Survey Data Source: National Household Education Surveys (NHES) Program 2019: Parent and Family Involvement in Education (PFI)

1) Measure an outcome variable and at least 5 predictors.

Outcome Variable: enjoy_school; Item 50: SEENJOY “How much do you agree or disagree with ‘This child enjoys school’?”

Variable 1: overall_grades; Item 51: SEGRADES “Overall, across all subjects, what grades does this child get?”

Variable 2: time_absent; Item 54: SEABSNT “Since the beginning of this school year, how many days has this child been absent from school?” (Recoded - higher value means less time absent, lower value means more time absent)

Variable 3: times_participated; Item 61: FSFREQ “During this school year, how many times has any adult in the household gone to meetings or participated in activities at this child’s school?”

Variable 4: hours_hw_perweek; Item 66: FHWKHRS “In an average week, how many hours does this child spend on homework outside of school?”

Variable 5: parent_interaction; Item 64E: FCSUPPRT “How satisfied or dissatisfied are you with the way that school staff interacts with parents?”

hw8data_imputed <- pfi19 %>%
  select (enjoy_school, overall_grades, time_absent, times_participated, hours_hw_perweek, parent_interaction, PPSU, PSTRATUM, FPWT)

hw8data_complete <- pfi19 %>%
  select (enjoy_school, overall_grades, time_absent, times_participated, hours_hw_perweek, parent_interaction, PPSU, PSTRATUM, FPWT) %>%
  filter (complete.cases(.))

2) Report the pattern of missingness among all of these variables.

##  enjoy_school              overall_grades            time_absent   
##  0   : 1868   1 Mostly As         :7866   1 0 to 5 Days    :13055  
##  1   :14122   2 Mostly Bs         :4528   2 6 to 10 Days   : 2250  
##  NA's:  456   3 Mostly Cs         :1345   3 11 to 20 Days  :  512  
##               4 Mostly Ds or Lower: 263   4 20 or More Days:  173  
##               NA's                :2444   NA's             :  456  
##                                                                    
##                                                                    
##  times_participated hours_hw_perweek               parent_interaction
##  Min.   : 0.000     Min.   : 0.00    1 Very Satisfied       :8680    
##  1st Qu.: 3.000     1st Qu.: 2.00    2 Somewhat Satisfied   :5512    
##  Median : 4.000     Median : 5.00    3 Somewhat Dissatisfied:1367    
##  Mean   : 7.023     Mean   : 5.88    4 Very Dissatisfied    : 461    
##  3rd Qu.: 8.000     3rd Qu.: 8.00    NA's                   : 426    
##  Max.   :99.000     Max.   :75.00                                    
##  NA's   :663        NA's   :1513
##          0          1       NA's 
## 0.11358385 0.85868904 0.02772711
##          1 Mostly As          2 Mostly Bs          3 Mostly Cs 
##           0.47829259           0.27532531           0.08178280 
## 4 Mostly Ds or Lower                 NA's 
##           0.01599173           0.14860756
##     1 0 to 5 Days    2 6 to 10 Days   3 11 to 20 Days 4 20 or More Days 
##        0.79381004        0.13681138        0.03113219        0.01051928 
##              NA's 
##        0.02772711
##        1 Very Satisfied    2 Somewhat Satisfied 3 Somewhat Dissatisfied 
##              0.52778791              0.33515749              0.08312052 
##     4 Very Dissatisfied                    NA's 
##              0.02803113              0.02590296
md.pattern(hw8data_imputed[, c("enjoy_school", "overall_grades", "time_absent", "times_participated", "hours_hw_perweek", "parent_interaction")])

