Analyses
In what follows, you can find the analyses for the results reported in the paper. To see the underlying code, click on the button “code”.
Setup
Packages
Let’s first load the packages we require for our analyses. Note that some need to be installed manually from github.
# install github packages devtools::install_github('tdienlin/td@v.0.0.2.3') # uncomment to install
# devtools::install_github('yrosseel/lavaan')
# define packages
packages <- c("brms", "cowplot", "devtools", "english", "faoutlier", "GGally", "kableExtra", "knitr", "lavaan", "magrittr",
"marginaleffects", "MVN", "pwr", "quanteda", "semTools", "tidyverse", "td")
# load packages
lapply(packages, library, character.only = TRUE, quietly = TRUE)
# If not wanting to compute all transformations etc., simply load workspace load('data/workspace.RData')Custom Functions
We now build some custom functions required for the analyses. Note that here you can find the syntax for the main analyses wrapped in helper functions, which we will then call below.
# define function to silence result output
# needed for brms
hush <-
function(
code
){
sink("/dev/null") # use /dev/null in UNIX
tmp = code
sink()
return(tmp)
}
# plot distributions of items
plot_dist <-
function(
item,
n_rows = 1
) {
ggplot(
gather(
select(
d,
starts_with(
item
)
)
),
mapping = aes(
x = value
)
) +
geom_bar() +
facet_wrap(
~key,
nrow = n_rows
) +
theme_bw()
}
get_desc <-
function(
item,
name
) {
desc <-
select(
d,
starts_with(
item
)
) %>%
apply(
1,
mean,
na.rm = TRUE
) %>%
as.data.frame() %>%
summarise(
m = mean(., na.rm = TRUE),
sd = sd(., na.rm = TRUE)
)
assign(
paste0(
"des_",
name
),
desc,
envir = .GlobalEnv
)
}
# Function to display diagnostics of fitted hurdle models
model_diagnostics <-
function(
model
){
plot_grid(
pp_check(
model,
type = "dens_overlay",
nsamples = 100
),
pp_check(
model,
type = "loo_pit_qq",
nsamples = 100
),
pp_check(
model,
type = "stat",
stat = "median",
nsamples = 100
),
pp_check(
model,
type = "stat",
stat = "mean",
nsamples = 100
),
labels = c("Density overlay", "LOO-PIT QQ", "Predicted medians", "Predicted means"),
ncol = 2,
label_size = 8,
hjust = 0,
vjust = 0,
label_x = 0,
label_y = 0.93
)
}
# wrapper function that runs relevant analyses for hurdle models
run_hurdles <-
function(
object,
name,
outcome,
predictor,
x_lab,
y_lab,
plot = FALSE,
...
){
# define define general model
formula_mdl <-
formula(
paste0(
outcome,
" ~ 1 + age + male + edu +",
predictor
)
)
# define hurdle model
formula_hrdl <-
formula(
paste0(
"hu ~ 1 + age + male + edu +",
predictor
)
)
# fit model
fit <-
hush(
brm(
bf(
formula_mdl,
formula_hrdl
),
data = object,
family = hurdle_gamma(),
silent = TRUE,
refresh = 0,
iter = 1000,
backend = "cmdstanr"
)
)
# export fit
assign(
paste0(
"fit_",
name),
fit,
envir = .GlobalEnv
)
# plot model
if(
isTRUE(plot
)) {
plot(fit, ask = FALSE)
}
# run diagnostics
fit_diagnostics <-
model_diagnostics(fit)
plot(fit_diagnostics)
# print summary
fit_summary <-
summary(fit)
print(fit_summary)
# calculate slopes
slopes <-
cbind(
Outcome = outcome,
avg_slopes(fit)
)
# print slopes
cat("\nResults of marginal effects:\n")
print(slopes)
# export as object to environment
assign(
paste0(
"slopes_",
name
),
slopes,
envir = .GlobalEnv
)
# make figure
fig_res <-
conditional_effects(
fit
, ask = FALSE
)
# make figure
fig <-
plot(
fig_res,
plot = FALSE
)[[predictor]] +
labs(
x = x_lab,
y = y_lab
)
# plot figure
print(fig)
# export as object to environment
assign(
paste0(
"fig_",
name
),
fig,
envir = .GlobalEnv
)
}Data-Wrangling
Let’s next tidy up and prepare the data, so we can analyze them.
# load data
# please note that in a prior step several different data sets were merged using information such as case_taken or IP-addresses. To guarantee the privacy of the participants, these data were deleted after merging.
d_raw <- read_csv("data/data_raw.csv")
# recode variables
d <-
d_raw %>%
dplyr::rename(
"male" = "SD01",
"age" = "SD02_01",
"state" = "SD03",
"edu_fac" = "SD04",
"TR02_01" = "TR01_01",
"TR02_02" = "TR01_05",
"TR02_03" = "TR01_09",
"TR01_01" = "TR01_02",
"TR01_02" = "TR01_03",
"TR01_03" = "TR01_04",
"TR01_04" = "TR01_06",
"TR01_05" = "TR01_07",
"TR01_06" = "TR01_08",
"TR01_07" = "TR01_10",
"TR01_08" = "TR01_11",
"TR01_09" = "TR01_12"
) %>%
mutate(
#general
version = factor(
VE01_01,
labels = c(
"Control",
"Like",
"Like & dislike"
)
),
version_lkdslk = factor(
version,
levels = c(
"Like & dislike",
"Like",
"Control"
)
),
version_no = recode(
version,
"Control" = 1,
"Like" = 2,
"Like & dislike" = 3
),
male = replace_na(
male,
"[3]") %>%
dplyr::recode(
"männlich [1]" = 1,
"weiblich [2]" = 0
),
age_grp = cut(
age,
breaks = c(0, 29, 39, 49, 59, 100),
labels = c("<30", "30-39", "40-49", "50-59", ">59")
),
# contrasts
like = recode(
version,
"Control" = 0,
"Like" = 1,
"Like & dislike" = 0
),
likedislike = recode(
version,
"Control" = 0,
"Like" = 0,
"Like & dislike" = 1
),
control = recode(
version,
"Control" = 1,
"Like" = 0,
"Like & dislike" = 0
),
# recode
edu = recode(
edu_fac,
"Hauptschulabschluss/Volksschulabschluss" = 1,
"Noch Schüler" = 1,
"Realschulabschluss (Mittlere Reife)" = 1,
"Abschluss Polytechnische Oberschule 10. Klasse (vor 1965: 8. Klasse)" = 1,
"Anderer Schulabschluss:" = 1,
"Fachhochschulreife (Abschluss einer Fachoberschule)" = 2,
"Abitur, allgemeine oder fachgebundene Hochschulreife (Gymnasium bzw. EOS)" = 2,
"Hochschulabschluss" = 3),
PC01_03 = 8 - PC01_03,
SE01_05 = 8 - SE01_05,
SE01_06 = 8 - SE01_06,
# behavioral data
words = replace_na(
words,
0
),
self_dis = words + (reactions * 2),
time_read_MIN = time_read / 60,
time_sum_min_t2 = TIME_SUM_t2 / 60,
# take logarithm
words_log = log1p(words),
self_dis_log = log1p(self_dis)
)
# variable labels
var_names <- c(
"Privacy concerns",
"Gratifications general",
"Gratifications specific",
"Privacy deliberation",
"Self-efficacy",
"Trust general",
"Trust specific",
"Words log"
)
var_names_breaks <- c(
"Privacy\nconcerns",
"Gratifications\ngeneral",
"Gratifications\nspecific",
"Privacy\ndeliberation",
"Self-\nefficacy",
"Trust\ngeneral",
"Trust\nspecific",
"Words (log)"
)
# Extract sample characteristics and descriptives.
# sample descriptives t1
n_t1 <- nrow(d)
age_t1_m <- mean(d$age, na.rm = TRUE)
male_t1_m <- mean(d$male, na.rm = TRUE)
college_t1_m <- table(d$edu)[3] / n_t1
# sample descriptives t2
# participants
n_t2 <-
filter(
d,
!is.na(
id_survey_t2
)
) %>%
nrow()
# age
age_t2_m <-
filter(
d,
!is.na(
id_survey_t2
)
) %>%
select(
age
) %>%
mean()
# gender
male_t2_m <-
filter(
d,
!is.na(
id_survey_t2
)
) %>%
select(
male
) %>%
mean(
na.rm = TRUE
)
# education
college_t2_m <-
filter(
d,
!is.na(
id_survey_t2
)
) %>%
select(
edu
) %>%
table() %>%
.[3] / n_t2
# descriptives users
n_users <-
filter(
d,
!is.na(
post_count
)
) %>%
nrow()
# characteristics of posts
n_comments <-
sum(
d$post_count,
na.rm = TRUE
)
n_words <-
sum(
d$words,
na.rm = TRUE
)
n_time <-
sum(
d$time_read_MIN,
na.rm = TRUE
)
n_posts <-
sum(
d$post_count,
na.rm = TRUE
)
# filter unmatched cases, use only completes
d <-
filter(
d,
!is.na(
post_count
) & !is.na(
id_survey_t2
)
)
n_matched <- nrow(d)
# save data file with all participants to compare results
d_all <- dFilter Participants
I filtered participants who answered the questionnaire in less than three minutes, which I considered to be unreasonably fast.
# filter speeders
time_crit <- 3 # minimum time on survey
# count number of speeders
n_speeding <- nrow(filter(d, time_sum_min_t2 < time_crit))
# Deletion of fast respondents
d <- filter(d, time_sum_min_t2 >= time_crit)I inspected the data manually for cases with obvious response patterns. The following cases show extreme response patterns (alongside fast response times), and were hence removed.
# identify response patterns
resp_pattern <- c("ANIEVLK9F2SW", "BN4MAOWZO7W2" # clear response pattern
)
n_resp_pattern <- length(resp_pattern)
d %>%
filter(case_token %in% resp_pattern) %>%
select(case_token, GR01_01:SE01_06, topics_entered:reactions, -SO01_01, TIME_SUM_t1, TIME_SUM_t2) %>%
kable() %>%
kable_styling("striped") %>%
scroll_box(width = "100%")| case_token | GR01_01 | GR01_02 | GR01_03 | GR01_04 | GR01_05 | GR01_06 | GR01_07 | GR01_08 | GR01_09 | GR01_10 | GR01_11 | GR01_12 | GR01_13 | GR01_14 | GR01_15 | GR02_01 | GR02_02 | GR02_03 | GR02_04 | GR02_05 | PC01_01 | PC01_02 | PC01_03 | PC01_04 | PC01_05 | PC01_06 | PC01_07 | TR02_01 | TR01_01 | TR01_02 | TR01_03 | TR02_02 | TR01_04 | TR01_05 | TR01_06 | TR02_03 | TR01_07 | TR01_08 | TR01_09 | PD01_01 | PD01_02 | PD01_03 | PD01_04 | PD01_05 | SE01_01 | SE01_02 | SE01_03 | SE01_04 | SE01_05 | SE01_06 | topics_entered | posts_read_count | time_read | topic_count | post_count | words | reactions | TIME_SUM_t1 | TIME_SUM_t2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BN4MAOWZO7W2 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 1 | 7 | 7 | 7 | 7 | 4 | 1 | 7 | 1 | 4 | 1 | 1 | 1 | 7 | 7 | 4 | 7 | 7 | 7 | 7 | 7 | 7 | 1 | 1 | 1 | 1 | 7 | 1 | 6 | 27 | 211 | 0 | 1 | 2 | 0 | 37 | 246 |
| ANIEVLK9F2SW | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 7 | 5 | 7 | 1 | 1 | 7 | 3 | 151 | 1518 | 0 | 2 | 73 | 0 | 66 | 469 |
d %<>%
filter(!case_token %in% resp_pattern)
# sample descriptives final data set
n_final <- nrow(d)
age_final_m <- mean(d$age)
male_final_m <- mean(d$male, na.rm = TRUE)
college_final_m <- table(d$edu)[[3]]/n_finalMeasures
Let’s next inspect all measures. I list all items, their distributions and CFAs.
Privacy concerns
Items
Using the participation platform I had …
- … concerns about what happens to my data.
- … concerns about disclosing information about myself.
- … no concerns. (reversed)
- … concerns that others could discover my real identity (i.e. my last and first name).
- … concerns that information about myself could fall into wrong hands.
- … concerns that others could discover what my political views are.
- … concerns about my privacy.
Distributions
# plot distribution
plot_dist(
item = "PC01"
)
# extract results
get_desc(
item = "PC01",
name = "pricon"
)CFA
name <- "pricon"
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_03 + PC01_04 + PC01_05 + PC01_06 + PC01_07
"
fit <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 16.1 14 0.307 0.999 0.998 0.0164 0.0104 0.947 0.944 0.724# export factor validity
assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std") %$%
lambda## pri_cn
## PC01_01 0.931
## PC01_02 0.901
## PC01_03 0.547
## PC01_04 0.890
## PC01_05 0.908
## PC01_06 0.795
## PC01_07 0.928Shows that PC01_03 doesn’t load well. As it’s an inverted item that’s not surprising. Also from a theoretic perspective it’s suboptimal, because it doesn’t explicitly focus on privacy, but just concerns in general. Will be deleted.
CFA 2
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
"
fit <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 11 9 0.273 0.998 0.997 0.0201 0.00963 0.96 0.959 0.799assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std"
) %$%
lambda## pri_cn
## PC01_01 0.931
## PC01_02 0.901
## PC01_04 0.891
## PC01_05 0.909
## PC01_06 0.796
## PC01_07 0.926Updated version shows good fit.
Gratifications general
Items
Using the participation platform …
- … had many benefits for me.
- … has paid off for me.
- … was worthwhile.
- … was fun.
- … has brought me further regarding content.
Distributions
plot_dist(
item = "GR02"
)
get_desc(
item = "GR02",
name = "gratsgen"
)CFA
name <- "gratsgen"
model <- "
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
"
fit <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 34 5 2.35e-06 0.976 0.951 0.102 0.0193 0.934 0.934 0.741assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std"
) %$%
lambda## grts_g
## GR02_01 0.853
## GR02_02 0.908
## GR02_03 0.851
## GR02_04 0.837
## GR02_05 0.848Model fit was good.
Gratifications specific
Items
Using the participation platform it has been possible for me …
Information
- … to learn things I would not otherwise have noticed.
- … to hear the opinion of others.
- … to learn how other people tick.
Relevance
- … to react to a subject that is very dear to me.
- … to react to a subject that is important to me.
- … to react to a subject that I am affected by.
Political participation
- … to engage politically.
- … to discuss political issues.
- … to pursue my political interest.
Idealism
- … to try to improve society.
- … to advocate to something meaningful.
- … to serve a good purpose.
Extrinsic benefits
- … to do the responsible persons a favor.
- … to soothe my guilty consciences.
- … to fulfil my civic duty.
Distributions
plot_dist(
item = "GR01",
n_rows = 2
)
get_desc(
item = "GR01",
name = "gratsspec"
)CFA
name <- "gratsspec"
model <- "
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
"
fit <- lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 270 85 0 0.941 0.928 0.0624 0.0527 0.946 0.933 0.586assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std"
) %$%
lambda## grts_n grts_r grts_p grts_d grts_x grts_s
## GR01_01 0.688 0.000 0.000 0.000 0.000 0
## GR01_02 0.819 0.000 0.000 0.000 0.000 0
## GR01_03 0.851 0.000 0.000 0.000 0.000 0
## GR01_04 0.000 0.891 0.000 0.000 0.000 0
## GR01_05 0.000 0.852 0.000 0.000 0.000 0
## GR01_06 0.000 0.704 0.000 0.000 0.000 0
## GR01_07 0.000 0.000 0.826 0.000 0.000 0
## GR01_08 0.000 0.000 0.811 0.000 0.000 0
## GR01_09 0.000 0.000 0.816 0.000 0.000 0
## GR01_10 0.000 0.000 0.000 0.796 0.000 0
## GR01_11 0.000 0.000 0.000 0.882 0.000 0
## GR01_12 0.000 0.000 0.000 0.762 0.000 0
## GR01_13 0.000 0.000 0.000 0.000 0.519 0
## GR01_14 0.000 0.000 0.000 0.000 0.513 0
## GR01_15 0.000 0.000 0.000 0.000 0.848 0Model fit was okay.
Privacy deliberation
Items
Using the participation platform …
- … I have considered whether I could be disadvantaged by writing a comment.
- … I have considered whether I could be advantaged by writing a comment.
- … I have weighed up the advantages and disadvantages of writing a comment.
- … I have thought about consequences of a possible comment.
- … I have considered whether I should write a comment or not.
Distributions
plot_dist(
item = "PD01"
)
get_desc(
item = "PD01",
name = "pridel"
)CFA
name <- "pridel"
model <- "
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
"
fit <- lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 15.6 5 0.00825 0.98 0.96 0.0614 0.0235 0.848 0.843 0.532assign(
paste0(
name,
"_facval"),
facval
)Factor loadings:
inspect(
fit,
what = "std"
) %$%
lambda## pr_dlb
## PD01_01 0.849
## PD01_02 0.653
## PD01_03 0.691
## PD01_04 0.752
## PD01_05 0.656Model fit was very good (which is nice, given it’s a newly designed scale).
Trust general
Items
- The other users seemed trustworthy.
- The operators of the participation platform seemed
trustworthy.
- The website seemed trustworthy.
Distributions
plot_dist(
item = "TR02"
)
get_desc(
item = "TR02",
name = "trustgen"
)CFA
Note that I constrained two loadings to be able to report model fit. I used operators and website, as these are theoretically closer.
name <- "trustgen"
model <- "
trust =~ TR02_01 + a*TR02_02 + a*TR02_03
"
fit <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 2.07 1 0.15 0.998 0.993 0.0438 0.0125 0.873 0.864 0.701assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std"
) %$%
lambda## trust
## TR02_01 0.660
## TR02_02 0.923
## TR02_03 0.898Note that I constrained Items 5 and Item 9 to be equal. Explanation: First, they are theoretically related. Second, not constraining would yield to just-identified model, for which model fit cannot be interpreted meaningfully.
Trust specific
Items
Community
- The comments of other users were useful.
- The other users had good intentions.
- I could rely on the statements of other users.
Provider
- The operators of the participation platform have done a good job.
- It was important to the operators that the users are satisfied with the participation platform.
- I could rely on the statements of the operators of the participation platform.
Information System
- The website worked well.
