#------------ setwd
setwd("C:/Users/C00252837/OneDrive - University of Louisiana Lafayette/101 PROJECTS_OneDrive/Student_Parents_Project/R")
#------------Read in Data
data<-read.csv("sutdentparents_survey_weighted.csv", header = T)
data<-as_tibble(data)
names(data)## [1] "Column1" "x1"
## [3] "ïColumn1" "x"
## [5] "classification" "enrollment"
## [7] "department" "q4"
## [9] "finanacial_assistance" "q24_8_text"
## [11] "q23" "q38"
## [13] "q5" "q43"
## [15] "employment_status" "gender"
## [17] "gender2" "q10_5_text"
## [19] "age_original" "race"
## [21] "race2" "q46"
## [23] "q46_4_text" "q47"
## [25] "q47_11_text" "pregnancy"
## [27] "parenthood" "numb_children"
## [29] "age_children" "q35_13"
## [31] "q35_14" "q35_15"
## [33] "q62" "column226"
## [35] "res_aware_both_mean" "res_aware_patuniq_mean"
## [37] "res_use_both_mean" "res_use_patuniq_mean"
## [39] "socialsupport_both_mean" "socialsupport_patuniq_mean"
## [41] "positive_exp_both_mean" "positive_exp_patuniq_mean"
## [43] "negative_exp_gen_both_mean" "negative_exp_gen_patuniq_mean"
## [45] "academic_diffty_both_mean" "academic_diffty_patuniq_mean"
## [47] "financial_ins_both_mean" "financial_ins_patuniq_mean"
## [49] "housing_ins_both_mean" "physical_health_both_mean"
## [51] "psycsocemo_health_both_mean" "psycsocemo_health_patuniq_mean"
## [53] "expectations_both_mean" "expectations_patuniq_mean"
## [55] "pat_childcare_patuniq_mean" "child_issues_patuniq_mean"
## [57] "pregnancy_patuniq_mean" "age_recoded"
## [59] "age_recoded_narm" "parenthood_age"
## [61] "parenthood_age_narm" "res_aware_both_mean_recode"
## [63] "res_use_both_mean_recode" "socialsupport_both_mean_recode"
## [65] "positive_exp_both_mean_recode" "negative_exp_gen_both_mean_recod"
## [67] "academic_diffty_both_mean_recode" "financial_ins_both_mean_recode"
## [69] "housing_ins_both_mean_recode" "physical_health_both_mean_recode"
## [71] "psycsocemo_health_both_mean_recode" "nurs_or_not"
## [73] "age" "agegroup"
## [75] "binary_gender_original" "binary_gender"
## [77] "race3" "graduate"
## [79] "agegroup_tot" "binary_gender_tot"
## [81] "race3_tot" "graduate_tot"
## [83] "counter1" "sample_tot"
## [85] "baseweight" "finalweight"data <- data%>%
rename(
ID = x)#Total count of the sample (N=738)
count(data)## # A tibble: 1 × 1
## n
## <int>
## 1 738#rename factor levels of parenthood
unique(data$parenthood)## [1] "No" "Yes"data$parenthood<-recode_factor (data$parenthood, No="nonP", Yes="P")
levels(data$parenthood)## [1] "nonP" "P"#parenthood status
table(data$parenthood)##
## nonP P
## 571 167prop.table(table(data$parenthood))##
## nonP P
## 0.7737127 0.2262873#graduate status
table(data$graduate)#0=undergraduate, 1=graduate##
## 0 1
## 535 203prop.table(table(data$graduate))##
## 0 1
## 0.7249322 0.2750678#parenthood by graduate status
table(data$parenthood, data$graduate)##
## 0 1
## nonP 445 126
## P 90 77prop.table(table(data$parenthood, data$graduate), margin=1)##
## 0 1
## nonP 0.7793345 0.2206655
## P 0.5389222 0.4610778#employment status
table(data$employment_status)##
## Full-time I do not work. Part-time
## 3 215 203 317prop.table(table(data$employment_status))##
## Full-time I do not work. Part-time
## 0.004065041 0.291327913 0.275067751 0.429539295#age groups
table(data$agegroup)##
## 1 2 3 4
## 134 200 115 289prop.table(table(data$agegroup))##
## 1 2 3 4
## 0.1815718 0.2710027 0.1558266 0.3915989#pregnancy
table(data$pregnancy)##
## No Yes
## 2 727 9prop.table(table(data$pregnancy))##
## No Yes
## 0.002710027 0.985094851 0.012195122library(survey)
des<-svydesign(ids=~1, weights=~finalweight, data = data)
#re-do the analysis from above using sample weights
library(questionr)#parenthood status
wtd.table(data$parenthood,weights = data$finalweight)## nonP P
## 12521 2203prop.table(wtd.table(data$parenthood,weights = data$finalweight))## nonP P
## 0.8503803 0.1496197#graduate status
wtd.table(data$graduate, weights = data$finalweight)#0=undergraduate, 1=graduate## 0 1
## 12353 2371prop.table(wtd.table(data$graduate, weights = data$finalweight))## 0 1
## 0.8389704 0.1610296#employment status
wtd.table(data$employment_status, weights = data$finalweight)## Full-time I do not work. Part-time
## 73 3150 4492 7009prop.table(wtd.table(data$employment_status, weights = data$finalweight))## Full-time I do not work. Part-time
## 0.004957892 0.213936430 0.305080141 0.476025537#race3
##race3=0(Other),race3=1(Asian),race3=2(Black or African American),race3=3(Hispanic or Latino or Spanish origin of any race),race3=4 (White or Caucasian)
wtd.table(data$race3, weights = data$finalweight)#race3=0(Other),race3=1(Asian),race3=2(Black or African American),race3=3(Hispanic or Latino or Spanish origin of any race),race3=4 (White or Caucasian)## 0 1 2 3 4
## 1342 426 2938 852 9166prop.table(wtd.table(data$race3, weights = data$finalweight))## 0 1 2 3 4
## 0.09114371 0.02893236 0.19953817 0.05786471 0.62252105#age
#agegroup=1(age<20), agegroup=2(age>=20 & age<22),agegroup=3 (age>=22 & age<25),agegroup=4 (age>=25 & age!=.
