3 Descriptives and Visualizations

3.1 Overview

In this section, I describe and visualize the sample and variables. We have variables on the meta-level (about the survey), the person-level, the app-level, and the day-level. App-level data is in the apps_long data file; all other in the dat data file.

Meta-level

• Duration of the entry survey, when participants reported traits (duration_personality)
• Duration of the exit survey, when participants reported their screen time (duration_screen_time)

Person-level

• Participant identifier (id)
• age in years
• ethnicity
• Notifications of social media apps over the past week (weekly_notifications)
• Basic Psychological Need Satisfaction (autonomy_trait, competence_trait, relatedness_trait) plus their individual items (starting with bpns_)
• Big Five (extraversion, agreeableness, conscientiousness, neuroticsim, openness) plus their individual items (starting with big_five_)

App-level

• What app participants report use for (app)
• On what rank was that app on participants’ top ten (rank)
• Notifications for that app for the week (notifications_per_week)
• Pickups for that app on that day (pickups)
• Screen time for that app on that day (social_media_objective)

Day-level

• Duration of the survey on that day (duration_diary)
• day the survey was answered
• Estimated time on social media on that day (social_media_subjective)
• Estimated pickups of social media apps on that day (pickups_subjective)
• Estimated notifications of social media apps on that day (notifications_subjective)
• Objective time on social media on that day (social_media_objective)
• Objective pickups of social media apps on that day (pickups_objective)
• Well-being on that day (well_being) plus its individual items (starting with low_ and high_)
• Basic psychological needs on that day (autonomy_state, competence_state, relatedness_state) plus their individual items (starting with autonomy_, competence_, relatedness_ respectively)
• Experiences of satisfaction, boredom, stress, enjoyment on that day (satisfied, boring, stressful, enoyable)

3.2 Meta-level

I begin with describing and plotting the duration of the entry and exit surveys. Table 3.1 shows descriptive stats; Figure 3.1 shows that twp participants had their entry surveys open for a day before pressing send, which skews the mean massively. However, those people’s data look good, so I wouldn’t exclude them here. Note: Colors are from here.
Table 3.1: Duration of entry and exit surveys
variable mean sd median min max range cilow cihigh
duration_personality 1H 5M 56S 4H 36M 55S 15M 40S 1M 22S 1d 9H 33M 36S 1d 9H 32M 14S 9M 50S 2H 2M 3S
duration_screen_time 20M 21S 20M 8S 13M 18S 1M 8S 1H 51M 29S 1H 50M 21S 16M 17S 24M 26S

3.3 Person-level

Let’s have a look at the final sample. Overall, our sample size is N = 96. The sample has a mean age of M = 20.45, SD = 1.32. The age ranges from 18 to 25 The sample consists mostly of women (66 women, 30 men, and one non-binary participant).

Most participants are Asian, followed by White, Black, and Hispanic, see Table 3.2
Table 3.2: Ethnicity of the sample
ethnicity count percent
Asian 40 42
White 26 27
Black or African American 11 11
Hispanic or Latino 10 10
Multiracial 6 6
NA 2 2
Native Hawaiian or Other Pacific Islander 1 1
Alright, next we look at the objective count of notifications over the past week, aggregated across all apps. Table 3.3 shows that participants received quite a lot of notifications from social media apps only. That distribution is heavily skewed (Figure 3.2 by a couple of participants who received several thousand notifications over the week.
Table 3.3: Weekly notifications (objective) across all apps
variable mean sd median min max range cilow cihigh
weekly_notifications 997.6354 673.4178 868.5 126 3382 3256 861.1883 1134.083
Now we look at the trait variables: the basic psychological need satisfaction and the big five. Note that I follow recent recommendations and calculate $$\omega$$ for reliability. Table 3.4 shows the descriptive information of the three psychological needs and the big five. Figure 3.3 shows their distribution. The sample isn’t too large, so considering the small size, I’d say everything looks pretty good.
Table 3.4: Descriptives for trait variables
variable mean sd median min max range cilow cihigh omega
autonomy_trait 4.51 0.88 4.50 1.25 6.88 5.62 4.33 4.68 0.82
competence_trait 5.16 0.70 5.31 3.50 6.50 3.00 5.01 5.30 0.75
relatedness_trait 4.54 1.03 4.56 2.38 6.62 4.25 4.33 4.75 0.88
extraversion 3.13 0.74 3.00 1.62 5.00 3.38 2.98 3.29 0.86
agreeableness 3.68 0.56 3.59 2.33 5.00 2.67 3.57 3.80 0.75
conscientiousness 3.50 0.58 3.50 2.00 4.89 2.89 3.39 3.62 0.75
neuroticism 3.29 0.50 3.38 2.00 4.38 2.38 3.19 3.39 0.59
openness 3.54 0.48 3.60 2.10 4.80 2.70 3.45 3.64 0.66
In Figure 3.4 we see the correlations between those traits. As expected psychological needs are correlated highly with each other. Credit for the lm lines goes to data prone, whose idea I adapted.