- I had the impression that my data was necessary for the use of the website.
- I found the website useful.
Distributions
plot_dist(
item = "TR01",
n_rows = 2
)
get_desc(
item = "TR01",
name = "trustspec"
)CFA
name <- "trustspec"
model <- "
trust_community =~ TR01_01 + TR01_02 + TR01_03
trust_provider =~ TR01_04 + TR01_05 + TR01_06
trust_system =~ TR01_07 + TR01_08 + TR01_09
trust_spec =~ trust_community + trust_provider + trust_system
"
fit <- lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 89.1 24 2.02e-09 0.963 0.944 0.0697 0.0351 0.93 0.921 0.613assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std") %$%
lambda## trst_c trst_p trst_sy trst_sp
## TR01_01 0.814 0.000 0.000 0
## TR01_02 0.765 0.000 0.000 0
## TR01_03 0.822 0.000 0.000 0
## TR01_04 0.000 0.884 0.000 0
## TR01_05 0.000 0.779 0.000 0
## TR01_06 0.000 0.793 0.000 0
## TR01_07 0.000 0.000 0.690 0
## TR01_08 0.000 0.000 0.662 0
## TR01_09 0.000 0.000 0.817 0Model fit was good (there’s a Highwood case on level 1, which is not too problematic in this case.)
Self-efficacy
Items
- In principle, I felt able to write a comment.
- I felt technically competent enough to write a comment.
- In terms of the topic, I felt competent enough to express my opinion.
- I found it easy to express my opinion regarding the topic.
- I found it complicated to write a comment. (reversed)
- I was overburdened to write a comment. (reversed)
Distributions
plot_dist(
item = "SE01"
)
get_desc(
item = "SE01",
name = "selfeff"
)CFA
name <- "self-eff"
model <- "
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04 + SE01_05 + SE01_06
"
fit <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 96.1 9 1.11e-16 0.86 0.766 0.132 0.0671 0.851 0.854 0.49assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std") %$%
lambda## slf_ff
## SE01_01 0.842
## SE01_02 0.689
## SE01_03 0.764
## SE01_04 0.801
## SE01_05 0.528
## SE01_06 0.633Shows significant misfit. I will delete inverted items, while allowing covariations between Items 1 and 2 (tech-oriented) and Items 3 and 4 (topic-oriented).
CFA 2
name <- "selfeff"
model <- "
self_eff_pos =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
"
fit <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML"
)Model fit:
facval <-
fit_tab(
fit,
reliability = TRUE,
scaled = TRUE
) %T>%
print()## chisq df pvalue cfi tli rmsea srmr omega alpha ave
## 1 3.23 1 0.0721 0.993 0.957 0.0633 0.0136 0.833 0.862 0.592assign(
paste0(
name,
"_facval"
),
facval
)Factor loadings:
inspect(
fit,
what = "std") %$%
lambda## slf_f_
## SE01_01 0.828
## SE01_02 0.675
## SE01_03 0.779
## SE01_04 0.787Adapted version shows better and adequate fit.
Communication
Density plot
ggplot(
gather(
select(
d,
words
)
),
mapping = aes(
x = value
)
) +
geom_density() +
facet_wrap(
~key,
nrow = 1
) +
theme_bw()
We can see that Communication is severely skewed. Let’s calculate how many participants actually commented.
no_words_perc <-
d %>%
select(words) %>%
table() %>%
prop.table() %>%
.[1] %>%
unlist()
words_m <-
mean(
d$words,
na.rm = TRUE
)0.58 percent wrote a comment. The average number of communicated words was 76.533.
Will hence be log-scaled for SEMs.
Communication Logged
ggplot(
gather(
select(
d,
words_log
)
),
mapping = aes(
x = value
)
) +
geom_density() +
facet_wrap(
~key,
nrow = 1
) +
theme_bw()
Baseline model
In what follows, please find the results of all variables combined in one model. This model will be used to extract factor scores.
model_baseline <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
trust_gen =~ TR02_01 + TR02_02 + TR02_03
trust_community =~ TR01_01 + TR01_02 + TR01_03
trust_provider =~ TR01_04 + TR01_05 + TR01_06
trust_system =~ TR01_07 + TR01_08 + TR01_09
trust_spec =~ trust_community + trust_provider + trust_system
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
Words_log =~ words_log
Words_log ~~ a1*pri_con + b1*grats_gen + c1*pri_delib + d1*self_eff + e1*trust_spec + f1*trust_gen + g1*grats_spec
"
fit_baseline <- lavaan::sem(model_baseline, data = d, missing = "ML")
summary(fit_baseline, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6.15 ended normally after 171 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 181
## Number of equality constraints 1
##
## Number of observations 559
## Number of missing patterns 3
##
## Model Test User Model:
##
## Test statistic 3210.778
## Degrees of freedom 1044
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 22544.020
## Degrees of freedom 1128
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.899
## Tucker-Lewis Index (TLI) 0.891
##
## Robust Comparative Fit Index (CFI) 0.899
## Robust Tucker-Lewis Index (TLI) 0.891
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -37699.172
## Loglikelihood unrestricted model (H1) -36093.783
##
## Akaike (AIC) 75758.344
## Bayesian (BIC) 76537.051
## Sample-size adjusted Bayesian (SABIC) 75965.644
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.061
## 90 Percent confidence interval - lower 0.059
## 90 Percent confidence interval - upper 0.063
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Robust RMSEA 0.061
## 90 Percent confidence interval - lower 0.059
## 90 Percent confidence interval - upper 0.063
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.065
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pri_con =~
## PC01_01 1.000 1.602 0.929
## PC01_02 0.994 0.027 36.762 0.000 1.592 0.901
## PC01_04 0.977 0.027 35.602 0.000 1.565 0.892
## PC01_05 1.002 0.026 38.002 0.000 1.605 0.910
## PC01_06 0.855 0.032 26.995 0.000 1.369 0.798
## PC01_07 0.996 0.025 40.248 0.000 1.595 0.925
## grats_gen =~
## GR02_01 1.000 1.128 0.841
## GR02_02 1.120 0.040 27.925 0.000 1.264 0.893
## GR02_03 1.032 0.040 25.952 0.000 1.164 0.870
## GR02_04 0.990 0.040 25.023 0.000 1.118 0.850
## GR02_05 1.073 0.042 25.275 0.000 1.210 0.845
## grats_inf =~
## GR01_01 1.000 0.978 0.696
## GR01_02 1.018 0.062 16.518 0.000 0.996 0.816
## GR01_03 1.114 0.066 16.803 0.000 1.089 0.847
## grats_rel =~
## GR01_04 1.000 1.175 0.891
## GR01_05 0.943 0.034 27.545 0.000 1.108 0.857
## GR01_06 0.878 0.046 19.086 0.000 1.032 0.698
## grats_par =~
## GR01_07 1.000 1.192 0.819
## GR01_08 0.938 0.042 22.086 0.000 1.119 0.816
## GR01_09 0.961 0.043 22.316 0.000 1.146 0.819
## grats_ide =~
## GR01_10 1.000 1.148 0.791
## GR01_11 1.000 0.043 23.251 0.000 1.148 0.885
## GR01_12 0.928 0.048 19.451 0.000 1.065 0.764
## grats_ext =~
## GR01_13 1.000 0.850 0.518
## GR01_14 1.003 0.108 9.311 0.000 0.853 0.509
## GR01_15 1.528 0.144 10.596 0.000 1.299 0.851
## grats_spec =~
## grats_inf 1.000 0.843 0.843
## grats_rel 1.314 0.089 14.743 0.000 0.922 0.922
## grats_par 1.379 0.097 14.187 0.000 0.954 0.954
## grats_ide 1.306 0.095 13.801 0.000 0.938 0.938
## grats_ext 0.805 0.088 9.162 0.000 0.781 0.781
## pri_delib =~
## PD01_01 1.000 1.494 0.866
## PD01_02 0.676 0.041 16.543 0.000 1.009 0.657
## PD01_03 0.704 0.043 16.324 0.000 1.052 0.676
## PD01_04 0.848 0.044 19.139 0.000 1.267 0.743
## PD01_05 0.718 0.045 15.904 0.000 1.073 0.648
## trust_gen =~
## TR02_01 1.000 0.814 0.706
## TR02_02 1.253 0.064 19.587 0.000 1.020 0.886
## TR02_03 1.355 0.068 19.998 0.000 1.103 0.914
## trust_community =~
## TR01_01 1.000 1.024 0.808
## TR01_02 0.824 0.043 18.982 0.000 0.844 0.768
## TR01_03 0.931 0.044 21.029 0.000 0.953 0.826
## trust_provider =~
## TR01_04 1.000 1.042 0.869
## TR01_05 0.866 0.038 22.891 0.000 0.902 0.779
## TR01_06 0.864 0.036 24.282 0.000 0.900 0.812
## trust_system =~
## TR01_07 1.000 0.812 0.690
## TR01_08 1.037 0.069 14.917 0.000 0.841 0.649
## TR01_09 1.376 0.073 18.761 0.000 1.117 0.831
## trust_spec =~
## trust_communty 1.000 0.853 0.853
## trust_provider 1.166 0.062 18.798 0.000 0.977 0.977
## trust_system 0.959 0.061 15.790 0.000 1.032 1.032
## self_eff =~
## SE01_01 1.000 1.135 0.821
## SE01_02 0.809 0.046 17.415 0.000 0.918 0.680
## SE01_03 0.923 0.048 19.106 0.000 1.048 0.781
## SE01_04 0.941 0.047 19.947 0.000 1.067 0.791
## Words_log =~
## words_log 1.000 2.311 1.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SE01_01 ~~
## .SE01_02 (x) 0.115 0.029 3.993 0.000 0.115 0.148
## .SE01_03 ~~
## .SE01_04 (x) 0.115 0.029 3.993 0.000 0.115 0.166
## pri_con ~~
## Words_log (a1) -0.559 0.161 -3.460 0.001 -0.151 -0.151
## grats_gen ~~
## Words_log (b1) 0.299 0.115 2.594 0.009 0.115 0.115
## pri_delib ~~
## Words_log (c1) -0.653 0.160 -4.090 0.000 -0.189 -0.189
## self_eff ~~
## Words_log (d1) 1.016 0.132 7.711 0.000 0.387 0.387
## trust_spec ~~
## Words_log (e1) 0.358 0.091 3.920 0.000 0.177 0.177
## trust_gen ~~
## Words_log (f1) 0.323 0.086 3.767 0.000 0.171 0.171
## grats_spec ~~
## Words_log (g1) 0.426 0.089 4.771 0.000 0.224 0.224
## pri_con ~~
## grats_gen -0.284 0.082 -3.465 0.001 -0.157 -0.157
## grats_spc -0.109 0.060 -1.825 0.068 -0.083 -0.083
## pri_delib 1.356 0.131 10.332 0.000 0.567 0.567
## trust_gen -0.548 0.068 -8.083 0.000 -0.420 -0.420
## trust_spc -0.414 0.068 -6.120 0.000 -0.296 -0.296
## self_eff -0.384 0.088 -4.352 0.000 -0.211 -0.211
## grats_gen ~~
## grats_spc 0.733 0.071 10.351 0.000 0.787 0.787
## pri_delib -0.073 0.080 -0.918 0.359 -0.043 -0.043
## trust_gen 0.561 0.056 9.943 0.000 0.610 0.610
## trust_spc 0.748 0.067 11.174 0.000 0.759 0.759
## self_eff 0.464 0.066 7.002 0.000 0.362 0.362
## grats_spec ~~
## pri_delib 0.011 0.059 0.189 0.850 0.009 0.009
## trust_gen 0.441 0.049 9.001 0.000 0.657 0.657
## trust_spc 0.562 0.058 9.619 0.000 0.780 0.780
## self_eff 0.496 0.059 8.428 0.000 0.530 0.530
## pri_delib ~~
## trust_gen -0.309 0.062 -4.974 0.000 -0.254 -0.254
## trust_spc -0.136 0.063 -2.150 0.032 -0.104 -0.104
## self_eff -0.335 0.087 -3.865 0.000 -0.197 -0.197
## trust_gen ~~
## trust_spc 0.664 0.060 11.079 0.000 0.934 0.934
## self_eff 0.486 0.055 8.838 0.000 0.526 0.526
## trust_spec ~~
## self_eff 0.534 0.059 9.072 0.000 0.539 0.539
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 3.293 0.073 45.160 0.000 3.293 1.910
## .PC01_02 3.327 0.075 44.525 0.000 3.327 1.883
## .PC01_04 3.222 0.074 43.394 0.000 3.222 1.835
## .PC01_05 3.263 0.075 43.748 0.000 3.263 1.850
## .PC01_06 3.004 0.073 41.410 0.000 3.004 1.751
## .PC01_07 3.224 0.073 44.188 0.000 3.224 1.869
## .GR02_01 4.281 0.057 75.413 0.000 4.281 3.190
## .GR02_02 4.596 0.060 76.742 0.000 4.596 3.246
## .GR02_03 5.131 0.057 90.628 0.000 5.131 3.833
## .GR02_04 5.089 0.056 91.559 0.000 5.089 3.873
## .GR02_05 4.692 0.061 77.447 0.000 4.692 3.276
## .GR01_01 4.878 0.059 82.009 0.000 4.878 3.469
## .GR01_02 5.436 0.052 105.391 0.000 5.436 4.458
## .GR01_03 5.283 0.054 97.076 0.000 5.283 4.106
## .GR01_04 4.925 0.056 88.265 0.000 4.925 3.733
## .GR01_05 5.086 0.055 93.032 0.000 5.086 3.935
## .GR01_06 4.660 0.063 74.538 0.000 4.660 3.153
## .GR01_07 4.682 0.062 76.077 0.000 4.682 3.218
## .GR01_08 5.066 0.058 87.375 0.000 5.066 3.696
## .GR01_09 4.841 0.059 81.782 0.000 4.841 3.459
## .GR01_10 4.547 0.061 74.048 0.000 4.547 3.132
## .GR01_11 4.964 0.055 90.449 0.000 4.964 3.826
## .GR01_12 4.760 0.059 80.678 0.000 4.760 3.412
## .GR01_13 4.079 0.069 58.781 0.000 4.079 2.486
## .GR01_14 3.039 0.071 42.918 0.000 3.039 1.815
## .GR01_15 4.410 0.065 68.283 0.000 4.410 2.888
## .PD01_01 3.658 0.073 50.136 0.000 3.658 2.121
## .PD01_02 3.352 0.065 51.628 0.000 3.352 2.184
## .PD01_03 4.191 0.066 63.662 0.000 4.191 2.693
## .PD01_04 4.081 0.072 56.578 0.000 4.081 2.393
## .PD01_05 4.351 0.070 62.149 0.000 4.351 2.629
## .TR02_01 4.846 0.049 99.399 0.000 4.846 4.204
## .TR02_02 5.383 0.049 110.608 0.000 5.383 4.678
## .TR02_03 5.390 0.051 105.542 0.000 5.390 4.464
## .TR01_01 4.764 0.054 88.924 0.000 4.764 3.761
## .TR01_02 4.844 0.046 104.196 0.000 4.844 4.407
## .TR01_03 4.615 0.049 94.568 0.000 4.615 4.000
## .TR01_04 5.403 0.051 106.479 0.000 5.403 4.504
## .TR01_05 5.200 0.049 106.180 0.000 5.200 4.491
## .TR01_06 5.129 0.047 109.405 0.000 5.129 4.627
## .TR01_07 5.725 0.050 114.940 0.000 5.725 4.864
## .TR01_08 4.834 0.055 88.152 0.000 4.834 3.728
## .TR01_09 5.179 0.057 91.089 0.000 5.179 3.853
## .SE01_01 5.277 0.058 90.256 0.000 5.277 3.820
## .SE01_02 5.523 0.057 96.667 0.000 5.523 4.092
## .SE01_03 5.224 0.057 92.028 0.000 5.224 3.895
## .SE01_04 5.137 0.057 89.906 0.000 5.137 3.805
## .words_log 1.834 0.098 18.765 0.000 1.834 0.794
## pri_con 0.000 0.000 0.000
## grats_gen 0.000 0.000 0.000
## .grats_inf 0.000 0.000 0.000
## .grats_rel 0.000 0.000 0.000
## .grats_par 0.000 0.000 0.000
## .grats_ide 0.000 0.000 0.000
## .grats_ext 0.000 0.000 0.000
## grats_spec 0.000 0.000 0.000
## pri_delib 0.000 0.000 0.000
## trust_gen 0.000 0.000 0.000
## .trust_communty 0.000 0.000 0.000
## .trust_provider 0.000 0.000 0.000
## .trust_system 0.000 0.000 0.000
## trust_spec 0.000 0.000 0.000
## self_eff 0.000 0.000 0.000
## Words_log 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 0.406 0.032 12.792 0.000 0.406 0.137
## .PC01_02 0.587 0.042 14.012 0.000 0.587 0.188
## .PC01_04 0.631 0.044 14.284 0.000 0.631 0.205
## .PC01_05 0.534 0.039 13.741 0.000 0.534 0.172
## .PC01_06 1.066 0.068 15.606 0.000 1.066 0.362
## .PC01_07 0.430 0.033 13.040 0.000 0.430 0.145
## .GR02_01 0.528 0.038 13.980 0.000 0.528 0.293
## .GR02_02 0.407 0.033 12.355 0.000 0.407 0.203
## .GR02_03 0.436 0.033 13.158 0.000 0.436 0.243
## .GR02_04 0.478 0.035 13.822 0.000 0.478 0.277
## .GR02_05 0.587 0.042 14.018 0.000 0.587 0.286
## .GR01_01 1.021 0.072 14.111 0.000 1.021 0.516
## .GR01_02 0.496 0.042 11.733 0.000 0.496 0.334
## .GR01_03 0.468 0.045 10.366 0.000 0.468 0.283
## .GR01_04 0.359 0.034 10.458 0.000 0.359 0.206
## .GR01_05 0.443 0.036 12.232 0.000 0.443 0.265
## .GR01_06 1.121 0.074 15.181 0.000 1.121 0.513
## .GR01_07 0.696 0.052 13.418 0.000 0.696 0.329
## .GR01_08 0.628 0.047 13.436 0.000 0.628 0.334
## .GR01_09 0.646 0.049 13.309 0.000 0.646 0.330
## .GR01_10 0.790 0.056 14.010 0.000 0.790 0.374
## .GR01_11 0.366 0.034 10.622 0.000 0.366 0.217
## .GR01_12 0.811 0.056 14.458 0.000 0.811 0.417
## .GR01_13 1.969 0.131 14.980 0.000 1.969 0.731
## .GR01_14 2.076 0.138 15.094 0.000 2.076 0.741
## .GR01_15 0.643 0.097 6.622 0.000 0.643 0.276
## .PD01_01 0.745 0.082 9.135 0.000 0.745 0.250
## .PD01_02 1.338 0.090 14.870 0.000 1.338 0.568
## .PD01_03 1.317 0.092 14.323 0.000 1.317 0.543
## .PD01_04 1.303 0.096 13.632 0.000 1.303 0.448
## .PD01_05 1.588 0.107 14.850 0.000 1.588 0.580
## .TR02_01 0.666 0.044 15.225 0.000 0.666 0.501
## .TR02_02 0.284 0.024 12.036 0.000 0.284 0.215
## .TR02_03 0.241 0.024 10.204 0.000 0.241 0.165
## .TR01_01 0.556 0.045 12.395 0.000 0.556 0.346
## .TR01_02 0.496 0.037 13.434 0.000 0.496 0.411
## .TR01_03 0.423 0.036 11.680 0.000 0.423 0.317
## .TR01_04 0.353 0.028 12.654 0.000 0.353 0.245
## .TR01_05 0.527 0.036 14.562 0.000 0.527 0.393
## .TR01_06 0.418 0.030 14.162 0.000 0.418 0.341
## .TR01_07 0.727 0.045 16.067 0.000 0.727 0.524
## .TR01_08 0.973 0.060 16.122 0.000 0.973 0.579
## .TR01_09 0.560 0.045 12.449 0.000 0.560 0.310
## .SE01_01 0.621 0.053 11.716 0.000 0.621 0.325
## .SE01_02 0.979 0.066 14.732 0.000 0.979 0.537
## .SE01_03 0.701 0.056 12.593 0.000 0.701 0.390
## .SE01_04 0.684 0.054 12.760 0.000 0.684 0.375
## .words_log 0.000 0.000 0.000
## pri_con 2.567 0.177 14.473 0.000 1.000 1.000
## grats_gen 1.273 0.105 12.120 0.000 1.000 1.000
## .grats_inf 0.277 0.038 7.350 0.000 0.290 0.290
## .grats_rel 0.207 0.033 6.248 0.000 0.150 0.150
## .grats_par 0.129 0.031 4.095 0.000 0.091 0.091
## .grats_ide 0.159 0.031 5.120 0.000 0.120 0.120
## .grats_ext 0.282 0.054 5.183 0.000 0.390 0.390
## grats_spec 0.680 0.092 7.415 0.000 1.000 1.000
## pri_delib 2.231 0.185 12.033 0.000 1.000 1.000
## trust_gen 0.663 0.071 9.319 0.000 1.000 1.000
## .trust_communty 0.286 0.036 7.946 0.000 0.272 0.272
## .trust_provider 0.050 0.019 2.591 0.010 0.046 0.046
## .trust_system -0.043 0.016 -2.739 0.006 -0.065 -0.065
## trust_spec 0.763 0.082 9.257 0.000 1.000 1.000
## self_eff 1.288 0.116 11.135 0.000 1.000 1.000
## Words_log 5.341 0.319 16.718 0.000 1.000 1.000Descriptive analyses
I first report the factor validity of all variables combined.