wtd.table(data$agegroup, weights = data$finalweight)## 1 2 3 4
## 4123 4096 3003 3502prop.table(wtd.table(data$agegroup, weights = data$finalweight))## 1 2 3 4
## 0.2800190 0.2781853 0.2039527 0.2378430#parenthood by age group
table(data$parenthood, data$agegroup)##
## 1 2 3 4
## nonP 134 196 110 131
## P 0 4 5 158prop.table(table(data$parenthood, data$agegroup), margin=2)##
## 1 2 3 4
## nonP 1.00000000 0.98000000 0.95652174 0.45328720
## P 0.00000000 0.02000000 0.04347826 0.54671280prop.test(x = c(0, 4, 5, 158), n = c(134, 200, 115, 289))##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(0, 4, 5, 158) out of c(134, 200, 115, 289)
## X-squared = 279.23, df = 3, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.00000000 0.02000000 0.04347826 0.54671280pairwise.prop.test(x = c(0, 4, 5, 158), n = c(134, 200, 115, 289), p.adjust.method="bonferroni", alternative="two.sided", correct = FALSE)##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(0, 4, 5, 158) out of c(134, 200, 115, 289)
##
## 1 2 3
## 2 0.597 - -
## 3 0.089 1.000 -
## 4 <2e-16 <2e-16 <2e-16
##
## P value adjustment method: bonferroni###Gender
#Gender
#binary_gender=1 (Female),binary_gender=2 (Male)
wtd.table(data$binary_gender, weights = data$finalweight)## 1 2
## 8461 6263prop.table(wtd.table(data$binary_gender, weights = data$finalweight))## 1 2
## 0.57464 0.42536#pregnancy
wtd.table(data$pregnancy, weights = data$finalweight)## No Yes
## 27 14571 126prop.table(wtd.table(data$pregnancy, weights = data$finalweight))## No Yes
## 0.001833741 0.989608802 0.008557457#parenthood by graduate status (weighted)
wtd.table(data$parenthood, data$graduate, weights = data$finalweight)## 0 1
## nonP 10949 1572
## P 1404 799prop.table(wtd.table(data$parenthood, data$graduate, weights = data$finalweight), margin=2)## 0 1
## nonP 0.8863434 0.6630114
## P 0.1136566 0.3369886compare_grad_status <-prop.test(x = c(799, 1404), n = c(799+1572, 1404+10949), correct=FALSE)
compare_grad_status##
## 2-sample test for equality of proportions without continuity correction
##
## data: c(799, 1404) out of c(799 + 1572, 1404 + 10949)
## X-squared = 779.79, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.2034997 0.2431643
## sample estimates:
## prop 1 prop 2
## 0.3369886 0.1136566#Proportion test_Resource Awareness by parenthood (weighted)
#drop "" from res_aware_both_mean_recode
data <- data[!(data$res_aware_both_mean_recode==""), ]
unique(data$res_aware_both_mean_recode)## [1] "Yes" "No"wtd.table(data$res_aware_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 4169 532
## Yes 5995 1268prop.table(wtd.table(data$res_aware_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.4101732 0.2955556
## Yes 0.5898268 0.7044444compare_res_aware <-prop.test(x = c(1268, 5995), n = c(1268+532, 5995+4169), correct=FALSE)
compare_res_aware ##
## 2-sample test for equality of proportions without continuity correction
##
## data: c(1268, 5995) out of c(1268 + 532, 5995 + 4169)
## X-squared = 84.219, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.09147087 0.13776434
## sample estimates:
## prop 1 prop 2
## 0.7044444 0.5898268#Proportion test_Resource Use by parenthood (weighted)
#drop "" from res_use_both_mean_recode
data <- data[!(data$res_use_both_mean_recode==""), ]
unique(data$res_use_both_mean_recode)## [1] "Yes" "No"wtd.table(data$res_use_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 6261 1041
## Yes 2188 421prop.table(wtd.table(data$res_use_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.7410344 0.7120383
## Yes 0.2589656 0.2879617compare_res_use <-prop.test(x = c(421, 2188), n = c(421+1041, 1901+ 6261))
compare_res_use##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(421, 2188) out of c(421 + 1041, 1901 + 6261)
## X-squared = 2.3828, df = 1, p-value = 0.1227
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.00563473 0.04541502
## sample estimates:
## prop 1 prop 2
## 0.2879617 0.2680716#Proportion test_positive experience by parenthood (weighted)
#drop "" from positive_exp_both_mean_recode
data <- data[!(data$positive_exp_both_mean_recode==""), ]
unique(data$positive_exp_both_mean_recode)## [1] "No" "Yes"wtd.table(data$positive_exp_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 7161 1037
## Yes 1078 260prop.table(wtd.table(data$positive_exp_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.8691589 0.7995374
## Yes 0.1308411 0.2004626compare_positive_exp <-prop.test(x = c(260, 1078), n = c(260+1037, 1078+ 7161))
compare_positive_exp##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(260, 1078) out of c(260 + 1037, 1078 + 7161)
## X-squared = 44.455, df = 1, p-value = 2.603e-11
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.04620285 0.09304012
## sample estimates:
## prop 1 prop 2
## 0.2004626 0.1308411#Proportion test_negative experience by parenthood (weighted)
#drop "" from negative_exp_gen_both_mean_recod
data <- data[!(data$negative_exp_gen_both_mean_recod==""), ]
unique(data$negative_exp_gen_both_mean_recod)## [1] "Yes" "No"wtd.table(data$negative_exp_gen_both_mean_recod, data$parenthood, weights = data$finalweight)## nonP P
## No 2912 772
## Yes 5212 525prop.table(wtd.table(data$negative_exp_gen_both_mean_recod, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.3584441 0.5952197
## Yes 0.6415559 0.4047803compare_negative_exp <-prop.test(x = c(525, 5212), n = c(525+772, 5212+2912 ))
compare_negative_exp##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(525, 5212) out of c(525 + 772, 5212 + 2912)
## X-squared = 262.32, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.2658991 -0.2076522
## sample estimates:
## prop 1 prop 2
## 0.4047803 0.6415559#Proportion test_housing insecurity by parenthood (weighted)
#drop "" from housing_ins_both_mean_recode
data <- data[!(data$housing_ins_both_mean_recode==""), ]
unique(data$housing_ins_both_mean_recode)## [1] "Yes" "No"wtd.table(data$housing_ins_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 7346 1215
## Yes 433 55prop.table(wtd.table(data$housing_ins_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.94433732 0.95669291
## Yes 0.05566268 0.04330709compare_housing_ins <-prop.test(x = c(55, 433), n = c(55+1215, 433+7346))
compare_housing_ins##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(55, 433) out of c(55 + 1215, 433 + 7346)
## X-squared = 3.029, df = 1, p-value = 0.08179
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.0251131075 0.0004019175
## sample estimates:
## prop 1 prop 2
## 0.04330709 0.05566268#Proportion test_financial insecurity by parenthood (weighted)
#drop "" from financial_ins_both_mean_recode
data <- data[!(data$financial_ins_both_mean_recode==""), ]
unique(data$financial_ins_both_mean_recode)## [1] "Yes" "No"wtd.table(data$financial_ins_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 3582 693
## Yes 3780 471prop.table(wtd.table(data$financial_ins_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.4865526 0.5953608
## Yes 0.5134474 0.4046392compare_financial_ins <-prop.test(x = c(471, 3780), n = c(471+693, 3780+ 3582))
compare_financial_ins##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(471, 3780) out of c(471 + 693, 3780 + 3582)
## X-squared = 47.164, df = 1, p-value = 6.529e-12
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.13972610 -0.07789042
## sample estimates:
## prop 1 prop 2
## 0.4046392 0.5134474#Proportion test_accademic difficulty by parenthood (weighted)
#drop "" from academic_diffty_both_mean_recode
data <- data[!(data$academic_diffty_both_mean_recode==""), ]
unique(data$academic_diffty_both_mean_recode)## [1] "Yes" "No"wtd.table(data$academic_diffty_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 2549 555
## Yes 4500 516prop.table(wtd.table(data$academic_diffty_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.3616116 0.5182073
## Yes 0.6383884 0.4817927compare_academic_diffty <-prop.test(x = c(516, 4500), n = c(516+555, 4500+2549 ))
compare_academic_diffty##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(516, 4500) out of c(516 + 555, 4500 + 2549)
## X-squared = 95.888, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.1890915 -0.1240999
## sample estimates:
## prop 1 prop 2
## 0.4817927 0.6383884#Proportion test_physical health by parenthood (weighted)
#drop "" from physical_health_both_mean_recode
data <- data[!(data$physical_health_both_mean_recode==""), ]
unique(data$physical_health_both_mean_recode)## [1] "Yes" "No"wtd.table(data$physical_health_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 3625 556
## Yes 3424 505prop.table(wtd.table(data$physical_health_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.5142573 0.5240339
## Yes 0.4857427 0.4759661compare_physical_health <-prop.test(x = c(505, 3424), n = c(505+556, 3424+ 3625 ))
compare_physical_health##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(505, 3424) out of c(505 + 556, 3424 + 3625)
