stroop_cronbach_incong 265
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 43"
## [1] "Cols: 6"
## File saved as: flanker_cronbach_cong_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width]
## flanker_cronbach_cong 2 0.830 0.819 0.879 0.060
## flanker_cronbach_cong 3 0.898 0.872 0.907 0.035
## flanker_cronbach_cong 4 0.925 0.924 0.940 0.016
## flanker_cronbach_cong 5 0.938 0.934 0.945 0.011
## flanker_cronbach_cong 6 0.947 0.939 0.952 0.013
## flanker_cronbach_cong 7 0.949 0.939 0.950 0.011
## flanker_cronbach_cong 8 0.957 0.956 0.967 0.011
## flanker_cronbach_cong 9 0.952 0.928 0.968 0.040
## flanker_cronbach_cong 10 0.957 0.955 0.966 0.011
## flanker_cronbach_cong 11 0.961 0.956 0.974 0.018
## flanker_cronbach_cong 12 0.963 0.948 0.967 0.019
## flanker_cronbach_cong 13 0.966 0.952 0.967 0.015
## flanker_cronbach_cong 14 0.967 0.964 0.970 0.006
## flanker_cronbach_cong 15 0.970 0.962 0.975 0.013
## flanker_cronbach_cong 16 0.972 0.960 0.975 0.015
## flanker_cronbach_cong 17 0.974 0.969 0.976 0.007
## flanker_cronbach_cong 18 0.975 0.970 0.979 0.009
## flanker_cronbach_cong 19 0.977 0.966 0.982 0.016
## flanker_cronbach_cong 20 0.978 0.975 0.981 0.006
## flanker_cronbach_cong 21 0.979 0.972 0.980 0.008
## flanker_cronbach_cong 22 0.979 0.976 0.984 0.008
## flanker_cronbach_cong 23 0.980 0.970 0.981 0.011
## flanker_cronbach_cong 24 0.981 0.977 0.984 0.007
## flanker_cronbach_cong 25 0.981 0.977 0.985 0.008
## flanker_cronbach_cong 26 0.982 0.981 0.984 0.003
## flanker_cronbach_cong 27 0.982 0.981 0.985 0.004
## flanker_cronbach_cong 28 0.983 0.981 0.985 0.004
## flanker_cronbach_cong 29 0.984 0.984 0.986 0.002
## flanker_cronbach_cong 30 0.984 0.983 0.986 0.003
## flanker_cronbach_cong 31 0.985 0.982 0.987 0.005
## flanker_cronbach_cong 32 0.985 0.980 0.987 0.007
## flanker_cronbach_cong 33 0.986 0.984 0.988 0.004
## flanker_cronbach_cong 34 0.986 0.980 0.988 0.008
## flanker_cronbach_cong 35 0.987 0.982 0.989 0.007
## flanker_cronbach_cong 36 0.987 0.985 0.987 0.002
## flanker_cronbach_cong 37 0.987 0.987 0.989 0.002
## flanker_cronbach_cong 38 0.988 0.986 0.988 0.002
## flanker_cronbach_cong 39 0.988 0.987 0.990 0.003
## flanker_cronbach_cong 40 0.988 0.986 0.989 0.003
## flanker_cronbach_cong 41 0.989 0.987 0.990 0.003
## flanker_cronbach_cong 42 0.989 0.987 0.990 0.003
## flanker_cronbach_cong 43 0.989 0.988 0.990 0.002
## flanker_cronbach_cong 44 0.989 0.988 0.991 0.003
## [Final trial sample Size]
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 364
## flanker_cronbach_cong 362
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 42"
