descriptives
distribution
## nc vs hc
xtabs(~ hormonal_contraception, all_surveys_included)
## hormonal_contraception
## FALSE TRUE
## 539 255
## single vs relationship
xtabs(~ relationship_status == 1, all_surveys_included)
## relationship_status == 1
## FALSE TRUE
## 531 263
## relationship status + contraception
xtabs(~ (relationship_status == 1) + hormonal_contraception, all_surveys_included)
## hormonal_contraception
## relationship_status == 1 FALSE TRUE
## FALSE 336 195
## TRUE 203 60
## percentage hc vs nc
round(prop.table(xtabs(~ hormonal_contraception, all_surveys_included))*100 ,2)
## hormonal_contraception
## FALSE TRUE
## 67.88 32.12
NC
all_surveys_included %>% filter(hormonal_contraception == F) %>% select(age, religiosity, first_time, menstruation_length, number_sexual_partner, menarche, bfi_agree, bfi_open, bfi_neuro, bfi_extra, bfi_consc) %>%
gather(variable, value) %>%
group_by(variable) %>%
filter(!is.na(value)) %>%
summarise(mean = mean(value), sd = sd(value),
median = median(value),
min = min(value), max = max(value)) %>%
data.frame()
## variable mean sd median min max
## 1 age 26.525 5.8479 26.000 18.000 49
## 2 bfi_agree 3.667 0.5903 3.667 1.444 5
## 3 bfi_consc 3.484 0.6544 3.556 1.667 5
## 4 bfi_extra 3.416 0.7615 3.500 1.375 5
## 5 bfi_neuro 2.980 0.7648 3.000 1.000 5
## 6 bfi_open 3.823 0.6183 3.900 1.500 5
## 7 first_time 17.062 2.8260 17.000 0.000 29
## 8 menarche 12.761 1.3797 13.000 8.000 18
## 9 menstruation_length 28.987 3.1502 28.000 19.000 50
## 10 number_sexual_partner 8.948 10.9999 6.000 0.000 100
## 11 religiosity 2.208 1.3279 2.000 1.000 6
HC
all_surveys_included %>% filter(hormonal_contraception == T) %>% select(age, religiosity, first_time, menstruation_length, number_sexual_partner, menarche, bfi_agree, bfi_open, bfi_neuro, bfi_extra, bfi_consc) %>%
gather(variable, value) %>%
group_by(variable) %>%
filter(!is.na(value)) %>%
dplyr::summarise(mean = mean(value), sd = sd(value),
median = median(value),
min = min(value), max = max(value)) %>%
data.frame()
## variable mean sd median min max
## 1 age 23.973 4.5778 23.000 18.000 46.000
## 2 bfi_agree 3.737 0.6158 3.778 1.889 4.889
## 3 bfi_consc 3.630 0.6900 3.667 1.667 5.000
## 4 bfi_extra 3.487 0.8091 3.500 1.125 5.000
## 5 bfi_neuro 2.931 0.7676 2.875 1.000 4.625
## 6 bfi_open 3.733 0.6073 3.700 1.700 4.900
## 7 first_time 16.755 2.6479 17.000 0.000 32.000
## 8 menarche 12.749 1.2642 13.000 9.000 18.000
## 9 menstruation_length 27.643 2.3110 28.000 15.000 36.000
## 10 number_sexual_partner 6.204 9.1362 3.000 0.000 110.000
## 11 religiosity 2.157 1.3395 2.000 1.000 6.000
single women
NC
all_surveys_included %>% filter(hormonal_contraception == F & relationship_status == 1) %>% select(age, religiosity, first_time, menstruation_length, number_sexual_partner, menarche, bfi_agree, bfi_open, bfi_neuro, bfi_extra, bfi_consc) %>%
gather(variable, value) %>%
group_by(variable) %>%
filter(!is.na(value)) %>%
dplyr::summarise(mean = mean(value), sd = sd(value),
median = median(value),
min = min(value), max = max(value)) %>%
data.frame()
## variable mean sd median min max
## 1 age 24.399 4.8730 23.000 18.000 43.000
## 2 bfi_agree 3.696 0.5570 3.778 2.111 4.889
## 3 bfi_consc 3.381 0.6295 3.333 1.778 4.778
## 4 bfi_extra 3.461 0.7201 3.500 1.375 4.875
## 5 bfi_neuro 2.950 0.7800 3.000 1.125 5.000
## 6 bfi_open 3.857 0.6034 3.900 2.300 5.000
## 7 first_time 17.533 2.6300 17.000 13.000 27.000
## 8 menarche 12.645 1.3688 13.000 8.000 16.000
## 9 menstruation_length 28.862 3.1135 28.000 20.000 40.000
## 10 number_sexual_partner 8.803 13.7380 5.000 0.000 100.000
## 11 religiosity 2.123 1.2741 2.000 1.000 6.000
HC