# extract model factor scores / predicted values for items & calc means
d_fs <- lavPredict(fit_baseline, type = "ov") %>%
as.data.frame() %>%
mutate(version = d$version, grats_gen_fs = rowMeans(select(., starts_with("GR02"))), grats_spec_fs = rowMeans(select(.,
starts_with("GR01"))), pri_con_fs = rowMeans(select(., starts_with("PC01"))), trust_gen_fs = rowMeans(select(., starts_with("TR02"))),
trust_spec_fs = rowMeans(select(., starts_with("TR01"))), pri_del_fs = rowMeans(select(., starts_with("PD01"))),
self_eff_fs = rowMeans(select(., starts_with("SE01")))) %>%
select(version, pri_con_fs, grats_gen_fs, grats_spec_fs, pri_del_fs, self_eff_fs, trust_gen_fs, trust_spec_fs, words_log)
# combine d with d factor scores
d %<>%
cbind(select(d_fs, -version, -words_log))
# rename for plotting
d_fs %<>%
set_names(c("version", var_names_breaks))
# means of model predicted values
des <- rbind(des_pricon, des_gratsgen, des_gratsspec, des_pridel, des_trustgen, des_trustspec, des_selfeff)
facval_tab <- rbind(pricon_facval, gratsgen_facval, gratsspec_facval, pridel_facval, selfeff_facval, trustgen_facval, trustspec_facval) %$%
cbind(des[-c(8), ], .) %>%
set_rownames(var_names[-c(8)])
facval_tab %>%
kable() %>%
kable_styling("striped") %>%
scroll_box(width = "100%")| m | sd | chisq | df | pvalue | cfi | tli | rmsea | srmr | omega | alpha | ave | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Privacy concerns | 3.21 | 1.514 | 16.10 | 14 | 0.307 | 0.999 | 0.998 | 0.016 | 0.010 | 0.947 | 0.944 | 0.724 |
| Gratifications general | 4.76 | 1.219 | 34.03 | 5 | 0.000 | 0.976 | 0.951 | 0.102 | 0.019 | 0.934 | 0.934 | 0.741 |
| Gratifications specific | 4.71 | 1.019 | 269.77 | 85 | 0.000 | 0.941 | 0.928 | 0.062 | 0.053 | 0.946 | 0.933 | 0.586 |
| Privacy deliberation | 3.93 | 1.285 | 15.55 | 5 | 0.008 | 0.980 | 0.960 | 0.061 | 0.024 | 0.848 | 0.843 | 0.532 |
| Self-efficacy | 5.21 | 1.039 | 3.23 | 1 | 0.072 | 0.993 | 0.957 | 0.063 | 0.014 | 0.833 | 0.862 | 0.592 |
| Trust general | 5.08 | 0.942 | 2.07 | 1 | 0.150 | 0.998 | 0.993 | 0.044 | 0.012 | 0.873 | 0.864 | 0.701 |
| Trust specific | 5.25 | 1.118 | 89.11 | 24 | 0.000 | 0.963 | 0.944 | 0.070 | 0.035 | 0.930 | 0.921 | 0.613 |
In what follows, I report zero-order correlations, distributions, and scatterplots of the variables’ factor scores.
corr_plot <-
ggpairs(
select(d_fs, -version),
upper = list(
continuous = cor_plot
),
lower = list(
continuous = wrap(
td::scat_plot,
coords = c(1, 7, 0, 7)
)
)
) +
theme_bw()
print(corr_plot)
ggsave("figures/results/cor_plot.png")Power analyses
In what follows, I report power analyses for our study. Please note that I conduct a rudimentary power-analysis, assuming bivariate correlations. (At the time I was not yet aware of the existence of power analyses for multivariate structural equation models.)
I first estimate the sample size necessary to find small effects in 95% of all cases.
# estimate pwr-samplesize
pwr.r.test(
r = r_sesoi,
sig.level = alpha,
power = power_desired,
alternative = "greater"
) %T>%
print %$%
n %>%
round(0) ->
n_desired##
## approximate correlation power calculation (arctangh transformation)
##
## n = 1077
## r = 0.1
## sig.level = 0.05
## power = 0.95
## alternative = greaterI then compute the power we have achieved with our final sample size to detect small effects.
# compute pwr-achieved
pwr.r.test(
n = n_final,
r = r_sesoi,
sig.level = alpha,
alternative = "greater"
) %T>%
print %$%
power %>%
round(2) ->
power_achieved##
## approximate correlation power calculation (arctangh transformation)
##
## n = 559
## r = 0.1
## sig.level = 0.05
## power = 0.765
## alternative = greaterI finally compute what effect size we are likely to find in 95% of all cases given our final sample size.
# estimate pwr-sensitivity
pwr.r.test(
n = n_final,
power = power_desired,
sig.level = alpha,
alternative = "greater"
) %T>%
print %$%
r %>%
round(2) ->
r_sensitive##
## approximate correlation power calculation (arctangh transformation)
##
## n = 559
## r = 0.138
## sig.level = 0.05
## power = 0.95
## alternative = greaterAssumptions
Multivariate normal distribution
# create subset of data with all items that were used
items_used <-
c(
"words",
"GR02_01", "GR02_02", "GR02_03", "GR02_04", "GR02_05",
"PC01_01", "PC01_02", "PC01_04", "PC01_05", "PC01_06", "PC01_07",
"TR01_01", "TR01_02", "TR01_03", "TR01_04", "TR01_05", "TR01_06", "TR01_07", "TR01_08", "TR01_09",
"TR02_01", "TR02_02", "TR02_03",
"PD01_01", "PD01_02", "PD01_03", "PD01_04", "PD01_05",
"SE01_01", "SE01_02", "SE01_03", "SE01_04",
"male", "age", "edu"
)
d_sub <- d[, items_used]
# test multivariate normal distribution
mvn_result <- mvn(d_sub, mvnTest = "mardia")
mvn_result$multivariateNormality## Test Statistic p value Result
## 1 Mardia Skewness 27553.2761555743 0 NO
## 2 Mardia Kurtosis 109.282869715688 0 NO
## 3 MVN <NA> <NA> NOShows that multivariate normal distribution is violated. I hence use maximum likelihood estimation with robust standard errors and a Satorra-Bentler scaled test statistic.
Influential cases
In what follows I test for influential cases in the baseline model, to detect potentially corrupt data (e.g., people who provided response patterns). Specifically, I compute Cook’s distance.
[Note: The following lines stopped working after some time, potentially due to changes in package. I can hence not recreate them here, but refer to results obtained before.]
cooks_dis <-
gCD(
d,
model_baseline
) %T>%
plot()The following ten cases have a particularly strong influence on the baseline model.
infl_cases <-
invisible(
rownames(
print(
cooks_dis
)
)
)Let’s inspect these cases.
infl_cases_tokens <-
d[infl_cases, "case_token"] %>%
as_vector()
d %>%
filter(
case_token %in% infl_cases_tokens
) %>%
select(
case_token,
GR01_01:SE01_06,
topics_entered:reactions,
-SO01_01,
TIME_SUM_t1,
TIME_SUM_t2
) %>%
kable() %>%
kable_styling("striped") %>%
scroll_box(width = "100%")These data do not reveal potential cases of response patterns. Indeed, answer times suggest that respondents were diligent.
Results
Preregistered
Privacy calculus
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_community =~ TR01_01 + TR01_02 + TR01_03
trust_provider =~ TR01_04 + TR01_05 + TR01_06
trust_system =~ TR01_07 + TR01_08 + TR01_09
trust_spec =~ trust_community + trust_provider + trust_system
words_log ~ a1*pri_con + b1*grats_gen + c1*pri_delib + d1*self_eff + e1*trust_spec
# Covariates
words_log + GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_01 + TR01_02 + TR01_03 + TR01_04 + TR01_05 + TR01_06 + TR01_07 + TR01_08 + TR01_09 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
# Covariances
male ~~ age + edu
age ~~ edu
"
fit_prereg <-
lavaan::sem(
model,
data = d,
estimator = "MLR",
missing = "ML"
)
summary(
fit_prereg,
fit = TRUE,
std = TRUE
)## lavaan 0.6.15 ended normally after 345 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 208
## Number of equality constraints 1
##
## Number of observations 559
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1220.157 934.980
## Degrees of freedom 387 387
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.305
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 13379.428 10044.844
## Degrees of freedom 528 528
## P-value 0.000 0.000
## Scaling correction factor 1.332
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.935 0.942
## Tucker-Lewis Index (TLI) 0.912 0.921
##
## Robust Comparative Fit Index (CFI) 0.944
## Robust Tucker-Lewis Index (TLI) 0.924
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -27300.107 -27300.107
## Scaling correction factor 1.242
## for the MLR correction
## Loglikelihood unrestricted model (H1) -26690.028 -26690.028
## Scaling correction factor 1.285
## for the MLR correction
##
## Akaike (AIC) 55014.214 55014.214
## Bayesian (BIC) 55909.727 55909.727
## Sample-size adjusted Bayesian (SABIC) 55252.609 55252.609
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062 0.050
## 90 Percent confidence interval - lower 0.058 0.047
## 90 Percent confidence interval - upper 0.066 0.054
## P-value H_0: RMSEA <= 0.050 0.000 0.434
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.057
## 90 Percent confidence interval - lower 0.052
## 90 Percent confidence interval - upper 0.062
## P-value H_0: Robust RMSEA <= 0.050 0.007
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.049 0.049
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pri_con =~
## PC01_01 1.000 1.596 0.926
## PC01_02 0.990 0.027 36.315 0.000 1.581 0.895
## PC01_04 0.972 0.027 35.734 0.000 1.551 0.884
## PC01_05 1.002 0.024 42.571 0.000 1.600 0.907
## PC01_06 0.855 0.038 22.731 0.000 1.365 0.796
## PC01_07 0.995 0.023 43.861 0.000 1.588 0.920
## grats_gen =~
## GR02_01 1.000 1.129 0.841
## GR02_02 1.120 0.033 33.538 0.000 1.265 0.893
## GR02_03 1.025 0.048 21.437 0.000 1.158 0.865
## GR02_04 0.988 0.048 20.466 0.000 1.115 0.849
## GR02_05 1.076 0.040 27.087 0.000 1.215 0.848
## pri_delib =~
## PD01_01 1.000 1.472 0.853
## PD01_02 0.669 0.048 13.902 0.000 0.985 0.642
## PD01_03 0.709 0.055 12.939 0.000 1.043 0.670
## PD01_04 0.843 0.047 17.856 0.000 1.240 0.727
## PD01_05 0.717 0.050 14.304 0.000 1.056 0.638
## self_eff =~
## SE01_01 1.000 1.116 0.808
## SE01_02 0.816 0.057 14.291 0.000 0.911 0.675
## SE01_03 0.934 0.046 20.343 0.000 1.043 0.778
## SE01_04 0.953 0.043 22.351 0.000 1.063 0.788
## trust_community =~
## TR01_01 1.000 1.026 0.810
## TR01_02 0.814 0.053 15.343 0.000 0.835 0.760
## TR01_03 0.917 0.048 19.282 0.000 0.941 0.815
## trust_provider =~
## TR01_04 1.000 1.057 0.881
## TR01_05 0.857 0.040 21.572 0.000 0.906 0.782
## TR01_06 0.833 0.040 20.611 0.000 0.881 0.794
## trust_system =~
## TR01_07 1.000 0.800 0.680
## TR01_08 1.058 0.081 13.016 0.000 0.846 0.653
## TR01_09 1.398 0.086 16.276 0.000 1.119 0.832
## trust_spec =~
## trust_communty 1.000 0.847 0.847
## trust_provider 1.160 0.080 14.545 0.000 0.954 0.954
## trust_system 0.975 0.079 12.345 0.000 1.059 1.059
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## words_log ~