##       parent_interaction enjoy_school time_absent times_participated
## 13045                  1            1           1                  1
## 1632                   1            1           1                  1
## 669                    1            1           1                  1
## 341                    1            1           1                  1
## 160                    1            1           1                  0
## 6                      1            1           1                  0
## 57                     1            1           1                  0
## 2                      1            1           1                  0
## 84                     1            0           0                  1
## 12                     1            0           0                  1
## 6                      1            0           0                  0
## 6                      1            0           0                  0
## 71                     0            1           1                  0
## 7                      0            1           1                  0
## 348                    0            0           0                  0
##                      426          456         456                663
##       hours_hw_perweek overall_grades     
## 13045                1              1    0
## 1632                 1              0    1
## 669                  0              1    1
## 341                  0              0    2
## 160                  1              1    1
## 6                    1              0    2
## 57                   0              1    2
## 2                    0              0    3
## 84                   1              0    3
## 12                   0              0    4
## 6                    1              0    4
## 6                    0              0    5
## 71                   0              1    3
## 7                    0              0    4
## 348                  0              0    6
##                   1513           2444 5958
md.pairs(hw8data_imputed[, c("enjoy_school", "overall_grades", "time_absent", "times_participated", "hours_hw_perweek", "parent_interaction")])
## $rr
##                    enjoy_school overall_grades time_absent times_participated
## enjoy_school              15990          14002       15990              15687
## overall_grades            14002          14002       14002              13714
## time_absent               15990          14002       15990              15687
## times_participated        15687          13714       15687              15783
## hours_hw_perweek          14843          13205       14843              14761
## parent_interaction        15912          13931       15912              15783
##                    hours_hw_perweek parent_interaction
## enjoy_school                  14843              15912
## overall_grades                13205              13931
## time_absent                   14843              15912
## times_participated            14761              15783
## hours_hw_perweek              14933              14933
## parent_interaction            14933              16020
## 
## $rm
##                    enjoy_school overall_grades time_absent times_participated
## enjoy_school                  0           1988           0                303
## overall_grades                0              0           0                288
## time_absent                   0           1988           0                303
## times_participated           96           2069          96                  0
## hours_hw_perweek             90           1728          90                172
## parent_interaction          108           2089         108                237
##                    hours_hw_perweek parent_interaction
## enjoy_school                   1147                 78
## overall_grades                  797                 71
## time_absent                    1147                 78
## times_participated             1022                  0
## hours_hw_perweek                  0                  0
## parent_interaction             1087                  0
## 
## $mr
##                    enjoy_school overall_grades time_absent times_participated
## enjoy_school                  0              0           0                 96
## overall_grades             1988              0        1988               2069
## time_absent                   0              0           0                 96
## times_participated          303            288         303                  0
## hours_hw_perweek           1147            797        1147               1022
## parent_interaction           78             71          78                  0
##                    hours_hw_perweek parent_interaction
## enjoy_school                     90                108
## overall_grades                 1728               2089
## time_absent                      90                108
## times_participated              172                237
## hours_hw_perweek                  0               1087
## parent_interaction                0                  0
## 
## $mm
##                    enjoy_school overall_grades time_absent times_participated
## enjoy_school                456            456         456                360
## overall_grades              456           2444         456                375
## time_absent                 456            456         456                360
## times_participated          360            375         360                663
## hours_hw_perweek            366            716         366                491
## parent_interaction          348            355         348                426
##                    hours_hw_perweek parent_interaction
## enjoy_school                    366                348
## overall_grades                  716                355
## time_absent                     366                348
## times_participated              491                426
## hours_hw_perweek               1513                426
## parent_interaction              426                426

3) Perform a mean (a mean for numeric data) or a modal imputation (for categorical data) of all values. Perform the analysis using this imputed data. What are your results?

enjoy_school - modal imputation

mcv.enjoy_school
## [1] 1
## Levels: 0 1
summary(hw8data_imputed$enjoy_school)
##     0     1  NA's 
##  1868 14122   456
prop.table(table(hw8data_imputed$enjoy_school))
## 
##        0        1 
## 0.116823 0.883177
table(hw8data_imputed$enjoy_school.imp)
## 
##     0     1 
##  1868 14578
prop.table(table(hw8data_imputed$enjoy_school.imp))
## 
##         0         1 
## 0.1135839 0.8864161