## [1] "Cols: 6"
## File saved as: flanker_cronbach_incong_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width]
## flanker_cronbach_incong 2 0.896 0.866 0.930 0.064
## flanker_cronbach_incong 3 0.916 0.880 0.935 0.055
## flanker_cronbach_incong 4 0.938 0.913 0.947 0.034
## flanker_cronbach_incong 5 0.948 0.940 0.960 0.020
## flanker_cronbach_incong 6 0.954 0.951 0.961 0.010
## flanker_cronbach_incong 7 0.958 0.955 0.963 0.008
## flanker_cronbach_incong 8 0.963 0.960 0.968 0.008
## flanker_cronbach_incong 9 0.966 0.958 0.969 0.011
## flanker_cronbach_incong 10 0.969 0.967 0.971 0.004
## flanker_cronbach_incong 11 0.971 0.968 0.973 0.005
## flanker_cronbach_incong 12 0.971 0.965 0.974 0.009
## flanker_cronbach_incong 13 0.972 0.970 0.974 0.004
## flanker_cronbach_incong 14 0.974 0.973 0.977 0.004
## flanker_cronbach_incong 15 0.976 0.970 0.980 0.010
## flanker_cronbach_incong 16 0.977 0.973 0.978 0.005
## flanker_cronbach_incong 17 0.978 0.975 0.979 0.004
## flanker_cronbach_incong 18 0.979 0.978 0.980 0.002
## flanker_cronbach_incong 19 0.980 0.978 0.981 0.003
## flanker_cronbach_incong 20 0.981 0.980 0.982 0.002
## flanker_cronbach_incong 21 0.981 0.980 0.983 0.003
## flanker_cronbach_incong 22 0.981 0.979 0.982 0.003
## flanker_cronbach_incong 23 0.982 0.978 0.985 0.007
## flanker_cronbach_incong 24 0.982 0.980 0.984 0.004
## flanker_cronbach_incong 25 0.983 0.981 0.984 0.003
## flanker_cronbach_incong 26 0.983 0.981 0.985 0.004
## flanker_cronbach_incong 27 0.984 0.980 0.984 0.004
## flanker_cronbach_incong 28 0.984 0.981 0.986 0.005
## flanker_cronbach_incong 29 0.985 0.985 0.987 0.002
## flanker_cronbach_incong 30 0.985 0.984 0.986 0.002
## flanker_cronbach_incong 31 0.986 0.986 0.987 0.001
## flanker_cronbach_incong 32 0.986 0.987 0.989 0.002
## flanker_cronbach_incong 33 0.987 0.985 0.988 0.003
## flanker_cronbach_incong 34 0.987 0.986 0.989 0.003
## flanker_cronbach_incong 35 0.988 0.985 0.989 0.004
## flanker_cronbach_incong 36 0.988 0.985 0.988 0.003
## flanker_cronbach_incong 37 0.988 0.986 0.989 0.003
## flanker_cronbach_incong 38 0.989 0.987 0.989 0.002
## flanker_cronbach_incong 39 0.989 0.989 0.990 0.001
## flanker_cronbach_incong 40 0.989 0.987 0.990 0.003
## flanker_cronbach_incong 41 0.989 0.987 0.990 0.003
## flanker_cronbach_incong 42 0.990 0.988 0.991 0.003
## flanker_cronbach_incong 43 0.990 0.989 0.991 0.002
## [Final trial sample Size]
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## flanker_cronbach_incong 364
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 71"
## [1] "Cols: 6"
## File saved as: gonogo_cronbach_go_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width]
## gonogo_cronbach_go 2 0.807 0.747 0.816 0.069
## gonogo_cronbach_go 3 0.857 0.841 0.867 0.026
## gonogo_cronbach_go 4 0.090 0.032 0.813 0.781
## gonogo_cronbach_go 5 0.157 0.104 0.921 0.817
## gonogo_cronbach_go 6 0.221 0.124 0.935 0.811
## gonogo_cronbach_go 7 0.281 0.195 0.942 0.747
## gonogo_cronbach_go 8 0.319 0.220 0.956 0.736
## gonogo_cronbach_go 9 0.360 0.423 0.960 0.537
## gonogo_cronbach_go 10 0.402 0.309 0.960 0.651
## gonogo_cronbach_go 11 0.439 0.300 0.962 0.662
## gonogo_cronbach_go 12 0.470 0.346 0.966 0.620
## gonogo_cronbach_go 13 0.506 0.473 0.970 0.497
## gonogo_cronbach_go 14 0.539 0.498 0.970 0.472
## gonogo_cronbach_go 15 0.571 0.394 0.969 0.575
## gonogo_cronbach_go 16 0.600 