all_surveys_included %>% filter(hormonal_contraception == T & relationship_status == 1) %>% select(age, religiosity, first_time, menstruation_length, number_sexual_partner, menarche, bfi_agree, bfi_open, bfi_neuro, bfi_extra, bfi_consc) %>%
gather(variable, value) %>%
group_by(variable) %>%
filter(!is.na(value)) %>%
dplyr::summarise(mean = mean(value), sd = sd(value),
median = median(value),
min = min(value), max = max(value)) %>%
data.frame()
## variable mean sd median min max
## 1 age 24.533 5.2866 23.500 18.000 42.000
## 2 bfi_agree 3.648 0.6010 3.667 2.111 4.889
## 3 bfi_consc 3.580 0.6847 3.611 1.667 4.778
## 4 bfi_extra 3.542 0.8207 3.625 1.500 4.875
## 5 bfi_neuro 2.894 0.7821 2.875 1.250 4.500
## 6 bfi_open 3.802 0.6816 3.800 1.700 4.900
## 7 first_time 16.793 2.2302 17.000 13.000 26.000
## 8 menarche 12.850 1.2599 13.000 10.000 16.000
## 9 menstruation_length 27.317 3.2703 28.000 15.000 35.000
## 10 number_sexual_partner 8.017 8.4462 5.000 0.000 40.000
## 11 religiosity 1.817 1.0813 1.000 1.000 5.000
compare groups
age
t.test(hc$age, nc$age)
Welch Two Sample t-test
data: hc$age and nc$age
t = -6.7, df = 622, p-value = 5e-11
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.302 -1.803
sample estimates:
mean of x mean of y
23.97 26.53
cohen.d(hc$age, nc$age, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: -0.4361 (small)
## 95 percent confidence interval:
## lower upper
## -0.5867 -0.2855
religiosity
t.test(hc$religiosity, nc$religiosity)
Welch Two Sample t-test
data: hc$religiosity and nc$religiosity
t = -0.5, df = 495, p-value = 0.6
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2504 0.1485
sample estimates:
mean of x mean of y
2.157 2.208
cohen.d(hc$religiosity, nc$religiosity, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: -0.03832 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.1874 0.1108
first time
t.test(hc$first_time, nc$first_time)
Welch Two Sample t-test
data: hc$first_time and nc$first_time
t = -1.5, df = 537, p-value = 0.1
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.7179 0.1035
sample estimates:
mean of x mean of y
16.75 17.06
cohen.d(hc$first_time, nc$first_time, hedges.correction = T, pooled = F, na.rm =T)
##
## Hedges's g
##
## g estimate: -0.1086 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.26005 0.04287
menarche
t.test(hc$menarche, nc$menarche)
Welch Two Sample t-test
data: hc$menarche and nc$menarche
t = -0.12, df = 540, p-value = 0.9
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2061 0.1828
sample estimates:
mean of x mean of y
12.75 12.76
cohen.d(hc$menarche, nc$menarche, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: -0.008435 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.1575 0.1406
cycle length
t.test(hc$menstruation_length, nc$menstruation_length)
Welch Two Sample t-test
data: hc$menstruation_length and nc$menstruation_length
t = -6.8, df = 657, p-value = 3e-11
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.7334 -0.9543
sample estimates:
mean of x mean of y
27.64 28.99
cohen.d(hc$menstruation_length, nc$menstruation_length, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: -0.4262 (small)
## 95 percent confidence interval:
## lower upper
## -0.5767 -0.2757
nr sex partner
t.test(hc$number_sexual_partner, nc$number_sexual_partner)
Welch Two Sample t-test
data: hc$number_sexual_partner and nc$number_sexual_partner
t = -3.7, df = 591, p-value = 0.0002
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.203 -1.285
sample estimates:
mean of x mean of y
6.204 8.948
cohen.d(hc$number_sexual_partner, nc$number_sexual_partner, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: -0.2492 (small)
## 95 percent confidence interval:
## lower upper
## -0.39879 -0.09967
agreeableness