overall_grades - modal imputation

mcv.overall_grades
## [1] 1 Mostly As
## Levels: 1 Mostly As 2 Mostly Bs 3 Mostly Cs 4 Mostly Ds or Lower
summary(hw8data_imputed$overall_grades)
##          1 Mostly As          2 Mostly Bs          3 Mostly Cs 
##                 7866                 4528                 1345 
## 4 Mostly Ds or Lower                 NA's 
##                  263                 2444
prop.table(table(hw8data_imputed$overall_grades))
## 
##          1 Mostly As          2 Mostly Bs          3 Mostly Cs 
##           0.56177689           0.32338237           0.09605771 
## 4 Mostly Ds or Lower 
##           0.01878303
table(hw8data_imputed$overall_grades.imp)
## 
##          1 Mostly As          2 Mostly Bs          3 Mostly Cs 
##                10310                 4528                 1345 
## 4 Mostly Ds or Lower 
##                  263
prop.table(table(hw8data_imputed$overall_grades.imp))
## 
##          1 Mostly As          2 Mostly Bs          3 Mostly Cs 
##           0.62690016           0.27532531           0.08178280 
## 4 Mostly Ds or Lower 
##           0.01599173

time_absent - modal imputation

mcv.time_absent <- factor(names(which.max(table(hw8data_imputed$time_absent))), levels=levels(hw8data_imputed$time_absent))

hw8data_imputed$time_absent.imp <- 
  as.factor(ifelse(is.na(hw8data_imputed$time_absent)==T, 
                   mcv.time_absent, 
                   hw8data_imputed$time_absent))

levels(hw8data_imputed$time_absent.imp)<-levels(hw8data_imputed$time_absent)
mcv.time_absent
## [1] 1 0 to 5 Days
## Levels: 1 0 to 5 Days 2 6 to 10 Days 3 11 to 20 Days 4 20 or More Days
summary(hw8data_imputed$time_absent)
##     1 0 to 5 Days    2 6 to 10 Days   3 11 to 20 Days 4 20 or More Days 
##             13055              2250               512               173 
##              NA's 
##               456
prop.table(table(hw8data_imputed$time_absent))
## 
##     1 0 to 5 Days    2 6 to 10 Days   3 11 to 20 Days 4 20 or More Days 
##        0.81644778        0.14071295        0.03202001        0.01081926
table(hw8data_imputed$time_absent.imp)
## 
##     1 0 to 5 Days    2 6 to 10 Days   3 11 to 20 Days 4 20 or More Days 
##             13511              2250               512               173
prop.table(table(hw8data_imputed$time_absent.imp))
## 
##     1 0 to 5 Days    2 6 to 10 Days   3 11 to 20 Days 4 20 or More Days 
##        0.82153715        0.13681138        0.03113219        0.01051928

times_participated - mean imputation

summary(hw8data_imputed$times_participated)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   3.000   4.000   7.023   8.000  99.000     663
mean(hw8data_imputed$times_participated, na.rm=T)
## [1] 7.023253
summary(hw8data_imputed$times_participated.imp.mean)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   3.000   5.000   7.023   8.000  99.000
mean(hw8data_imputed$times_participated.imp.mean)
## [1] 7.023253

hours_hw_perweek - mean imputation

summary(hw8data_imputed$hours_hw_perweek)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    2.00    5.00    5.88    8.00   75.00    1513
mean(hw8data_imputed$hours_hw_perweek, na.rm=T)
## [1] 5.87993
summary(hw8data_imputed$hours_hw_perweek.imp.mean)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    2.00    5.00    5.88    7.00   75.00
mean(hw8data_imputed$hours_hw_perweek.imp.mean)
## [1] 5.87993