0.409 0.630 0.221
## gonogo_cronbach_go 17 0.628 0.500 0.975 0.475
## gonogo_cronbach_go 18 0.652 0.365 0.947 0.582
## gonogo_cronbach_go 19 0.676 0.460 0.948 0.488
## gonogo_cronbach_go 20 0.698 0.477 0.979 0.502
## gonogo_cronbach_go 21 0.716 0.710 0.981 0.271
## gonogo_cronbach_go 22 0.734 0.484 0.957 0.473
## gonogo_cronbach_go 23 0.750 0.601 0.962 0.361
## gonogo_cronbach_go 24 0.763 0.747 0.960 0.213
## gonogo_cronbach_go 25 0.776 0.675 0.962 0.287
## gonogo_cronbach_go 26 0.785 0.662 0.985 0.323
## gonogo_cronbach_go 27 0.795 0.575 0.810 0.235
## gonogo_cronbach_go 28 0.804 0.677 0.982 0.305
## gonogo_cronbach_go 29 0.814 0.695 0.983 0.288
## gonogo_cronbach_go 30 0.822 0.662 0.970 0.308
## gonogo_cronbach_go 31 0.830 0.736 0.970 0.234
## gonogo_cronbach_go 32 0.836 0.756 0.971 0.215
## gonogo_cronbach_go 33 0.843 0.666 0.969 0.303
## gonogo_cronbach_go 34 0.850 0.846 0.967 0.121
## gonogo_cronbach_go 35 0.855 0.845 0.986 0.141
## gonogo_cronbach_go 36 0.861 0.619 0.985 0.366
## gonogo_cronbach_go 37 0.866 0.867 0.988 0.121
## gonogo_cronbach_go 38 0.871 0.803 0.988 0.185
## gonogo_cronbach_go 39 0.876 0.811 0.988 0.177
## gonogo_cronbach_go 40 0.880 0.787 0.987 0.200
## gonogo_cronbach_go 41 0.883 0.741 0.987 0.246
## gonogo_cronbach_go 42 0.887 0.754 0.985 0.231
## gonogo_cronbach_go 43 0.891 0.828 0.987 0.159
## gonogo_cronbach_go 44 0.894 0.703 0.900 0.197
## gonogo_cronbach_go 45 0.898 0.813 0.987 0.174
## gonogo_cronbach_go 46 0.901 0.781 0.989 0.208
## gonogo_cronbach_go 47 0.904 0.815 0.980 0.165
## gonogo_cronbach_go 48 0.907 0.859 0.989 0.130
## gonogo_cronbach_go 49 0.910 0.860 0.990 0.130
## gonogo_cronbach_go 50 0.913 0.921 0.989 0.068
## gonogo_cronbach_go 51 0.915 0.869 0.980 0.111
## gonogo_cronbach_go 52 0.918 0.840 0.988 0.148
## gonogo_cronbach_go 53 0.920 0.874 0.984 0.110
## gonogo_cronbach_go 54 0.923 0.798 0.980 0.182
## gonogo_cronbach_go 55 0.925 0.846 0.984 0.138
## gonogo_cronbach_go 56 0.927 0.835 0.928 0.093
## gonogo_cronbach_go 57 0.929 0.930 0.991 0.061
## gonogo_cronbach_go 58 0.931 0.924 0.990 0.066
## gonogo_cronbach_go 59 0.933 0.835 0.985 0.150
## gonogo_cronbach_go 60 0.935 0.927 0.992 0.065
## gonogo_cronbach_go 61 0.936 0.933 0.991 0.058
## gonogo_cronbach_go 62 0.938 0.884 0.991 0.107
## gonogo_cronbach_go 63 0.940 0.837 0.944 0.107
## gonogo_cronbach_go 64 0.941 0.892 0.991 0.099
## gonogo_cronbach_go 65 0.943 0.942 0.991 0.049
## gonogo_cronbach_go 66 0.944 0.865 0.939 0.074
## gonogo_cronbach_go 67 0.946 0.940 0.954 0.014
## gonogo_cronbach_go 68 0.947 0.873 0.987 0.114
## gonogo_cronbach_go 69 0.948 0.951 0.994 0.043
## gonogo_cronbach_go 70 0.949 0.912 0.993 0.081
## gonogo_cronbach_go 71 0.950 0.903 0.993 0.090
## gonogo_cronbach_go 72 0.952 0.953 0.993 0.040
## [Final trial sample Size]
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 334
## gonogo_cronbach_go 333
## gonogo_cronbach_go 331
## gonogo_cronbach_go 331
## gonogo_cronbach_go 323
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 7"