## pri_con (a1) -0.035 0.079 -0.447 0.655 -0.057 -0.025
## grats_gen (b1) -0.010 0.147 -0.068 0.946 -0.011 -0.005
## pri_delib (c1) -0.164 0.093 -1.764 0.078 -0.242 -0.105
## self_eff (d1) 0.770 0.142 5.424 0.000 0.860 0.372
## trust_spc (e1) -0.087 0.240 -0.363 0.716 -0.076 -0.033
## male 0.020 0.199 0.098 0.922 0.020 0.004
## age 0.005 0.006 0.805 0.421 0.005 0.033
## edu 0.230 0.117 1.967 0.049 0.230 0.084
## GR02_01 ~
## male -0.127 0.115 -1.098 0.272 -0.127 -0.047
## age 0.000 0.004 0.089 0.929 0.000 0.004
## edu 0.006 0.068 0.082 0.935 0.006 0.004
## GR02_02 ~
## male -0.068 0.120 -0.565 0.572 -0.068 -0.024
## age 0.006 0.004 1.538 0.124 0.006 0.068
## edu -0.078 0.071 -1.107 0.268 -0.078 -0.047
## GR02_03 ~
## male -0.027 0.116 -0.230 0.818 -0.027 -0.010
## age 0.001 0.004 0.303 0.762 0.001 0.013
## edu -0.080 0.067 -1.199 0.230 -0.080 -0.050
## GR02_04 ~
## male 0.027 0.113 0.239 0.811 0.027 0.010
## age 0.005 0.004 1.296 0.195 0.005 0.057
## edu -0.070 0.067 -1.035 0.301 -0.070 -0.045
## GR02_05 ~
## male -0.141 0.123 -1.141 0.254 -0.141 -0.049
## age -0.004 0.004 -0.878 0.380 -0.004 -0.039
## edu 0.014 0.072 0.193 0.847 0.014 0.008
## PC01_01 ~
## male -0.184 0.151 -1.218 0.223 -0.184 -0.053
## age -0.004 0.005 -0.830 0.407 -0.004 -0.037
## edu 0.114 0.087 1.309 0.190 0.114 0.056
## PC01_02 ~
## male -0.304 0.154 -1.977 0.048 -0.304 -0.086
## age -0.008 0.005 -1.674 0.094 -0.008 -0.072
## edu 0.051 0.089 0.577 0.564 0.051 0.025
## PC01_04 ~
## male -0.227 0.152 -1.487 0.137 -0.227 -0.065
## age -0.010 0.005 -1.990 0.047 -0.010 -0.086
## edu 0.117 0.089 1.320 0.187 0.117 0.056
## PC01_05 ~
## male -0.100 0.154 -0.649 0.516 -0.100 -0.028
## age -0.006 0.005 -1.174 0.240 -0.006 -0.051
## edu 0.094 0.090 1.049 0.294 0.094 0.045
## PC01_06 ~
## male -0.110 0.150 -0.734 0.463 -0.110 -0.032
## age -0.005 0.005 -1.065 0.287 -0.005 -0.046
## edu 0.047 0.087 0.540 0.589 0.047 0.023
## PC01_07 ~
## male -0.176 0.150 -1.172 0.241 -0.176 -0.051
## age -0.007 0.005 -1.347 0.178 -0.007 -0.059
## edu 0.086 0.087 0.987 0.324 0.086 0.042
## TR01_01 ~
## male -0.296 0.108 -2.742 0.006 -0.296 -0.117
## age -0.004 0.004 -1.101 0.271 -0.004 -0.049
## edu 0.005 0.061 0.075 0.940 0.005 0.003
## TR01_02 ~
## male -0.139 0.095 -1.464 0.143 -0.139 -0.063
## age -0.002 0.003 -0.557 0.577 -0.002 -0.025
## edu 0.021 0.053 0.385 0.700 0.021 0.016
## TR01_03 ~
## male -0.133 0.099 -1.343 0.179 -0.133 -0.058
## age -0.004 0.003 -1.201 0.230 -0.004 -0.054
## edu -0.006 0.060 -0.098 0.922 -0.006 -0.004
## TR01_04 ~
## male -0.086 0.104 -0.825 0.409 -0.086 -0.036
## age 0.000 0.003 0.114 0.909 0.000 0.005
## edu -0.053 0.058 -0.903 0.366 -0.053 -0.037
## TR01_05 ~
## male -0.043 0.099 -0.429 0.668 -0.043 -0.018
## age 0.001 0.003 0.357 0.721 0.001 0.016
## edu 0.014 0.058 0.240 0.810 0.014 0.010
## TR01_06 ~
## male 0.048 0.096 0.498 0.619 0.048 0.021
## age -0.004 0.003 -1.235 0.217 -0.004 -0.053
## edu 0.021 0.056 0.370 0.712 0.021 0.016
## TR01_07 ~
## male 0.093 0.100 0.927 0.354 0.093 0.039
## age -0.004 0.003 -1.166 0.244 -0.004 -0.050
## edu -0.058 0.058 -0.995 0.320 -0.058 -0.042
## TR01_08 ~
## male 0.028 0.112 0.255 0.799 0.028 0.011
## age 0.003 0.004 0.831 0.406 0.003 0.036
## edu -0.096 0.065 -1.473 0.141 -0.096 -0.062
## TR01_09 ~
## male -0.120 0.115 -1.039 0.299 -0.120 -0.045
## age -0.002 0.004 -0.401 0.688 -0.002 -0.018
## edu -0.148 0.068 -2.182 0.029 -0.148 -0.092
## PD01_01 ~
## male -0.176 0.148 -1.194 0.232 -0.176 -0.051
## age -0.015 0.005 -3.274 0.001 -0.015 -0.137
## edu -0.027 0.085 -0.322 0.748 -0.027 -0.013
## PD01_02 ~
## male -0.118 0.131 -0.900 0.368 -0.118 -0.038
## age -0.014 0.004 -3.439 0.001 -0.014 -0.141
## edu 0.030 0.077 0.384 0.701 0.030 0.016
## PD01_03 ~
## male -0.321 0.132 -2.425 0.015 -0.321 -0.103
## age -0.004 0.004 -1.025 0.306 -0.004 -0.044
## edu 0.065 0.080 0.811 0.418 0.065 0.035
## PD01_04 ~
## male -0.411 0.144 -2.846 0.004 -0.411 -0.120
## age -0.009 0.005 -1.904 0.057 -0.009 -0.082
## edu 0.102 0.085 1.206 0.228 0.102 0.051
## PD01_05 ~
## male -0.205 0.142 -1.441 0.150 -0.205 -0.062
## age -0.012 0.004 -2.698 0.007 -0.012 -0.111
## edu -0.002 0.084 -0.018 0.985 -0.002 -0.001
## SE01_01 ~
## male 0.121 0.118 1.020 0.308 0.121 0.044
## age 0.000 0.004 0.015 0.988 0.000 0.001
## edu 0.207 0.068 3.046 0.002 0.207 0.126
## SE01_02 ~
## male 0.060 0.112 0.537 0.591 0.060 0.022
## age -0.013 0.004 -3.590 0.000 -0.013 -0.151
## edu 0.194 0.066 2.938 0.003 0.194 0.121
## SE01_03 ~
## male 0.195 0.114 1.705 0.088 0.195 0.073
## age 0.001 0.004 0.254 0.800 0.001 0.011
## edu 0.138 0.067 2.065 0.039 0.138 0.087
## SE01_04 ~
## male 0.054 0.115 0.473 0.636 0.054 0.020
## age 0.007 0.004 2.055 0.040 0.007 0.086
## edu 0.122 0.066 1.837 0.066 0.122 0.076
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SE01_01 ~~
## .SE01_02 (x) 0.106 0.044 2.400 0.016 0.106 0.140
## .SE01_03 ~~
## .SE01_04 (x) 0.106 0.044 2.400 0.016 0.106 0.156
## male ~~
## age 0.757 0.328 2.312 0.021 0.757 0.097
## edu 0.052 0.018 2.966 0.003 0.052 0.125
## age ~~
## edu -1.018 0.547 -1.862 0.063 -1.018 -0.078
## pri_con ~~
## grats_gen -0.279 0.095 -2.924 0.003 -0.155 -0.155
## pri_delib 1.320 0.130 10.121 0.000 0.562 0.562
## self_eff -0.383 0.091 -4.210 0.000 -0.215 -0.215
## trust_spec -0.411 0.069 -5.932 0.000 -0.296 -0.296
## grats_gen ~~
## pri_delib -0.068 0.102 -0.669 0.503 -0.041 -0.041
## self_eff 0.466 0.067 6.999 0.000 0.370 0.370
## trust_spec 0.746 0.080 9.308 0.000 0.760 0.760
## pri_delib ~~
## self_eff -0.323 0.094 -3.437 0.001 -0.197 -0.197
## trust_spec -0.140 0.079 -1.768 0.077 -0.109 -0.109
## self_eff ~~
## trust_spec 0.532 0.059 8.938 0.000 0.548 0.548
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 3.360 0.292 11.525 0.000 3.360 1.949
## .PC01_02 3.760 0.304 12.368 0.000 3.760 2.128
## .PC01_04 3.562 0.297 11.993 0.000 3.562 2.029
## .PC01_05 3.405 0.304 11.200 0.000 3.405 1.931
## .PC01_06 3.207 0.288 11.128 0.000 3.207 1.870
## .PC01_07 3.452 0.294 11.749 0.000 3.452 2.001
## .GR02_01 4.318 0.224 19.261 0.000 4.318 3.217
## .GR02_02 4.489 0.244 18.372 0.000 4.489 3.170
## .GR02_03 5.239 0.221 23.652 0.000 5.239 3.914
## .GR02_04 4.983 0.221 22.508 0.000 4.983 3.792
## .GR02_05 4.902 0.254 19.325 0.000 4.902 3.422
## .PD01_01 4.495 0.290 15.524 0.000 4.495 2.605
## .PD01_02 4.000 0.248 16.125 0.000 4.000 2.605
## .PD01_03 4.432 0.269 16.447 0.000 4.432 2.847
## .PD01_04 4.507 0.295 15.294 0.000 4.507 2.643
## .PD01_05 4.999 0.276 18.098 0.000 4.999 3.020
## .SE01_01 4.833 0.249 19.394 0.000 4.833 3.500
## .SE01_02 5.739 0.234 24.499 0.000 5.739 4.252
## .SE01_03 4.828 0.226 21.365 0.000 4.828 3.602
## .SE01_04 4.542 0.234 19.415 0.000 4.542 3.366
## .TR01_01 5.084 0.219 23.244 0.000 5.084 4.014
## .TR01_02 4.956 0.189 26.210 0.000 4.956 4.509
## .TR01_03 4.877 0.200 24.387 0.000 4.877 4.227
## .TR01_04 5.524 0.204 27.020 0.000 5.524 4.605
## .TR01_05 5.142 0.197 26.057 0.000 5.142 4.441
## .TR01_06 5.240 0.189 27.798 0.000 5.240 4.728
## .TR01_07 5.960 0.193 30.944 0.000 5.960 5.063
## .TR01_08 4.860 0.218 22.299 0.000 4.860 3.749
## .TR01_09 5.584 0.231 24.200 0.000 5.584 4.154
## .words_log 1.169 0.380 3.079 0.002 1.169 0.506
## male 0.493 0.021 23.296 0.000 0.493 0.986
## age 46.132 0.658 70.151 0.000 46.132 2.967
## edu 1.852 0.036 51.966 0.000 1.852 2.198
## pri_con 0.000 0.000 0.000
## grats_gen 0.000 0.000 0.000
## pri_delib 0.000 0.000 0.000
## self_eff 0.000 0.000 0.000
## .trust_communty 0.000 0.000 0.000
## .trust_provider 0.000 0.000 0.000
## .trust_system 0.000 0.000 0.000
## trust_spec 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 0.403 0.050 8.075 0.000 0.403 0.135
## .PC01_02 0.579 0.103 5.652 0.000 0.579 0.186
## .PC01_04 0.626 0.077 8.134 0.000 0.626 0.203
## .PC01_05 0.533 0.064 8.359 0.000 0.533 0.171
## .PC01_06 1.067 0.116 9.239 0.000 1.067 0.363
## .PC01_07 0.430 0.065 6.570 0.000 0.430 0.144
## .GR02_01 0.523 0.053 9.777 0.000 0.523 0.290
## .GR02_02 0.389 0.040 9.823 0.000 0.389 0.194
## .GR02_03 0.446 0.073 6.073 0.000 0.446 0.249
## .GR02_04 0.473 0.048 9.904 0.000 0.473 0.274
## .GR02_05 0.567 0.062 9.160 0.000 0.567 0.277
## .PD01_01 0.742 0.110 6.717 0.000 0.742 0.249
## .PD01_02 1.332 0.127 10.455 0.000 1.332 0.565
## .PD01_03 1.301 0.128 10.186 0.000 1.301 0.537
## .PD01_04 1.297 0.147 8.844 0.000 1.297 0.446
## .PD01_05 1.576 0.128 12.340 0.000 1.576 0.575
## .SE01_01 0.625 0.087 7.177 0.000 0.625 0.328
## .SE01_02 0.917 0.118 7.772 0.000 0.917 0.504
## .SE01_03 0.684 0.095 7.212 0.000 0.684 0.381
## .SE01_04 0.667 0.078 8.544 0.000 0.667 0.366
## .TR01_01 0.524 0.067 7.792 0.000 0.524 0.327
## .TR01_02 0.505 0.057 8.907 0.000 0.505 0.418
## .TR01_03 0.437 0.045 9.653 0.000 0.437 0.329
## .TR01_04 0.317 0.034 9.461 0.000 0.317 0.220
## .TR01_05 0.520 0.053 9.726 0.000 0.520 0.388
## .TR01_06 0.449 0.042 10.628 0.000 0.449 0.365
## .TR01_07 0.739 0.057 13.027 0.000 0.739 0.533
## .TR01_08 0.955 0.079 12.047 0.000 0.955 0.568
## .TR01_09 0.534 0.061 8.705 0.000 0.534 0.295
## .words_log 4.459 0.215 20.710 0.000 4.459 0.835
## male 0.250 0.000 817.190 0.000 0.250 1.000
## age 241.747 9.947 24.303 0.000 241.747 1.000
## edu 0.710 0.021 34.591 0.000 0.710 1.000
## pri_con 2.549 0.144 17.662 0.000 1.000 1.000
## grats_gen 1.275 0.114 11.180 0.000 1.000 1.000
## pri_delib 2.167 0.157 13.796 0.000 1.000 1.000
## self_eff 1.245 0.113 10.991 0.000 1.000 1.000
## .trust_communty 0.297 0.045 6.672 0.000 0.282 0.282
## .trust_provider 0.100 0.031 3.213 0.001 0.089 0.089
## .trust_system -0.078 0.019 -4.092 0.000 -0.122 -0.122
## trust_spec 0.756 0.095 7.920 0.000 1.000 1.000rsquare_fit_prereg <-
inspect(
fit_prereg,
what = "rsquare"
)["words"]Results show that there’s only one significant predictor of Communication, being self-efficacy. The other predictors are in the direction as planned, albeit not significant. Trust, however, shows the inverse relation as effect, that is, more trust, less communication.
Effects of popularity cues
Confidence intervals
The easiest way to assess the effect of the experimental manipulation on the variables is by visualizing their means. If 83% confidence intervals don’t overlap, the variables differ significantly across the conditions. One can quickly see that there aren’t any major effects.
# violin plot
fig_fs_m <-
ggplot(
gather(
d_fs,
variable,
value,
-version) %>%
mutate(
variable = factor(
variable,
levels = var_names_breaks
)
),
aes(
x = version,
y = value,
fill = version
)
) +
geom_violin(
trim = TRUE
) +
stat_summary(
fun.y = mean,
geom = "point"
) +
stat_summary(
fun.data = mean_se,
fun.args = list(mult = 1.39),
geom = "errorbar",
width = .5
) +
facet_wrap(
~ variable,
nrow = 2
) +
theme_bw() +
theme(
axis.title.y = element_blank(),
axis.title.x = element_blank(),
plot.title = element_text(hjust = .5),
panel.spacing = unit(.9, "lines"),
text = element_text(size = 12),
legend.position="none",
legend.title = element_blank()) +
coord_cartesian(
ylim = c(0, 7)
) +
scale_fill_brewer(
palette = "Greys"
)
ggsave(
"figures/results/violin_plot.png",
width = 8,
height = 6
)
fig_fs_m
SEM
In what follows, I also report explicit statistical tests of the differences between the conditions using contrasts.