3.4 App-level

First, Figure 3.5 shows what apps mostly nominated (i.e., used). We see that out of the sample, most participants had Messaging, Snapchat, Whatsapp etc. as part of their top ten. Next, I visualize how many minutes each of those apps was used across the sample. For that, I need to reshape the data a bit to get the mean minutes per app across all days and all participants. In Figure 3.6, I show the average objective time per app. Note that the CIs are across the entire data and not nested by app or day. Also, a high mean doesn’t mean that much because it could just be from one participant who used it a lot on two days. The size of the points shows how often an app was reported across the entire sample. For apps that only had one entry, those CI will be nonexistent. In addition, I now exclude entries on social_media_objective that have NA. The NA here can mean participants just didn’t fill in anything, or they had zero duration on that day. Because adding up the raw scores across apps was so close to the daily total, I’ll exclude NAs here. I’ll do the same for objective pickups per app, averaged across day and participant. Figure 3.7 shows that the same apps that got a lot of screen time had a lot of pickups. Last, I check which apps got the most notifications over the week in Figure 3.8, on average. It’s interesting to see that Facebook had a lot of screen time and pickups, but much fewer notifications. Also, these notifications are per week, and not per day, as the previous two figures.

3.5 Day level

Alright, we’re at the most interesting section, the daily surveys. I first look at how long people typically took for a survey. Table 3.5 shows that the mean is highly skewed because of outliers and the median more appropriate to describe the duration. In Figure 3.9 we see that a couple of people took a long time from opening to submitting the survey. I checked those participants who took a long time in the data processing section. The maximum duration here from someone who didn’t open the survey on a Friday. So that duration is just the survey closing automatically after two days, which really drives up the mean.
Table 3.5: Duration of daily surveys
variable mean sd median min max range cilow cihigh
duration_diary 50M 22S 2H 44M 17S 16M 0S 25S 2d 0H 27M 48S 2d 0H 27M 23S 35M 5S 1H 5M 40S

Alright, next I inspect overall response rate in the final sample, aka how many valid surveys do we have among the final sample. Each participant received five surveys, one for each day, so 96 participants x 5 = 480. We have 435 surveys in the final sample where participants actually responded, which means a 91% response rate among the final sample.

Let’s inspect response rate per day. As is to be expected, participants lost motivation over the course of the week. However, even the response rate on Friday is really high (at least among our sample of valid responses). We should still consider to take the day grouping into account when modelling the data later in the analysis. Next, I describe and plot the distributions of the social media use variables. The distribution and CI is of the entire sample, not aggregated by participant or day first. Table 3.6 shows that participants weren’t too far off in their estimates, which is interesting. As expected (Figure 3.11), the social media variables are a bit skewed, but overall, they look fine.
Table 3.6: Descriptive information on social media variables
variable mean sd median min max range cilow cihigh
social_media_subjective 153 112 130 5 692 687 143 164
social_media_objective 139 94 118 2 565 563 130 148
error 46 156 -3 -96 1186 1282 31 61
pickups_subjective 34 43 17 0 259 259 30 38
pickups_objective 49 31 44 0 196 196 46 52
notifications_subjective 61 99 30 0 700 700 52 71
I also want to see how much variability there is between the objective and subjective measures. In Figure 3.12 we see per participant the difference between objective and subjective social media use. The numbers in the grey box show whether the subjective report is an underestimate (negative number) or overestimate (positive number). Inspiration for the plot from here and here. Now let’s look at the state well-being and psychological needs variables plus the four experiences (e.g., boring). Again, I calculate $$\omega$$, but this time for the entire sample in Table 3.7. That will necessarily bias the estimate because there’s multiple measures per person. I’m not aware of a consensus reliability procedure for repeated measures. Figure 3.13 shows that the data look pretty good.
Table 3.7: Descriptives for state variables
variable mean sd median min max range cilow cihigh omega
well_being_state 3.23 0.70 3.17 1.25 5 3.75 3.17 3.30 0.85
autonomy_state 4.50 1.16 4.25 1.00 7 6.00 4.39 4.60 0.69
competence_state 4.60 1.27 4.50 1.00 7 6.00 4.48 4.72 0.79
relatedness_state 5.33 1.07 5.50 2.50 7 4.50 5.23 5.43 0.70
satisfied 4.61 1.33 5.00 1.00 7 6.00 4.49 4.74 NA
boring 3.40 1.55 3.00 1.00 7 6.00 3.25 3.55 NA
stressful 3.96 1.81 4.00 1.00 7 6.00 3.79 4.13 NA
enjoyable 4.27 1.41 4.00 1.00 7 6.00 4.14 4.40 NA
In Figure 3.14 we see the correlations between variables on the state level. In Figure 3.15 we see the correlations between use variables on the state level and the trait level.