## [1] "Cols: 6"
## File saved as: gonogo_cronbach_nogo_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width]
## gonogo_cronbach_nogo 2 0.297 0.305 0.458 0.153
## gonogo_cronbach_nogo 3 0.381 0.317 0.474 0.157
## gonogo_cronbach_nogo 4 0.435 0.381 0.470 0.089
## gonogo_cronbach_nogo 5 0.495 0.426 0.587 0.161
## gonogo_cronbach_nogo 6 0.544 0.471 0.543 0.072
## gonogo_cronbach_nogo 7 0.557 0.474 0.561 0.087
## gonogo_cronbach_nogo 8 0.599 0.561 0.632 0.071
## [Final trial sample Size]
## gonogo_cronbach_nogo 334
## gonogo_cronbach_nogo 334
## gonogo_cronbach_nogo 334
## gonogo_cronbach_nogo 334
## gonogo_cronbach_nogo 334
## gonogo_cronbach_nogo 333
## gonogo_cronbach_nogo 325
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 30"
## [1] "Cols: 6"
## File saved as: humi_cronbach_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width] [Final trial sample Size]
## humi_cronbach 2 0.720 0.679 0.793 0.114 358
## humi_cronbach 3 0.439 0.386 0.505 0.119 358
## humi_cronbach 4 0.538 0.491 0.562 0.071 358
## humi_cronbach 5 0.613 0.547 0.653 0.106 358
## humi_cronbach 6 0.668 0.621 0.683 0.062 358
## humi_cronbach 7 0.645 0.590 0.678 0.088 358
## humi_cronbach 8 0.640 0.623 0.684 0.061 358
## humi_cronbach 9 0.643 0.618 0.672 0.054 358
## humi_cronbach 10 0.659 0.640 0.693 0.053 358
## humi_cronbach 11 0.684 0.683 0.719 0.036 358
## humi_cronbach 12 0.688 0.644 0.686 0.042 358
## humi_cronbach 13 0.686 0.658 0.692 0.034 358
## humi_cronbach 14 0.688 0.662 0.679 0.017 358
## humi_cronbach 15 0.693 0.654 0.727 0.073 358
## humi_cronbach 16 0.690 0.653 0.700 0.047 358
## humi_cronbach 17 0.697 0.652 0.736 0.084 358
## humi_cronbach 18 0.697 0.656 0.701 0.045 357
## humi_cronbach 19 0.697 0.673 0.741 0.068 357
## humi_cronbach 20 0.703 0.706 0.756 0.050 357
## humi_cronbach 21 0.712 0.675 0.736 0.061 356
## humi_cronbach 22 0.716 0.687 0.733 0.046 356
## humi_cronbach 23 0.717 0.708 0.758 0.050 356
## humi_cronbach 24 0.687 0.654 0.722 0.068 353
## humi_cronbach 25 0.684 0.632 0.701 0.069 353
## humi_cronbach 26 0.686 0.663 0.742 0.079 353
## humi_cronbach 27 0.667 0.628 0.695 0.067 351
## humi_cronbach 28 0.669 0.645 0.724 0.079 351
## humi_cronbach 29 0.665 0.630 0.662 0.032 350
## humi_cronbach 30 0.619 0.572 0.622 0.050 346
## humi_cronbach 31 0.622 0.542 0.667 0.125 344
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 87"
## [1] "Cols: 6"
## File saved as: nback_cronbach_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width] [Final trial sample Size]
## nback_cronbach 2 0.616 0.553 0.762 0.209 269
## nback_cronbach 3 0.420 0.266 0.476 0.210 269
## nback_cronbach 4 0.543 0.481 0.543 0.062 269
## nback_cronbach 5 0.586 0.494 0.645 0.151 269
## nback_cronbach 6 0.587 0.536 0.669 0.133 269
## nback_cronbach 7 0.598 0.530 0.620 0.090 269
## nback_cronbach 8 0.617 0.550 0.672 0.122 269
## nback_cronbach 9 0.626 0.561 0.653 0.092 269
## nback_cronbach 10 0.642 0.610 0.689 0.079 269
## nback_cronbach 11 0.660 