Like & Like-Dislike vs. Control
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_community =~ TR01_01 + TR01_02 + TR01_03
trust_provider =~ TR01_04 + TR01_05 + TR01_06
trust_system =~ TR01_07 + TR01_08 + TR01_09
trust_spec =~ trust_community + trust_provider + trust_system
pri_con + grats_gen + pri_delib + self_eff + trust_spec ~ like + likedislike
words ~ a*pri_con + b*grats_gen + c*pri_delib + d*self_eff + e*trust_spec + f*like + g*likedislike
# Covariates
words + GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_01 + TR01_02 + TR01_03 + TR01_04 + TR01_05 + TR01_06 + TR01_07 + TR01_08 + TR01_09 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_lik_ctrl <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML",
fixed.x = FALSE
)
summary(
fit_lik_ctrl,
fit = TRUE,
std = TRUE
)## lavaan 0.6.15 ended normally after 598 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 221
## Number of equality constraints 1
##
## Number of observations 559
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2019.407 1485.951
## Degrees of freedom 445 445
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.359
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 13359.444 10346.817
## Degrees of freedom 585 585
## P-value 0.000 0.000
## Scaling correction factor 1.291
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.877 0.893
## Tucker-Lewis Index (TLI) 0.838 0.860
##
## Robust Comparative Fit Index (CFI) 0.888
## Robust Tucker-Lewis Index (TLI) 0.853
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -30981.342 -30981.342
## Scaling correction factor 1.201
## for the MLR correction
## Loglikelihood unrestricted model (H1) -29971.639 -29971.639
## Scaling correction factor 1.308
## for the MLR correction
##
## Akaike (AIC) 62402.685 62402.685
## Bayesian (BIC) 63354.438 63354.438
## Sample-size adjusted Bayesian (SABIC) 62656.052 62656.052
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.080 0.065
## 90 Percent confidence interval - lower 0.076 0.062
## 90 Percent confidence interval - upper 0.083 0.068
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.422 0.000
##
## Robust RMSEA 0.075
## 90 Percent confidence interval - lower 0.071
## 90 Percent confidence interval - upper 0.080
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.036
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.188 0.188
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pri_con =~
## PC01_01 1.000 1.599 0.927
## PC01_02 0.988 0.027 36.235 0.000 1.580 0.895
## PC01_04 0.969 0.027 35.679 0.000 1.550 0.883
## PC01_05 0.999 0.023 42.633 0.000 1.598 0.906
## PC01_06 0.851 0.038 22.657 0.000 1.361 0.793
## PC01_07 0.994 0.023 43.524 0.000 1.590 0.922
## grats_gen =~
## GR02_01 1.000 1.145 0.853
## GR02_02 1.120 0.033 33.637 0.000 1.283 0.906
## GR02_03 0.992 0.046 21.721 0.000 1.136 0.849
## GR02_04 0.958 0.046 20.688 0.000 1.098 0.835
## GR02_05 1.064 0.039 27.133 0.000 1.219 0.851
## pri_delib =~
## PD01_01 1.000 1.443 0.836
## PD01_02 0.679 0.051 13.436 0.000 0.979 0.638
## PD01_03 0.737 0.058 12.626 0.000 1.063 0.683
## PD01_04 0.873 0.049 17.786 0.000 1.260 0.739
## PD01_05 0.740 0.052 14.183 0.000 1.067 0.645
## self_eff =~
## SE01_01 1.000 1.126 0.815
## SE01_02 0.805 0.060 13.435 0.000 0.907 0.671
## SE01_03 0.918 0.051 17.902 0.000 1.033 0.772
## SE01_04 0.939 0.044 21.178 0.000 1.058 0.785
## trust_community =~
## TR01_01 1.000 1.022 0.807
## TR01_02 0.819 0.054 15.111 0.000 0.837 0.762
## TR01_03 0.922 0.048 19.178 0.000 0.943 0.817
## trust_provider =~
## TR01_04 1.000 1.058 0.882
## TR01_05 0.854 0.040 21.372 0.000 0.903 0.780
## TR01_06 0.834 0.041 20.194 0.000 0.882 0.796
## trust_system =~
## TR01_07 1.000 0.810 0.688
## TR01_08 1.060 0.084 12.677 0.000 0.859 0.662
## TR01_09 1.348 0.084 16.058 0.000 1.093 0.813
## trust_spec =~
## trust_communty 1.000 0.838 0.838
## trust_provider 1.185 0.083 14.342 0.000 0.961 0.961
## trust_system 1.009 0.081 12.402 0.000 1.067 1.067
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pri_con ~
## like 0.032 0.163 0.197 0.844 0.020 0.010
## likedislik 0.177 0.169 1.043 0.297 0.111 0.051
## grats_gen ~
## like -0.167 0.120 -1.388 0.165 -0.146 -0.069
## likedislik -0.221 0.122 -1.811 0.070 -0.193 -0.090
## pri_delib ~
## like 0.002 0.159 0.011 0.992 0.001 0.001
## likedislik -0.084 0.169 -0.499 0.617 -0.058 -0.027
## self_eff ~
## like -0.088 0.126 -0.693 0.488 -0.078 -0.037
## likedislik -0.066 0.131 -0.501 0.616 -0.058 -0.027
## trust_spec ~
## like -0.154 0.091 -1.702 0.089 -0.180 -0.086
## likedislik -0.151 0.094 -1.594 0.111 -0.176 -0.082
## words ~
## pri_con (a) 0.620 6.851 0.090 0.928 0.991 0.004
## grats_gen (b) 11.167 13.072 0.854 0.393 12.790 0.051
## pri_delib (c) -18.340 7.948 -2.307 0.021 -26.457 -0.106
## self_eff (d) 52.682 14.678 3.589 0.000 59.340 0.238
## trust_spec (e) -10.061 14.918 -0.674 0.500 -8.624 -0.035
## like (f) -29.288 26.879 -1.090 0.276 -29.288 -0.056
## likedislik (g) -40.080 27.169 -1.475 0.140 -40.080 -0.075
## male -8.863 23.351 -0.380 0.704 -8.863 -0.018
## age 0.339 0.558 0.608 0.543 0.339 0.021
## edu 12.182 15.227 0.800 0.424 12.182 0.041
## GR02_01 ~
## male -0.130 0.116 -1.129 0.259 -0.130 -0.049
## age 0.000 0.004 0.079 0.937 0.000 0.003
## edu 0.004 0.069 0.051 0.959 0.004 0.002
## GR02_02 ~
## male -0.072 0.120 -0.600 0.548 -0.072 -0.025
## age 0.006 0.004 1.529 0.126 0.006 0.067
## edu -0.081 0.071 -1.142 0.253 -0.081 -0.048
## GR02_03 ~
## male -0.030 0.115 -0.263 0.793 -0.030 -0.011
## age 0.001 0.004 0.292 0.771 0.001 0.013
## edu -0.082 0.067 -1.230 0.219 -0.082 -0.052
## GR02_04 ~
## male 0.023 0.113 0.207 0.836 0.023 0.009
## age 0.005 0.004 1.287 0.198 0.005 0.056
## edu -0.072 0.067 -1.063 0.288 -0.072 -0.046
## GR02_05 ~
## male -0.145 0.123 -1.172 0.241 -0.145 -0.051
## age -0.004 0.004 -0.890 0.374 -0.004 -0.040
## edu 0.012 0.073 0.162 0.871 0.012 0.007
## PC01_01 ~
## male -0.179 0.151 -1.191 0.234 -0.179 -0.052
## age -0.004 0.005 -0.795 0.427 -0.004 -0.035
## edu 0.119 0.087 1.361 0.173 0.119 0.058
## PC01_02 ~
## male -0.300 0.154 -1.950 0.051 -0.300 -0.085
## age -0.008 0.005 -1.643 0.100 -0.008 -0.070
## edu 0.056 0.089 0.624 0.533 0.056 0.026
## PC01_04 ~
## male -0.223 0.152 -1.463 0.144 -0.223 -0.063
## age -0.009 0.005 -1.959 0.050 -0.009 -0.084
## edu 0.121 0.089 1.370 0.171 0.121 0.058
## PC01_05 ~
## male -0.096 0.154 -0.622 0.534 -0.096 -0.027
## age -0.006 0.005 -1.137 0.256 -0.006 -0.049
## edu 0.099 0.090 1.097 0.272 0.099 0.047
## PC01_06 ~
## male -0.106 0.150 -0.711 0.477 -0.106 -0.031
## age -0.005 0.005 -1.034 0.301 -0.005 -0.045
## edu 0.050 0.087 0.582 0.560 0.050 0.025
## PC01_07 ~
## male -0.172 0.150 -1.146 0.252 -0.172 -0.050
## age -0.006 0.005 -1.313 0.189 -0.006 -0.057
## edu 0.090 0.086 1.039 0.299 0.090 0.044
## TR01_01 ~
## male -0.298 0.108 -2.751 0.006 -0.298 -0.118
## age -0.004 0.004 -1.091 0.275 -0.004 -0.048
## edu 0.004 0.061 0.066 0.947 0.004 0.003
## TR01_02 ~
## male -0.140 0.096 -1.467 0.142 -0.140 -0.064
## age -0.002 0.003 -0.547 0.585 -0.002 -0.025
## edu 0.020 0.053 0.377 0.706 0.020 0.015
## TR01_03 ~
## male -0.134 0.100 -1.349 0.177 -0.134 -0.058
## age -0.004 0.003 -1.190 0.234 -0.004 -0.054
## edu -0.006 0.060 -0.107 0.915 -0.006 -0.005
## TR01_04 ~
## male -0.088 0.104 -0.847 0.397 -0.088 -0.037
## age 0.000 0.003 0.127 0.899 0.000 0.006
## edu -0.053 0.058 -0.922 0.357 -0.053 -0.037
## TR01_05 ~
## male -0.044 0.099 -0.446 0.655 -0.044 -0.019
## age 0.001 0.003 0.371 0.711 0.001 0.016
## edu 0.013 0.058 0.233 0.816 0.013 0.010
## TR01_06 ~
## male 0.046 0.096 0.479 0.632 0.046 0.021
## age -0.004 0.003 -1.229 0.219 -0.004 -0.052
## edu 0.020 0.056 0.360 0.719 0.020 0.015
## TR01_07 ~
## male 0.090 0.100 0.903 0.367 0.090 0.038
## age -0.004 0.003 -1.156 0.248 -0.004 -0.049
## edu -0.058 0.058 -1.003 0.316 -0.058 -0.042
## TR01_08 ~
## male 0.027 0.112 0.237 0.812 0.027 0.010
## age 0.003 0.004 0.841 0.400 0.003 0.036
## edu -0.096 0.065 -1.479 0.139 -0.096 -0.063
## TR01_09 ~
## male -0.122 0.115 -1.060 0.289 -0.122 -0.045
## age -0.002 0.004 -0.389 0.697 -0.002 -0.018
## edu -0.148 0.067 -2.201 0.028 -0.148 -0.093
## PD01_01 ~
## male -0.178 0.148 -1.208 0.227 -0.178 -0.052
## age -0.015 0.005 -3.294 0.001 -0.015 -0.138
## edu -0.030 0.085 -0.352 0.725 -0.030 -0.015
## PD01_02 ~
## male -0.119 0.131 -0.909 0.363 -0.119 -0.039
## age -0.014 0.004 -3.453 0.001 -0.014 -0.142
## edu 0.028 0.077 0.362 0.718 0.028 0.015
## PD01_03 ~
## male -0.322 0.132 -2.435 0.015 -0.322 -0.104
## age -0.004 0.004 -1.043 0.297 -0.004 -0.045
## edu 0.063 0.080 0.788 0.431 0.063 0.034
## PD01_04 ~
## male -0.413 0.144 -2.861 0.004 -0.413 -0.121
## age -0.009 0.005 -1.927 0.054 -0.009 -0.083
## edu 0.100 0.085 1.183 0.237 0.100 0.050
## PD01_05 ~
## male -0.206 0.142 -1.453 0.146 -0.206 -0.062
## age -0.012 0.004 -2.714 0.007 -0.012 -0.112
## edu -0.003 0.084 -0.041 0.968 -0.003 -0.002
## SE01_01 ~
## male 0.123 0.118 1.043 0.297 0.123 0.045
## age 0.000 0.004 0.052 0.959 0.000 0.002
## edu 0.210 0.068 3.097 0.002 0.210 0.128
## SE01_02 ~
## male 0.062 0.112 0.557 0.577 0.062 0.023
## age -0.013 0.004 -3.542 0.000 -0.013 -0.149
## edu 0.197 0.066 2.974 0.003 0.197 0.123
## SE01_03 ~
## male 0.198 0.115 1.722 0.085 0.198 0.074
## age 0.001 0.004 0.289 0.773 0.001 0.013
## edu 0.142 0.067 2.110 0.035 0.142 0.089
## SE01_04 ~
## male 0.057 0.115 0.495 0.621 0.057 0.021
## age 0.008 0.004 2.088 0.037 0.008 0.087
## edu 0.125 0.067 1.884 0.060 0.125 0.078
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SE01_01 ~~
## .SE01_02 (x) 0.110 0.045 2.455 0.014 0.110 0.147
## .SE01_03 ~~
## .SE01_04 (x) 0.110 0.045 2.455 0.014 0.110 0.161
## like ~~
## likedislik -0.109 0.007 -16.483 0.000 -0.109 -0.494
## male 0.006 0.010 0.596 0.551 0.006 0.025
## age 0.364 0.313 1.161 0.246 0.364 0.049
## edu 0.016 0.017 0.916 0.360 0.016 0.039
## likedislike ~~
## male -0.009 0.010 -0.942 0.346 -0.009 -0.040
## age -0.321 0.306 -1.047 0.295 -0.321 -0.044
## edu -0.019 0.017 -1.169 0.243 -0.019 -0.050
## male ~~
## age 0.758 0.328 2.312 0.021 0.758 0.097
## edu 0.052 0.018 2.964 0.003 0.052 0.125
## age ~~
## edu -1.018 0.547 -1.862 0.063 -1.018 -0.078
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 3.275 0.298 11.003 0.000 3.275 1.899