## X-squared = 0.31486, df = 1, p-value = 0.5747
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.04255524 0.02300206
## sample estimates:
## prop 1 prop 2
## 0.4759661 0.4857427#Proportion test_psychosocioemotional health by parenthood (weighted)
#drop "" from psycsocemo_health_both_mean_recode
data <- data[!(data$psycsocemo_health_both_mean_recode==""), ]
unique(data$psycsocemo_health_both_mean_recode)## [1] "Yes" "No"wtd.table(data$psycsocemo_health_both_mean_recode, data$parenthood, weights = data$finalweight)## nonP P
## No 1900 407
## Yes 4972 553prop.table(wtd.table(data$psycsocemo_health_both_mean_recode, data$parenthood, weights = data$finalweight), margin=2)## nonP P
## No 0.2764843 0.4239583
## Yes 0.7235157 0.5760417compare_psycsocemo_health <-prop.test(x = c(553, 4972), n = c(553+407, 4972+1900))
compare_psycsocemo_health##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(553, 4972) out of c(553 + 407, 4972 + 1900)
## X-squared = 87.453, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.1810686 -0.1138795
## sample estimates:
## prop 1 prop 2
## 0.5760417 0.7235157unique(data$parenthood_age_narm)## [1] "young non-parents" "old parents" "old non-parents"
## [4] "young parents" ""data <- data[!(data$parenthood_age_narm==""), ]
unique(data$parenthood_age_narm)## [1] "young non-parents" "old parents" "old non-parents"
## [4] "young parents"#parenthood_age_narm by graduate status (weighted)
wtd.table(data$parenthood_age_narm, data$graduate, weights = data$finalweight)## 0 1
## old non-parents 185 551
## old parents 524 283
## young non-parents 5684 438
## young parents 101 0prop.table(wtd.table(data$parenthood_age_narm, data$graduate, weights = data$finalweight), margin=1)## 0 1
## old non-parents 0.25135870 0.74864130
## old parents 0.64931846 0.35068154
## young non-parents 0.92845475 0.07154525
## young parents 1.00000000 0.00000000compare_parenthood_age <-prop.test(x = c(551, 283, 438, 0), n = c(185+551, 524+283, 5684+438, 101 ))
compare_parenthood_age##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(551, 283, 438, 0) out of c(185 + 551, 524 + 283, 5684 + 438, 101)
## X-squared = 2444, df = 3, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.74864130 0.35068154 0.07154525 0.00000000compare_parenthood_age_pairwise <-pairwise.prop.test(x = c(551, 283, 438, 0), n = c(185+551, 524+283, 5684+438, 101 ))
compare_parenthood_age_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(551, 283, 438, 0) out of c(185 + 551, 524 + 283, 5684 + 438, 101)
##
## 1 2 3
## 2 < 2e-16 - -
## 3 < 2e-16 < 2e-16 -
## 4 < 2e-16 3.3e-12 0.0095
##
## P value adjustment method: holm#Proportion test_Resource Awareness by parenthood_age_narm (weighted)
#drop "" from res_aware_both_mean_recode
data <- data[!(data$res_aware_both_mean_recode==""), ]
unique(data$res_aware_both_mean_recode)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$res_aware_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 285 451
## old parents 269 538
## young non-parents 2577 3545
## young parents 14 87prop.table(wtd.table(data$parenthood_age_narm, data$res_aware_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.3872283 0.6127717
## old parents 0.3333333 0.6666667
## young non-parents 0.4209409 0.5790591
## young parents 0.1386139 0.8613861compare_res_aware <-prop.test(x = c(451, 538, 3545, 87), n = c(451+285, 538+269, 3545+2577, 87+14 ))
compare_res_aware##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(451, 538, 3545, 87) out of c(451 + 285, 538 + 269, 3545 + 2577, 87 + 14)
## X-squared = 54.364, df = 3, p-value = 9.384e-12
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.6127717 0.6666667 0.5790591 0.8613861compare_res_aware_pairwise <-pairwise.prop.test(x = c(451, 538, 3545, 87), n = c(451+285, 538+269, 3545+2577, 87+14))
compare_res_aware_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(451, 538, 3545, 87) out of c(451 + 285, 538 + 269, 3545 + 2577, 87 + 14)
##
## 1 2 3
## 2 0.06294 - -
## 3 0.08674 9.6e-06 -
## 4 8.8e-06 0.00033 1.2e-07
##
## P value adjustment method: holm#Proportion test_Resource Use by parenthood_age_narm (weighted)
#drop "" from res_use_both_mean_recode
data <- data[!(data$res_use_both_mean_recode==""), ]
unique(data$res_use_both_mean_recode)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$res_use_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 489 247
## old parents 573 234
## young non-parents 4468 1654
## young parents 77 24prop.table(wtd.table(data$parenthood_age_narm, data$res_use_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.6644022 0.3355978
## old parents 0.7100372 0.2899628
## young non-parents 0.7298269 0.2701731
## young parents 0.7623762 0.2376238compare_res_use <-prop.test(x = c(247, 234, 1654, 24), n = c(247+489, 234+573, 1654+4468, 24+77 ))
compare_res_use##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(247, 234, 1654, 24) out of c(247 + 489, 234 + 573, 1654 + 4468, 24 + 77)
## X-squared = 15.429, df = 3, p-value = 0.001485
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.3355978 0.2899628 0.2701731 0.2376238compare_res_use_pairwise <-pairwise.prop.test(x = c(247, 234, 1654, 24), n = c(247+489, 234+573, 1654+4468, 24+77))
compare_res_use_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(247, 234, 1654, 24) out of c(247 + 489, 234 + 573, 1654 + 4468, 24 + 77)
##
## 1 2 3
## 2 0.3019 - -
## 3 0.0013 0.7569 -
## 4 0.3019 0.7569 0.7569
##
## P value adjustment method: holm#Proportion test_positive experience by parenthood_age_narm (weighted)
#drop "" from positive_exp_both_mean_recode
data <- data[!(data$positive_exp_both_mean_recode==""), ]
unique(data$positive_exp_both_mean_recode)## [1] "No" "Yes"wtd.table(data$parenthood_age_narm, data$positive_exp_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 648 88
## old parents 631 176
## young non-parents 5377 745
## young parents 89 12prop.table(wtd.table(data$parenthood_age_narm, data$positive_exp_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.8804348 0.1195652
## old parents 0.7819083 0.2180917
## young non-parents 0.8783077 0.1216923
## young parents 0.8811881 0.1188119compare_positive_exp <-prop.test(x = c(88, 176, 745, 12), n = c(88+648, 176+631, 745+5377, 12+89 ))
compare_positive_exp##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(88, 176, 745, 12) out of c(88 + 648, 176 + 631, 745 + 5377, 12 + 89)
## X-squared = 59.21, df = 3, p-value = 8.67e-13
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.1195652 0.2180917 0.1216923 0.1188119compare_positive_exp_pairwise <-pairwise.prop.test(x = c(88, 176, 745, 59), n = c(88+648, 176+631, 745+5377, 12+89))
compare_positive_exp_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(88, 176, 745, 59) out of c(88 + 648, 176 + 631, 745 + 5377, 12 + 89)
##
## 1 2 3
## 2 8.2e-07 - -
## 3 0.91 1.6e-13 -
## 4 < 2e-16 2.5e-14 < 2e-16
##
## P value adjustment method: holm#Proportion test_negative experience by parenthood_age_narm (weighted)
#drop "" from negative_exp_gen_both_mean_recod
data <- data[!(data$negative_exp_gen_both_mean_recod==""), ]
unique(data$negative_exp_gen_both_mean_recod)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$negative_exp_gen_both_mean_recod, weights = data$finalweight)## No Yes
## old non-parents 337 399
## old parents 513 294
## young non-parents 2134 3988
## young parents 12 89prop.table(wtd.table(data$parenthood_age_narm, data$negative_exp_gen_both_mean_recod, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.4578804 0.5421196
## old parents 0.6356877 0.3643123
## young non-parents 0.3485789 0.6514211
## young parents 0.1188119 0.8811881compare_negative_exp <-prop.test(x = c(399, 294, 3988, 89), n = c(399+337, 294+513, 3988+2134, 89+12 ))
compare_negative_exp##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(399, 294, 3988, 89) out of c(399 + 337, 294 + 513, 3988 + 2134, 89 + 12)
## X-squared = 294.98, df = 3, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.5421196 0.3643123 0.6514211 0.8811881compare_negative_exp_pairwise <-pairwise.prop.test(x = c(399, 294, 3988, 89), n = c(399+337, 294+513, 3988+2134, 89+12))
compare_negative_exp_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(399, 294, 3988, 89) out of c(399 + 337, 294 + 513, 3988 + 2134, 89 + 12)
##
## 1 2 3
## 2 1.3e-11 - -
## 3 1.4e-08 < 2e-16 -
## 4 5.6e-10 < 2e-16 2.4e-06
##
## P value adjustment method: holm#Proportion test_accademic difficulty by parenthood_age_narm (weighted)
#drop "" from academic_diffty_both_mean_recode
data <- data[!(data$academic_diffty_both_mean_recode==""), ]
unique(data$academic_diffty_both_mean_recode)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$academic_diffty_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 269 467
