Loading, setting up
d_to_model <- params$d_to_model
Descriptives
d_to_model %>%
select(n_tweets_by_user_in_state,
voices,
national_school_lunch_program_fy_2016_fns_usda_gov,
pct_students_frpl,
state_spending_on_public_elementary_secondary_spending_fy_2016_census_in_thousands,
state_spending_per_child,
urban_percentage_of_state_population_in_2015,
three_year_average_of_state_poverty_rates_2015_2017,
teacher_salaries_nea_2016,
c_ideology,
g_ideology,
region_by_census,
enrollment_in_public_ele_sec_education_digest_of_ed_stats_fall_2015_projected,
staff_teachers,
coordinators_teachers,
time_of_account
) %>%
skimr::skim()
Data summary
| Name |
Piped data |
| Number of rows |
616208 |
| Number of columns |
16 |
| _______________________ |
|
| Column type frequency: |
|
| numeric |
16 |
| ________________________ |
|
| Group variables |
None |
Variable type: numeric
| n_tweets_by_user_in_state |
0 |
1 |
308.63 |
520.31 |
1.00 |
13.00 |
79.00 |
344.00 |
3230.00 |
▇▁▁▁▁ |
| voices |
0 |
1 |
4997.19 |
3692.13 |
104.00 |
1908.00 |
5009.00 |
7958.00 |
12799.00 |
▇▆▃▃▃ |
| national_school_lunch_program_fy_2016_fns_usda_gov |
0 |
1 |
1034926.29 |
1075027.49 |
50876.06 |
342539.73 |
598665.35 |
1013673.02 |
3377166.13 |
▇▃▁▁▂ |
| pct_students_frpl |
318 |
1 |
0.51 |
0.08 |
0.28 |
0.45 |
0.49 |
0.59 |
0.75 |
▂▅▅▇▁ |
| state_spending_on_public_elementary_secondary_spending_fy_2016_census_in_thousands |
0 |
1 |
21459518.17 |
22995511.74 |
1473360.00 |
6129735.00 |
11314646.00 |
27945116.00 |
82153002.00 |
▇▂▁▂▁ |
| state_spending_per_child |
0 |
1 |
12.28 |
3.65 |
7.19 |
9.65 |
11.54 |
13.19 |
25.07 |
▅▇▂▁▁ |
| urban_percentage_of_state_population_in_2015 |
441 |
1 |
75.99 |
12.11 |
38.70 |
66.20 |
74.60 |
84.70 |
95.00 |
▁▂▆▆▇ |
| three_year_average_of_state_poverty_rates_2015_2017 |
441 |
1 |
12.73 |
1.78 |
6.70 |
11.70 |
13.40 |
13.80 |
20.00 |
▁▂▇▁▁ |
| teacher_salaries_nea_2016 |
441 |
1 |
54110.48 |
9500.70 |
42025.00 |
47755.00 |
51386.00 |
58140.00 |
79152.00 |
▇▆▂▁▂ |
| c_ideology |
0 |
1 |
46.36 |
14.94 |
23.98 |
38.24 |
44.41 |
58.04 |
97.00 |
▅▇▅▁▁ |
| g_ideology |
0 |
1 |
33.04 |
15.45 |
18.11 |
21.99 |
24.80 |
45.08 |
69.95 |
▇▁▁▁▂ |
| region_by_census |
0 |
1 |
2.58 |
0.90 |
1.00 |
2.00 |
3.00 |
3.00 |
4.00 |
▂▆▁▇▃ |
| enrollment_in_public_ele_sec_education_digest_of_ed_stats_fall_2015_projected |
0 |
1 |
1727295.50 |
1804836.38 |
95400.00 |
646200.00 |
917000.00 |
1715000.00 |
6294600.00 |
▇▃▁▁▂ |
| staff_teachers |
0 |
1 |
2.06 |
0.28 |
1.51 |
1.90 |
2.02 |
2.06 |
3.17 |
▁▇▁▁▁ |
| coordinators_teachers |
0 |
1 |
0.02 |
0.02 |
0.00 |
0.01 |
0.02 |
0.03 |
0.12 |
▇▂▁▁▁ |
| time_of_account |
0 |
1 |
4.04 |
2.31 |
-0.49 |
2.19 |
3.95 |
6.32 |
9.46 |
▅▇▇▇▂ |
Correlations
d_to_model %>%
select(n_tweets_by_user_in_state,
voices,
national_school_lunch_program_fy_2016_fns_usda_gov,
pct_students_frpl,