parent_interaction - modal imputation

mcv.parent_interaction
## [1] 1 Very Satisfied
## 4 Levels: 1 Very Satisfied 2 Somewhat Satisfied ... 4 Very Dissatisfied
summary(hw8data_imputed$parent_interaction)
##        1 Very Satisfied    2 Somewhat Satisfied 3 Somewhat Dissatisfied 
##                    8680                    5512                    1367 
##     4 Very Dissatisfied                    NA's 
##                     461                     426
prop.table(table(hw8data_imputed$parent_interaction))
## 
##        1 Very Satisfied    2 Somewhat Satisfied 3 Somewhat Dissatisfied 
##              0.54182272              0.34406991              0.08533084 
##     4 Very Dissatisfied 
##              0.02877653
table(hw8data_imputed$parent_interaction.imp)
## 
##        1 Very Satisfied    2 Somewhat Satisfied 3 Somewhat Dissatisfied 
##                    9106                    5512                    1367 
##     4 Very Dissatisfied 
##                     461
prop.table(table(hw8data_imputed$parent_interaction.imp))
## 
##        1 Very Satisfied    2 Somewhat Satisfied 3 Somewhat Dissatisfied 
##              0.55369087              0.33515749              0.08312052 
##     4 Very Dissatisfied 
##              0.02803113

4) Perform a multiple imputation of all values. Perform the analysis using this imputed data set. What are your results?

hw8imp <- mice(data = hw8data_imputed[, c("enjoy_school", "overall_grades", "time_absent", "times_participated", "hours_hw_perweek", "parent_interaction", "PPSU", "PSTRATUM", "FPWT")], seed = 13, m = 10)
print(hw8imp)
## Class: mids
## Number of multiple imputations:  10 
## Imputation methods:
##       enjoy_school     overall_grades        time_absent times_participated 
##           "logreg"          "polyreg"          "polyreg"              "pmm" 
##   hours_hw_perweek parent_interaction               PPSU           PSTRATUM 
##              "pmm"          "polyreg"                 ""                 "" 
##               FPWT 
##                 "" 
## PredictorMatrix:
##                    enjoy_school overall_grades time_absent times_participated
## enjoy_school                  0              1           1                  1
## overall_grades                1              0           1                  1
## time_absent                   1              1           0                  1
## times_participated            1              1           1                  0
## hours_hw_perweek              1              1           1                  1
## parent_interaction            1              1           1                  1
##                    hours_hw_perweek parent_interaction PPSU PSTRATUM FPWT
## enjoy_school                      1                  1    1        1    1
## overall_grades                    1                  1    1        1    1
## time_absent                       1                  1    1        1    1
## times_participated                1                  1    1        1    1
## hours_hw_perweek                  0                  1    1        1    1
## parent_interaction                1                  0    1        1    1
plot(hw8imp)

summary(hw8imp)
## Class: mids
## Number of multiple imputations:  10 
## Imputation methods:
##       enjoy_school     overall_grades        time_absent times_participated 
##           "logreg"          "polyreg"          "polyreg"              "pmm" 
##   hours_hw_perweek parent_interaction               PPSU           PSTRATUM 
##              "pmm"          "polyreg"                 ""                 "" 
##               FPWT 
##                 "" 
## PredictorMatrix:
##                    enjoy_school overall_grades time_absent times_participated
## enjoy_school                  0              1           1                  1
## overall_grades                1              0           1                  1
## time_absent                   1              1           0                  1
## times_participated            1              1           1                  0
## hours_hw_perweek              1              1           1                  1
## parent_interaction            1              1           1                  1
##                    hours_hw_perweek parent_interaction PPSU PSTRATUM FPWT
## enjoy_school                      1                  1    1        1    1
## overall_grades                    1                  1    1        1    1
## time_absent                       1                  1    1        1    1
## times_participated                1                  1    1        1    1
## hours_hw_perweek                  0                  1    1        1    1
## parent_interaction                1                  0    1        1    1
hw8dataimp <- complete (hw8imp, action = 1)

head(hw8dataimp)
options(survey.lonely.psu = "adjust")

hw8design_imp <- svydesign(ids = ~PPSU,
                       strata = ~PSTRATUM,
                       weights = ~FPWT,
                       data = hw8dataimp,
                       nest = TRUE)