## old parents 441 366
## young non-parents 2155 3967
## young parents 0 101prop.table(wtd.table(data$parenthood_age_narm, data$academic_diffty_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.3654891 0.6345109
## old parents 0.5464684 0.4535316
## young non-parents 0.3520091 0.6479909
## young parents 0.0000000 1.0000000compare_academic_diffty <-prop.test(x = c(467, 366, 3967, 101), n = c(467+269, 366+441, 3967+2155, 101+0 ))
compare_academic_diffty##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(467, 366, 3967, 101) out of c(467 + 269, 366 + 441, 3967 + 2155, 101 + 0)
## X-squared = 175.87, df = 3, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.6345109 0.4535316 0.6479909 1.0000000compare_academic_diffty_pairwise <-pairwise.prop.test(x = c(467, 366, 3967, 101), n = c(467+269, 366+441, 3967+2155, 101+0))
compare_academic_diffty_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(467, 366, 3967, 101) out of c(467 + 269, 366 + 441, 3967 + 2155, 101 + 0)
##
## 1 2 3
## 2 3.0e-12 - -
## 3 0.5 < 2e-16 -
## 4 1.4e-12 < 2e-16 1.4e-12
##
## P value adjustment method: holm#Proportion test_financial insecurity by parenthood_age_narm (weighted)
#drop "" from financial_ins_both_mean_recode
data <- data[!(data$financial_ins_both_mean_recode==""), ]
unique(data$financial_ins_both_mean_recode)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$financial_ins_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 315 421
## old parents 455 352
## young non-parents 3014 3108
## young parents 42 59prop.table(wtd.table(data$parenthood_age_narm, data$financial_ins_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.4279891 0.5720109
## old parents 0.5638166 0.4361834
## young non-parents 0.4923228 0.5076772
## young parents 0.4158416 0.5841584compare_financial_ins <-prop.test(x = c(421, 352, 3108, 59), n = c(421+315 , 352+455, 3108+3014, 59+42 ))
compare_financial_ins##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(421, 352, 3108, 59) out of c(421 + 315, 352 + 455, 3108 + 3014, 59 + 42)
## X-squared = 31.05, df = 3, p-value = 8.295e-07
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.5720109 0.4361834 0.5076772 0.5841584compare_financial_ins_pairwise <-pairwise.prop.test(x = c(421, 352, 3108, 59), n = c(421+315 , 352+455, 3108+3014, 59+42))
compare_financial_ins_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(421, 352, 3108, 59) out of c(421 + 315, 352 + 455, 3108 + 3014, 59 + 42)
##
## 1 2 3
## 2 7.8e-07 - -
## 3 0.00445 0.00078 -
## 4 0.90116 0.02015 0.30853
##
## P value adjustment method: holm#Proportion test_housing insecurity by parenthood_age_narm (weighted)
#drop "" from housing_ins_both_mean_recode
data <- data[!(data$housing_ins_both_mean_recode==""), ]
unique(data$housing_ins_both_mean_recode)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$housing_ins_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 648 88
## old parents 775 32
## young non-parents 5797 325
## young parents 78 23prop.table(wtd.table(data$parenthood_age_narm, data$housing_ins_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.88043478 0.11956522
## old parents 0.96034696 0.03965304
## young non-parents 0.94691277 0.05308723
## young parents 0.77227723 0.22772277compare_housing_ins <-prop.test(x = c(88, 32, 325, 23), n = c(88+648 , 32+775, 325+5797, 23+78 ))
compare_housing_ins##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(88, 32, 325, 23) out of c(88 + 648, 32 + 775, 325 + 5797, 23 + 78)
## X-squared = 107.34, df = 3, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.11956522 0.03965304 0.05308723 0.22772277compare_housing_ins_pairwise <-pairwise.prop.test(x = c(88, 32, 325, 23), n = c(88+648 , 32+775, 325+5797, 23+78))
compare_housing_ins_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(88, 32, 325, 23) out of c(88 + 648, 32 + 775, 325 + 5797, 23 + 78)
##
## 1 2 3
## 2 2.5e-08 - -
## 3 5.7e-12 0.1240 -
## 4 0.0088 2.1e-12 1.1e-12
##
## P value adjustment method: holm#Proportion test_physical health by parenthood_age_narm (weighted)
#drop "" from physical_health_both_mean_recode
data <- data[!(data$physical_health_both_mean_recode==""), ]
unique(data$physical_health_both_mean_recode)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$physical_health_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 309 427
## old parents 424 383
## young non-parents 3207 2915
## young parents 68 33prop.table(wtd.table(data$parenthood_age_narm, data$physical_health_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.4198370 0.5801630
## old parents 0.5254027 0.4745973
## young non-parents 0.5238484 0.4761516
## young parents 0.6732673 0.3267327compare_physical_health <-prop.test(x = c(427, 383, 2915, 33), n = c(427+309, 383+424, 2915+3207, 33+68 ))
compare_physical_health##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(427, 383, 2915, 33) out of c(427 + 309, 383 + 424, 2915 + 3207, 33 + 68)
## X-squared = 39.05, df = 3, p-value = 1.694e-08
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.5801630 0.4745973 0.4761516 0.3267327compare_physical_health_pairwise <-pairwise.prop.test(x = c(427, 383, 2915, 33), n = c(427+309, 383+424, 2915+3207, 33+68))
compare_physical_health_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(427, 383, 2915, 33) out of c(427 + 309, 383 + 424, 2915 + 3207, 33 + 68)
##
## 1 2 3
## 2 0.00017 - -
## 3 7.1e-07 0.96362 -
## 4 1.3e-05 0.01363 0.01184
##
## P value adjustment method: holm#Proportion test_psychosocioemotional health by parenthood_age_narm (weighted)
#drop "" from psycsocemo_health_both_mean_recode
data <- data[!(data$psycsocemo_health_both_mean_recode==""), ]
unique(data$psycsocemo_health_both_mean_recode)## [1] "Yes" "No"wtd.table(data$parenthood_age_narm, data$psycsocemo_health_both_mean_recode, weights = data$finalweight)## No Yes
## old non-parents 244 492
## old parents 323 484
## young non-parents 1656 4466
## young parents 42 59prop.table(wtd.table(data$parenthood_age_narm, data$psycsocemo_health_both_mean_recode, weights = data$finalweight), margin=1)## No Yes
## old non-parents 0.3315217 0.6684783
## old parents 0.4002478 0.5997522
## young non-parents 0.2704998 0.7295002
## young parents 0.4158416 0.5841584compare_psychosocioemotional_health <-prop.test(x = c(492, 484, 4466, 59), n = c(492+244, 484+323, 4466+1656, 59+42))
compare_psychosocioemotional_health##
## 4-sample test for equality of proportions without continuity correction
##
## data: c(492, 484, 4466, 59) out of c(492 + 244, 484 + 323, 4466 + 1656, 59 + 42)
## X-squared = 72.528, df = 3, p-value = 1.227e-15
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4
## 0.6684783 0.5997522 0.7295002 0.5841584compare_psychosocioemotional_health_pairwise <-pairwise.prop.test(x = c(492, 484, 4466, 59), n = c(492+244, 484+323, 4466+1656, 59+42))
compare_psychosocioemotional_health_pairwise##
## Pairwise comparisons using Pairwise comparison of proportions
##
## data: c(492, 484, 4466, 59) out of c(492 + 244, 484 + 323, 4466 + 1656, 59 + 42)
##
## 1 2 3
## 2 0.0182 - -
## 3 0.0028 1.4e-13 -
## 4 0.2358 0.8464 0.0068
##
## P value adjustment method: holm###################### academic_difficulty ##########################################################
#weighted
Hmisc::describe(data$academic_diffty_both_mean, weights = data$finalweight)## data$academic_diffty_both_mean
## n missing distinct Info Mean
## 7766 0 5 0.89 3.373
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 35 1175 3263 2446 847
## Proportion 0.005 0.151 0.420 0.315 0.109t.test(academic_diffty_both_mean~parenthood, data=data, weights = data$finalweight, var.equal=TRUE)##
## Two Sample t-test
##
## data: academic_diffty_both_mean by parenthood
## t = 2.0651, df = 384, p-value = 0.03958
## alternative hypothesis: true difference in means between group nonP and group P is not equal to 0
## 95 percent confidence interval:
## 0.01174103 0.47817805
## sample estimates:
## mean in group nonP mean in group P
## 3.375394 3.130435###################### psycsocemo_health_issues ##########################################################
#weighted
Hmisc::describe(data$psycsocemo_health_both_mean, weights = data$finalweight)## data$psycsocemo_health_both_mean
## n missing distinct Info Mean
## 7766 0 4 0.873 3.598
##
## Value 2 3 4 5
## Frequency 824 2459 3501 982
## Proportion 0.106 0.317 0.451 0.126t.test(psycsocemo_health_both_mean~parenthood, data=data, weights = data$finalweight, var.equal=TRUE)##
## Two Sample t-test
##
## data: psycsocemo_health_both_mean by parenthood
## t = 2.1661, df = 384, p-value = 0.03092
## alternative hypothesis: true difference in means between group nonP and group P is not equal to 0
## 95 percent confidence interval:
## 0.02212051 0.45710044
## sample estimates:
## mean in group nonP mean in group P
## 3.630915 3.391304reg_psycsocemo_health_both<-lm(psycsocemo_health_both_mean~age + + binary_gender + race3 + graduate + parenthood + socialsupport_both_mean + financial_ins_both_mean + physical_health_both_mean+negative_exp_gen_both_mean+res_aware_both_mean+res_use_both_mean, data=data,weights = data$finalweight, var.equal=TRUE)
summary(reg_psycsocemo_health_both)##