state_spending_on_public_elementary_secondary_spending_fy_2016_census_in_thousands,
state_spending_per_child,
urban_percentage_of_state_population_in_2015,
three_year_average_of_state_poverty_rates_2015_2017,
teacher_salaries_nea_2016,
c_ideology,
g_ideology,
region_by_census,
enrollment_in_public_ele_sec_education_digest_of_ed_stats_fall_2015_projected,
staff_teachers,
coordinators_teachers,
time_of_account
) %>%
corrr::correlate() %>%
arrange(desc(n_tweets_by_user_in_state)) %>%
corrr::shave() %>%
corrr::fashion() %>%
knitr::kable()
##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
| time_of_account |
.12 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| region_by_census |
.03 |
.15 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| three_year_average_of_state_poverty_rates_2015_2017 |
.02 |
.41 |
.32 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| voices |
.02 |
|
.67 |
.60 |
|
|
|
|
|
|
|
|
|
|
|
|
| pct_students_frpl |
.00 |
.60 |
.43 |
|
.29 |
|
|
|
|
|
|
|
|
|
|
|
| staff_teachers |
-.02 |
-.04 |
.06 |
-.18 |
.07 |
.05 |
|
|
|
|
|
|
|
|
|
|
| coordinators_teachers |
-.06 |
-.31 |
.10 |
-.30 |
.20 |
.09 |
.31 |
|
|
|
|
|
|
|
|
|
| state_spending_per_child |
-.10 |
-.29 |
.02 |
-.49 |
.29 |
|
.28 |
-.32 |
|
|
|
|
|
|
|
|
| urban_percentage_of_state_population_in_2015 |
-.10 |
.13 |
.50 |
-.09 |
.59 |
.28 |
|
-.28 |
.58 |
|
|
|
|
|
|
|
| national_school_lunch_program_fy_2016_fns_usda_gov |
-.12 |
.67 |
|
.43 |
.93 |
.02 |
.50 |
.32 |
.31 |
.21 |
|
|
|
|
|
|
| enrollment_in_public_ele_sec_education_digest_of_ed_stats_fall_2015_projected |
-.13 |
.62 |
.99 |
.40 |
.95 |
.05 |
.53 |
.29 |
.39 |
.26 |
.14 |
|
|
|
|
|
| state_spending_on_public_elementary_secondary_spending_fy_2016_census_in_thousands |
-.13 |
.50 |
.93 |
.29 |
|
.29 |
.59 |
.21 |
.58 |
.41 |
.33 |
.10 |
|
|
|
|
| teacher_salaries_nea_2016 |
-.13 |
-.17 |
.31 |
-.29 |
.58 |
.72 |
.58 |
-.26 |
|
.80 |
.74 |
-.28 |
.39 |
|
|
|
| g_ideology |
-.13 |
-.35 |
.06 |
-.23 |
.33 |
.62 |
.35 |
-.33 |
.74 |
.71 |
|
-.23 |
.14 |
-.11 |
|
|
| c_ideology |
-.18 |
-.28 |
.21 |
-.34 |
.41 |
.63 |
.46 |
-.24 |
.80 |
|
.71 |
-.44 |
.26 |
-.00 |
.14 |
|
| n_tweets_by_user_in_state |
|
.02 |
-.12 |
.00 |
-.13 |
-.10 |
-.10 |
.02 |
-.13 |
-.18 |
-.13 |
.03 |
-.13 |
-.02 |
-.06 |
.12 |
d_user_level <- d_to_model %>%
distinct(screen_name, .keep_all = TRUE)
Analysis - at the individual level
d_user_level$pct_students_frpl_std <- as.vector(scale(d_user_level$pct_students_frpl * 100))
d_user_level$voices_std <- as.vector(scale(d_user_level$voices))
d_user_level$full_time_equivalent_fte_teachers_state_2015_16_std <- as.vector(scale(d_user_level$full_time_equivalent_fte_teachers_state_2015_16))
d_user_level$c_ideology_std <- as.vector(scale(d_user_level$c_ideology))
d_user_level$g_ideology_std <- as.vector(scale(d_user_level$g_ideology))
