Intelligence and religious stance
Emil O. W. Kirkegaard
What
R data analysis for:
- Intelligence and religious stance (in prep)
Start
options(digits = 2, width = 600)
library(pacman)
p_load(kirkegaard, readr, dplyr, rms, polycor, mirt)
#redefine describe
describe = function(...) psych::describe(...) %>% as.matrix()Data
#load
vars = readr::read_csv2("data/question_data.csv")## ℹ Using ',' as decimal and '.' as grouping mark. Use `read_delim()` for more control.## Warning: Missing column names filled in: 'X1' [1]##
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## X1 = col_character(),
## text = col_character(),
## option_1 = col_character(),
## option_2 = col_character(),
## option_3 = col_character(),
## option_4 = col_character(),
## N = col_double(),
## Type = col_character(),
## Order = col_character(),
## Keywords = col_character()
## )d = readr::read_rds("data/parsed_data.rds")
d_orig = d
#test items
iq_items_meta = read_csv("data/test_items.csv")## Warning: Missing column names filled in: 'X1' [1]##
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## X1 = col_character(),
## ID = col_double(),
## text = col_character(),
## option_1 = col_character(),
## option_2 = col_character(),
## option_3 = col_character(),
## option_4 = col_character(),
## option_correct = col_double()
## )#limited to persons with at last 5 IQ items
#included IQ items, some were removed due to ~0 datapoints
CA_items = intersect(iq_items_meta$X1, names(d))
d$CA_items = d[CA_items] %>% miss_by_case(reverse = T)
d = filter(d, CA_items >= 5)Recode
#rename
d$age = d$d_age
d$sex = d$gender
d$country = d$d_country
d$sexual_orientation = d$gender_orientation
#anglophone = USA states, or regions or Canada (not Quebec) or a few others
d$anglophone = (d$country %in% c("Ontario", "British Columbia", "Alberta", "Manitoba", "Nova Scotia", "Saskatchewan", "New Brunswick", "Prince Edward Island", "Australia", "Singapore", "Ireland", "New Zealand")) | str_length(d$country) == 2
d$western = (d$country %in% c("Ontario", "British Columbia", "Alberta", "Manitoba", "Nova Scotia", "Saskatchewan", "New Brunswick", "Prince Edward Island", "Australia", "Ireland", "New Zealand")) | (str_length(d$country) == 2) | (d$country %in% c("Germany", "Netherlands", "Denmark", "Italy", "Sweden", "France", "Finland", "Israel", "Belgium", "Spain", "Austria", "Switzerland", "Norway", "Greece", "Portugal", "Iceland", "Luxembourg"))
#religion
#recode order
d$religion_seriousness = ordered(d$d_religion_seriosity,
levels = c("and laughing about it", "but not too serious about it", "and somewhat serious about it", "and very serious about it"))
d$religion = d$d_religion_type %>% plyr::mapvalues("-", NA) %>% factor()
d$religion_combined = str_glue("{d$religion} + {d$religion_seriousness}")Extract intelligence
#get scoring key
v_correct_answers_text = apply(iq_items_meta %>% filter(X1 %in% CA_items), MARGIN = 1, function(item) {
item["option_" + 1:4][item["option_correct"] %>% as.numeric()]
})
#score items
CA_scored_items = score_items(d[CA_items], key = v_correct_answers_text)