## Call:
## lm(formula = psycsocemo_health_both_mean ~ age + +binary_gender +
## race3 + graduate + parenthood + socialsupport_both_mean +
## financial_ins_both_mean + physical_health_both_mean + negative_exp_gen_both_mean +
## res_aware_both_mean + res_use_both_mean, data = data, weights = data$finalweight,
## var.equal = TRUE)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -9.9558 -1.5265 0.1087 1.5922 13.4465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.368800 0.338793 6.992 1.25e-11 ***
## age 0.006451 0.005330 1.210 0.226921
## binary_gender 0.005208 0.062285 0.084 0.933401
## race3 -0.013951 0.024623 -0.567 0.571340
## graduate -0.136666 0.093377 -1.464 0.144145
## parenthoodP -0.115179 0.113491 -1.015 0.310824
## socialsupport_both_mean -0.218174 0.041975 -5.198 3.33e-07 ***
## financial_ins_both_mean 0.066379 0.043689 1.519 0.129522
## physical_health_both_mean 0.121245 0.033233 3.648 0.000301 ***
## negative_exp_gen_both_mean 0.339729 0.040253 8.440 7.06e-16 ***
## res_aware_both_mean -0.082772 0.036682 -2.256 0.024616 *
## res_use_both_mean 0.091820 0.053869 1.705 0.089114 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.629 on 374 degrees of freedom
## Multiple R-squared: 0.5283, Adjusted R-squared: 0.5144
## F-statistic: 38.07 on 11 and 374 DF, p-value: < 2.2e-16reg_psycsocemo_health_both<-lm(psycsocemo_health_both_mean~age + + binary_gender + race3 + graduate + parenthood + socialsupport_both_mean + financial_ins_both_mean + physical_health_both_mean+negative_exp_gen_both_mean+res_aware_both_mean+res_use_both_mean, data=data,weights = data$finalweight, var.equal=TRUE)
summary(reg_psycsocemo_health_both)##
## Call:
## lm(formula = psycsocemo_health_both_mean ~ age + +binary_gender +
## race3 + graduate + parenthood + socialsupport_both_mean +
## financial_ins_both_mean + physical_health_both_mean + negative_exp_gen_both_mean +
## res_aware_both_mean + res_use_both_mean, data = data, weights = data$finalweight,
## var.equal = TRUE)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -9.9558 -1.5265 0.1087 1.5922 13.4465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.368800 0.338793 6.992 1.25e-11 ***
## age 0.006451 0.005330 1.210 0.226921
## binary_gender 0.005208 0.062285 0.084 0.933401
## race3 -0.013951 0.024623 -0.567 0.571340
## graduate -0.136666 0.093377 -1.464 0.144145
## parenthoodP -0.115179 0.113491 -1.015 0.310824
## socialsupport_both_mean -0.218174 0.041975 -5.198 3.33e-07 ***
## financial_ins_both_mean 0.066379 0.043689 1.519 0.129522
## physical_health_both_mean 0.121245 0.033233 3.648 0.000301 ***
## negative_exp_gen_both_mean 0.339729 0.040253 8.440 7.06e-16 ***
## res_aware_both_mean -0.082772 0.036682 -2.256 0.024616 *
## res_use_both_mean 0.091820 0.053869 1.705 0.089114 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.629 on 374 degrees of freedom
## Multiple R-squared: 0.5283, Adjusted R-squared: 0.5144
## F-statistic: 38.07 on 11 and 374 DF, p-value: < 2.2e-16#un-weighted (lots of missing values!)
Hmisc::describe(data$res_aware_patuniq_mean)## data$res_aware_patuniq_mean
## n missing distinct Info Mean Gmd
## 55 331 5 0.801 2.527 0.6882
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 2 25 26 1 1
## Proportion 0.036 0.455 0.473 0.018 0.018Hmisc::describe(data$res_use_patuniq_mean)## data$res_use_patuniq_mean
## n missing distinct Info Mean Gmd
## 34 352 3 0.545 3.265 0.4296
##
## Value 3 4 5
## Frequency 26 7 1
## Proportion 0.765 0.206 0.029Hmisc::describe(data$socialsupport_patuniq_mean)## data$socialsupport_patuniq_mean
## n missing distinct Info Mean Gmd
## 52 334 4 0.857 3.481 0.8318
##
## Value 2 3 4 5
## Frequency 5 21 22 4
## Proportion 0.096 0.404 0.423 0.077Hmisc::describe(data$positive_exp_patuniq_mean)## data$positive_exp_patuniq_mean
## n missing distinct Info Mean Gmd
## 51 335 3 0.727 3.784 0.571
##
## Value 3 4 5
## Frequency 15 32 4
## Proportion 0.294 0.627 0.078Hmisc::describe(data$negative_exp_gen_patuniq_mean)## data$negative_exp_gen_patuniq_mean
## n missing distinct Info Mean Gmd
## 51 335 3 0.727 3.784 0.571
##
## Value 3 4 5
## Frequency 15 32 4
## Proportion 0.294 0.627 0.078Hmisc::describe(data$financial_ins_patuniq_mean)## data$financial_ins_patuniq_mean
## n missing distinct Info Mean Gmd
## 386 0 5 0.899 3.251 0.983
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 3 79 155 116 33
## Proportion 0.008 0.205 0.402 0.301 0.085Hmisc::describe(data$academic_diffty_patuniq_mean)## data$academic_diffty_patuniq_mean
## n missing distinct Info Mean Gmd
## 55 331 4 0.92 3.473 1.091
##
## Value 2 3 4 5
## Frequency 10 18 18 9
## Proportion 0.182 0.327 0.327 0.164Hmisc::describe(data$psycsocemo_health_patuniq_mean)## data$psycsocemo_health_patuniq_mean
## n missing distinct Info Mean Gmd
## 52 334 4 0.829 3.462 0.816
##
## Value 2 3 4 5
## Frequency 7 16 27 2
## Proportion 0.135 0.308 0.519 0.038Hmisc::describe(data$pat_childcare_patuniq_mean)## data$pat_childcare_patuniq_mean
## n missing distinct Info Mean Gmd
## 50 336 4 0.82 3.62 0.7257
##
## Value 2 3 4 5
## Frequency 2 19 25 4
## Proportion 0.04 0.38 0.50 0.08Hmisc::describe(data$child_issues_patuniq_mean)## data$child_issues_patuniq_mean
## n missing distinct Info Mean Gmd
## 43 343 4 0.873 3.512 0.8948
##
## Value 2 3 4 5
## Frequency 4 18 16 5
## Proportion 0.093 0.419 0.372 0.116Hmisc::describe(data$physical_health_both_mean)## data$physical_health_both_mean
## n missing distinct Info Mean Gmd
## 386 0 5 0.927 3.513 1.145
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 7 61 121 121 76
## Proportion 0.018 0.158 0.313 0.313 0.197#weighted (such missingness warrants cautious generalization!)
Hmisc::describe(data$res_aware_patuniq_mean, weights = data$finalweight)## data$res_aware_patuniq_mean
## n missing distinct Info Mean
## 732 7034 5 0.798 2.598
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 31 286 382 12 21
## Proportion 0.042 0.391 0.522 0.016 0.029Hmisc::describe(data$res_use_patuniq_mean, weights = data$finalweight)## data$res_use_patuniq_mean
## n missing distinct Info Mean
## 516 7250 3 0.568 3.308
##
## Value 3 4 5
## Frequency 388 97 31
## Proportion 0.752 0.188 0.060Hmisc::describe(data$socialsupport_patuniq_mean, weights = data$finalweight)## data$socialsupport_patuniq_mean
## n missing distinct Info Mean
## 684 7082 4 0.856 3.398
##
## Value 2 3 4 5
## Frequency 75 312 247 50
## Proportion 0.110 0.456 0.361 0.073Hmisc::describe(data$positive_exp_patuniq_mean, weights = data$finalweight)## data$positive_exp_patuniq_mean
## n missing distinct Info Mean
## 684 7082 3 0.744 3.81
##
## Value 3 4 5
## Frequency 197 420 67
## Proportion 0.288 0.614 0.098Hmisc::describe(data$negative_exp_gen_patuniq_mean, weights = data$finalweight)## data$negative_exp_gen_patuniq_mean
## n missing distinct Info Mean
## 684 7082 3 0.744 3.81
##
## Value 3 4 5
## Frequency 197 420 67
## Proportion 0.288 0.614 0.098Hmisc::describe(data$financial_ins_patuniq_mean, weights = data$finalweight)## data$financial_ins_patuniq_mean
## n missing distinct Info Mean
## 7766 0 5 0.897 3.229
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 72 1622 3212 2177 683
## Proportion 0.009 0.209 0.414 0.280 0.088Hmisc::describe(data$academic_diffty_patuniq_mean, weights = data$finalweight)## data$academic_diffty_patuniq_mean
## n missing distinct Info Mean
## 722 7044 4 0.93 3.316
##
## Value 2 3 4 5
## Frequency 205 199 203 115
## Proportion 0.284 0.276 0.281 0.159Hmisc::describe(data$psycsocemo_health_patuniq_mean, weights = data$finalweight)## data$psycsocemo_health_patuniq_mean
## n missing distinct Info Mean
## 691 7075 4 0.808 3.486
##
## Value 2 3 4 5
## Frequency 96 188 382 25
## Proportion 0.139 0.272 0.553 0.036Hmisc::describe(data$pat_childcare_patuniq_mean, weights = data$finalweight)## data$pat_childcare_patuniq_mean
## n missing distinct Info Mean
## 674 7092 4 0.82 3.558
##
## Value 2 3 4 5
## Frequency 20 309 294 51
## Proportion 0.030 0.458 0.436 0.076Hmisc::describe(data$child_issues_patuniq_mean, weights = data$finalweight)## data$child_issues_patuniq_mean
## n missing distinct Info Mean
## 612 7154 4 0.88 3.43
##
## Value 2 3 4 5
## Frequency 77 260 210 65
## Proportion 0.126 0.425 0.343 0.106Hmisc::describe(data$physical_health_both_mean, weights = data$finalweight)## data$physical_health_both_mean
## n missing distinct Info Mean
## 7766 0 5 0.933 3.426
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 165 1546 2297 2331 1427
## Proportion 0.021 0.199 0.296 0.300 0.184patuniq <- dplyr::select(data, res_aware_patuniq_mean, res_use_patuniq_mean, socialsupport_patuniq_mean, negative_exp_gen_patuniq_mean, academic_diffty_patuniq_mean, financial_ins_patuniq_mean, physical_health_both_mean, psycsocemo_health_patuniq_mean, pat_childcare_patuniq_mean, child_issues_patuniq_mean)
#pairwise deletion: [rcorr(as.matrix(patuniq), type="pearson")]
#The default of rcorr() is pairwise deletion. To do listwide deletion, remove incomplete data first.