fit.imp <- svyglm (enjoy_school ~ overall_grades + time_absent + times_participated + hours_hw_perweek + parent_interaction,
                 hw8design_imp,
                 family = quasibinomial(link="logit"))
summary(fit.imp)
## 
## Call:
## svyglm(formula = enjoy_school ~ overall_grades + time_absent + 
##     times_participated + hours_hw_perweek + parent_interaction, 
##     design = hw8design_imp, family = quasibinomial(link = "logit"))
## 
## Survey design:
## svydesign(ids = ~PPSU, strata = ~PSTRATUM, weights = ~FPWT, data = hw8dataimp, 
##     nest = TRUE)
## 
## Coefficients:
##                                            Estimate Std. Error t value Pr(>|t|)
## (Intercept)                                3.557835   0.103649  34.326  < 2e-16
## overall_grades2 Mostly Bs                 -0.661366   0.087901  -7.524 5.59e-14
## overall_grades3 Mostly Cs                 -1.623166   0.115428 -14.062  < 2e-16
## overall_grades4 Mostly Ds or Lower        -2.369951   0.179528 -13.201  < 2e-16
## time_absent2 6 to 10 Days                 -0.332130   0.099566  -3.336 0.000852
## time_absent3 11 to 20 Days                -1.050156   0.153051  -6.861 7.06e-12
## time_absent4 20 or More Days              -1.474559   0.292230  -5.046 4.56e-07
## times_participated                         0.010521   0.005279   1.993 0.046292
## hours_hw_perweek                          -0.006896   0.007808  -0.883 0.377136
## parent_interaction2 Somewhat Satisfied    -1.002062   0.089885 -11.148  < 2e-16
## parent_interaction3 Somewhat Dissatisfied -1.449428   0.118566 -12.225  < 2e-16
## parent_interaction4 Very Dissatisfied     -1.962881   0.167206 -11.739  < 2e-16
##                                              
## (Intercept)                               ***
## overall_grades2 Mostly Bs                 ***
## overall_grades3 Mostly Cs                 ***
## overall_grades4 Mostly Ds or Lower        ***
## time_absent2 6 to 10 Days                 ***
## time_absent3 11 to 20 Days                ***
## time_absent4 20 or More Days              ***
## times_participated                        *  
## hours_hw_perweek                             
## parent_interaction2 Somewhat Satisfied    ***
## parent_interaction3 Somewhat Dissatisfied ***
## parent_interaction4 Very Dissatisfied     ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 0.9872161)
## 
## Number of Fisher Scoring iterations: 5

5) Were the results similar between the mean/modal and multiple imputed data sets? How do the results compare to the results from the model fit with the data source with missing values?

options(survey.lonely.psu = "adjust")

hw8design <- svydesign(ids = ~PPSU,
                       strata = ~PSTRATUM,
                       weights = ~FPWT,
                       data = hw8data_complete,
                       nest = TRUE)