## .PC01_02 3.676 0.307 11.968 0.000 3.676 2.080
## .PC01_04 3.480 0.300 11.601 0.000 3.480 1.982
## .PC01_05 3.320 0.306 10.833 0.000 3.320 1.882
## .PC01_06 3.134 0.289 10.855 0.000 3.134 1.828
## .PC01_07 3.367 0.299 11.276 0.000 3.367 1.952
## .GR02_01 4.453 0.231 19.294 0.000 4.453 3.318
## .GR02_02 4.640 0.253 18.336 0.000 4.640 3.277
## .GR02_03 5.373 0.230 23.328 0.000 5.373 4.014
## .GR02_04 5.113 0.230 22.193 0.000 5.113 3.890
## .GR02_05 5.046 0.261 19.336 0.000 5.046 3.522
## .PD01_01 4.532 0.308 14.699 0.000 4.532 2.627
## .PD01_02 4.025 0.260 15.495 0.000 4.025 2.622
## .PD01_03 4.459 0.286 15.583 0.000 4.459 2.865
## .PD01_04 4.539 0.312 14.529 0.000 4.539 2.662
## .PD01_05 5.026 0.291 17.286 0.000 5.026 3.037
## .SE01_01 4.871 0.265 18.408 0.000 4.871 3.526
## .SE01_02 5.769 0.245 23.522 0.000 5.769 4.271
## .SE01_03 4.863 0.241 20.181 0.000 4.863 3.631
## .SE01_04 4.578 0.250 18.348 0.000 4.578 3.395
## .TR01_01 5.185 0.228 22.713 0.000 5.185 4.094
## .TR01_02 5.039 0.194 25.967 0.000 5.039 4.584
## .TR01_03 4.971 0.206 24.072 0.000 4.971 4.308
## .TR01_04 5.644 0.213 26.486 0.000 5.644 4.705
## .TR01_05 5.245 0.204 25.677 0.000 5.245 4.529
## .TR01_06 5.340 0.196 27.213 0.000 5.340 4.818
## .TR01_07 6.062 0.201 30.123 0.000 6.062 5.150
## .TR01_08 4.968 0.229 21.729 0.000 4.968 3.832
## .TR01_09 5.721 0.244 23.490 0.000 5.721 4.256
## .words 68.057 30.401 2.239 0.025 68.057 0.273
## like 0.347 0.020 17.237 0.000 0.347 0.729
## likedislike 0.315 0.020 16.027 0.000 0.315 0.678
## male 0.493 0.021 23.297 0.000 0.493 0.986
## age 46.132 0.658 70.151 0.000 46.132 2.967
## edu 1.852 0.036 51.966 0.000 1.852 2.198
## .pri_con 0.000 0.000 0.000
## .grats_gen 0.000 0.000 0.000
## .pri_delib 0.000 0.000 0.000
## .self_eff 0.000 0.000 0.000
## .trust_communty 0.000 0.000 0.000
## .trust_provider 0.000 0.000 0.000
## .trust_system 0.000 0.000 0.000
## .trust_spec 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 0.395 0.050 7.916 0.000 0.395 0.133
## .PC01_02 0.580 0.103 5.618 0.000 0.580 0.186
## .PC01_04 0.632 0.078 8.072 0.000 0.632 0.205
## .PC01_05 0.539 0.065 8.277 0.000 0.539 0.173
## .PC01_06 1.078 0.117 9.225 0.000 1.078 0.366
## .PC01_07 0.424 0.064 6.634 0.000 0.424 0.142
## .GR02_01 0.486 0.052 9.401 0.000 0.486 0.270
## .GR02_02 0.344 0.036 9.592 0.000 0.344 0.172
## .GR02_03 0.496 0.074 6.721 0.000 0.496 0.277
## .GR02_04 0.513 0.051 10.118 0.000 0.513 0.297
## .GR02_05 0.558 0.061 9.156 0.000 0.558 0.272
## .PD01_01 0.828 0.129 6.408 0.000 0.828 0.278
## .PD01_02 1.344 0.130 10.342 0.000 1.344 0.570
## .PD01_03 1.259 0.131 9.606 0.000 1.259 0.520
## .PD01_04 1.248 0.147 8.468 0.000 1.248 0.429
## .PD01_05 1.553 0.129 12.052 0.000 1.553 0.567
## .SE01_01 0.602 0.093 6.452 0.000 0.602 0.315
## .SE01_02 0.927 0.121 7.672 0.000 0.927 0.508
## .SE01_03 0.698 0.105 6.678 0.000 0.698 0.389
## .SE01_04 0.673 0.080 8.429 0.000 0.673 0.370
## .TR01_01 0.532 0.070 7.549 0.000 0.532 0.331
## .TR01_02 0.501 0.057 8.724 0.000 0.501 0.415
## .TR01_03 0.434 0.045 9.648 0.000 0.434 0.326
## .TR01_04 0.316 0.034 9.218 0.000 0.316 0.220
## .TR01_05 0.524 0.055 9.610 0.000 0.524 0.391
## .TR01_06 0.447 0.043 10.458 0.000 0.447 0.364
## .TR01_07 0.723 0.056 12.840 0.000 0.723 0.522
## .TR01_08 0.934 0.079 11.819 0.000 0.934 0.556
## .TR01_09 0.592 0.067 8.808 0.000 0.592 0.328
## .words 57409.641 0.006 10016654.498 0.000 57409.641 0.921
## .pri_con 2.551 0.144 17.751 0.000 0.998 0.998
## .grats_gen 1.303 0.113 11.534 0.000 0.993 0.993
## .pri_delib 2.080 0.167 12.433 0.000 0.999 0.999
## .self_eff 1.267 0.115 10.996 0.000 0.999 0.999
## .trust_communty 0.311 0.048 6.500 0.000 0.297 0.297
## .trust_provider 0.087 0.038 2.255 0.024 0.077 0.077
## .trust_system -0.091 0.026 -3.509 0.000 -0.138 -0.138
## .trust_spec 0.729 0.093 7.834 0.000 0.993 0.993
## like 0.227 0.006 36.792 0.000 0.227 1.000
## likedislike 0.216 0.007 29.655 0.000 0.216 1.000
## male 0.250 0.000 817.431 0.000 0.250 1.000
## age 241.746 9.947 24.303 0.000 241.746 1.000
## edu 0.710 0.021 34.591 0.000 0.710 1.000No significant effects of popularity cues on privacy calculus.
Like-Dislike & Control vs. Like
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_community =~ TR01_01 + TR01_02 + TR01_03
trust_provider =~ TR01_04 + TR01_05 + TR01_06
trust_system =~ TR01_07 + TR01_08 + TR01_09
trust_spec =~ trust_community + trust_provider + trust_system
pri_con + grats_gen + pri_delib + self_eff + trust_spec + words ~ likedislike + control
# Covariates
words + GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_01 + TR01_02 + TR01_03 + TR01_04 + TR01_05 + TR01_06 + TR01_07 + TR01_08 + TR01_09 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_lik_ctrl <-
lavaan::sem(
model = model,
data = d,
estimator = "MLR",
missing = "ML")
summary(
fit_lik_ctrl,
fit = TRUE,
std = TRUE
)## lavaan 0.6.15 ended normally after 552 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 211
## Number of equality constraints 1
##
## Used Total
## Number of observations 558 559
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1254.996 923.368
## Degrees of freedom 435 435
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.359
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 13325.570 10323.740
## Degrees of freedom 585 585
## P-value 0.000 0.000
## Scaling correction factor 1.291
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.936 0.950
## Tucker-Lewis Index (TLI) 0.913 0.933
##
## Robust Comparative Fit Index (CFI) 0.948
## Robust Tucker-Lewis Index (TLI) 0.930
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -26475.947 -26475.947
## Scaling correction factor 1.246
## for the MLR correction
## Loglikelihood unrestricted model (H1) -25848.449 -25848.449
## Scaling correction factor 1.324
## for the MLR correction
##
## Akaike (AIC) 53371.893 53371.893
## Bayesian (BIC) 54280.008 54280.008
## Sample-size adjusted Bayesian (SABIC) 53613.368 53613.368
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058 0.045
## 90 Percent confidence interval - lower 0.054 0.041
## 90 Percent confidence interval - upper 0.062 0.048
## P-value H_0: RMSEA <= 0.050 0.000 0.993
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.052
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.057
## P-value H_0: Robust RMSEA <= 0.050 0.250
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046 0.046
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pri_con =~
## PC01_01 1.000 1.595 0.926
## PC01_02 0.990 0.027 36.192 0.000 1.579 0.894
## PC01_04 0.972 0.027 35.662 0.000 1.551 0.884
## PC01_05 1.002 0.024 42.459 0.000 1.599 0.907
## PC01_06 0.855 0.038 22.667 0.000 1.363 0.795
## PC01_07 0.995 0.023 43.750 0.000 1.587 0.920
## grats_gen =~
## GR02_01 1.000 1.130 0.841
## GR02_02 1.120 0.033 33.549 0.000 1.265 0.893
## GR02_03 1.025 0.048 21.463 0.000 1.158 0.865
## GR02_04 0.988 0.048 20.449 0.000 1.116 0.849
## GR02_05 1.075 0.040 27.055 0.000 1.215 0.847
## pri_delib =~
## PD01_01 1.000 1.478 0.856
## PD01_02 0.665 0.048 13.894 0.000 0.983 0.640
## PD01_03 0.703 0.055 12.854 0.000 1.038 0.667
## PD01_04 0.841 0.047 17.753 0.000 1.243 0.729
## PD01_05 0.714 0.050 14.264 0.000 1.056 0.637
## self_eff =~
## SE01_01 1.000 1.111 0.805
## SE01_02 0.816 0.058 14.127 0.000 0.906 0.672
## SE01_03 0.936 0.046 20.258 0.000 1.040 0.776
## SE01_04 0.960 0.044 21.890 0.000 1.066 0.790
## trust_community =~
## TR01_01 1.000 1.028 0.811
## TR01_02 0.811 0.053 15.321 0.000 0.834 0.759
## TR01_03 0.914 0.047 19.282 0.000 0.940 0.815
## trust_provider =~
## TR01_04 1.000 1.059 0.882
## TR01_05 0.854 0.040 21.557 0.000 0.904 0.782
## TR01_06 0.830 0.040 20.584 0.000 0.879 0.794
## trust_system =~
## TR01_07 1.000 0.799 0.679
## TR01_08 1.060 0.082 12.941 0.000 0.846 0.653
## TR01_09 1.403 0.087 16.210 0.000 1.120 0.833
## trust_spec =~
## trust_communty 1.000 0.847 0.847
## trust_provider 1.160 0.080 14.542 0.000 0.954 0.954
## trust_system 0.970 0.079 12.353 0.000 1.058 1.058
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## pri_con ~
## likedislike 0.158 0.174 0.904 0.366 0.099 0.046
## control -0.031 0.162 -0.191 0.849 -0.019 -0.009
## grats_gen ~
## likedislike -0.045 0.125 -0.361 0.718 -0.040 -0.018
## control 0.164 0.119 1.386 0.166 0.145 0.069
## pri_delib ~
## likedislike -0.083 0.166 -0.501 0.617 -0.056 -0.026
## control 0.004 0.162 0.028 0.978 0.003 0.001
## self_eff ~
## likedislike 0.007 0.127 0.051 0.959 0.006 0.003
## control 0.083 0.125 0.666 0.506 0.075 0.035
## trust_spec ~
## likedislike -0.002 0.095 -0.016 0.987 -0.002 -0.001
## control 0.159 0.092 1.728 0.084 0.183 0.087
## words ~
## likedislike -9.593 19.548 -0.491 0.624 -9.593 -0.018
## control 32.972 28.926 1.140 0.254 32.972 0.062
## male -8.817 23.401 -0.377 0.706 -8.817 -0.018
## age 0.336 0.558 0.603 0.547 0.336 0.021
## edu 11.696 15.254 0.767 0.443 11.696 0.039
## GR02_01 ~
## male -0.130 0.116 -1.124 0.261 -0.130 -0.048
## age 0.000 0.004 0.083 0.934 0.000 0.004
## edu 0.003 0.069 0.048 0.962 0.003 0.002
## GR02_02 ~
## male -0.071 0.120 -0.592 0.554 -0.071 -0.025
## age 0.006 0.004 1.535 0.125 0.006 0.068
## edu -0.082 0.071 -1.156 0.248 -0.082 -0.049
## GR02_03 ~
## male -0.029 0.116 -0.250 0.802 -0.029 -0.011
## age 0.001 0.004 0.301 0.763 0.001 0.013
## edu -0.084 0.067 -1.264 0.206 -0.084 -0.053
## GR02_04 ~
## male 0.025 0.113 0.219 0.827 0.025 0.009
## age 0.005 0.004 1.297 0.195 0.005 0.057
## edu -0.074 0.067 -1.096 0.273 -0.074 -0.047
## GR02_05 ~
## male -0.144 0.124 -1.165 0.244 -0.144 -0.050
## age -0.004 0.004 -0.883 0.377 -0.004 -0.039
## edu 0.011 0.073 0.147 0.883 0.011 0.006
## PC01_01 ~
## male -0.177 0.150 -1.175 0.240 -0.177 -0.051
## age -0.004 0.005 -0.781 0.435 -0.004 -0.034
## edu 0.114 0.087 1.308 0.191 0.114 0.056
## PC01_02 ~
## male -0.297 0.153 -1.937 0.053 -0.297 -0.084
## age -0.008 0.005 -1.629 0.103 -0.008 -0.070
## edu 0.051 0.089 0.571 0.568 0.051 0.024
## PC01_04 ~
## male -0.220 0.152 -1.448 0.148 -0.220 -0.063
## age -0.009 0.005 -1.946 0.052 -0.009 -0.084
## edu 0.117 0.089 1.320 0.187 0.117 0.056
## PC01_05 ~
## male -0.093 0.154 -0.605 0.545 -0.093 -0.026
## age -0.006 0.005 -1.123 0.261 -0.006 -0.049
## edu 0.094 0.090 1.046 0.295 0.094 0.045
## PC01_06 ~
## male -0.104 0.150 -0.695 0.487 -0.104 -0.030
## age -0.005 0.005 -1.022 0.307 -0.005 -0.044
## edu 0.046 0.087 0.535 0.593 0.046 0.023
## PC01_07 ~
## male -0.169 0.150 -1.130 0.259 -0.169 -0.049
## age -0.006 0.005 -1.299 0.194 -0.006 -0.056
## edu 0.085 0.086 0.987 0.324 0.085 0.042
## TR01_01 ~
## male -0.299 0.109 -2.757 0.006 -0.299 -0.118
## age -0.004 0.004 -1.095 0.274 -0.004 -0.048
## edu 0.005 0.061 0.076 0.939 0.005 0.003
## TR01_02 ~
## male -0.142 0.095 -1.489 0.137 -0.142 -0.065
## age -0.002 0.003 -0.557 0.577 -0.002 -0.025
## edu 0.023 0.053 0.426 0.670 0.023 0.017
## TR01_03 ~
## male -0.136 0.099 -1.374 0.169 -0.136 -0.059
## age -0.004 0.003 -1.202 0.229 -0.004 -0.054
## edu -0.003 0.060 -0.055 0.956 -0.003 -0.002
## TR01_04 ~
## male -0.089 0.104 -0.857 0.392 -0.089 -0.037
## age 0.000 0.003 0.120 0.904 0.000 0.005
## edu -0.052 0.058 -0.899 0.369 -0.052 -0.037
## TR01_05 ~
## male -0.047 0.099 -0.473 0.636 -0.047 -0.020
## age 0.001 0.003 0.356 0.722 0.001 0.015
## edu 0.017 0.058 0.301 0.763 0.017 0.013
## TR01_06 ~
## male 0.044 0.095 0.457 0.648 0.044 0.020
## age -0.004 0.003 -1.247 0.212 -0.004 -0.053
## edu 0.024 0.056 0.434 0.664 0.024 0.018
## TR01_07 ~
## male 0.090 0.100 0.894 0.371 0.090 0.038
## age -0.004 0.003 -1.165 0.244 -0.004 -0.050
## edu -0.056 0.058 -0.963 0.336 -0.056 -0.040
## TR01_08 ~
## male 0.025 0.112 0.224 0.822 0.025 0.010
## age 0.003 0.004 0.832 0.405 0.003 0.036
## edu -0.094 0.065 -1.442 0.149 -0.094 -0.061
## TR01_09 ~
## male -0.124 0.115 -1.071 0.284 -0.124 -0.046
## age -0.002 0.004 -0.396 0.692 -0.002 -0.018
## edu -0.147 0.067 -2.176 0.030 -0.147 -0.092
## PD01_01 ~
## male -0.179 0.148 -1.210 0.226 -0.179 -0.052
## age -0.015 0.005 -3.294 0.001 -0.015 -0.138
## edu -0.028 0.085 -0.335 0.738 -0.028 -0.014
## PD01_02 ~
## male -0.121 0.132 -0.915 0.360 -0.121 -0.039
## age -0.014 0.004 -3.455 0.001 -0.014 -0.142
## edu 0.030 0.077 0.387 0.699 0.030 0.016
## PD01_03 ~
## male -0.323 0.133 -2.434 0.015 -0.323 -0.104
## age -0.004 0.004 -1.041 0.298 -0.004 -0.045
## edu 0.063 0.080 0.790 0.430 0.063 0.034
## PD01_04 ~
## male -0.414 0.145 -2.861 0.004 -0.414 -0.121
## age -0.009 0.005 -1.925 0.054 -0.009 -0.083
## edu 0.101 0.085 1.189 0.234 0.101 0.050
## PD01_05 ~
## male -0.206 0.142 -1.450 0.147 -0.206 -0.062
## age -0.012 0.004 -2.710 0.007 -0.012 -0.112
## edu -0.004 0.084 -0.043 0.966 -0.004 -0.002
## SE01_01 ~
## male 0.118 0.118 1.003 0.316 0.118 0.043
## age 0.000 0.004 0.014 0.989 0.000 0.001
## edu 0.211 0.068 3.104 0.002 0.211 0.129
## SE01_02 ~
## male 0.058 0.112 0.520 0.603 0.058 0.022
## age -0.013 0.004 -3.578 0.000 -0.013 -0.151
## edu 0.198 0.066 2.986 0.003 0.198 0.124
## SE01_03 ~
## male 0.193 0.115 1.686 0.092 0.193 0.072
## age 0.001 0.004 0.251 0.801 0.001 0.011
## edu 0.142 0.067 2.120 0.034 0.142 0.090
## SE01_04 ~
## male 0.052 0.115 0.453 0.651 0.052 0.019
## age 0.007 0.004 2.050 0.040 0.007 0.086
## edu 0.126 0.067 1.897 0.058 0.126 0.079
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SE01_01 ~~
## .SE01_02 (x) 0.105 0.044 2.407 0.016 0.105 0.138
## .SE01_03 ~~
## .SE01_04 (x) 0.105 0.044 2.407 0.016 0.105 0.156
## .pri_con ~~
## .grats_gen -0.277 0.096 -2.902 0.004 -0.155 -0.155
## .pri_delib 1.331 0.130 10.213 0.000 0.566 0.566
## .self_eff -0.373 0.092 -4.055 0.000 -0.211 -0.211
## .trust_spec -0.405 0.070 -5.824 0.000 -0.293 -0.293
## .words -35.118 18.522 -1.896 0.058 -22.044 -0.088
## .grats_gen ~~
## .pri_delib -0.073 0.102 -0.708 0.479 -0.044 -0.044
## .self_eff 0.465 0.067 6.914 0.000 0.372 0.372
## .trust_spec 0.744 0.080 9.326 0.000 0.761 0.761
## .words 29.451 11.287 2.609 0.009 26.151 0.105
## .pri_delib ~~
## .self_eff -0.326 0.095 -3.416 0.001 -0.199 -0.199
## .trust_spec -0.145 0.080 -1.819 0.069 -0.113 -0.113
## .words -52.100 15.972 -3.262 0.001 -35.266 -0.141
## .self_eff ~~
## .trust_spec 0.526 0.060 8.774 0.000 0.546 0.546
## .words 70.815 17.043 4.155 0.000 63.779 0.256
## .trust_spec ~~
## .words 26.191 6.881 3.806 0.000 30.181 0.121
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 3.310 0.309 10.718 0.000 3.310 1.921
## .PC01_02 3.711 0.322 11.523 0.000 3.711 2.102
## .PC01_04 3.514 0.316 11.132 0.000 3.514 2.003
## .PC01_05 3.355 0.326 10.306 0.000 3.355 1.904
## .PC01_06 3.165 0.302 10.477 0.000 3.165 1.846
## .PC01_07 3.402 0.313 10.860 0.000 3.402 1.974
## .GR02_01 4.284 0.244 17.527 0.000 4.284 3.189
## .GR02_02 4.453 0.265 16.820 0.000 4.453 3.142
## .GR02_03 5.208 0.242 21.495 0.000 5.208 3.890
## .GR02_04 4.954 0.238 20.836 0.000 4.954 3.768
## .GR02_05 4.867 0.270 17.994 0.000 4.867 3.395
## .PD01_01 4.528 0.306 14.787 0.000 4.528 2.622
## .PD01_02 4.020 0.257 15.611 0.000 4.020 2.616
## .PD01_03 4.456 0.276 16.136 0.000 4.456 2.860
## .PD01_04 4.536 0.302 14.998 0.000 4.536 2.657
## .PD01_05 5.025 0.284 17.674 0.000 5.025 3.033
## .SE01_01 4.793 0.270 17.755 0.000 4.793 3.474
## .SE01_02 5.705 0.250 22.800 0.000 5.705 4.231
## .SE01_03 4.790 0.242 19.806 0.000 4.790 3.575
## .SE01_04 4.503 0.248 18.129 0.000 4.503 3.339
## .TR01_01 5.030 0.227 22.161 0.000 5.030 3.968
## .TR01_02 4.909 0.196 25.023 0.000 4.909 4.466
## .TR01_03 4.823 0.207 23.259 0.000 4.823 4.181
## .TR01_04 5.460 0.217 25.199 0.000 5.460 4.549
## .TR01_05 5.082 0.205 24.848 0.000 5.082 4.395
## .TR01_06 5.181 0.197 26.315 0.000 5.181 4.682
## .TR01_07 5.904 0.201 29.369 0.000 5.904 5.016
## .TR01_08 4.801 0.229 20.969 0.000 4.801 3.703
## .TR01_09 5.509 0.249 22.145 0.000 5.509 4.096
## .words 35.618 39.123 0.910 0.363 35.618 0.142
## .pri_con 0.000 0.000 0.000
## .grats_gen 0.000 0.000 0.000
## .pri_delib 0.000 0.000 0.000
## .self_eff 0.000 0.000 0.000
## .trust_communty 0.000 0.000 0.000
## .trust_provider 0.000 0.000 0.000
## .trust_system 0.000 0.000 0.000
## .trust_spec 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PC01_01 0.404 0.050 8.070 0.000 0.404 0.136
## .PC01_02 0.581 0.102 5.666 0.000 0.581 0.186
## .PC01_04 0.627 0.077 8.133 0.000 0.627 0.204
## .PC01_05 0.534 0.064 8.368 0.000 0.534 0.172
## .PC01_06 1.069 0.116 9.242 0.000 1.069 0.364
## .PC01_07 0.431 0.065 6.580 0.000 0.431 0.145
## .GR02_01 0.524 0.054 9.788 0.000 0.524 0.290
## .GR02_02 0.391 0.040 9.826 0.000 0.391 0.195
## .GR02_03 0.445 0.073 6.069 0.000 0.445 0.248
## .GR02_04 0.472 0.048 9.876 0.000 0.472 0.273
## .GR02_05 0.570 0.062 9.191 0.000 0.570 0.277
## .PD01_01 0.730 0.111 6.594 0.000 0.730 0.245
## .PD01_02 1.339 0.127 10.505 0.000 1.339 0.567
## .PD01_03 1.315 0.129 10.230 0.000 1.315 0.542
## .PD01_04 1.295 0.147 8.829 0.000 1.295 0.444
## .PD01_05 1.582 0.128 12.342 0.000 1.582 0.577
## .SE01_01 0.631 0.088 7.138 0.000 0.631 0.332
## .SE01_02 0.921 0.120 7.680 0.000 0.921 0.507
## .SE01_03 0.687 0.096 7.164 0.000 0.687 0.382
## .SE01_04 0.658 0.078 8.461 0.000 0.658 0.362
## .TR01_01 0.522 0.067 7.745 0.000 0.522 0.325
## .TR01_02 0.506 0.057 8.908 0.000 0.506 0.419
## .TR01_03 0.438 0.045 9.650 0.000 0.438 0.329
## .TR01_04 0.316 0.034 9.416 0.000 0.316 0.220
## .TR01_05 0.519 0.053 9.733 0.000 0.519 0.388
## .TR01_06 0.448 0.042 10.621 0.000 0.448 0.366
## .TR01_07 0.741 0.057 13.002 0.000 0.741 0.535
## .TR01_08 0.956 0.079 12.044 0.000 0.956 0.569
## .TR01_09 0.533 0.061 8.675 0.000 0.533 0.294
## .words 62215.810 0.042 1467956.418 0.000 62215.810 0.993
## .pri_con 2.538 0.144 17.580 0.000 0.997 0.997
## .grats_gen 1.268 0.113 11.182 0.000 0.994 0.994
## .pri_delib 2.183 0.157 13.864 0.000 0.999 0.999
## .self_eff 1.233 0.117 10.546 0.000 0.999 0.999
## .trust_communty 0.298 0.045 6.656 0.000 0.282 0.282
## .trust_provider 0.100 0.031 3.198 0.001 0.089 0.089
## .trust_system -0.077 0.019 -4.050 0.000 -0.120 -0.120
## .trust_spec 0.753 0.095 7.912 0.000 0.992 0.992No significant effects of popularity cues on privacy calculus.
Exploratory analyses
The bivariate relations showed several strong correlations. People who trusted the providers more also experienced more gratifications (r = .79). In multiple regression, we analze the potential effect of one variable on the outcome while holding all others constant. In this case, I tested whether trust increases communication while holding constant gratifications, privacy concerns, privacy deliberations, and self-efficacy. Given the close theoretical and empirical interrelations between the predictors, this creates an unlikely and artifical scenario. If people say loose trust in a provider, they will likely experience more concerns and less benefits. But it is not even necessary to control for these mediating factors. When trying to analyze the causal effect, it is necessary to control for counfounding variables, but importantly not for mediators (Rohrer 2018). Confunding variables impact both the independent variable and the outcome. Sociodemographic variables are often ideal candidates for confounders. For example, men are generally less concerned about their privacy and post more online. Hence, controlling for gender hence helps isolate the actual causal effect. As a result, I reran the analyses controlling for confounding variables that are not mediators.