patuniq_complete<-patuniq[complete.cases(patuniq),]
cor.matrix<-rcorr(as.matrix(patuniq_complete), type="pearson")
cor.matrix## res_aware_patuniq_mean res_use_patuniq_mean
## res_aware_patuniq_mean 1.00 0.01
## res_use_patuniq_mean 0.01 1.00
## socialsupport_patuniq_mean 0.25 0.11
## negative_exp_gen_patuniq_mean -0.21 0.29
## academic_diffty_patuniq_mean -0.36 -0.02
## financial_ins_patuniq_mean 0.09 -0.17
## physical_health_both_mean 0.22 -0.25
## psycsocemo_health_patuniq_mean -0.08 0.03
## pat_childcare_patuniq_mean -0.07 0.12
## child_issues_patuniq_mean 0.18 -0.01
## socialsupport_patuniq_mean
## res_aware_patuniq_mean 0.25
## res_use_patuniq_mean 0.11
## socialsupport_patuniq_mean 1.00
## negative_exp_gen_patuniq_mean 0.21
## academic_diffty_patuniq_mean -0.07
## financial_ins_patuniq_mean -0.09
## physical_health_both_mean 0.01
## psycsocemo_health_patuniq_mean -0.17
## pat_childcare_patuniq_mean -0.05
## child_issues_patuniq_mean 0.08
## negative_exp_gen_patuniq_mean
## res_aware_patuniq_mean -0.21
## res_use_patuniq_mean 0.29
## socialsupport_patuniq_mean 0.21
## negative_exp_gen_patuniq_mean 1.00
## academic_diffty_patuniq_mean 0.05
## financial_ins_patuniq_mean -0.09
## physical_health_both_mean -0.19
## psycsocemo_health_patuniq_mean -0.28
## pat_childcare_patuniq_mean -0.21
## child_issues_patuniq_mean -0.36
## academic_diffty_patuniq_mean
## res_aware_patuniq_mean -0.36
## res_use_patuniq_mean -0.02
## socialsupport_patuniq_mean -0.07
## negative_exp_gen_patuniq_mean 0.05
## academic_diffty_patuniq_mean 1.00
## financial_ins_patuniq_mean 0.40
## physical_health_both_mean 0.22
## psycsocemo_health_patuniq_mean 0.49
## pat_childcare_patuniq_mean 0.57
## child_issues_patuniq_mean 0.32
## financial_ins_patuniq_mean
## res_aware_patuniq_mean 0.09
## res_use_patuniq_mean -0.17
## socialsupport_patuniq_mean -0.09
## negative_exp_gen_patuniq_mean -0.09
## academic_diffty_patuniq_mean 0.40
## financial_ins_patuniq_mean 1.00
## physical_health_both_mean 0.45
## psycsocemo_health_patuniq_mean 0.29
## pat_childcare_patuniq_mean 0.45
## child_issues_patuniq_mean 0.49
## physical_health_both_mean
## res_aware_patuniq_mean 0.22
## res_use_patuniq_mean -0.25
## socialsupport_patuniq_mean 0.01
## negative_exp_gen_patuniq_mean -0.19
## academic_diffty_patuniq_mean 0.22
## financial_ins_patuniq_mean 0.45
## physical_health_both_mean 1.00
## psycsocemo_health_patuniq_mean 0.40
## pat_childcare_patuniq_mean 0.27
## child_issues_patuniq_mean 0.47
## psycsocemo_health_patuniq_mean
## res_aware_patuniq_mean -0.08
## res_use_patuniq_mean 0.03
## socialsupport_patuniq_mean -0.17
## negative_exp_gen_patuniq_mean -0.28
## academic_diffty_patuniq_mean 0.49
## financial_ins_patuniq_mean 0.29
## physical_health_both_mean 0.40
## psycsocemo_health_patuniq_mean 1.00
## pat_childcare_patuniq_mean 0.74
## child_issues_patuniq_mean 0.45
## pat_childcare_patuniq_mean
## res_aware_patuniq_mean -0.07
## res_use_patuniq_mean 0.12
## socialsupport_patuniq_mean -0.05
## negative_exp_gen_patuniq_mean -0.21
## academic_diffty_patuniq_mean 0.57
## financial_ins_patuniq_mean 0.45
## physical_health_both_mean 0.27
## psycsocemo_health_patuniq_mean 0.74
## pat_childcare_patuniq_mean 1.00
## child_issues_patuniq_mean 0.58
## child_issues_patuniq_mean
## res_aware_patuniq_mean 0.18
## res_use_patuniq_mean -0.01
## socialsupport_patuniq_mean 0.08
## negative_exp_gen_patuniq_mean -0.36
## academic_diffty_patuniq_mean 0.32
## financial_ins_patuniq_mean 0.49
## physical_health_both_mean 0.47
## psycsocemo_health_patuniq_mean 0.45
## pat_childcare_patuniq_mean 0.58
## child_issues_patuniq_mean 1.00
##
## n= 29
##
##
## P
## res_aware_patuniq_mean res_use_patuniq_mean
## res_aware_patuniq_mean 0.9501
## res_use_patuniq_mean 0.9501
## socialsupport_patuniq_mean 0.1972 0.5868
## negative_exp_gen_patuniq_mean 0.2788 0.1231
## academic_diffty_patuniq_mean 0.0546 0.9121
## financial_ins_patuniq_mean 0.6567 0.3681
## physical_health_both_mean 0.2558 0.1827
## psycsocemo_health_patuniq_mean 0.6843 0.8597
## pat_childcare_patuniq_mean 0.7035 0.5337
## child_issues_patuniq_mean 0.3474 0.9501
## socialsupport_patuniq_mean
## res_aware_patuniq_mean 0.1972
## res_use_patuniq_mean 0.5868
## socialsupport_patuniq_mean
## negative_exp_gen_patuniq_mean 0.2788
## academic_diffty_patuniq_mean 0.7054
## financial_ins_patuniq_mean 0.6567
## physical_health_both_mean 0.9546
## psycsocemo_health_patuniq_mean 0.3826
## pat_childcare_patuniq_mean 0.7884
## child_issues_patuniq_mean 0.6745
## negative_exp_gen_patuniq_mean
## res_aware_patuniq_mean 0.2788
## res_use_patuniq_mean 0.1231
## socialsupport_patuniq_mean 0.2788
## negative_exp_gen_patuniq_mean
## academic_diffty_patuniq_mean 0.8059
## financial_ins_patuniq_mean 0.6334
## physical_health_both_mean 0.3188
## psycsocemo_health_patuniq_mean 0.1342
## pat_childcare_patuniq_mean 0.2735
## child_issues_patuniq_mean 0.0587
## academic_diffty_patuniq_mean
## res_aware_patuniq_mean 0.0546
## res_use_patuniq_mean 0.9121
## socialsupport_patuniq_mean 0.7054
## negative_exp_gen_patuniq_mean 0.8059
## academic_diffty_patuniq_mean
## financial_ins_patuniq_mean 0.0320
## physical_health_both_mean 0.2479
## psycsocemo_health_patuniq_mean 0.0065
## pat_childcare_patuniq_mean 0.0011
## child_issues_patuniq_mean 0.0935
## financial_ins_patuniq_mean
## res_aware_patuniq_mean 0.6567
## res_use_patuniq_mean 0.3681
## socialsupport_patuniq_mean 0.6567
## negative_exp_gen_patuniq_mean 0.6334
## academic_diffty_patuniq_mean 0.0320
## financial_ins_patuniq_mean
## physical_health_both_mean 0.0146
## psycsocemo_health_patuniq_mean 0.1235
## pat_childcare_patuniq_mean 0.0132
## child_issues_patuniq_mean 0.0074
## physical_health_both_mean
## res_aware_patuniq_mean 0.2558
## res_use_patuniq_mean 0.1827
## socialsupport_patuniq_mean 0.9546
## negative_exp_gen_patuniq_mean 0.3188
## academic_diffty_patuniq_mean 0.2479
## financial_ins_patuniq_mean 0.0146
## physical_health_both_mean