#factor analysis / IRT
# irt_CA = irt.fa(CA_scored_items)
irt_CA = mirt(CA_scored_items, model = 1)##
Iteration: 1, Log-Lik: -151544.753, Max-Change: 3.37241
Iteration: 2, Log-Lik: -137049.017, Max-Change: 0.45960
Iteration: 3, Log-Lik: -135942.068, Max-Change: 0.28963
Iteration: 4, Log-Lik: -135346.752, Max-Change: 0.23959
Iteration: 5, Log-Lik: -135000.070, Max-Change: 0.16686
Iteration: 6, Log-Lik: -134805.781, Max-Change: 0.25781
Iteration: 7, Log-Lik: -134681.766, Max-Change: 0.09668
Iteration: 8, Log-Lik: -134606.564, Max-Change: 0.24591
Iteration: 9, Log-Lik: -134548.208, Max-Change: 0.14237
Iteration: 10, Log-Lik: -134519.358, Max-Change: 0.08380
Iteration: 11, Log-Lik: -134499.564, Max-Change: 0.06449
Iteration: 12, Log-Lik: -134485.274, Max-Change: 0.05566
Iteration: 13, Log-Lik: -134474.237, Max-Change: 0.03508
Iteration: 14, Log-Lik: -134468.174, Max-Change: 0.04288
Iteration: 15, Log-Lik: -134463.962, Max-Change: 0.03212
Iteration: 16, Log-Lik: -134461.212, Max-Change: 0.04777
Iteration: 17, Log-Lik: -134455.203, Max-Change: 0.01162
Iteration: 18, Log-Lik: -134454.203, Max-Change: 0.00664
Iteration: 19, Log-Lik: -134453.895, Max-Change: 0.00367
Iteration: 20, Log-Lik: -134453.713, Max-Change: 0.00224
Iteration: 21, Log-Lik: -134453.625, Max-Change: 0.00139
Iteration: 22, Log-Lik: -134453.609, Max-Change: 0.00142
Iteration: 23, Log-Lik: -134453.582, Max-Change: 0.00074
Iteration: 24, Log-Lik: -134453.567, Max-Change: 0.00094
Iteration: 25, Log-Lik: -134453.551, Max-Change: 0.00071
Iteration: 26, Log-Lik: -134453.547, Max-Change: 0.00026
Iteration: 27, Log-Lik: -134453.545, Max-Change: 0.00025
Iteration: 28, Log-Lik: -134453.544, Max-Change: 0.00020
Iteration: 29, Log-Lik: -134453.544, Max-Change: 0.00018
Iteration: 30, Log-Lik: -134453.543, Max-Change: 0.00019
Iteration: 31, Log-Lik: -134453.543, Max-Change: 0.00014
Iteration: 32, Log-Lik: -134453.543, Max-Change: 0.00016
Iteration: 33, Log-Lik: -134453.543, Max-Change: 0.00015
Iteration: 34, Log-Lik: -134453.543, Max-Change: 0.00012
Iteration: 35, Log-Lik: -134453.543, Max-Change: 0.00011
Iteration: 36, Log-Lik: -134453.542, Max-Change: 0.00011
Iteration: 37, Log-Lik: -134453.542, Max-Change: 0.00010
Iteration: 38, Log-Lik: -134453.542, Max-Change: 0.00009irt_CA##
## Call:
## mirt(data = CA_scored_items, model = 1)
##
## Full-information item factor analysis with 1 factor(s).
## Converged within 1e-04 tolerance after 38 EM iterations.
## mirt version: 1.33.2
## M-step optimizer: BFGS
## EM acceleration: Ramsay
## Number of rectangular quadrature: 61
## Latent density type: Gaussian
##
## Log-likelihood = -134454
## Estimated parameters: 28
## AIC = 268963; AICc = 268963
## BIC = 269202; SABIC = 269113irt_CA@Fit## $G2
## [1] NaN
##
## $p
## [1] NaN
##
## $TLI
## [1] NaN
##
## $CFI
## [1] NaN
##
## $RMSEA
## [1] NaN
##
## $df
## [1] 16355
##
## $AIC
## [1] 268963
##
## $AICc
## [1] 268963
##
## $BIC
## [1] 269202
##
## $SABIC
## [1] 269113
##
## $DIC
## [1] 268963
##
## $HQ
## [1] 269039
##
## $logLik
## [1] -134454
##
## $logPrior
## [1] 0
##
## $SElogLik
## [1] 0
##
## $F
## F1
## q178 0.74
## q255 0.70
## q1201 0.75
## q14835 0.38
## q8672 0.72
## q18154 0.74
## q12625 0.58
## q477 0.60
## q256 0.37
## q43639 0.75
## q267 0.48
## q18698 0.69
## q511 0.68
## q57844 0.69
##
## $h2
## q178 q255 q1201 q14835 q8672 q18154 q12625 q477 q256 q43639 q267 q18698 q511 q57844
## 0.54 0.50 0.57 0.14 0.52 0.55 0.34 0.36 0.14 0.56 0.23 0.48 0.46 0.48#score
# irt_CA_scores = scoreIrt(irt_CA, CA_scored_items)
irt_CA_scores = fscores(irt_CA, full.scores = T, full.scores.SE = T)
#save
d$CA_old = d$CA %>% standardize()
d$CA = irt_CA_scores[, 1] %>% standardize()
#plot scores
GG_scatter(d, "CA", "CA_old")## `geom_smooth()` using formula 'y ~ x'
Subsets
#white Americans only
d_white_amer = d %>% filter(race == "White", country %in% state.abb)Analyses
Descriptive
#Sex
d$sex %>% table2()#sexual orientation
d$sexual_orientation %>% table2()#age
d$age %>% describe()## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36975 33 7.8 32 32 7.4 18 100 82 0.93 2 0.041#country / state
d$country %>% table2()#anglo
d$anglophone %>% table2()#western
d$western %>% table2()Religious stance and seriousness of the belief
Whole sample
#plot
d %>%
GG_group_means("CA", groupvar = "religion", subgroupvar = "religion_seriousness", min_n = 5) +
theme(axis.text.x = element_text(angle = -30, hjust = 0)) +
scale_y_continuous("Mean intelligence") +
scale_x_discrete("Religious position") +
scale_fill_discrete("Seriousness")## Missing values were removed.## Warning: Problem with `mutate()` input `ci_bar`.
## ℹ NaNs produced
## ℹ Input `ci_bar` is `qt(1 - ((1 - CI)/1.96), df = n - 1)`.## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.## Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
GG_save("figs/main.png")
#values
d %$% describeBy(CA, religion_combined, mat = T)#without subgroups
d %>%
GG_group_means("CA", groupvar = "religion", min_n = 5) +
theme(axis.text.x = element_text(angle = -30, hjust = 0)) +
scale_y_continuous("Mean intelligence") +
scale_x_discrete("Religious position") +
scale_fill_discrete("Seriousness")## Missing values were removed.## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
GG_save("figs/main_alt.png")
#values
d %$% describeBy(CA, religion, mat = T)#correlations by group
plyr::ddply(d, "religion", .fun = function(dd) {
# fit_r = polyserial(dd$CA, dd$religion_seriousness %>% as.numeric() %>% as.data.frame())
fit_r = polycor::hetcor(dd[c("CA", "religion_seriousness")])
data_frame(
n = nrow(dd),
mean = wtd_mean(dd$CA),
r = fit_r$correlations[1, 2],
se = fit_r$std.errors[1, 2],
p = pnorm(abs(r) / se, lower.tail = F)
)
})## Warning: `data_frame()` is deprecated as of tibble 1.1.0.
## Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.White Americans
#plot
d_white_amer %>%
GG_group_means("CA", groupvar = "religion", subgroupvar = "religion_seriousness", min_n = 5) +
theme(axis.text.x = element_text(angle = -30, hjust = 0)) +
scale_y_continuous("Mean intelligence") +
scale_x_discrete("Religious position") +
scale_fill_discrete("Seriousness") +
ggtitle("Religion by group and subgroup\nWhite Americans subset")## Missing values were removed.## Warning: Problem with `mutate()` input `ci_bar`.