t.test(hc$bfi_agree, nc$bfi_agree)
Welch Two Sample t-test
data: hc$bfi_agree and nc$bfi_agree
t = 1.5, df = 480, p-value = 0.1
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.02018 0.16135
sample estimates:
mean of x mean of y
3.737 3.667
cohen.d(hc$bfi_agree, nc$bfi_agree, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: 0.1195 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.0297 0.2686
neuroticism
t.test(hc$bfi_neuro, nc$bfi_neuro)
Welch Two Sample t-test
data: hc$bfi_neuro and nc$bfi_neuro
t = -0.84, df = 497, p-value = 0.4
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.16318 0.06582
sample estimates:
mean of x mean of y
2.931 2.980
cohen.d(hc$bfi_neuro, nc$bfi_neuro, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: -0.06359 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.2127 0.0855
conscientiousness
t.test(hc$bfi_consc, nc$bfi_consc)
Welch Two Sample t-test
data: hc$bfi_consc and nc$bfi_consc
t = 2.8, df = 476, p-value = 0.005
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.04403 0.24677
sample estimates:
mean of x mean of y
3.630 3.484
cohen.d(hc$bfi_consc, nc$bfi_consc, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: 0.222 (small)
## 95 percent confidence interval:
## lower upper
## 0.07251 0.37142
openness
t.test(hc$bfi_open, nc$bfi_open)
Welch Two Sample t-test
data: hc$bfi_open and nc$bfi_open
t = -1.9, df = 507, p-value = 0.05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.181260 0.001173
sample estimates:
mean of x mean of y
3.733 3.823
cohen.d(hc$bfi_open, nc$bfi_open, hedges.correction = T, pooled = F)
##
## Hedges's g
##
## g estimate: -0.1455 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.294730 0.003724
Time spent with whom
diary %>% filter(reasons_for_exclusion == "") %>%
select(time_family, person_is_related_man, person_is_related_woman) %>% cor(use = 'p') %>% round(2)
## time_family person_is_related_man person_is_related_woman
## time_family 1.00 0.21 0.24
## person_is_related_man 0.21 1.00 0.40
## person_is_related_woman 0.24 0.40 1.00
diary %>% filter(reasons_for_exclusion == "") %>%
xtabs(~ time_family + I(person_is_related_man>0) + I(person_is_related_woman>0), .)
## , , I(person_is_related_woman > 0) = FALSE
##
## I(person_is_related_man > 0)
## time_family FALSE TRUE
## 0 1643 25
## 1 1959 24
## 2 2158 39
## 3 1049 44
## 4 478 50
##
## , , I(person_is_related_woman > 0) = TRUE
##
## I(person_is_related_man > 0)
## time_family FALSE TRUE
## 0 51 19
## 1 101 30
## 2 206 58
## 3 182 85
## 4 172 139
Exclusion reasons
Between subjects
exclusion_reasons <- all_surveys %>%
mutate(reasons_for_exclusion = str_sub(reasons_for_exclusion, 1, -3)) %>%
select(session, reasons_for_exclusion) %>%
drop_na(session) %>%
comma_separated_to_columns(reasons_for_exclusion)
exclusion_reasons_table <- exclusion_reasons %>%
select(-session) %>%
summarise_all(sum) %>%
sort() %>%
gather(reason, n) %>%
left_join(diary %>% mutate(reason = str_sub(reasons_for_exclusion, 1, -3)) %>% group_by(reason) %>% summarise(unique = n())) %>%
mutate(unique = if_else(is.na(unique), 0L, unique)) %>%
left_join(exclusion_reasons %>%
gather(reason, n, -session) %>%
filter(n > 0) %>%
distinct(session, reason, n) %>%
group_by(reason) %>%
summarise(n_women = sum(n)))
library(UpSetR)
exclusion_reasons %>%
filter(included == 0) %>%
select(-included) %>%
as.data.frame() %>%
{
upset(., ncol(.), 20, show.numbers = TRUE, order.by = "freq",
main.bar.color = "#6E8691",
matrix.color = "#6E8691",
sets.bar.color = "#53AC9B")
}
exclusion_reasons_table %>%
knitr::kable(caption = "Reasons for exclusion. _n_ shows the number of affected women, _unique_ those who for whom this was the only reason to be excluded.")