## psycsocemo_health_patuniq_mean 0.0313
## pat_childcare_patuniq_mean 0.1543
## child_issues_patuniq_mean 0.0102
## psycsocemo_health_patuniq_mean
## res_aware_patuniq_mean 0.6843
## res_use_patuniq_mean 0.8597
## socialsupport_patuniq_mean 0.3826
## negative_exp_gen_patuniq_mean 0.1342
## academic_diffty_patuniq_mean 0.0065
## financial_ins_patuniq_mean 0.1235
## physical_health_both_mean 0.0313
## psycsocemo_health_patuniq_mean
## pat_childcare_patuniq_mean 0.0000
## child_issues_patuniq_mean 0.0144
## pat_childcare_patuniq_mean
## res_aware_patuniq_mean 0.7035
## res_use_patuniq_mean 0.5337
## socialsupport_patuniq_mean 0.7884
## negative_exp_gen_patuniq_mean 0.2735
## academic_diffty_patuniq_mean 0.0011
## financial_ins_patuniq_mean 0.0132
## physical_health_both_mean 0.1543
## psycsocemo_health_patuniq_mean 0.0000
## pat_childcare_patuniq_mean
## child_issues_patuniq_mean 0.0010
## child_issues_patuniq_mean
## res_aware_patuniq_mean 0.3474
## res_use_patuniq_mean 0.9501
## socialsupport_patuniq_mean 0.6745
## negative_exp_gen_patuniq_mean 0.0587
## academic_diffty_patuniq_mean 0.0935
## financial_ins_patuniq_mean 0.0074
## physical_health_both_mean 0.0102
## psycsocemo_health_patuniq_mean 0.0144
## pat_childcare_patuniq_mean 0.0010
## child_issues_patuniq_mean##correlation coefficients(repetitive codes)
# r's
#cor_df_r<-as.data.frame.matrix(round(cor.matrix$r,2)) #round to 2 d.p.
#cor_df_r# p's
cor_df_p<-as.data.frame.matrix(round(cor.matrix$P,3)) #round to 3 d.p.
cor_df_p[cor_df_p == 0] <- "< .001"
cor_df_p[is.na(cor_df_p)] <- "-"
cor_df_p## res_aware_patuniq_mean res_use_patuniq_mean
## res_aware_patuniq_mean - 0.95
## res_use_patuniq_mean 0.95 -
## socialsupport_patuniq_mean 0.197 0.587
## negative_exp_gen_patuniq_mean 0.279 0.123
## academic_diffty_patuniq_mean 0.055 0.912
## financial_ins_patuniq_mean 0.657 0.368
## physical_health_both_mean 0.256 0.183
## psycsocemo_health_patuniq_mean 0.684 0.86
## pat_childcare_patuniq_mean 0.704 0.534
## child_issues_patuniq_mean 0.347 0.95
## socialsupport_patuniq_mean
## res_aware_patuniq_mean 0.197
## res_use_patuniq_mean 0.587
## socialsupport_patuniq_mean -
## negative_exp_gen_patuniq_mean 0.279
## academic_diffty_patuniq_mean 0.705
## financial_ins_patuniq_mean 0.657
## physical_health_both_mean 0.955
## psycsocemo_health_patuniq_mean 0.383
## pat_childcare_patuniq_mean 0.788
## child_issues_patuniq_mean 0.674
## negative_exp_gen_patuniq_mean
## res_aware_patuniq_mean 0.279
## res_use_patuniq_mean 0.123
## socialsupport_patuniq_mean 0.279
## negative_exp_gen_patuniq_mean -
## academic_diffty_patuniq_mean 0.806
## financial_ins_patuniq_mean 0.633
## physical_health_both_mean 0.319
## psycsocemo_health_patuniq_mean 0.134
## pat_childcare_patuniq_mean 0.274
## child_issues_patuniq_mean 0.059
## academic_diffty_patuniq_mean
## res_aware_patuniq_mean 0.055
## res_use_patuniq_mean 0.912
## socialsupport_patuniq_mean 0.705
## negative_exp_gen_patuniq_mean 0.806
## academic_diffty_patuniq_mean -
## financial_ins_patuniq_mean 0.032
## physical_health_both_mean 0.248
## psycsocemo_health_patuniq_mean 0.007
## pat_childcare_patuniq_mean 0.001
## child_issues_patuniq_mean 0.094
## financial_ins_patuniq_mean
## res_aware_patuniq_mean 0.657
## res_use_patuniq_mean 0.368
## socialsupport_patuniq_mean 0.657
## negative_exp_gen_patuniq_mean 0.633
## academic_diffty_patuniq_mean 0.032
## financial_ins_patuniq_mean -
## physical_health_both_mean 0.015
## psycsocemo_health_patuniq_mean 0.123
## pat_childcare_patuniq_mean 0.013
## child_issues_patuniq_mean 0.007
## physical_health_both_mean
## res_aware_patuniq_mean 0.256
## res_use_patuniq_mean 0.183
## socialsupport_patuniq_mean 0.955
## negative_exp_gen_patuniq_mean 0.319
## academic_diffty_patuniq_mean 0.248
## financial_ins_patuniq_mean 0.015
## physical_health_both_mean -
## psycsocemo_health_patuniq_mean 0.031
## pat_childcare_patuniq_mean 0.154
## child_issues_patuniq_mean 0.01
## psycsocemo_health_patuniq_mean
## res_aware_patuniq_mean 0.684
## res_use_patuniq_mean 0.86
## socialsupport_patuniq_mean 0.383
## negative_exp_gen_patuniq_mean 0.134
## academic_diffty_patuniq_mean 0.007
## financial_ins_patuniq_mean 0.123
## physical_health_both_mean 0.031
## psycsocemo_health_patuniq_mean -
## pat_childcare_patuniq_mean < .001
## child_issues_patuniq_mean 0.014
## pat_childcare_patuniq_mean
## res_aware_patuniq_mean 0.704
## res_use_patuniq_mean 0.534
## socialsupport_patuniq_mean 0.788
## negative_exp_gen_patuniq_mean 0.274
## academic_diffty_patuniq_mean 0.001
## financial_ins_patuniq_mean 0.013
## physical_health_both_mean 0.154
## psycsocemo_health_patuniq_mean < .001
## pat_childcare_patuniq_mean -
## child_issues_patuniq_mean 0.001
## child_issues_patuniq_mean
## res_aware_patuniq_mean 0.347
## res_use_patuniq_mean 0.95
## socialsupport_patuniq_mean 0.674
## negative_exp_gen_patuniq_mean 0.059
## academic_diffty_patuniq_mean 0.094
## financial_ins_patuniq_mean 0.007
## physical_health_both_mean 0.01
## psycsocemo_health_patuniq_mean 0.014
## pat_childcare_patuniq_mean 0.001
## child_issues_patuniq_mean -#un-weighted
reg_psycsocemo_health_patuniq_unweighted<-lm(psycsocemo_health_patuniq_mean~age + binary_gender + race3 + graduate + socialsupport_patuniq_mean + financial_ins_patuniq_mean + physical_health_both_mean+negative_exp_gen_patuniq_mean+res_aware_patuniq_mean+res_use_patuniq_mean +pat_childcare_patuniq_mean + child_issues_patuniq_mean, data=data,var.equal=TRUE)
summary(reg_psycsocemo_health_patuniq_unweighted)##
## Call:
## lm(formula = psycsocemo_health_patuniq_mean ~ age + binary_gender +
## race3 + graduate + socialsupport_patuniq_mean + financial_ins_patuniq_mean +
## physical_health_both_mean + negative_exp_gen_patuniq_mean +
## res_aware_patuniq_mean + res_use_patuniq_mean + pat_childcare_patuniq_mean +
## child_issues_patuniq_mean, data = data, var.equal = TRUE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7923 -0.2697 -0.0885 0.1665 1.1131