d_user_level$teacher_student_ratio_std <- as.vector(scale(d_user_level$teacher_student_ratio))
d_user_level$state_spending_per_child_std <- as.vector(scale(d_user_level$state_spending_per_child))
d_user_level$time_of_account_std <- as.vector(scale(d_user_level$time_of_account))
mnull <- glmer(n_tweets_by_user_in_state ~ 1 +
#scale(time_of_account)+
#scale(voices) +
#scale(c_ideology) +
#scale(g_ideology) +
#three_year_average_of_state_poverty_rates_2015_2017 +
#fct_students_frpl +
#state_spending_per_child +
#total_students_state_2015_16 +
#full_time_equivalent_fte_teachers_state_2015_16 +
#scale(urban_percentage_of_state_population_in_2015) +
(1|state),
data = d_user_level,
family = "poisson")
performance::icc(mnull)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.638
## Conditional ICC: 0.638
sjPlot::tab_model(mnull, show.std = "std")
## No variables could be standardized.
|
|
n tweets by user in state
|
|
Predictors
|
Incidence Rate Ratios
|
std. Beta
|
CI
|
standardized CI
|
p
|
|
(Intercept)
|
6.05
|
1.80
|
5.37 – 6.82
|
1.68 – 1.92
|
<0.001
|
|
Random Effects
|
|
σ2
|
0.15
|
|
τ00 state
|
0.27
|
|
ICC
|
0.64
|
|
N state
|
45
|
|
Observations
|
57690
|
|
Marginal R2 / Conditional R2
|
0.000 / 0.638
|
m1 <- glmer(n_tweets_by_user_in_state ~ 1 +
time_of_account_std +
voices_std +
full_time_equivalent_fte_teachers_state_2015_16_std +
c_ideology_std +
g_ideology_std +
pct_students_frpl_std +
teacher_student_ratio_std +
state_spending_per_child_std +
(1|state),
data = d_user_level,
family = "poisson")
performance::icc(m1)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.519
## Conditional ICC: 0.342
sjPlot::tab_model(m1, show.std = "std")
|
|
n tweets by user in state
|
|
Predictors
|
Incidence Rate Ratios
|
std. Beta
|
CI
|
standardized CI
|
p
|
std. p
|
|
(Intercept)
|
6.43
|
1.86
|
5.23 – 7.90
|
1.65 – 2.07
|
<0.001
|
<0.001
|
|
time_of_account_std
|
1.21
|
0.19
|
1.21 – 1.22
|
0.19 – 0.20
|
<0.001
|
<0.001
|
|
voices_std
|
1.21
|
0.19
|
0.93 – 1.58
|
-0.08 – 0.47
|
0.148
|
0.165
|
|
full_time_equivalent_fte_teachers_state_2015_16_std
|
0.72
|
-0.33
|
0.52 – 0.98
|
-0.65 – -0.02
|
0.035
|
0.038
|
|
c_ideology_std
|
0.83
|
-0.18
|
0.70 – 0.99
|
-0.36 – -0.01
|
0.041
|
0.043
|
|
g_ideology_std
|
0.94
|
-0.06
|
0.77 – 1.16
|
-0.27 – 0.15
|
0.570
|
0.576
|
|
pct_students_frpl_std
|
0.99
|
-0.01
|
0.87 – 1.13
|
-0.14 – 0.13
|
0.934
|
0.935
|
|
teacher_student_ratio_std
|
1.11
|
0.11
|
0.94 – 1.33
|
-0.07 – 0.28
|
0.222
|
0.227
|
|
state_spending_per_child_std
|
1.05
|
0.05
|
0.86 – 1.28
|
-0.16 – 0.25
|
0.651
|
0.658
|
|
Random Effects
|
|
σ2
|
0.15
|
|
τ00 state
|
0.17
|
|
ICC
|
0.52
|
|
N state
|
44
|
|
Observations
|
57642
|
|
Marginal R2 / Conditional R2
|
0.341 / 0.683
|
performance::check_model(m1)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 57619 rows containing missing values (geom_text_repel).