3.6 Demographics and social media

To compare our findings with previous research which found gender and age differences in social media use, we also check for those differences in our data set. We don’t report those results in the paper because a) they’re less relevant to our research questions, b) space limits.

First, let’s create a correlation matrix between age and the social media use variables. Figure 3.16 shows that age is pretty much unrelated to the social media use variables, possibly because the age range is quite narrow. Next, I visualize differences in the three social media use variables between genders. Figure (fig:visualize-gender-differences) shows little visual indication that there might be differences.

I’ll test that formally with a t-test on the aggregated data. For none of the outcomes is the difference between genders significant.

my_t_test <- function(model) {
out1 <- broom::tidy(model) %>%
select(
contains("estimate"),
statistic,
p.value
) %>%
rename(
Difference = estimate,
Men = estimate1,
Women = estimate2,
p = p.value
)
}

tmp %>%
group_by(variable) %>%
group_modify(~my_t_test(t.test(value ~ gender, data = .x))) %>%
rename(Outcome = variable) %>%
kable(digits = c(2,2,2,2))
Outcome Difference Men Women statistic p
error 15.71 59.50 43.79 0.50 0.62
social_media_objective -13.12 131.38 144.50 -0.68 0.50
social_media_subjective -11.47 147.57 159.04 -0.52 0.60

3.7 Plots for paper

Here, I’ll create summary figures for the paper. I’ll begin with plotting the traits.

For the plot, the data need to be in the long format.

tmp <-
dat %>%
group_by(id) %>%
slice(1) %>%
ungroup() %>%
select(all_of(c("id", trait_descriptives$variable))) %>% pivot_longer( -id, names_to = "variable", values_to = "value" ) rename_levels <- c( "Autonomy", "Competence", "Relatedness", "Agreeableness", "Conscientiousness", "Extraversion", "Neuroticism", "Openness" ) my_string <- "_trait" # reorder and rename factor levels clean_plot_data <- function( dat, levels_to_rename, string_to_remove ){ dat <- dat %>% mutate( # in case it's social media variables variable = case_when( variable == "social_media_objective" ~ "Objective (h)", variable == "social_media_subjective" ~ "Subjective (h)", variable == "error" ~ "Accuracy (%)", TRUE ~ variable ), # remove _trait at the end and capitalize variable = str_to_sentence(str_remove(variable, string_to_remove)), variable = as.factor(variable), variable = str_replace(variable, "_", "-"), # reorder factor levels variable = fct_relevel( variable, levels_to_rename ) ) return(dat) } tmp <- clean_plot_data(tmp, rename_levels, my_string) trait_descriptives <- clean_plot_data(trait_descriptives, rename_levels, my_string) Okay, we already have the aggregated info in trait_descriptives, so we can get to plotting. # function for breaks my_breaks <- function(x) { if (max(x) > 5){ 1:7 } else { 1:5 } } # function for limits my_limits <- function(x) { if (max(x) > 5){ c(1,7) } else { c(1,5) } } # color palette (not needed anymore after review that colors made the figure harder to see) cb_palette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") # plot ggplot( tmp, aes( x = value, y = 1 ) ) + geom_quasirandom(groupOnX=FALSE, size = 0.7, shape = 20, color = "black") + facet_wrap( ~ variable, scales = "free_x" ) + scale_x_continuous(breaks = my_breaks, limits = my_limits) + geom_text( data = trait_descriptives, aes( x = 1.6, y = 1.4, label = paste0("M = ", mean), family = "Corbel" ), size = 2.5, color = "black" ) + geom_text( data = trait_descriptives, aes( x = 1.6, y = 1.3, label = paste0("SD = ", sd), family = "Corbel" ), size = 2.5, color = "black" ) + geom_text( data = trait_descriptives, aes( x = 1.6, y = 1.2, label = paste0("\u03a9 = ", omega), family = "Corbel" ), size = 2.5, color = "black" ) + theme_cowplot() + # scale_colour_manual(values=cb_palette) + # scale_fill_manual(values = cb_palette) + theme( axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), strip.background.x = element_blank(), strip.background.y = element_blank(), legend.position = "none", text = element_text(family = "Corbel") ) -> figure1 figure1 ggsave( here("figures", "figure1.tiff"), plot = figure1, width = 21 * 0.8, height = 29.7 * 0.4, units = "cm", dpi = 300 ) Okay, next the state variables. tmp <- dat %>% select(all_of(c("id", state_descriptives$variable))) %>%
pivot_longer(
-id,
names_to = "variable",
values_to = "value"
)

rename_levels <- c(
"Autonomy",
"Competence",
"Relatedness",
"Boring",
"Enjoyable",
"Satisfied",
"Stressful",
"Well-being"
)

my_string <- "_state"

tmp <- clean_plot_data(tmp, rename_levels, my_string)
state_descriptives <- clean_plot_data(state_descriptives, rename_levels, my_string)