## ℹ NaNs produced
## ℹ Input `ci_bar` is `qt(1 - ((1 - CI)/1.96), df = n - 1)`.
## Warning: Problem with `mutate()` input `ci_bar`.
## ℹ NaNs produced
## ℹ Input `ci_bar` is `qt(1 - ((1 - CI)/1.96), df = n - 1)`.
## Warning: Problem with `mutate()` input `ci_bar`.
## ℹ NaNs produced
## ℹ Input `ci_bar` is `qt(1 - ((1 - CI)/1.96), df = n - 1)`.## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.## Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
GG_save("figs/main_whites.png")
#values
d_white_amer %$% describeBy(CA, religion_combined, mat = T)#without subgroups
d_white_amer %>%
GG_group_means("CA", groupvar = "religion", min_n = 5) +
theme(axis.text.x = element_text(angle = -30, hjust = 0)) +
scale_y_continuous("Mean intelligence") +
scale_x_discrete("Religious position") +
scale_fill_discrete("Seriousness") +
ggtitle("Religion by group\nWhite Americans subset")## Missing values were removed.## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
GG_save("figs/main_alt_whites.png")
#values
d_white_amer %$% describeBy(CA, religion, mat = T)#correlations by group
plyr::ddply(d_white_amer, "religion", .fun = function(dd) {
# fit_r = polyserial(dd$CA, dd$religion_seriousness %>% as.numeric() %>% as.data.frame())
fit_r = polycor::hetcor(dd[c("CA", "religion_seriousness")])
data_frame(
n = nrow(dd),
mean = wtd_mean(dd$CA),
r = fit_r$correlations[1, 2],
se = fit_r$std.errors[1, 2],
p = pnorm(abs(r) / se, lower.tail = F)
)
})## Warning in hetcor.data.frame(dd[c("CA", "religion_seriousness")]): could not compute polyserial correlation between variables 2 and 1
## Message: Error in if (any(diff(cts) < 0)) return(Inf) :
## missing value where TRUE/FALSE needed## Warning in hetcor.data.frame(dd[c("CA", "religion_seriousness")]): could not compute polyserial correlation between variables 2 and 1
## Message: Error in if (any(diff(cts) < 0)) return(Inf) :
## missing value where TRUE/FALSE neededReligious factor
Latent factor approach.
#subset items
d_religion = d[c("q41", "q42", "q61786", "q210", "q156917")]
#convert to integer for psych
d_religion_int = map_df(d_religion, as.integer)
#rows to keep (some are completely empty)
d_religion_miss = miss_by_case(d_religion)
d_religion_miss_thres = 3
d_religion_int = d_religion_int[d_religion_miss < d_religion_miss_thres, ]
d = d[d_religion_miss < d_religion_miss_thres, ]
#factor analyze / IRT
# irt_religion = irt.fa(d_religion_int, fm = "ml")
irt_religion = mirt(d_religion_int, model = 1)##
Iteration: 1, Log-Lik: -89794.887, Max-Change: 1.34487
Iteration: 2, Log-Lik: -77968.925, Max-Change: 1.57812
Iteration: 3, Log-Lik: -71638.757, Max-Change: 1.22459
Iteration: 4, Log-Lik: -69113.679, Max-Change: 0.93371
Iteration: 5, Log-Lik: -68093.657, Max-Change: 0.76060
Iteration: 6, Log-Lik: -67669.301, Max-Change: 0.56779
Iteration: 7, Log-Lik: -67480.120, Max-Change: 0.41534
Iteration: 8, Log-Lik: -67390.119, Max-Change: 0.29961
Iteration: 9, Log-Lik: -67346.210, Max-Change: 0.23439
Iteration: 10, Log-Lik: -67321.961, Max-Change: 0.17359
Iteration: 11, Log-Lik: -67308.597, Max-Change: 0.12710
Iteration: 12, Log-Lik: -67301.075, Max-Change: 0.09588
Iteration: 13, Log-Lik: -67296.791, Max-Change: 0.07066
Iteration: 14, Log-Lik: -67294.059, Max-Change: 0.05101
Iteration: 15, Log-Lik: -67292.370, Max-Change: 0.03862
Iteration: 16, Log-Lik: -67289.439, Max-Change: 0.00687
Iteration: 17, Log-Lik: -67289.159, Max-Change: 0.00714
Iteration: 18, Log-Lik: -67288.936, Max-Change: 0.00610
Iteration: 19, Log-Lik: -67288.665, Max-Change: 0.00523
Iteration: 20, Log-Lik: -67288.540, Max-Change: 0.00470
Iteration: 21, Log-Lik: -67288.443, Max-Change: 0.00389
Iteration: 22, Log-Lik: -67288.234, Max-Change: 0.00819
Iteration: 23, Log-Lik: -67288.177, Max-Change: 0.00279
Iteration: 24, Log-Lik: -67288.143, Max-Change: 0.00259
Iteration: 25, Log-Lik: -67288.062, Max-Change: 0.00252
Iteration: 26, Log-Lik: -67288.042, Max-Change: 0.00177
Iteration: 27, Log-Lik: -67288.032, Max-Change: 0.00168
Iteration: 28, Log-Lik: -67288.014, Max-Change: 0.00177
Iteration: 29, Log-Lik: -67288.006, Max-Change: 0.00099
Iteration: 30, Log-Lik: -67288.002, Max-Change: 0.00093
Iteration: 31, Log-Lik: -67287.984, Max-Change: 0.00033
Iteration: 32, Log-Lik: -67287.984, Max-Change: 0.00020
Iteration: 33, Log-Lik: -67287.983, Max-Change: 0.00020
Iteration: 34, Log-Lik: -67287.982, Max-Change: 0.00019
Iteration: 35, Log-Lik: -67287.982, Max-Change: 0.00089
Iteration: 36, Log-Lik: -67287.981, Max-Change: 0.00033
Iteration: 37, Log-Lik: -67287.980, Max-Change: 0.00018
Iteration: 38, Log-Lik: -67287.980, Max-Change: 0.00072
Iteration: 39, Log-Lik: -67287.980, Max-Change: 0.00056