fit.complete <- svyglm (enjoy_school ~ overall_grades + time_absent + times_participated + hours_hw_perweek + parent_interaction,
                 hw8design,
                 family = quasibinomial(link="logit"))
summary(fit.complete)
## 
## Call:
## svyglm(formula = enjoy_school ~ overall_grades + time_absent + 
##     times_participated + hours_hw_perweek + parent_interaction, 
##     design = hw8design, family = quasibinomial(link = "logit"))
## 
## Survey design:
## svydesign(ids = ~PPSU, strata = ~PSTRATUM, weights = ~FPWT, data = hw8data_complete, 
##     nest = TRUE)
## 
## Coefficients:
##                                            Estimate Std. Error t value Pr(>|t|)
## (Intercept)                                3.580259   0.117353  30.508  < 2e-16
## overall_grades2 Mostly Bs                 -0.619664   0.098709  -6.278 3.55e-10
## overall_grades3 Mostly Cs                 -1.565636   0.134529 -11.638  < 2e-16
## overall_grades4 Mostly Ds or Lower        -2.240160   0.213224 -10.506  < 2e-16
## time_absent2 6 to 10 Days                 -0.463999   0.110187  -4.211 2.56e-05
## time_absent3 11 to 20 Days                -1.099713   0.172853  -6.362 2.06e-10
## time_absent4 20 or More Days              -1.589897   0.350769  -4.533 5.88e-06
## times_participated                         0.008948   0.005407   1.655    0.098
## hours_hw_perweek                          -0.006207   0.008970  -0.692    0.489
## parent_interaction2 Somewhat Satisfied    -1.005494   0.105476  -9.533  < 2e-16
## parent_interaction3 Somewhat Dissatisfied -1.484003   0.134410 -11.041  < 2e-16
## parent_interaction4 Very Dissatisfied     -2.284021   0.171042 -13.354  < 2e-16
##                                              
## (Intercept)                               ***
## overall_grades2 Mostly Bs                 ***
## overall_grades3 Mostly Cs                 ***
## overall_grades4 Mostly Ds or Lower        ***
## time_absent2 6 to 10 Days                 ***
## time_absent3 11 to 20 Days                ***
## time_absent4 20 or More Days              ***
## times_participated                        .  
## hours_hw_perweek                             
## parent_interaction2 Somewhat Satisfied    ***
## parent_interaction3 Somewhat Dissatisfied ***
## parent_interaction4 Very Dissatisfied     ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasibinomial family taken to be 0.975302)
## 
## Number of Fisher Scoring iterations: 5
---
title: "7283_HW8"
author: "Ryan Labio"
date: "4/4/2022"
output:
   html_document:
    df_print: paged
    fig_height: 7
    fig_width: 7
    toc: yes
    toc_float: yes
    code_download: true
---

Survey Data Source: National Household Education Surveys (NHES) Program 2019: Parent and Family Involvement in Education (PFI)

```{r, echo=FALSE, results="hide", message=FALSE, warning=FALSE}

library(haven)
library(car)
library(stargazer)
library(survey)
library(ggplot2)
library(dplyr)
library(gtsummary)
library(factoextra)
library(FactoMineR)
library(mice)

# Read Stata file

pfi19 = read_dta(file = "C:\\UTSA\\OneDrive - University of Texas at San Antonio\\1_M_7283_StatsII\\Homework\\pfi_pu_pert_dat_dta.dta")

# Recode variables

pfi19$SEENJOY <- as.numeric(pfi19$SEENJOY)

pfi19$enjoy_school <- Recode(pfi19$SEENJOY, recodes="1:2=1; 3:4=0; else=NA", as.factor=T)

pfi19$SEGRADES <- as.numeric(pfi19$SEGRADES)

pfi19$overall_grades <- Recode(pfi19$SEGRADES, recodes="1='1 Mostly As'; 
                               2='2 Mostly Bs'; 
                               3='3 Mostly Cs'; 
                               4='4 Mostly Ds or Lower'; 
                               else=NA", as.factor=T)

pfi19$SEABSNT <- as.numeric(pfi19$SEABSNT)

pfi19$time_absent <- Recode(pfi19$SEABSNT, recodes="1='1 0 to 5 Days'; 
                            2='2 6 to 10 Days'; 
                            3='3 11 to 20 Days'; 
                            4='4 20 or More Days'; 
                            else=NA", as.factor=T)

pfi19$FSFREQ <- as.numeric(pfi19$FSFREQ)

pfi19$times_participated <- Recode(pfi19$FSFREQ, recodes="-1=NA; -9=NA", as.factor=F)

pfi19$FHWKHRS <- as.numeric(pfi19$FHWKHRS)

pfi19$hours_hw_perweek <- Recode(pfi19$FHWKHRS, recodes="-1=NA; -9=NA", as.factor=F)

pfi19$FCSUPPRT <- as.numeric(pfi19$FCSUPPRT)

pfi19$parent_interaction <- Recode(pfi19$FCSUPPRT, recodes="1='1 Very Satisfied';
                                   2='2 Somewhat Satisfied';
                                   3='3 Somewhat Dissatisfied';
                                   4='4 Very Dissatisfied';
                                   else=NA", as.factor=T)

pfi19$PARGRADEX <- as.numeric(pfi19$PARGRADEX)

pfi19$parent_educ <- Recode(pfi19$PARGRADEX, recodes="1='0Less than HS';
                            2='1HS Grad';
                            3='2Some College';
                            4='3College Grad';
                            5='4Grad School'; 
                            else=NA", as.factor=T)

pfi19$parent_educ <- relevel(pfi19$parent_educ, ref = '1HS Grad')

pfi19$RACEETH <- as.numeric(pfi19$RACEETH)

pfi19$race_eth <- Recode(pfi19$RACEETH, recodes="1='NH White';
                            2='NH Black';
                            3='Hispanic';
                            4='NH Asian';
                            5='Other'; 
                            else=NA", as.factor=T)

pfi19$race_eth <- relevel(pfi19$race_eth, ref='NH White')

pfi19$SEFUTUREX <- as.numeric(pfi19$SEFUTUREX)

pfi19$expectation <- Recode(pfi19$SEFUTUREX, recodes="1='0Less than HS';
                            2='1HS Grad';
                            3='2Vocational Technical';
                            4='3Two Yrs College';
                            5='4Bachelors'; 
                            6='5Graduate Degree'; 
                            else=NA", as.factor=T)

pfi19$expectation <- relevel(pfi19$expectation, ref = '0Less than HS')