Reasons for exclusion. n shows the number of affected women, unique those who for whom this was the only reason to be excluded.
| sex_hormones |
7 |
123 |
7 |
| non_heterosexual_relationship |
9 |
0 |
9 |
| switched_contraception |
18 |
135 |
18 |
| pregnant |
23 |
0 |
23 |
| not_heterosexual_female |
26 |
175 |
26 |
| breast_feeding |
28 |
0 |
28 |
| older_than_50 |
35 |
0 |
35 |
| menopausal |
41 |
0 |
41 |
| diary_days_not_usable |
45 |
0 |
45 |
| didnt_finish_demographics |
53 |
0 |
53 |
| didnt_do_diary |
66 |
0 |
66 |
| fertility_never_estimable |
143 |
0 |
143 |
| living_with_parents |
145 |
1476 |
145 |
| didnt_finish_personality |
196 |
0 |
196 |
| no_regular_menstruation |
302 |
2349 |
302 |
| included |
794 |
0 |
794 |
Diary
exclusion_reasons_diary <- diary %>%
mutate(reasons_for_exclusion = str_sub(reasons_for_exclusion, 1, -3)) %>%
select(session, created_date, reasons_for_exclusion) %>%
drop_na(session, created_date) %>%
comma_separated_to_columns(reasons_for_exclusion) %>%
select( -created_date)
exclusion_reasons_diary_table <- exclusion_reasons_diary %>%
select(-session) %>%
summarise_all(sum) %>%
sort() %>%
gather(reason, n) %>%
left_join(diary %>% mutate(reason = str_sub(reasons_for_exclusion, 1, -3)) %>% group_by(reason) %>% summarise(unique = n())) %>%
mutate(unique = if_else(is.na(unique), 0L, unique)) %>%
left_join(exclusion_reasons_diary %>%
gather(reason, n, -session) %>%
filter(n > 0) %>%
distinct(session, reason, n) %>%
group_by(reason) %>%
summarise(n_women = sum(n)))
library(UpSetR)
exclusion_reasons_diary %>%
filter(included == 0) %>%
select(-included) %>%
as.data.frame() %>%
{
upset(., ncol(.), 20, show.numbers = TRUE, order.by = "freq",
main.bar.color = "#6E8691",
matrix.color = "#6E8691",
sets.bar.color = "#53AC9B")
}
exclusion_reasons_diary_table %>%
knitr::kable(caption = "Reasons for exclusion. _n_ shows the number of affected women, _unique_ those who for whom this was the only reason to be excluded.")
Reasons for exclusion. n shows the number of affected women, unique those who for whom this was the only reason to be excluded.
| dishonest_answer |
150 |
60 |
86 |
| didnt_do_diary |
187 |
0 |
28 |
| non_heterosexual_relationship |
338 |
0 |
8 |
| sex_hormones |
442 |
123 |
7 |
| did_not_finish_entry |
856 |
123 |
542 |
| not_heterosexual_female |
896 |
175 |
16 |
| pregnant |
1047 |
0 |
19 |
| breast_feeding |
1137 |
0 |
22 |
| switched_contraception |
1178 |
135 |
18 |
| older_than_50 |
1560 |
0 |
28 |
| menopausal |
2166 |
0 |
39 |
| diary_days_not_usable |
2361 |
0 |
45 |
| fertility_never_estimable |
2527 |
0 |
143 |
| next_menstrual_onset_unobserved |
4100 |
575 |
207 |
| cycle_shorter_than_20 |
4736 |
830 |
476 |
| living_with_parents |
6587 |
1476 |
123 |
| cycle_longer_than_40 |
7461 |
536 |
222 |
| included |
12251 |
0 |
263 |
| no_regular_menstruation |
13237 |
2349 |
245 |
| skipped_diary_entry |
15321 |
1960 |
1245 |
| not_single |
51899 |
22659 |
922 |