0.643 0.713 0.070 269
## nback_cronbach 12 0.652 0.612 0.669 0.057 269
## nback_cronbach 13 0.668 0.642 0.699 0.057 269
## nback_cronbach 14 0.685 0.677 0.722 0.045 269
## nback_cronbach 15 0.695 0.656 0.729 0.073 269
## nback_cronbach 16 0.715 0.656 0.716 0.060 269
## nback_cronbach 17 0.732 0.704 0.780 0.076 269
## nback_cronbach 18 0.739 0.700 0.784 0.084 269
## nback_cronbach 19 0.749 0.726 0.755 0.029 269
## nback_cronbach 20 0.763 0.728 0.803 0.075 269
## nback_cronbach 21 0.777 0.742 0.811 0.069 269
## nback_cronbach 22 0.779 0.766 0.810 0.044 269
## nback_cronbach 23 0.787 0.782 0.820 0.038 269
## nback_cronbach 24 0.791 0.789 0.816 0.027 269
## nback_cronbach 25 0.802 0.761 0.812 0.051 269
## nback_cronbach 26 0.810 0.778 0.837 0.059 269
## nback_cronbach 27 0.814 0.797 0.834 0.037 269
## nback_cronbach 28 0.823 0.808 0.845 0.037 269
## nback_cronbach 29 0.831 0.807 0.841 0.034 269
## nback_cronbach 30 0.839 0.822 0.853 0.031 269
## nback_cronbach 31 0.842 0.836 0.858 0.022 269
## nback_cronbach 32 0.846 0.799 0.863 0.064 269
## nback_cronbach 33 0.849 0.835 0.880 0.045 269
## nback_cronbach 34 0.855 0.851 0.858 0.007 269
## nback_cronbach 35 0.861 0.842 0.881 0.039 269
## nback_cronbach 36 0.862 0.841 0.881 0.040 269
## nback_cronbach 37 0.864 0.837 0.881 0.044 269
## nback_cronbach 38 0.867 0.845 0.874 0.029 269
## nback_cronbach 39 0.869 0.859 0.883 0.024 269
## nback_cronbach 40 0.873 0.849 0.877 0.028 269
## nback_cronbach 41 0.876 0.864 0.884 0.020 269
## nback_cronbach 42 0.877 0.858 0.884 0.026 269
## nback_cronbach 43 0.880 0.866 0.891 0.025 269
## nback_cronbach 44 0.883 0.863 0.894 0.031 269
## nback_cronbach 45 0.884 0.872 0.898 0.026 269
## nback_cronbach 46 0.888 0.857 0.893 0.036 269
## nback_cronbach 47 0.889 0.875 0.909 0.034 269
## nback_cronbach 48 0.892 0.872 0.897 0.025 269
## nback_cronbach 49 0.894 0.877 0.904 0.027 269
## nback_cronbach 50 0.896 0.899 0.904 0.005 269
## nback_cronbach 51 0.898 0.886 0.907 0.021 269
## nback_cronbach 52 0.899 0.885 0.902 0.017 269
## nback_cronbach 53 0.901 0.882 0.909 0.027 269
## nback_cronbach 54 0.902 0.880 0.918 0.038 269
## nback_cronbach 55 0.904 0.903 0.920 0.017 269
## nback_cronbach 56 0.904 0.891 0.919 0.028 269
## nback_cronbach 57 0.906 0.905 0.910 0.005 269
## nback_cronbach 58 0.906 0.891 0.913 0.022 269
## nback_cronbach 59 0.909 0.884 0.906 0.022 269
## nback_cronbach 60 0.910 0.891 0.922 0.031 269
## nback_cronbach 61 0.912 0.901 0.926 0.025 269
## nback_cronbach 62 0.913 0.902 0.916 0.014 269
## nback_cronbach 63 0.914 0.905 0.927 0.022 269
## nback_cronbach 64 0.916 0.904 0.920 0.016 269
## nback_cronbach 65 0.917 0.898 0.920 0.022 269
## nback_cronbach 66 0.917 0.902 0.935 0.033 269
## nback_cronbach 67 0.918 0.910 0.931 0.021 269
## nback_cronbach 68 0.919 0.894 0.930 0.036 269
## nback_cronbach 69 0.920 0.904 0.931 0.027 269
## nback_cronbach 70 0.921 0.915 0.933 0.018 269
## nback_cronbach 71 0.922 0.913 0.925 0.012 269
## nback_cronbach 72 0.922 