```

## 1) Measure an outcome variable and at least 5 predictors.

Outcome Variable: enjoy_school; Item 50: SEENJOY "How much do you agree or disagree with 'This child enjoys school'?"

Variable 1: overall_grades; Item 51: SEGRADES "Overall, across all subjects, what grades does this child get?"

Variable 2: time_absent; Item 54: SEABSNT "Since the beginning of this school year, how many days has this child been absent from school?" (Recoded - higher value means less time absent, lower value means more time absent)

Variable 3: times_participated; Item 61: FSFREQ "During this school year, how many times has any adult in the household gone to meetings or participated in activities at this child's school?"

Variable 4: hours_hw_perweek; Item 66: FHWKHRS "In an average week, how many hours does this child spend on homework outside of school?"

Variable 5: parent_interaction; Item 64E: FCSUPPRT "How satisfied or dissatisfied are you with the way that school staff interacts with parents?"

```{r, results='hide', message=FALSE}

hw8data_imputed <- pfi19 %>%
  select (enjoy_school, overall_grades, time_absent, times_participated, hours_hw_perweek, parent_interaction, PPSU, PSTRATUM, FPWT)

hw8data_complete <- pfi19 %>%
  select (enjoy_school, overall_grades, time_absent, times_participated, hours_hw_perweek, parent_interaction, PPSU, PSTRATUM, FPWT) %>%
  filter (complete.cases(.))

```

## 2) Report the pattern of missingness among all of these variables.

```{r, echo=FALSE}

summary(hw8data_imputed[, c("enjoy_school", "overall_grades", "time_absent", "times_participated", "hours_hw_perweek", "parent_interaction")])

prop.table(summary(hw8data_imputed$enjoy_school))

prop.table(summary(hw8data_imputed$overall_grades))

prop.table(summary(hw8data_imputed$time_absent))

prop.table(summary(hw8data_imputed$parent_interaction))

```

```{r, message=FALSE}

md.pattern(hw8data_imputed[, c("enjoy_school", "overall_grades", "time_absent", "times_participated", "hours_hw_perweek", "parent_interaction")])

md.pairs(hw8data_imputed[, c("enjoy_school", "overall_grades", "time_absent", "times_participated", "hours_hw_perweek", "parent_interaction")])

```

## 3) Perform a mean (a mean for numeric data) or a modal imputation (for categorical data) of all values. Perform the analysis using this imputed data. What are your results?