Experimental Factors
Words
I first compare likes and likes plus dislikes to the control condition.
run_hurdles(object = d, name = "wrds_ctrl", outcome = "words", predictor = "version", y_lab = "Words", x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: words ~ 1 + age + male + edu + version
## hu ~ 1 + age + male + edu + version
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 5.62 0.37 4.90 6.36 1.00 2948 1703
## hu_Intercept 1.05 0.36 0.30 1.76 1.00 4517 1512
## age -0.00 0.01 -0.01 0.01 1.00 4341 1678
## male -0.18 0.16 -0.49 0.15 1.00 3142 1738
## edu 0.01 0.09 -0.17 0.19 1.00 3462 1067
## versionLike -0.39 0.19 -0.77 0.01 1.00 2493 1433
## versionLike&dislike -0.46 0.20 -0.88 -0.05 1.00 2595 1663
## hu_age -0.01 0.01 -0.02 0.00 1.00 5530 1716
## hu_male -0.06 0.18 -0.42 0.29 1.00 3342 1364
## hu_edu -0.21 0.11 -0.41 -0.00 1.00 3898 1729
## hu_versionLike 0.03 0.20 -0.37 0.44 1.00 2405 1516
## hu_versionLike&dislike 0.24 0.21 -0.19 0.65 1.01 2691 1808
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.70 0.06 0.59 0.81 1.00 3778 1418
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 words age dY/dX 0.25 -0.855 1.36
## 2 words edu dY/dX 9.94 -6.384 27.85
## 3 words male 1 - 0 -11.31 -43.054 19.71
## 4 words version Like & dislike - Control -48.40 -93.201 -10.50
## 5 words version Like - Control -35.24 -82.306 3.82
Let’s find out by how much people in like and like plus dislike condition wrote fewer words.
words_m_ctrl <- fig_wrds_ctrl$data %>%
filter(effect1__ == "Control") %>%
select(estimate__)
words_m_lk <- fig_wrds_ctrl$data %>%
filter(effect1__ == "Like") %>%
select(estimate__)
words_m_lkdslk <- fig_wrds_ctrl$data %>%
filter(effect1__ == "Like & dislike") %>%
select(estimate__)
effect_lk_wrds <- words_m_ctrl - words_m_lk
effect_lkdslk_wrds <- words_m_ctrl - words_m_lkdslk
effect_lk_prcnt <- (effect_lk_wrds/words_m_ctrl) * 100 %>%
round()
effect_lkdlk_prcnt <- (effect_lkdslk_wrds/words_m_ctrl) * 100 %>%
round()Compared to the control condition, participants wrote 45.644 percent fewer words on the platform with likes and dislikes.
I then compare likes and likes plus dislikes to the control condition.
run_hurdles(object = d, name = "wrds_lkdslk", outcome = "words", predictor = "version_lkdslk", y_lab = "Words", x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: words ~ 1 + age + male + edu + version_lkdslk
## hu ~ 1 + age + male + edu + version_lkdslk
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 5.16 0.39 4.43 5.96 1.00 3618 1599
## hu_Intercept 1.29 0.36 0.60 1.98 1.00 3622 1654
## age -0.00 0.01 -0.01 0.01 1.00 4658 1633
## male -0.18 0.16 -0.51 0.14 1.00 3017 1601
## edu 0.01 0.10 -0.19 0.21 1.00 3154 1409
## version_lkdslkLike 0.07 0.21 -0.34 0.47 1.00 2014 1571
## version_lkdslkControl 0.46 0.20 0.04 0.85 1.00 2417 1192
## hu_age -0.01 0.01 -0.02 0.00 1.00 4011 1479
## hu_male -0.06 0.18 -0.39 0.29 1.00 3644 1614
## hu_edu -0.20 0.10 -0.40 -0.00 1.00 3180 1663
## hu_version_lkdslkLike -0.22 0.21 -0.62 0.19 1.00 2719 1847
## hu_version_lkdslkControl -0.24 0.22 -0.65 0.17 1.00 2316 1640
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.70 0.06 0.59 0.82 1.00 4152 1715
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 words age dY/dX 0.252 -0.85 1.28
## 2 words edu dY/dX 10.029 -7.82 27.98
## 3 words male 1 - 0 -11.697 -43.97 18.47
## 4 words version_lkdslk Control - Like & dislike 47.077 9.30 90.61
## 5 words version_lkdslk Like - Like & dislike 12.623 -19.47 44.38
Privacy Concerns
I first compare likes and likes plus dislikes to the control condition.
run_hurdles(object = d, name = "pricon_ctrl", outcome = "pri_con_fs", predictor = "version", y_lab = "Privacy concerns",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: pri_con_fs ~ 1 + age + male + edu + version
## hu ~ 1 + age + male + edu + version
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.21 0.09 1.04 1.39 1.00 1925 1734
## hu_Intercept -9.94 6.20 -23.56 0.33 1.01 1170 1162
## age -0.00 0.00 -0.00 0.00 1.00 1900 1139
## male -0.06 0.04 -0.14 0.03 1.00 2004 1589
## edu 0.03 0.03 -0.03 0.08 1.00 1935 1506
## versionLike 0.01 0.05 -0.09 0.11 1.00 1556 1523
## versionLike&dislike 0.06 0.05 -0.05 0.16 1.00 1537 1359
## hu_age 0.00 0.08 -0.16 0.17 1.00 1423 1221
## hu_male -0.31 3.36 -7.70 6.32 1.00 1160 718
## hu_edu -0.17 1.77 -4.09 3.15 1.00 1529 1035
## hu_versionLike -0.01 4.43 -9.85 9.11 1.00 1038 816
## hu_versionLike&dislike -0.07 4.51 -9.80 8.99 1.00 873 859
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 3.95 0.23 3.48 4.40 1.00 1835 1270
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 pri_con_fs age dY/dX -0.00594 -0.0149 0.00237
## 2 pri_con_fs edu dY/dX 0.08229 -0.0894 0.25608
## 3 pri_con_fs male 1 - 0 -0.17534 -0.4498 0.09783
## 4 pri_con_fs version Like & dislike - Control 0.18073 -0.1596 0.52073
## 5 pri_con_fs version Like - Control 0.03836 -0.2850 0.34313
I then compare the likes and control conditions to the likes plus dislikes condition.
run_hurdles(object = d, name = "pricon_lkdslk", outcome = "pri_con_fs", predictor = "version_lkdslk", y_lab = "Privacy concerns",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: pri_con_fs ~ 1 + age + male + edu + version_lkdslk
## hu ~ 1 + age + male + edu + version_lkdslk
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.27 0.09 1.10 1.44 1.00 2089 1717
## hu_Intercept -9.78 5.76 -22.77 -0.30 1.00 1213 1277
## age -0.00 0.00 -0.00 0.00 1.00 2210 1224
## male -0.05 0.04 -0.13 0.03 1.00 2012 1592
## edu 0.03 0.03 -0.03 0.08 1.00 1911 1471
## version_lkdslkLike -0.04 0.05 -0.14 0.06 1.00 1505 1287
## version_lkdslkControl -0.06 0.05 -0.16 0.05 1.00 1725 1467
## hu_age -0.00 0.08 -0.16 0.16 1.00 1356 906
## hu_male 0.00 3.09 -6.10 6.56 1.00 1465 1024
## hu_edu -0.15 1.67 -3.93 2.97 1.00 1233 873
## hu_version_lkdslkLike -0.14 4.02 -9.13 7.78 1.01 764 960
## hu_version_lkdslkControl -0.08 4.23 -9.67 8.01 1.01 875 838
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 3.94 0.23 3.51 4.43 1.00 1812 1362
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 pri_con_fs age dY/dX -0.00584 -0.0141 0.0027
## 2 pri_con_fs edu dY/dX 0.08733 -0.0862 0.2475
## 3 pri_con_fs male 1 - 0 -0.18008 -0.4269 0.0855
## 4 pri_con_fs version_lkdslk Control - Like & dislike -0.18348 -0.5237 0.1656
## 5 pri_con_fs version_lkdslk Like - Like & dislike -0.14385 -0.4751 0.2028
Gratifications General
I first compare likes and likes plus dislikes to the control condition.
run_hurdles(object = d, name = "grats_ctrl", outcome = "grats_gen_fs", predictor = "version", y_lab = "Gratifications general",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: grats_gen_fs ~ 1 + age + male + edu + version
## hu ~ 1 + age + male + edu + version
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.60 0.05 1.51 1.69 1.00 2311 1825
## hu_Intercept -9.95 6.13 -23.72 0.11 1.00 1399 1077
## age 0.00 0.00 -0.00 0.00 1.00 2019 1540
## male -0.01 0.02 -0.06 0.03 1.00 2232 1615
## edu -0.01 0.01 -0.04 0.02 1.00 2803 1482
## versionLike -0.04 0.03 -0.09 0.02 1.00 1569 1427
## versionLike&dislike -0.04 0.03 -0.10 0.01 1.00 1746 1322
## hu_age 0.00 0.08 -0.15 0.17 1.01 1828 1340
## hu_male -0.15 3.20 -6.80 5.89 1.00 1466 1020
## hu_edu -0.18 1.71 -3.83 3.11 1.00 1510 905
## hu_versionLike 0.09 4.32 -8.87 8.75 1.00 1182 1045
## hu_versionLike&dislike -0.26 4.60 -10.38 9.04 1.00 1188 867
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 13.76 0.77 12.31 15.26 1.00 1875 1341
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 grats_gen_fs age dY/dX 0.00135 -0.00591 0.0086
## 2 grats_gen_fs edu dY/dX -0.05034 -0.17928 0.0906
## 3 grats_gen_fs male 1 - 0 -0.07147 -0.27931 0.1477
## 4 grats_gen_fs version Like & dislike - Control -0.20608 -0.47665 0.0665
## 5 grats_gen_fs version Like - Control -0.16933 -0.43182 0.0784
I then compare the likes and control conditions to the likes plus dislikes condition.
run_hurdles(object = d, name = "grats_lkdslk", outcome = "grats_gen_fs", predictor = "version_lkdslk", y_lab = "Gratifications",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: grats_gen_fs ~ 1 + age + male + edu + version_lkdslk
## hu ~ 1 + age + male + edu + version_lkdslk
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.56 0.05 1.46 1.65 1.00 2366 1733
## hu_Intercept -10.25 6.33 -25.86 -0.22 1.01 1379 1230
## age 0.00 0.00 -0.00 0.00 1.00 1937 1423
## male -0.01 0.02 -0.06 0.03 1.00 2105 1182
## edu -0.01 0.01 -0.04 0.02 1.00 2381 1314
## version_lkdslkLike 0.01 0.03 -0.05 0.06 1.00 2081 1397
## version_lkdslkControl 0.04 0.03 -0.01 0.10 1.01 2106 1481
## hu_age 0.00 0.09 -0.15 0.20 1.00 1849 1135
## hu_male -0.16 3.24 -6.97 6.32 1.00 1502 1029
## hu_edu -0.21 1.75 -3.95 3.09 1.00 1474 1083
## hu_version_lkdslkLike 0.28 4.29 -8.27 9.03 1.00 1173 845
## hu_version_lkdslkControl 0.02 4.61 -9.75 9.52 1.00 1005 913
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 13.74 0.82 12.17 15.40 1.00 2659 1520
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 grats_gen_fs age dY/dX 0.00144 -0.00585 0.00851
## 2 grats_gen_fs edu dY/dX -0.05066 -0.17734 0.08283
## 3 grats_gen_fs male 1 - 0 -0.06653 -0.27867 0.14476
## 4 grats_gen_fs version_lkdslk Control - Like & dislike 0.20930 -0.05690 0.47180
## 5 grats_gen_fs version_lkdslk Like - Like & dislike 0.03302 -0.23338 0.29847
Privacy Deliberation
I first compare likes and likes plus dislikes to the control condition.
run_hurdles(object = d, name = "pridel_ctrl", outcome = "pri_del_fs", predictor = "version", y_lab = "Privacy deliberation",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: pri_del_fs ~ 1 + age + male + edu + version
## hu ~ 1 + age + male + edu + version
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.50 0.05 1.40 1.60 1.00 1750 1463
## hu_Intercept -10.06 6.15 -23.66 0.03 1.00 1483 1061
## age -0.00 0.00 -0.00 -0.00 1.00 2242 1472
## male -0.05 0.03 -0.11 -0.00 1.00 2110 1455
## edu 0.00 0.02 -0.03 0.03 1.00 2034 1344
## versionLike -0.00 0.03 -0.06 0.06 1.00 1536 1212
## versionLike&dislike -0.01 0.03 -0.08 0.05 1.00 1497 1191
## hu_age 0.01 0.08 -0.15 0.18 1.00 1786 1101
## hu_male -0.07 3.14 -6.74 6.73 1.00 1858 1292
## hu_edu -0.14 1.72 -3.87 3.13 1.00 1632 993
## hu_versionLike -0.14 4.43 -9.57 8.87 1.01 1188 889
## hu_versionLike&dislike -0.38 4.59 -10.08 8.93 1.00 1178 925
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 11.43 0.69 10.15 12.75 1.00 2327 1599
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 pri_del_fs age dY/dX -0.00937 -0.016 -0.00333
## 2 pri_del_fs edu dY/dX 0.01309 -0.110 0.13428
## 3 pri_del_fs male 1 - 0 -0.20390 -0.414 -0.01421
## 4 pri_del_fs version Like & dislike - Control -0.04455 -0.296 0.20012
## 5 pri_del_fs version Like - Control -0.00598 -0.248 0.24446
I then compare the likes and control conditions to the likes plus dislikes condition.
run_hurdles(object = d, name = "pridel_lkdslk", outcome = "pri_del_fs", predictor = "version_lkdslk", y_lab = "Privacy deliberation",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: pri_del_fs ~ 1 + age + male + edu + version_lkdslk
## hu ~ 1 + age + male + edu + version_lkdslk
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.49 0.05 1.38 1.59 1.00 1951 1696
## hu_Intercept -10.25 6.23 -24.23 -0.18 1.00 1196 1080
## age -0.00 0.00 -0.00 -0.00 1.00 1832 1491
## male -0.05 0.03 -0.10 -0.01 1.00 2106 1534
## edu 0.00 0.02 -0.03 0.04 1.00 1905 1208
## version_lkdslkLike 0.01 0.03 -0.05 0.08 1.00 1532 1198
## version_lkdslkControl 0.01 0.03 -0.05 0.07 1.00 1617 1617
## hu_age 0.00 0.08 -0.16 0.18 1.00 1940 1210
## hu_male -0.16 3.27 -6.89 6.06 1.00 1710 917
## hu_edu -0.16 1.62 -3.50 2.91 1.00 1869 1443
## hu_version_lkdslkLike 0.26 4.36 -8.84 9.39 1.00 1036 689
## hu_version_lkdslkControl 0.47 4.37 -8.29 9.88 1.00 1000 781
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 11.39 0.69 10.07 12.72 1.01 1965 1311
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 pri_del_fs age dY/dX -0.00923 -0.0155 -0.00294
## 2 pri_del_fs edu dY/dX 0.01376 -0.1111 0.14462
## 3 pri_del_fs male 1 - 0 -0.20709 -0.4009 -0.01224
## 4 pri_del_fs version_lkdslk Control - Like & dislike 0.04962 -0.1982 0.28237
## 5 pri_del_fs version_lkdslk Like - Like & dislike 0.04180 -0.1939 0.30032
Trust general
I first compare likes and likes plus dislikes to the control condition.
run_hurdles(object = d, name = "trust_ctrl", outcome = "trust_gen_fs", predictor = "version", y_lab = "Trust general", x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: trust_gen_fs ~ 1 + age + male + edu + version
## hu ~ 1 + age + male + edu + version
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.69 0.03 1.63 1.76 1.00 2442 1782
## hu_Intercept -9.88 6.20 -23.41 0.89 1.00 1184 1035
## age -0.00 0.00 -0.00 0.00 1.00 2345 1681
## male 0.00 0.02 -0.03 0.03 1.00 2145 1458
## edu -0.00 0.01 -0.02 0.02 1.00 2890 1650
## versionLike -0.03 0.02 -0.07 0.01 1.00 1441 1350
## versionLike&dislike -0.03 0.02 -0.07 0.02 1.00 1398 1435
## hu_age 0.01 0.09 -0.15 0.19 1.00 1317 873
## hu_male -0.10 3.12 -6.51 6.35 1.00 1149 968
## hu_edu -0.26 1.77 -4.17 3.04 1.00 1232 1028
## hu_versionLike -0.16 4.31 -8.91 8.35 1.00 848 676
## hu_versionLike&dislike -0.48 4.65 -10.73 7.92 1.01 1000 744
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 27.49 1.65 24.21 30.72 1.00 1750 1369
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 trust_gen_fs age dY/dX -0.003045 -0.00935 0.00217
## 2 trust_gen_fs edu dY/dX 0.000451 -0.10797 0.11114
## 3 trust_gen_fs male 1 - 0 0.014549 -0.15291 0.18037
## 4 trust_gen_fs version Like & dislike - Control -0.132077 -0.35386 0.08660
## 5 trust_gen_fs version Like - Control -0.151385 -0.36271 0.05299
I then compare the likes and control conditions to the likes plus dislikes condition.
run_hurdles(object = d, name = "trust_lkdslk", outcome = "trust_gen_fs", predictor = "version_lkdslk", y_lab = "Trust general",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: trust_gen_fs ~ 1 + age + male + edu + version_lkdslk
## hu ~ 1 + age + male + edu + version_lkdslk
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.67 0.03 1.60 1.73 1.00 2065 1623
## hu_Intercept -10.32 6.12 -24.73 -0.48 1.00 1403 1227
## age -0.00 0.00 -0.00 0.00 1.00 1962 1381
## male 0.00 0.02 -0.03 0.04 1.00 2541 1303
## edu 0.00 0.01 -0.02 0.02 1.00 2428 1329
## version_lkdslkLike -0.00 0.02 -0.04 0.03 1.00 1833 1351
## version_lkdslkControl 0.02 0.02 -0.02 0.07 1.00 1797 1221
## hu_age 0.01 0.08 -0.16 0.18 1.00 1538 1014
## hu_male -0.05 3.20 -5.98 6.75 1.00 1328 874
## hu_edu -0.24 1.69 -3.87 2.95 1.00 1461 1066
## hu_version_lkdslkLike 0.21 4.73 -9.51 10.30 1.00 963 1000
## hu_version_lkdslkControl 0.37 4.49 -8.85 10.05 1.00 1121 982
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 27.46 1.65 24.32 30.76 1.01 2307 1494
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 trust_gen_fs age dY/dX -0.00302 -0.0089 0.0026
## 2 trust_gen_fs edu dY/dX 0.00291 -0.1102 0.1156
## 3 trust_gen_fs male 1 - 0 0.01542 -0.1548 0.1906
## 4 trust_gen_fs version_lkdslk Control - Like & dislike 0.12741 -0.0779 0.3487
## 5 trust_gen_fs version_lkdslk Like - Like & dislike -0.01983 -0.2263 0.1847
Self-Efficacy
I first compare likes and likes plus dislikes to the control condition.