## `geom_smooth()` using formula 'y ~ x'
margins::margins(m1)
## Average marginal effects
## time_of_account_std voices_std
## 1.395 1.383
## full_time_equivalent_fte_teachers_state_2015_16_std c_ideology_std
## -2.399 -1.314
## g_ideology_std pct_students_frpl_std teacher_student_ratio_std
## -0.4227 -0.03996 0.778
## state_spending_per_child_std
## 0.3313
Without voices
m2 <- glmer(n_tweets_by_user_in_state ~ 1 +
time_of_account_std +
full_time_equivalent_fte_teachers_state_2015_16_std +
c_ideology_std +
g_ideology_std +
pct_students_frpl_std +
teacher_student_ratio_std +
state_spending_per_child_std +
(1|state),
data = d_user_level,
family = "poisson")
performance::icc(m2)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.529
## Conditional ICC: 0.374
sjPlot::tab_model(m2, show.std = "std")
|
|
n tweets by user in state
|
|
Predictors
|
Incidence Rate Ratios
|
std. Beta
|
CI
|
standardized CI
|
p
|
std. p
|
|
(Intercept)
|
5.89
|
1.77
|
4.95 – 7.02
|
1.60 – 1.95
|
<0.001
|
<0.001
|
|
time_of_account_std
|
1.21
|
0.19
|
1.21 – 1.22
|
0.19 – 0.20
|
<0.001
|
<0.001
|
|
full_time_equivalent_fte_teachers_state_2015_16_std
|
0.83
|
-0.18
|
0.65 – 1.06
|
-0.43 – 0.06
|
0.141
|
0.142
|
|
c_ideology_std
|
0.82
|
-0.19
|
0.69 – 0.99
|
-0.37 – -0.02
|
0.035
|
0.034
|
|
g_ideology_std
|
0.93
|
-0.07
|
0.76 – 1.15
|
-0.28 – 0.14
|
0.520
|
0.516
|
|
pct_students_frpl_std
|
0.98
|
-0.02
|
0.86 – 1.13
|
-0.15 – 0.12
|
0.818
|
0.819
|
|
teacher_student_ratio_std
|
1.10
|
0.10
|
0.92 – 1.32
|
-0.08 – 0.27
|
0.292
|
0.286
|
|
state_spending_per_child_std
|
1.03
|
0.03
|
0.83 – 1.26
|
-0.18 – 0.23
|
0.812
|
0.811
|
|
Random Effects
|
|
σ2
|
0.15
|
|
τ00 state
|
0.17
|
|
ICC
|
0.53
|
|
N state
|
44
|
|
Observations
|
57642
|
|
Marginal R2 / Conditional R2
|
0.293 / 0.667
|
performance::check_model(m2)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 57616 rows containing missing values (geom_text_repel).
## `geom_smooth()` using formula 'y ~ x'
margins::margins(m2)
## Average marginal effects
## time_of_account_std full_time_equivalent_fte_teachers_state_2015_16_std
## 1.395 -1.316
## c_ideology_std g_ideology_std pct_students_frpl_std teacher_student_ratio_std
## -1.395 -0.4944 -0.114 0.6903
## state_spending_per_child_std
## 0.1794