Iteration: 40, Log-Lik: -67287.979, Max-Change: 0.00019
Iteration: 41, Log-Lik: -67287.979, Max-Change: 0.00068
Iteration: 42, Log-Lik: -67287.979, Max-Change: 0.00063
Iteration: 43, Log-Lik: -67287.978, Max-Change: 0.00018
Iteration: 44, Log-Lik: -67287.978, Max-Change: 0.00063
Iteration: 45, Log-Lik: -67287.978, Max-Change: 0.00058
Iteration: 46, Log-Lik: -67287.978, Max-Change: 0.00016
Iteration: 47, Log-Lik: -67287.978, Max-Change: 0.00060
Iteration: 48, Log-Lik: -67287.977, Max-Change: 0.00053
Iteration: 49, Log-Lik: -67287.977, Max-Change: 0.00015
Iteration: 50, Log-Lik: -67287.977, Max-Change: 0.00058
Iteration: 51, Log-Lik: -67287.977, Max-Change: 0.00049
Iteration: 52, Log-Lik: -67287.976, Max-Change: 0.00014
Iteration: 53, Log-Lik: -67287.976, Max-Change: 0.00057
Iteration: 54, Log-Lik: -67287.976, Max-Change: 0.00047
Iteration: 55, Log-Lik: -67287.976, Max-Change: 0.00013
Iteration: 56, Log-Lik: -67287.976, Max-Change: 0.00055
Iteration: 57, Log-Lik: -67287.975, Max-Change: 0.00044
Iteration: 58, Log-Lik: -67287.975, Max-Change: 0.00013
Iteration: 59, Log-Lik: -67287.975, Max-Change: 0.00054
Iteration: 60, Log-Lik: -67287.975, Max-Change: 0.00043
Iteration: 61, Log-Lik: -67287.975, Max-Change: 0.00012
Iteration: 62, Log-Lik: -67287.975, Max-Change: 0.00053
Iteration: 63, Log-Lik: -67287.974, Max-Change: 0.00041
Iteration: 64, Log-Lik: -67287.974, Max-Change: 0.00012
Iteration: 65, Log-Lik: -67287.974, Max-Change: 0.00052
Iteration: 66, Log-Lik: -67287.974, Max-Change: 0.00040
Iteration: 67, Log-Lik: -67287.974, Max-Change: 0.00011
Iteration: 68, Log-Lik: -67287.974, Max-Change: 0.00051
Iteration: 69, Log-Lik: -67287.974, Max-Change: 0.00038
Iteration: 70, Log-Lik: -67287.973, Max-Change: 0.00011
Iteration: 71, Log-Lik: -67287.973, Max-Change: 0.00050
Iteration: 72, Log-Lik: -67287.973, Max-Change: 0.00037
Iteration: 73, Log-Lik: -67287.973, Max-Change: 0.00011
Iteration: 74, Log-Lik: -67287.973, Max-Change: 0.00049
Iteration: 75, Log-Lik: -67287.973, Max-Change: 0.00036
Iteration: 76, Log-Lik: -67287.973, Max-Change: 0.00010
Iteration: 77, Log-Lik: -67287.972, Max-Change: 0.00048
Iteration: 78, Log-Lik: -67287.972, Max-Change: 0.00035
Iteration: 79, Log-Lik: -67287.972, Max-Change: 0.00010
Iteration: 80, Log-Lik: -67287.972, Max-Change: 0.00048
Iteration: 81, Log-Lik: -67287.972, Max-Change: 0.00035
Iteration: 82, Log-Lik: -67287.972, Max-Change: 0.00010irt_religion##
## Call:
## mirt(data = d_religion_int, model = 1)
##
## Full-information item factor analysis with 1 factor(s).
## Converged within 1e-04 tolerance after 82 EM iterations.
## mirt version: 1.33.2
## M-step optimizer: BFGS
## EM acceleration: Ramsay
## Number of rectangular quadrature: 61
## Latent density type: Gaussian
##
## Log-likelihood = -67288
## Estimated parameters: 12
## AIC = 134600; AICc = 134600
## BIC = 134699; SABIC = 134661irt_religion@Fit## $G2
## [1] NaN
##
## $p
## [1] NaN
##
## $TLI
## [1] NaN
##
## $CFI
## [1] NaN
##
## $RMSEA
## [1] NaN
##
## $df
## [1] 51
##
## $AIC
## [1] 134600
##
## $AICc
## [1] 134600
##
## $BIC
## [1] 134699
##
## $SABIC
## [1] 134661
##
## $DIC
## [1] 134600
##
## $HQ
## [1] 134632
##
## $logLik
## [1] -67288
##
## $logPrior
## [1] 0
##
## $SElogLik
## [1] 0
##
## $F
## F1
## q41 0.94
## q42 0.95
## q61786 0.69
## q210 0.97
## q156917 -0.95
##
## $h2
## q41 q42 q61786 q210 q156917
## 0.89 0.90 0.47 0.95 0.89#latent cors
d_religion_int %>% mixed.cor()##
## mixed.cor is deprecated, please use mixedCor.## Call: mixedCor(data = x, c = NULL, p = p, d = d, smooth = smooth, correct = correct,
## global = global, ncat = ncat, use = use, method = method,
## weight = weight)
## q41 q42 q6178 q210 q1569
## q41 1.00
## q42 0.89 1.00
## q61786 0.68 0.77 1.00
## q210 0.90 0.82 0.62 1.00
## q156917 -0.87 -0.73 -0.61 -0.94 1.00#two items for example as a contingency table
table(believe_god = d$q210, atheist = d$q156917) %>% prop.table(margin = 1)## atheist
## believe_god Yes No
## Yes 0.033 0.967
## No 0.761 0.239#score subjects
# irt_religion_scores = scoreIrt(irt_religion, items = d_religion_int, mod = "normal")
irt_religion_scores = fscores(irt_religion, full.scores = T, full.scores.SE = T)
#the normal here makes the scoring not give tons of warnings, and somehow prevents a bug with R notebook
#standardize
d$latent_religion = irt_religion_scores[, 1] %>% standardize() %>% multiply_by(-1)
#plot
GG_scatter(d, "CA", "latent_religion")## `geom_smooth()` using formula 'y ~ x'
#correct for measurement error
#religion
(religion_reliability = empirical_rxx(irt_religion_scores))## F1
## 0.8#CA
(CA_reliability = empirical_rxx(irt_CA_scores))## F1