0.923 0.933 0.010 269
## nback_cronbach 73 0.923 0.921 0.929 0.008 269
## nback_cronbach 74 0.924 0.917 0.935 0.018 269
## nback_cronbach 75 0.922 0.911 0.933 0.022 268
## nback_cronbach 76 0.922 0.920 0.930 0.010 268
## nback_cronbach 77 0.923 0.891 0.932 0.041 268
## nback_cronbach 78 0.921 0.920 0.930 0.010 267
## nback_cronbach 79 0.920 0.914 0.925 0.011 266
## nback_cronbach 80 0.918 0.904 0.928 0.024 265
## nback_cronbach 81 0.916 0.901 0.920 0.019 264
## nback_cronbach 82 0.917 0.915 0.926 0.011 264
## nback_cronbach 83 0.918 0.912 0.928 0.016 264
## nback_cronbach 84 0.919 0.907 0.922 0.015 264
## nback_cronbach 85 0.918 0.907 0.924 0.017 263
## nback_cronbach 86 0.915 0.905 0.928 0.023 261
## nback_cronbach 87 0.916 0.912 0.923 0.011 260
## nback_cronbach 88 0.916 0.900 0.921 0.021 256
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "Rows: 35"
## [1] "Cols: 6"
## File saved as: card_cronbach_full_Cronbachalpha_2023-10-13.csv
## File saved in: C:/Users/mark/Documents/Current Projects/Reliability/update from clayton/Clayton analyses[1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [items] [alpha] [LCI] [UCI] [CI width] [Final trial sample Size]
## card_cronbach 2 0.642 0.542 0.810 0.268 281
## card_cronbach 3 0.773 0.657 0.810 0.153 281
## card_cronbach 4 0.814 0.795 0.832 0.037 281
## card_cronbach 5 0.847 0.829 0.896 0.067 281
## card_cronbach 6 0.647 0.536 0.651 0.115 281
## card_cronbach 7 0.638 0.601 0.673 0.072 281
## card_cronbach 8 0.671 0.600 0.690 0.090 281
## card_cronbach 9 0.713 0.691 0.762 0.071 281
## card_cronbach 10 0.750 0.747 0.786 0.039 281
## card_cronbach 11 0.780 0.763 0.814 0.051 281
## card_cronbach 12 0.784 0.771 0.818 0.047 281
## card_cronbach 13 0.782 0.741 0.801 0.060 281
## card_cronbach 14 0.779 0.748 0.788 0.040 281
## card_cronbach 15 0.786 0.768 0.809 0.041 281
## card_cronbach 16 0.804 0.797 0.822 0.025 281
## card_cronbach 17 0.817 0.789 0.833 0.044 281
## card_cronbach 18 0.826 0.803 0.851 0.048 281
## card_cronbach 19 0.835 0.812 0.833 0.021 281
## card_cronbach 20 0.844 0.812 0.849 0.037 281
## card_cronbach 21 0.851 0.835 0.865 0.030 281
## card_cronbach 22 0.858 0.842 0.882 0.040 281
## card_cronbach 23 0.866 0.847 0.875 0.028 281
## card_cronbach 24 0.872 0.868 0.889 0.021 281
## card_cronbach 25 0.878 0.868 0.889 0.021 281
## card_cronbach 26 0.882 0.881 0.888 0.007 281
## card_cronbach 27 0.888 0.869 0.898 0.029 281
## card_cronbach 28 0.893 0.890 0.902 0.012 281
## card_cronbach 29 0.897 0.891 0.905 0.014 281
## card_cronbach 30 0.900 0.893 0.897 0.004 281
## card_cronbach 31 0.904 0.889 0.905 0.016 281
## card_cronbach 32 0.908 0.890 0.920 0.030 281
## card_cronbach 33 0.911 0.894 0.920 0.026 281
## card_cronbach 34 0.914 0.908 0.920 0.012 281
## card_cronbach 35 0.917 0.917 0.926 0.009 281
## card_cronbach 36 0.919 0.918 0.923 0.005 274
## NULL
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"
## [1] "#######----------------------------------------------------#######"