# position of the text (so it doesn't overlap with points)
state_descriptives <-
state_descriptives %>%
mutate(
x_position = 1.6,
y_position = 1.45
)

Then to plotting.

# plot
ggplot(
tmp,
aes(
x = value,
y = 1
)
) +
geom_quasirandom(groupOnX=FALSE, size = 0.7, shape = 20, color = "black") +
facet_wrap(
~ variable,
scales = "free_x"
) +
scale_x_continuous(breaks = my_breaks, limits = my_limits) +
geom_text(
data = state_descriptives,
aes(
x = x_position,
y = y_position + 0.1,
label = paste0("M = ", mean),
family = "Corbel"
),
size = 2.5,
color = "black"
) +
geom_text(
data = state_descriptives,
aes(
x = x_position,
y = y_position,
label = paste0("SD = ", sd),
family = "Corbel"
),
size = 2.5,
color = "black"
) +
geom_text(
data = state_descriptives,
aes(
x = x_position,
y = y_position - 0.1,
label = paste0("\u03a9 = ", omega),
family = "Corbel"
),
size = 2.5,
color = "black",
alpha = if_else(is.na(state_descriptives\$omega), 0, 1) # one-item measure don't have omega, so I make those see through
) +
theme_cowplot() +
# scale_colour_manual(values=cb_palette) +
# scale_fill_manual(values = cb_palette) +
theme(
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank(),
strip.background.x = element_blank(),
strip.background.y = element_blank(),
legend.position = "none",
text = element_text(family = "Corbel")
) -> figure2

figure2

ggsave(
here("figures", "figure2.tiff"),
plot = figure2,
width = 21 * 0.8,
height = 29.7 * 0.4,
units = "cm",
dpi = 300
)

Alright, last the smartphone use variables.

tmp <-
dat %>%
select(all_of(c("id", "social_media_objective", "social_media_subjective", "error"))) %>%
# turn to hours
mutate(
across(
contains("social_media"),
~ .x /60
)
) %>%
pivot_longer(
-id,
names_to = "variable",
values_to = "value"
)

rename_levels <- c(
"Objective (h)",
"Subjective (h)",
"Accuracy (%)"
)

my_string <- "_state"

tmp <- clean_plot_data(tmp, rename_levels, my_string)
social_media2 <- clean_plot_data(social_media, rename_levels, my_string) %>%
filter(variable %in% c("Objective (h)", "Subjective (h)", "Accuracy (%)")) %>%
# add x axis position for geom_text
mutate(
x_position = case_when(
variable == "Accuracy (%)" ~ 1200*0.8,
TRUE ~ 0.8*13
)
)

And the plot.

# function for breaks
my_breaks <-
function(x) {
if (max(x) < 100){
seq(0, 13, 2)
} else {
c(-200, 0, 400, 800, 1200)
}
}

# function for limits
my_limits <-
function(x) {
if (max(x) < 100){
c(0, 13)
} else {
c(-200, 1200)
}
}

# plot
ggplot(
tmp,
aes(
x = value,
y = 1
)
) +
geom_quasirandom(groupOnX=FALSE, size = 0.7, shape = 20, color = "black") +
facet_wrap(
~ variable,
scales = "free_x"
) +
scale_x_continuous(breaks = my_breaks, limits = my_limits) +
geom_text(
data = social_media2,
aes(
x = x_position,
y = 0.7,
label = paste0("M = ", mean),
family = "Corbel"
),
size = 3,
color = "black"
) +
geom_text(
data = social_media2,
aes(
x = x_position,
y = 0.67,
label = paste0("SD = ", sd),
family = "Corbel"
),
size = 3,
color = "black"
) +
theme_cowplot() +
scale_colour_manual(values=cb_palette) +
scale_fill_manual(values = cb_palette) +
theme(
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank(),
strip.background.x = element_blank(),
strip.background.y = element_blank(),
legend.position = "none",
text = element_text(family = "Corbel")
) -> figure3

figure3

ggsave(
here("figures", "figure3.tiff"),
plot = figure3,
width = 21 * 0.8,
height = 29.7 * 0.4,
units = "cm",
dpi = 300
)