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.42807 1.62843 0.877 0.3935
## age 0.01415 0.01644 0.861 0.4022
## binary_gender 0.20427 0.29543 0.691 0.4992
## race3 -0.06297 0.09712 -0.648 0.5260
## graduate -0.23274 0.27284 -0.853 0.4062
## socialsupport_patuniq_mean -0.06094 0.16966 -0.359 0.7242
## financial_ins_patuniq_mean -0.10477 0.15789 -0.664 0.5164
## physical_health_both_mean 0.24282 0.13009 1.867 0.0804 .
## negative_exp_gen_patuniq_mean -0.24271 0.23091 -1.051 0.3088
## res_aware_patuniq_mean -0.17220 0.19451 -0.885 0.3891
## res_use_patuniq_mean 0.10375 0.32747 0.317 0.7555
## pat_childcare_patuniq_mean 0.75509 0.19747 3.824 0.0015 **
## child_issues_patuniq_mean -0.15076 0.24300 -0.620 0.5437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5728 on 16 degrees of freedom
## (357 observations deleted due to missingness)
## Multiple R-squared: 0.6943, Adjusted R-squared: 0.4651
## F-statistic: 3.029 on 12 and 16 DF, p-value: 0.02046#weighted
reg_psycsocemo_health_patuniq_weighted<-lm(psycsocemo_health_patuniq_mean~age + binary_gender + race3 + graduate + socialsupport_patuniq_mean + financial_ins_patuniq_mean + physical_health_both_mean+negative_exp_gen_patuniq_mean+res_aware_patuniq_mean+res_use_patuniq_mean + pat_childcare_patuniq_mean + child_issues_patuniq_mean, data=data,weights = data$finalweight, var.equal=TRUE)
summary(reg_psycsocemo_health_patuniq_weighted)##
## Call:
## lm(formula = psycsocemo_health_patuniq_mean ~ age + binary_gender +
## race3 + graduate + socialsupport_patuniq_mean + financial_ins_patuniq_mean +
## physical_health_both_mean + negative_exp_gen_patuniq_mean +
## res_aware_patuniq_mean + res_use_patuniq_mean + pat_childcare_patuniq_mean +
## child_issues_patuniq_mean, data = data, weights = data$finalweight,
## var.equal = TRUE)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -2.5919 -1.1164 -0.6156 1.2654 4.3410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.408845 1.616934 -0.253 0.8036
## age 0.009410 0.015068 0.624 0.5411
## binary_gender 0.283421 0.313416 0.904 0.3793
## race3 -0.072514 0.091520 -0.792 0.4398
## graduate -0.191627 0.286573 -0.669 0.5132
## socialsupport_patuniq_mean -0.023887 0.169517 -0.141 0.8897
## financial_ins_patuniq_mean -0.225472 0.154401 -1.460 0.1636
## physical_health_both_mean 0.327707 0.135277 2.422 0.0277 *
## negative_exp_gen_patuniq_mean 0.059944 0.247000 0.243 0.8113
## res_aware_patuniq_mean -0.203627 0.203901 -0.999 0.3328
## res_use_patuniq_mean 0.154256 0.369242 0.418 0.6817
## pat_childcare_patuniq_mean 0.791590 0.210326 3.764 0.0017 **
## child_issues_patuniq_mean -0.004046 0.242370 -0.017 0.9869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.206 on 16 degrees of freedom
## (357 observations deleted due to missingness)
## Multiple R-squared: 0.7137, Adjusted R-squared: 0.499
## F-statistic: 3.324 on 12 and 16 DF, p-value: 0.01357reg_academic_diffty_patuniq_unweighted<-lm(academic_diffty_patuniq_mean~
age + binary_gender + race3 + graduate +
socialsupport_patuniq_mean +
financial_ins_patuniq_mean +
physical_health_both_mean +
psycsocemo_health_patuniq_mean +
negative_exp_gen_patuniq_mean +
res_aware_patuniq_mean +
res_use_patuniq_mean +
pat_childcare_patuniq_mean +
child_issues_patuniq_mean,
data=data,var.equal=TRUE)
summary(reg_academic_diffty_patuniq_unweighted)##
## Call:
## lm(formula = academic_diffty_patuniq_mean ~ age + binary_gender +
## race3 + graduate + socialsupport_patuniq_mean + financial_ins_patuniq_mean +
## physical_health_both_mean + psycsocemo_health_patuniq_mean +
## negative_exp_gen_patuniq_mean + res_aware_patuniq_mean +
## res_use_patuniq_mean + pat_childcare_patuniq_mean + child_issues_patuniq_mean,
## data = data, var.equal = TRUE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.17366 -0.62162 -0.01944 0.40002 1.52485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.076356 2.688804 0.772 0.452
## age -0.024930 0.027123 -0.919 0.373
## binary_gender -0.784702 0.483554 -1.623 0.125
## race3 -0.045029 0.158685 -0.284 0.780
## graduate 0.001662 0.449939 0.004 0.997
## socialsupport_patuniq_mean -0.082060 0.274741 -0.299 0.769
## financial_ins_patuniq_mean 0.254290 0.258133 0.985 0.340
## physical_health_both_mean -0.054593 0.231529 -0.236 0.817
## psycsocemo_health_patuniq_mean 0.478687 0.403213 1.187 0.254
## negative_exp_gen_patuniq_mean 0.497920 0.385074 1.293 0.216
## res_aware_patuniq_mean -0.179564 0.321307 -0.559 0.585
## res_use_patuniq_mean -0.526679 0.529812 -0.994 0.336
## pat_childcare_patuniq_mean 0.430508 0.440609 0.977 0.344
## child_issues_patuniq_mean 0.028351 0.396612 0.071 0.944
##
## Residual standard error: 0.9238 on 15 degrees of freedom
## (357 observations deleted due to missingness)
## Multiple R-squared: 0.6325, Adjusted R-squared: 0.3139
## F-statistic: 1.986 on 13 and 15 DF, p-value: 0.1023reg_academic_diffty_patuniq_weighted<-lm(academic_diffty_patuniq_mean~
age + binary_gender + race3 + graduate +
socialsupport_patuniq_mean +
financial_ins_patuniq_mean +
physical_health_both_mean +
psycsocemo_health_patuniq_mean +
negative_exp_gen_patuniq_mean +
res_aware_patuniq_mean +
res_use_patuniq_mean +
pat_childcare_patuniq_mean +
child_issues_patuniq_mean,
data=data, weights = data$finalweight, var.equal=TRUE)
summary(reg_academic_diffty_patuniq_weighted)##
## Call:
## lm(formula = academic_diffty_patuniq_mean ~ age + binary_gender +
## race3 + graduate + socialsupport_patuniq_mean + financial_ins_patuniq_mean +
## physical_health_both_mean + psycsocemo_health_patuniq_mean +
## negative_exp_gen_patuniq_mean + res_aware_patuniq_mean +
## res_use_patuniq_mean + pat_childcare_patuniq_mean + child_issues_patuniq_mean,
## data = data, weights = data$finalweight, var.equal = TRUE)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.9139 -1.9722 -0.1502 1.5082 5.6924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.76852 2.47929 1.117 0.282
## age -0.02559 0.02334 -1.097 0.290
## binary_gender -0.83012 0.49171 -1.688 0.112
## race3 -0.07054 0.14277 -0.494 0.628
## graduate 0.14652 0.44462 0.330 0.746
## socialsupport_patuniq_mean -0.10793 0.25957 -0.416 0.683
## financial_ins_patuniq_mean 0.18911 0.25153 0.752 0.464
## physical_health_both_mean -0.11702 0.24202 -0.484 0.636
## psycsocemo_health_patuniq_mean 0.40088 0.38257 1.048 0.311
## negative_exp_gen_patuniq_mean 0.49531 0.37867 1.308 0.211
## res_aware_patuniq_mean -0.18876 0.32160 -0.587 0.566
## res_use_patuniq_mean -0.71109 0.56811 -1.252 0.230
## pat_childcare_patuniq_mean 0.55355 0.44193 1.253 0.230
## child_issues_patuniq_mean 0.14464 0.37089 0.390 0.702
##
## Residual standard error: 3.376 on 15 degrees of freedom
## (357 observations deleted due to missingness)
## Multiple R-squared: 0.7059, Adjusted R-squared: 0.4509
## F-statistic: 2.769 on 13 and 15 DF, p-value: 0.0312
Social Support