## 0.63#correct
cor_matrix(d[c("CA", "latent_religion")], reliabilities = c(CA_reliability, religion_reliability))## CA latent_religion
## CA 0.63 -0.42
## latent_religion -0.42 0.80#jews
d %>%
filter(q156914 == "Yes") %>%
{
list(
n = nrow(.),
cor = wtd.cor(.$CA, .$latent_religion),
cor2 = cor.test(.$CA, .$latent_religion)
)
}## $n
## [1] 1490
##
## $cor
## correlation std.err t.value p.value
## Y -0.24 0.025 -9.5 1.1e-20
##
## $cor2
##
## Pearson's product-moment correlation
##
## data: .$CA and .$latent_religion
## t = -9, df = 1488, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.29 -0.19
## sample estimates:
## cor
## -0.24Models
list(
ols(latent_religion ~ CA, data = d),
ols(latent_religion ~ CA + rcs(age), data = d),
ols(latent_religion ~ CA + gender_orientation + rcs(age), data = d),
ols(latent_religion ~ CA + race + rcs(age), data = d),
ols(latent_religion ~ CA + gender_orientation + race + rcs(age), data = d),
ols(latent_religion ~ CA + gender_orientation + race + rcs(age) + country, data = d),
ols(latent_religion ~ CA * anglophone + gender_orientation + race + rcs(age), data = d),
ols(latent_religion ~ CA * western + gender_orientation + race + rcs(age), data = d)
) %>%
print() %>%
summarize_models()## [[1]]
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA, data = d)
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 29169 LR chi2 2688.19 R2 0.088
## sigma0.9550 d.f. 1 R2 adj 0.088
## d.f. 29167 Pr(> chi2) 0.0000 g 0.338
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.15477 -0.83376 -0.03887 0.69711 2.70535
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 0.0263 0.0056 4.68 <0.0001
## CA -0.3027 0.0057 -53.06 <0.0001
##
##
## [[2]]
## Frequencies of Missing Values Due to Each Variable
## latent_religion CA age
## 0 0 616
##
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA + rcs(age), data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 28553 LR chi2 3182.36 R2 0.105
## sigma0.9458 d.f. 5 R2 adj 0.105
## d.f. 28547 Pr(> chi2) 0.0000 g 0.369
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.25269 -0.81478 -0.04737 0.67781 2.81113
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.8730 0.1175 -7.43 <0.0001
## CA -0.3044 0.0058 -52.93 <0.0001
## age 0.0293 0.0047 6.29 <0.0001
## age' -0.0192 0.0359 -0.54 0.5923
## age'' -0.0846 0.1526 -0.55 0.5793
## age''' 0.2783 0.2027 1.37 0.1697
##
##
## [[3]]
## Frequencies of Missing Values Due to Each Variable
## latent_religion CA gender_orientation age
## 0 0 961 616
##
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA + gender_orientation + rcs(age),
## data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 28208 LR chi2 3432.37 R2 0.115
## sigma0.9417 d.f. 10 R2 adj 0.114
## d.f. 28197 Pr(> chi2) 0.0000 g 0.384
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.42430 -0.80503 -0.04524 0.67363 2.88721
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.6760 0.1204 -5.62 <0.0001
## CA -0.2958 0.0058 -51.16 <0.0001
## gender_orientation=Bisexual_female -0.2938 0.0269 -10.92 <0.0001
## gender_orientation=Gay_female -0.1450 0.0516 -2.81 0.0050
## gender_orientation=Gay_male -0.1113 0.0278 -4.01 <0.0001
## gender_orientation=Bisexual_male -0.3112 0.0407 -7.65 <0.0001
## gender_orientation=Hetero_male -0.2203 0.0139 -15.86 <0.0001
## age 0.0277 0.0047 5.84 <0.0001
## age' -0.0030 0.0361 -0.08 0.9330
## age'' -0.1224 0.1532 -0.80 0.4243
## age''' 0.2886 0.2032 1.42 0.1554
##
##
## [[4]]
## Frequencies of Missing Values Due to Each Variable
## latent_religion CA race age
## 0 0 2433 616
##
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA + race + rcs(age), data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 26736 LR chi2 3843.62 R2 0.134
## sigma0.9314 d.f. 14 R2 adj 0.133
## d.f. 26721 Pr(> chi2) 0.0000 g 0.411
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.40564 -0.79196 -0.04104 0.66203 2.86298
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -1.1188 0.1212 -9.23 <0.0001
## CA -0.2788 0.0059 -46.91 <0.0001
## race=Mixed 0.2785 0.0194 14.35 <0.0001
## race=Asian 0.3086 0.0294 10.51 <0.0001
## race=Hispanic / Latin 0.3923 0.0297 13.20 <0.0001
## race=Black 0.7154 0.0301 23.78 <0.0001
## race=Other 0.0672 0.0359 1.87 0.0615
## race=Indian 0.2694 0.0603 4.47 <0.0001
## race=Middle Eastern 0.3097 0.0953 3.25 0.0012
## race=Native American 0.2989 0.1294 2.31 0.0209
## race=Pacific Islander 0.4465 0.1375 3.25 0.0012
## age 0.0350 0.0048 7.30 <0.0001
## age' -0.0309 0.0368 -0.84 0.4016
## age'' -0.0775 0.1561 -0.50 0.6196
## age''' 0.3188 0.2072 1.54 0.1240
##
##
## [[5]]
## Frequencies of Missing Values Due to Each Variable
## latent_religion CA gender_orientation race age
## 0 0 961 2433 616
##
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA + gender_orientation + race +