run_hurdles(object = d, name = "selfeff_ctrl", outcome = "self_eff_fs", predictor = "version", y_lab = "Self-efficacy", x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: self_eff_fs ~ 1 + age + male + edu + version
## hu ~ 1 + age + male + edu + version
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.62 0.03 1.56 1.69 1.00 1964 1679
## hu_Intercept -10.16 6.10 -23.50 -0.05 1.00 1279 1300
## age -0.00 0.00 -0.00 0.00 1.00 1827 1100
## male 0.01 0.02 -0.02 0.05 1.00 1911 1559
## edu 0.03 0.01 0.01 0.04 1.00 2375 1193
## versionLike -0.02 0.02 -0.05 0.02 1.00 1706 1415
## versionLike&dislike -0.02 0.02 -0.06 0.02 1.00 1702 1208
## hu_age 0.00 0.08 -0.16 0.17 1.00 1307 1351
## hu_male -0.18 3.20 -7.08 6.31 1.00 1334 958
## hu_edu -0.15 1.70 -3.82 3.09 1.00 1749 1136
## hu_versionLike 0.08 4.62 -9.44 9.60 1.00 1014 804
## hu_versionLike&dislike -0.29 4.80 -10.23 9.45 1.00 903 853
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 27.90 1.72 24.78 31.40 1.00 2120 1360
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 self_eff_fs age dY/dX -5.83e-05 -0.00638 0.00597
## 2 self_eff_fs edu dY/dX 1.33e-01 0.02646 0.24309
## 3 self_eff_fs male 1 - 0 7.91e-02 -0.09294 0.25118
## 4 self_eff_fs version Like & dislike - Control -9.17e-02 -0.29982 0.12091
## 5 self_eff_fs version Like - Control -9.03e-02 -0.29554 0.11603
I then compare the likes and control conditions to the likes plus dislikes condition.
run_hurdles(object = d, name = "selfeff_lkdslk", outcome = "self_eff_fs", predictor = "version_lkdslk", y_lab = "Self-efficacy",
x_lab = "Experimental conditions")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: self_eff_fs ~ 1 + age + male + edu + version_lkdslk
## hu ~ 1 + age + male + edu + version_lkdslk
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.61 0.03 1.54 1.67 1.00 2287 1729
## hu_Intercept -10.11 6.38 -24.58 -0.48 1.00 1053 631
## age 0.00 0.00 -0.00 0.00 1.00 2025 1503
## male 0.01 0.02 -0.02 0.05 1.00 2510 1622
## edu 0.02 0.01 0.01 0.04 1.00 2549 1498
## version_lkdslkLike 0.00 0.02 -0.04 0.04 1.00 1771 1742
## version_lkdslkControl 0.02 0.02 -0.02 0.06 1.00 1870 1569
## hu_age 0.00 0.08 -0.16 0.18 1.00 1302 1079
## hu_male -0.19 3.23 -6.62 6.77 1.01 1008 621
## hu_edu -0.29 1.77 -4.18 2.86 1.00 1404 1094
## hu_version_lkdslkLike 0.41 4.64 -8.52 10.59 1.00 820 641
## hu_version_lkdslkControl 0.48 4.38 -7.74 10.39 1.01 753 613
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 27.98 1.66 24.78 31.40 1.00 2571 1327
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 self_eff_fs age dY/dX -3.35e-05 -0.00623 0.00556
## 2 self_eff_fs edu dY/dX 1.33e-01 0.03295 0.23518
## 3 self_eff_fs male 1 - 0 7.77e-02 -0.08997 0.25232
## 4 self_eff_fs version_lkdslk Control - Like & dislike 9.07e-02 -0.11725 0.29872
## 5 self_eff_fs version_lkdslk Like - Like & dislike -3.38e-04 -0.20015 0.21419
Words
Privacy Concerns
run_hurdles(object = d, name = "wrds_pricon", outcome = "words", predictor = "pri_con_fs", y_lab = "Words", x_lab = "Privacy concerns")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: words ~ 1 + age + male + edu + pri_con_fs
## hu ~ 1 + age + male + edu + pri_con_fs
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 5.65 0.42 4.86 6.47 1.00 3674 1900
## hu_Intercept 0.54 0.41 -0.26 1.33 1.00 4752 1616
## age -0.00 0.01 -0.01 0.01 1.00 3967 1825
## male -0.08 0.16 -0.39 0.23 1.00 4278 1463
## edu -0.01 0.10 -0.19 0.19 1.00 4080 1498
## pri_con_fs -0.10 0.06 -0.21 0.02 1.00 3271 1566
## hu_age -0.01 0.01 -0.02 0.00 1.00 4798 1772
## hu_male -0.04 0.18 -0.39 0.32 1.00 4138 1452
## hu_edu -0.23 0.11 -0.45 -0.02 1.00 4804 1477
## hu_pri_con_fs 0.19 0.06 0.07 0.30 1.00 4531 1416
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.69 0.05 0.59 0.80 1.00 3323 1590
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 words age dY/dX 0.192 -0.827 1.21
## 2 words edu dY/dX 9.020 -8.285 27.94
## 3 words male 1 - 0 -4.444 -34.586 25.46
## 4 words pri_con_fs dY/dX -15.620 -27.731 -5.06
Let’s explore how number of communicated words differ for people very much concerned versus not concerned at all.
# let's extract effects for 1
wrds_pricon_1 <- fig_wrds_pricon %$%
data %>%
filter(pri_con_fs == min(.$pri_con_fs)) %$%
estimate__ %>%
round(0)
# let's extract effects for 7
wrds_pricon_7 <- fig_wrds_pricon %$%
data %>%
filter(pri_con_fs == max(.$pri_con_fs)) %$%
estimate__ %>%
round(0)People who were not concerned at all communicated on average 115 words, whereas people strongly concerned communicated on average 32 words.
Gratifications General
run_hurdles(object = d, name = "wrds_grats", outcome = "words", predictor = "grats_gen_fs", y_lab = "Words", x_lab = "Gratifications general")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: words ~ 1 + age + male + edu + grats_gen_fs
## hu ~ 1 + age + male + edu + grats_gen_fs
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 4.28 0.46 3.40 5.20 1.00 3580 1571
## hu_Intercept 2.26 0.51 1.24 3.27 1.00 4151 1465
## age -0.00 0.01 -0.02 0.01 1.00 3940 1618
## male -0.08 0.16 -0.39 0.23 1.00 3822 1667
## edu 0.03 0.09 -0.16 0.22 1.01 3721 1273
## grats_gen_fs 0.22 0.07 0.08 0.36 1.00 4126 1911
## hu_age -0.01 0.01 -0.02 0.00 1.00 3490 1433
## hu_male -0.08 0.18 -0.44 0.29 1.00 4925 1423
## hu_edu -0.22 0.10 -0.43 -0.03 1.01 4808 1530
## hu_grats_gen_fs -0.22 0.08 -0.38 -0.07 1.00 5329 1420
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.71 0.06 0.60 0.82 1.01 5097 1613
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 words age dY/dX 0.0449 -0.944 1.04
## 2 words edu dY/dX 12.1728 -3.388 30.15
## 3 words grats_gen_fs dY/dX 26.6470 12.786 43.98
## 4 words male 1 - 0 -2.9281 -31.721 26.14
Privacy Deliberation
run_hurdles(object = d, name = "wrds_pridel", outcome = "words", predictor = "pri_del_fs", y_lab = "Words", x_lab = "Privacy deliberation")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: words ~ 1 + age + male + edu + pri_del_fs
## hu ~ 1 + age + male + edu + pri_del_fs
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 6.50 0.50 5.55 7.49 1.00 3363 1381
## hu_Intercept -0.16 0.47 -1.09 0.73 1.00 4267 1607
## age -0.01 0.01 -0.02 0.00 1.00 3397 1588
## male -0.08 0.15 -0.38 0.23 1.01 3394 1481
## edu -0.02 0.09 -0.20 0.15 1.00 3862 1585
## pri_del_fs -0.26 0.07 -0.41 -0.11 1.00 2853 1596
## hu_age -0.01 0.01 -0.02 0.00 1.00 3408 1469
## hu_male -0.00 0.18 -0.36 0.34 1.01 4099 1450
## hu_edu -0.22 0.10 -0.42 -0.02 1.00 4385 1468
## hu_pri_del_fs 0.30 0.08 0.15 0.46 1.00 4565 1303
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.71 0.06 0.60 0.84 1.00 4191 1575
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 words age dY/dX -0.163 -1.23 0.77
## 2 words edu dY/dX 7.349 -8.69 23.29
## 3 words male 1 - 0 -6.536 -35.47 23.54
## 4 words pri_del_fs dY/dX -33.086 -50.80 -19.51
Trust General
run_hurdles(object = d, name = "wrds_trust", outcome = "words", predictor = "trust_gen_fs", y_lab = "Words", x_lab = "Trust general")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: words ~ 1 + age + male + edu + trust_gen_fs
## hu ~ 1 + age + male + edu + trust_gen_fs
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 4.25 0.61 3.05 5.51 1.00 3053 1348
## hu_Intercept 3.10 0.63 1.87 4.28 1.00 4058 1775
## age -0.00 0.01 -0.01 0.01 1.00 3646 1570
## male -0.07 0.17 -0.41 0.27 1.00 4094 1304
## edu -0.01 0.09 -0.20 0.18 1.00 3915 1478
## trust_gen_fs 0.21 0.10 0.01 0.41 1.00 3166 1733
## hu_age -0.01 0.01 -0.02 0.00 1.00 3252 1581
## hu_male -0.07 0.17 -0.42 0.27 1.01 4579 1652
## hu_edu -0.22 0.11 -0.43 -0.01 1.00 5407 1430
## hu_trust_gen_fs -0.36 0.10 -0.54 -0.17 1.00 4243 1371
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.70 0.05 0.59 0.80 1.00 3576 1532
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 words age dY/dX 0.279 -0.80 1.37
## 2 words edu dY/dX 8.021 -8.74 25.84
## 3 words male 1 - 0 -3.071 -33.26 27.75
## 4 words trust_gen_fs dY/dX 31.345 13.95 53.68
Self-Efficacy
run_hurdles(object = d, name = "wrds_selfeff", outcome = "words", predictor = "self_eff_fs", y_lab = "Words", x_lab = "Self-efficacy")
## Family: hurdle_gamma
## Links: mu = log; shape = identity; hu = logit
## Formula: words ~ 1 + age + male + edu + self_eff_fs
## hu ~ 1 + age + male + edu + self_eff_fs
## Data: object (Number of observations: 558)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 2.17 0.50 1.20 3.17 1.00 3294 1436
## hu_Intercept 5.76 0.70 4.43 7.17 1.00 2986 1639
## age -0.00 0.01 -0.01 0.01 1.00 3970 1389
## male -0.15 0.15 -0.44 0.14 1.00 3172 1306
## edu -0.06 0.08 -0.23 0.10 1.01 3704 1646
## self_eff_fs 0.58 0.07 0.43 0.72 1.00 2484 1600
## hu_age -0.01 0.01 -0.02 0.00 1.00 3926 1420
## hu_male -0.01 0.18 -0.38 0.34 1.00 3127 1432
## hu_edu -0.12 0.11 -0.34 0.10 1.00 3116 1498
## hu_self_eff_fs -0.88 0.11 -1.10 -0.67 1.00 3226 1563
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.80 0.06 0.68 0.94 1.00 4124 1538
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
##
## Results of marginal effects:
## Outcome term contrast estimate conf.low conf.high
## 1 words age dY/dX 0.0355 -0.868 0.919
## 2 words edu dY/dX -1.0912 -16.157 13.279
## 3 words male 1 - 0 -11.2411 -38.103 12.515
## 4 words self_eff_fs dY/dX 72.7838 55.148 95.591
The effects is markedly exponential. Let’s inspect the changes across values in self-efficacy.
effects_wrds_selfeff <- slopes(fit_wrds_selfeff, newdata = datagrid(self_eff_fs = c(1, 6, 7)), grid_type = "counterfactual") %>%
filter(term == "self_eff_fs") %T>%
print()##
## Term Contrast Estimate 2.5 % 97.5 % age male edu self_eff_fs
## self_eff_fs dY/dX 0.232 0.0823 0.602 46.1 0.493 1.85 1
## self_eff_fs dY/dX 103.972 79.4271 135.915 46.1 0.493 1.85 6
## self_eff_fs dY/dX 203.860 135.2249 309.608 46.1 0.493 1.85 7
##
## Columns: rowid, term, contrast, estimate, conf.low, conf.high, predicted, predicted_hi, predicted_lo, tmp_idx, words, age, male, edu, self_eff_fs# let's extract effects for 1
effects_wrds_selfeff_12 <- effects_wrds_selfeff %>%
filter(self_eff_fs == 1 & term == "self_eff_fs") %>%
select(estimate)
# let's extract effects for 7
effects_wrds_selfeff_67 <- effects_wrds_selfeff %>%
filter(self_eff_fs == 6 & term == "self_eff_fs") %>%
select(estimate)Whereas a change in self-efficacy from 1 to 2 led to an increase of 0 words, a change from 6 to 7 led to an increase of 104 words.
Let’s explore how number of communicated words differ for people very much self-efficacious versus not self-efficacious at all.
# let's extract effects for 1
wrds_selfeff_1 <- fig_wrds_selfeff %$%
data %>%
filter(self_eff_fs == min(.$self_eff_fs)) %$%
estimate__ %>%
round(0)
# let's extract effects for 7
wrds_selfeff_7 <- fig_wrds_selfeff %$%
data %>%
filter(self_eff_fs == max(.$self_eff_fs)) %$%
estimate__ %>%
round(0)People who reported no self-efficacy at all communicated on average 1 words, whereas people with very high self-efficacy communicated on average 254 words.
Visualization
fig_results <- plot_grid(fig_wrds_pricon, fig_wrds_grats, fig_wrds_pridel, fig_wrds_trust, fig_wrds_selfeff, fig_wrds_ctrl) %T>%
print()
# Save visualizations
ggsave("figures/results/effects.pdf")
ggsave("figures/results/effects.png")Table
Make table of slopes
tab_slopes <- rbind(slopes_wrds_pricon, slopes_wrds_grats, slopes_wrds_pridel, slopes_wrds_trust, slopes_wrds_selfeff, slopes_wrds_ctrl,
slopes_wrds_lkdslk, slopes_pricon_ctrl, slopes_pricon_lkdslk, slopes_grats_ctrl, slopes_grats_lkdslk, slopes_pridel_ctrl,
slopes_pridel_lkdslk, slopes_trust_ctrl, slopes_trust_lkdslk, slopes_selfeff_ctrl, slopes_selfeff_lkdslk) %>%
as.data.frame() %>%
mutate(term = ifelse(.$contrast == "Like - Control", "Like - Control", ifelse(.$contrast == "Like & dislike - Control",
"Like & dislike - Control", ifelse(.$contrast == "Like - Like & dislike", "Like - Like & dislike", .$term))), Predictor = recode(term,
`Like - Control` = "Like vs. control", `Like & dislike - Control` = "Like & dislike vs. control", `Like - Like & dislike` = "Like vs. like & dislike",
pri_con_fs = "Privacy concerns", pri_del_fs = "Privacy deliberation", grats_gen_fs = "Expected gratifications", trust_gen_fs = "Trust",
self_eff_fs = "Self-efficacy"), Outcome = recode(Outcome, pri_con_fs = "Privacy concerns", pri_del_fs = "Privacy deliberation",
grats_gen_fs = "Expected gratifications", trust_gen_fs = "Trust", self_eff_fs = "Self-efficacy")) %>%
filter(Predictor %in% c("Like vs. control", "Like & dislike vs. control", "Like vs. like & dislike", "Privacy concerns",
"Privacy deliberation", "Expected gratifications", "Trust", "Self-efficacy")) %>%
mutate(Predictor = factor(Predictor, levels = c("Self-efficacy", "Trust", "Privacy deliberation", "Expected gratifications",
"Privacy concerns", "Like vs. like & dislike", "Like & dislike vs. control", "Like vs. control")), Estimate = ifelse(Outcome ==
"words", round(estimate), estimate), LL = ifelse(Outcome == "words", round(conf.low), conf.low), UL = ifelse(Outcome ==
"words", round(conf.high), conf.high), ) %>%
select(Outcome, Predictor, Estimate, LL, UL)
tab_slopes %>%
kable() %>%
kable_styling("striped") %>%
scroll_box(width = "100%")| Outcome | Predictor | Estimate | LL | UL |
|---|---|---|---|---|
| words | Privacy concerns | -16.000 | -28.000 | -5.000 |
| words | Expected gratifications | 27.000 | 13.000 | 44.000 |
| words | Privacy deliberation | -33.000 | -51.000 | -20.000 |
| words | Trust | 31.000 | 14.000 | 54.000 |
| words | Self-efficacy | 73.000 | 56.000 | 94.000 |
| words | Like & dislike vs. control | -48.000 | -93.000 | -10.000 |
| words | Like vs. control | -35.000 | -82.000 | 4.000 |
| words | Like vs. like & dislike | 13.000 | -19.000 | 44.000 |
| Privacy concerns | Like & dislike vs. control | 0.181 | -0.160 | 0.521 |
| Privacy concerns | Like vs. control | 0.038 | -0.285 | 0.343 |
| Privacy concerns | Like vs. like & dislike | -0.144 | -0.475 | 0.203 |
| Expected gratifications | Like & dislike vs. control | -0.206 | -0.477 | 0.067 |
| Expected gratifications | Like vs. control | -0.169 | -0.432 | 0.078 |
| Expected gratifications | Like vs. like & dislike | 0.033 | -0.233 | 0.298 |
| Privacy deliberation | Like & dislike vs. control | -0.045 | -0.296 | 0.200 |
| Privacy deliberation | Like vs. control | -0.006 | -0.248 | 0.244 |
| Privacy deliberation | Like vs. like & dislike | 0.042 | -0.194 | 0.300 |
| Trust | Like & dislike vs. control | -0.132 | -0.354 | 0.087 |
| Trust | Like vs. control | -0.151 | -0.363 | 0.053 |
| Trust | Like vs. like & dislike | -0.020 | -0.226 | 0.185 |
| Self-efficacy | Like & dislike vs. control | -0.092 | -0.300 | 0.121 |
| Self-efficacy | Like vs. control | -0.090 | -0.296 | 0.116 |
| Self-efficacy | Like vs. like & dislike | 0.000 | -0.200 | 0.214 |
Rohrer, Julia M. 2018. “Thinking Clearly about Correlations and
Causation: Graphical Causal Models for Observational
Data.” Advances in Methods and Practices in Psychological
Science 24 (2): 251524591774562. https://doi.org/10.1177/2515245917745629.