## rcs(age), data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 26421 LR chi2 4110.45 R2 0.144
## sigma0.9266 d.f. 19 R2 adj 0.143
## d.f. 26401 Pr(> chi2) 0.0000 g 0.427
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.37502 -0.78128 -0.04184 0.65334 2.95127
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.9277 0.1241 -7.48 <0.0001
## CA -0.2700 0.0060 -45.25 <0.0001
## gender_orientation=Bisexual_female -0.2894 0.0278 -10.41 <0.0001
## gender_orientation=Gay_female -0.1778 0.0538 -3.31 0.0010
## gender_orientation=Gay_male -0.1484 0.0280 -5.30 <0.0001
## gender_orientation=Bisexual_male -0.3145 0.0423 -7.44 <0.0001
## gender_orientation=Hetero_male -0.2359 0.0142 -16.61 <0.0001
## race=Mixed 0.2927 0.0195 15.00 <0.0001
## race=Asian 0.2742 0.0293 9.34 <0.0001
## race=Hispanic / Latin 0.4045 0.0297 13.61 <0.0001
## race=Black 0.7121 0.0301 23.66 <0.0001
## race=Other 0.0794 0.0360 2.21 0.0274
## race=Indian 0.2988 0.0603 4.96 <0.0001
## race=Middle Eastern 0.3222 0.0948 3.40 0.0007
## race=Native American 0.3063 0.1288 2.38 0.0174
## race=Pacific Islander 0.4295 0.1369 3.14 0.0017
## age 0.0339 0.0049 6.96 <0.0001
## age' -0.0162 0.0370 -0.44 0.6625
## age'' -0.1092 0.1567 -0.70 0.4860
## age''' 0.3205 0.2076 1.54 0.1226
##
##
## [[6]]
## Frequencies of Missing Values Due to Each Variable
## latent_religion CA gender_orientation race age country
## 0 0 961 2433 616 616
##
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA + gender_orientation + race +
## rcs(age) + country, data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 26421 LR chi2 6247.81 R2 0.211
## sigma0.8928 d.f. 191 R2 adj 0.205
## d.f. 26229 Pr(> chi2) 0.0000 g 0.517
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.48101 -0.70582 -0.04804 0.61752 3.09660
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.2834 0.1754 -1.62 0.1062
## CA -0.2421 0.0058 -41.56 <0.0001
## gender_orientation=Bisexual_female -0.2606 0.0269 -9.68 <0.0001
## gender_orientation=Gay_female -0.1777 0.0520 -3.42 0.0006
## gender_orientation=Gay_male -0.1869 0.0272 -6.87 <0.0001
## gender_orientation=Bisexual_male -0.2908 0.0409 -7.11 <0.0001
## gender_orientation=Hetero_male -0.2366 0.0138 -17.12 <0.0001
## race=Mixed 0.2451 0.0191 12.80 <0.0001
## race=Asian 0.1791 0.0321 5.59 <0.0001
## race=Hispanic / Latin 0.3499 0.0297 11.77 <0.0001
## race=Black 0.6415 0.0294 21.83 <0.0001
## race=Other 0.1478 0.0350 4.22 <0.0001
## race=Indian 0.3066 0.0649 4.72 <0.0001
## race=Middle Eastern 0.3052 0.0961 3.18 0.0015
## race=Native American 0.1770 0.1244 1.42 0.1548
## race=Pacific Islander 0.4598 0.1324 3.47 0.0005
## age 0.0158 0.0048 3.32 0.0009
## age' 0.0397 0.0359 1.11 0.2687
## age'' -0.2137 0.1517 -1.41 0.1588
## age''' 0.3166 0.2011 1.57 0.1154
## country=AL 0.2570 0.1500 1.71 0.0866
## country=Alberta -0.3249 0.1477 -2.20 0.0279
## country=Algeria 0.9918 0.9024 1.10 0.2718
## country=AR 0.2328 0.1582 1.47 0.1411
## country=Argentina -0.8530 0.2161 -3.95 <0.0001
## country=Australia -0.5539 0.1327 -4.18 <0.0001
## country=Austria -0.5735 0.1825 -3.14 0.0017
## country=AZ -0.0769 0.1346 -0.57 0.5675
## country=Bahamas 1.4974 0.9019 1.66 0.0969
## country=Bahrain -1.0000 0.9018 -1.11 0.2675
## country=Bangladesh 1.5590 0.9023 1.73 0.0840
## country=Belarus 0.4460 0.9018 0.49 0.6209
## country=Belgium -0.6747 0.1660 -4.07 <0.0001
## country=Bermuda -0.5038 0.6440 -0.78 0.4340
## country=Bosnia and Herzegovina 0.8514 0.6439 1.32 0.1861
## country=Brazil -0.3302 0.1559 -2.12 0.0342
## country=British Columbia -0.4866 0.1385 -3.51 0.0004
## country=Brunei 0.3654 0.9023 0.40 0.6855
## country=Bulgaria -0.4795 0.2507 -1.91 0.0559
## country=CA -0.1402 0.1275 -1.10 0.2714
## country=Cambodia 0.1062 0.9023 0.12 0.9063
## country=Chile 0.0993 0.3237 0.31 0.7590
## country=China -0.3288 0.1765 -1.86 0.0625
## country=CO -0.0404 0.1335 -0.30 0.7624
## country=Colombia -0.1372 0.3606 -0.38 0.7036
## country=Costa Rica -0.3925 0.2872 -1.37 0.1718
## country=Croatia -0.5424 0.2285 -2.37 0.0176
## country=CT -0.0302 0.1398 -0.22 0.8292
## country=Cyprus 0.0867 0.6456 0.13 0.8932
## country=Czech Republic -0.5182 0.2508 -2.07 0.0388
## country=DC -0.1993 0.1456 -1.37 0.1710
## country=DE -0.1557 0.1968 -0.79 0.4288
## country=Denmark -0.5389 0.1415 -3.81 0.0001
## country=Dominican Republic -0.0329 0.4190 -0.08 0.9375
## country=Ecuador -0.8281 0.5311 -1.56 0.1189
## country=Egypt 0.3570 0.3411 1.05 0.2953
## country=El Salvador 1.1155 0.9022 1.24 0.2163
## country=Estonia -1.1413 0.4640 -2.46 0.0139
## country=Falkland Islands (Islas Malvinas) -1.0522 0.9071 -1.16 0.2461
## country=Finland -0.5041 0.1540 -3.27 0.0011
## country=FL 0.0112 0.1299 0.09 0.9312
## country=France -0.6718 0.1504 -4.47 <0.0001
## country=GA 0.1596 0.1328 1.20 0.2296
## country=Georgia 0.5318 0.6439 0.83 0.4089
## country=Germany -0.5350 0.1343 -3.98 <0.0001
## country=Greece -0.4647 0.2004 -2.32 0.0204
## country=GU 0.5208 0.9018 0.58 0.5636
## country=Guatemala 0.1060 0.5309 0.20 0.8418
## country=Haiti 0.0795 0.6440 0.12 0.9018
## country=HI -0.0321 0.1603 -0.20 0.8415
## country=Honduras -1.4480 0.9022 -1.60 0.1085
## country=Hong Kong -0.2858 0.2141 -1.33 0.1819
## country=Hungary -0.3475 0.2363 -1.47 0.1414
## country=IA 0.1728 0.1452 1.19 0.2339
## country=Iceland -0.7859 0.3093 -2.54 0.0111
## country=ID 0.0298 0.1610 0.19 0.8531
## country=IL -0.0712 0.1300 -0.55 0.5840
## country=IN 0.0618 0.1356 0.46 0.6485
## country=India -0.0975 0.1719 -0.57 0.5705
## country=Indonesia 0.5445 0.2104 2.59 0.0097
## country=Iran -0.5709 0.4665 -1.22 0.2210
## country=Ireland -0.5195 0.1511 -3.44 0.0006
## country=Isle of Man -0.7408 0.9018 -0.82 0.4114
## country=Israel -0.2878 0.1628 -1.77 0.0770
## country=Italy -0.2875 0.1493 -1.92 0.0542
## country=Jamaica 0.5243 0.6449 0.81 0.4163
## country=Japan -0.2748 0.1883 -1.46 0.1444
## country=Jordan -0.6489 0.6457 -1.01 0.3149
## country=Kazakhstan 0.8785 0.6441 1.36 0.1726
## country=Kenya -1.2541 0.6441 -1.95 0.0515
## country=KS 0.1178 0.1466 0.80 0.4218
## country=Kuwait 1.7544 0.6442 2.72 0.0065
## country=KY 0.1307 0.1430 0.91 0.3609
## country=LA -0.0295 0.1349 -0.22 0.8270
## country=Latvia -0.7967 0.4644 -1.72 0.0862
## country=Lebanon 1.1637 0.9019 1.29 0.1970
## country=Lesotho 0.5350 0.9025 0.59 0.5534
## country=Lithuania -0.3614 0.4640 -0.78 0.4360
## country=Luxembourg -0.9283 0.4189 -2.22 0.0267
## country=MA -0.1173 0.1310 -0.90 0.3703
## country=Macau -0.5850 0.9022 -0.65 0.5167
## country=Macedonia 0.0019 0.6443 0.00 0.9977
## country=Malaysia 0.2402 0.2100 1.14 0.2527
## country=Mali -1.4155 0.9103 -1.55 0.1200
## country=Malta -0.0932 0.4188 -0.22 0.8239
## country=Manitoba -0.6668 0.2022 -3.30 0.0010
## country=MD -0.0070 0.1338 -0.05 0.9583
## country=ME -0.2255 0.1631 -1.38 0.1667
## country=Mexico -0.2710 0.1759 -1.54 0.1235
## country=MI 0.0587 0.1320 0.44 0.6566
## country=MN -0.0200 0.1339 -0.15 0.8816
## country=MO 0.1793 0.1360 1.32 0.1873
## country=Morocco 1.6352 0.9023 1.81 0.0699
## country=MS 0.2491 0.1703 1.46 0.1436
## country=MT 0.1558 0.1796 0.87 0.3857
## country=Namibia 0.6339 0.9024 0.70 0.4824
## country=NC 0.1723 0.1332 1.29 0.1959
## country=ND -0.1451 0.2159 -0.67 0.5017
## country=NE -0.1057 0.1549 -0.68 0.4950
## country=Nepal 0.5757 0.9023 0.64 0.5234
## country=Netherlands -0.7246 0.1396 -5.19 <0.0001
## country=Netherlands Antilles -1.1875 0.9017 -1.32 0.1879
## country=New Brunswick -0.0434 0.2565 -0.17 0.8656
## country=New Zealand -0.5262 0.1882 -2.80 0.0052
## country=NH -0.1581 0.1544 -1.02 0.3060
## country=Nicaragua 1.9284 0.9018 2.14 0.0325
## country=Nigeria 1.3816 0.9025 1.53 0.1258
## country=NJ -0.0014 0.1326 -0.01 0.9915
## country=NM -0.0782 0.1517 -0.52 0.6064
## country=Norway -0.7962 0.1895 -4.20 <0.0001
## country=Nova Scotia -0.5540 0.2042 -2.71 0.0067
## country=NV -0.1094 0.1416 -0.77 0.4395
## country=NY -0.1648 0.1279 -1.29 0.1976
## country=OH 0.0623 0.1318 0.47 0.6366
## country=OK 0.2322 0.1429 1.63 0.1041
## country=Oman 1.8470 0.9024 2.05 0.0407
## country=Ontario -0.3594 0.1322 -2.72 0.0066
## country=OR -0.2364 0.1324 -1.78 0.0743
## country=PA 0.0002 0.1302 0.00 0.9985
## country=Pakistan 1.1681 0.5310 2.20 0.0278
## country=Panama 1.1738 0.9022 1.30 0.1933
## country=Paraguay -0.2628 0.9022 -0.29 0.7709
## country=Peru 0.1301 0.3095 0.42 0.6742
## country=Philippines 0.7926 0.1675 4.73 <0.0001
## country=Poland -0.5157 0.2407 -2.14 0.0322
## country=Portugal -0.3814 0.2042 -1.87 0.0617
## country=PR -0.2820 0.2413 -1.17 0.2425
## country=Qatar 0.4514 0.6447 0.70 0.4838
## country=Quebec -0.5687 0.1533 -3.71 0.0002
## country=RI -0.1073 0.1572 -0.68 0.4949
## country=Romania -0.2929 0.1969 -1.49 0.1368
## country=Russia -0.0216 0.2043 -0.11 0.9158
## country=Saskatchewan -0.4524 0.2408 -1.88 0.0603
## country=Saudi Arabia 1.1005 0.3166 3.48 0.0005
## country=SC 0.1170 0.1435 0.82 0.4148
## country=SD 0.4915 0.2408 2.04 0.0412
## country=Serbia -0.3635 0.3400 -1.07 0.2851
## country=Singapore 0.3398 0.1528 2.22 0.0262
## country=Slovakia 0.1531 0.4188 0.37 0.7147
## country=Slovenia -0.5883 0.3604 -1.63 0.1026
## country=South Africa -0.1460 0.1923 -0.76 0.4475
## country=South Korea -0.0651 0.2189 -0.30 0.7660
## country=Spain -0.8590 0.1745 -4.92 <0.0001
## country=Sri Lanka 0.1381 0.9023 0.15 0.8783
## country=Suriname 0.2825 0.9022 0.31 0.7542
## country=Svalbard -0.2329 0.9027 -0.26 0.7964
## country=Sweden -0.6427 0.1467 -4.38 <0.0001
## country=Switzerland -0.4488 0.1847 -2.43 0.0151
## country=Taiwan -0.1621 0.2292 -0.71 0.4796
## country=Tajikistan 0.1062 0.9018 0.12 0.9062
## country=Thailand -0.5895 0.2456 -2.40 0.0164
## country=TN 0.2079 0.1378 1.51 0.1314
## country=Trinidad and Tobago 1.2021 0.5312 2.26 0.0236
## country=Tunisia 1.6849 0.9019 1.87 0.0617
## country=Turkey 0.0127 0.1970 0.06 0.9485
## country=TX 0.1295 0.1287 1.01 0.3144
## country=UK -0.6340 0.1281 -4.95 <0.0001
## country=Ukraine -0.4440 0.3604 -1.23 0.2179
## country=United Arab Emirates 0.0761 0.2632 0.29 0.7726
## country=Uruguay 0.2925 0.6440 0.45 0.6498
## country=UT 0.0871 0.1449 0.60 0.5480
## country=VA 0.1221 0.1324 0.92 0.3563
## country=Vanuatu -0.9902 0.9018 -1.10 0.2722
## country=Venezuela 0.6402 0.3865 1.66 0.0977
## country=VI -1.3656 0.9018 -1.51 0.1300
## country=Vietnam -0.5458 0.3251 -1.68 0.0932
## country=VT -0.1129 0.1745 -0.65 0.5174
## country=WA -0.1579 0.1301 -1.21 0.2249
## country=WI -0.0313 0.1352 -0.23 0.8168
## country=WV 0.1220 0.1753 0.70 0.4865
## country=WY -0.1760 0.2700 -0.65 0.5145
## country=Zimbabwe 0.6161 0.9018 0.68 0.4945
##
##
## [[7]]
## Frequencies of Missing Values Due to Each Variable
## latent_religion CA anglophone gender_orientation race age
## 0 0 616 961 2433 616
##
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA * anglophone + gender_orientation +
## race + rcs(age), data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 26421 LR chi2 4363.05 R2 0.152
## sigma0.9222 d.f. 21 R2 adj 0.152
## d.f. 26399 Pr(> chi2) 0.0000 g 0.440
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.36902 -0.77041 -0.04684 0.64820 3.17457
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -1.1855 0.1246 -9.52 <0.0001
## CA -0.2474 0.0191 -12.98 <0.0001
## anglophone 0.2996 0.0192 15.60 <0.0001
## gender_orientation=Bisexual_female -0.2873 0.0277 -10.38 <0.0001
## gender_orientation=Gay_female -0.1851 0.0536 -3.46 0.0005
## gender_orientation=Gay_male -0.1608 0.0279 -5.76 <0.0001
## gender_orientation=Bisexual_male -0.3093 0.0421 -7.35 <0.0001
## gender_orientation=Hetero_male -0.2356 0.0141 -16.66 <0.0001
## race=Mixed 0.2883 0.0194 14.84 <0.0001
## race=Asian 0.3154 0.0294 10.73 <0.0001
## race=Hispanic / Latin 0.4078 0.0296 13.78 <0.0001
## race=Black 0.6966 0.0300 23.24 <0.0001
## race=Other 0.0931 0.0358 2.60 0.0094
## race=Indian 0.3552 0.0601 5.91 <0.0001
## race=Middle Eastern 0.3963 0.0945 4.19 <0.0001
## race=Native American 0.2789 0.1282 2.18 0.0296
## race=Pacific Islander 0.4151 0.1362 3.05 0.0023
## age 0.0335 0.0049 6.91 <0.0001
## age' -0.0171 0.0368 -0.47 0.6418
## age'' -0.1002 0.1560 -0.64 0.5204
## age''' 0.3031 0.2066 1.47 0.1423
## CA * anglophone -0.0188 0.0200 -0.94 0.3478
##
##
## [[8]]
## Frequencies of Missing Values Due to Each Variable
## latent_religion CA western gender_orientation race age
## 0 0 616 961 2433 616
##
## Linear Regression Model
##
## ols(formula = latent_religion ~ CA * western + gender_orientation +
## race + rcs(age), data = d)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 26421 LR chi2 4111.27 R2 0.144
## sigma0.9266 d.f. 21 R2 adj 0.143
## d.f. 26399 Pr(> chi2) 0.0000 g 0.427
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.40041 -0.78095 -0.04133 0.65376 2.96079
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -0.9057 0.1276 -7.10 <0.0001
## CA -0.2865 0.0287 -9.98 <0.0001
## western -0.0206 0.0283 -0.73 0.4670
## gender_orientation=Bisexual_female -0.2893 0.0278 -10.40 <0.0001
## gender_orientation=Gay_female -0.1772 0.0538 -3.29 0.0010
## gender_orientation=Gay_male -0.1480 0.0280 -5.28 <0.0001
## gender_orientation=Bisexual_male -0.3142 0.0423 -7.44 <0.0001
## gender_orientation=Hetero_male -0.2358 0.0142 -16.60 <0.0001
## race=Mixed 0.2923 0.0195 14.97 <0.0001
## race=Asian 0.2673 0.0305 8.78 <0.0001
## race=Hispanic / Latin 0.4033 0.0298 13.55 <0.0001
## race=Black 0.7122 0.0301 23.66 <0.0001
## race=Other 0.0786 0.0360 2.18 0.0291
## race=Indian 0.2921 0.0608 4.80 <0.0001
## race=Middle Eastern 0.3178 0.0950 3.34 0.0008
## race=Native American 0.3073 0.1288 2.39 0.0170
## race=Pacific Islander 0.4299 0.1369 3.14 0.0017
## age 0.0338 0.0049 6.94 <0.0001
## age' -0.0157 0.0370 -0.42 0.6722
## age'' -0.1105 0.1567 -0.71 0.4807
## age''' 0.3212 0.2076 1.55 0.1217
## CA * western 0.0173 0.0293 0.59 0.5554
##