### enjoy_school - modal imputation

```{r, echo=FALSE}

mcv.enjoy_school <- factor(names(which.max(table(hw8data_imputed$enjoy_school))), levels=levels(hw8data_imputed$enjoy_school))

hw8data_imputed$enjoy_school.imp <- 
  as.factor(ifelse(is.na(hw8data_imputed$enjoy_school)==T, 
                   mcv.enjoy_school, 
                   hw8data_imputed$enjoy_school))

levels(hw8data_imputed$enjoy_school.imp)<-levels(hw8data_imputed$enjoy_school)

```

```{r}

mcv.enjoy_school

summary(hw8data_imputed$enjoy_school)

prop.table(table(hw8data_imputed$enjoy_school))

table(hw8data_imputed$enjoy_school.imp)

prop.table(table(hw8data_imputed$enjoy_school.imp))

```

### overall_grades - modal imputation

```{r, echo=FALSE}

mcv.overall_grades <- factor(names(which.max(table(hw8data_imputed$overall_grades))), levels=levels(hw8data_imputed$overall_grades))

hw8data_imputed$overall_grades.imp <- 
  as.factor(ifelse(is.na(hw8data_imputed$overall_grades)==T, 
                   mcv.overall_grades, 
                   hw8data_imputed$overall_grades))

levels(hw8data_imputed$overall_grades.imp)<-levels(hw8data_imputed$overall_grades)

```

```{r}

mcv.overall_grades

summary(hw8data_imputed$overall_grades)

prop.table(table(hw8data_imputed$overall_grades))

table(hw8data_imputed$overall_grades.imp)

prop.table(table(hw8data_imputed$overall_grades.imp))

```

### time_absent - modal imputation

```{r}

mcv.time_absent <- factor(names(which.max(table(hw8data_imputed$time_absent))), levels=levels(hw8data_imputed$time_absent))

hw8data_imputed$time_absent.imp <- 
  as.factor(ifelse(is.na(hw8data_imputed$time_absent)==T, 
                   mcv.time_absent, 
                   hw8data_imputed$time_absent))

levels(hw8data_imputed$time_absent.imp)<-levels(hw8data_imputed$time_absent)

```

```{r}

mcv.time_absent

summary(hw8data_imputed$time_absent)

prop.table(table(hw8data_imputed$time_absent))

table(hw8data_imputed$time_absent.imp)

prop.table(table(hw8data_imputed$time_absent.imp))

```

### times_participated - mean imputation

```{r, echo=FALSE}

hw8data_imputed$times_participated.imp.mean <- 
  ifelse(is.na(hw8data_imputed$times_participated)==T, 
         mean(hw8data_imputed$times_participated, na.rm=T), 
         hw8data_imputed$times_participated)

```

```{r}

summary(hw8data_imputed$times_participated)

mean(hw8data_imputed$times_participated, na.rm=T)

summary(hw8data_imputed$times_participated.imp.mean)

mean(hw8data_imputed$times_participated.imp.mean)

```

### hours_hw_perweek - mean imputation

```{r, echo=FALSE}

hw8data_imputed$hours_hw_perweek.imp.mean <- 
  ifelse(is.na(hw8data_imputed$hours_hw_perweek)==T, 
         mean(hw8data_imputed$hours_hw_perweek, na.rm=T), 
         hw8data_imputed$hours_hw_perweek)
```

```{r}

summary(hw8data_imputed$hours_hw_perweek)

mean(hw8data_imputed$hours_hw_perweek, na.rm=T)

summary(hw8data_imputed$hours_hw_perweek.imp.mean)

mean(hw8data_imputed$hours_hw_perweek.imp.mean)

```

### parent_interaction - modal imputation

```{r, echo=FALSE}

mcv.parent_interaction <- factor(names(which.max(table(hw8data_imputed$parent_interaction))), levels=levels(hw8data_imputed$parent_interaction))

hw8data_imputed$parent_interaction.imp <- 
  as.factor(ifelse(is.na(hw8data_imputed$parent_interaction)==T, 
                   mcv.parent_interaction, 
                   hw8data_imputed$parent_interaction))

levels(hw8data_imputed$parent_interaction.imp)<-levels(hw8data_imputed$parent_interaction)

```

```{r}

mcv.parent_interaction

summary(hw8data_imputed$parent_interaction)

prop.table(table(hw8data_imputed$parent_interaction))

table(hw8data_imputed$parent_interaction.imp)

prop.table(table(hw8data_imputed$parent_interaction.imp))

```

## 4) Perform a multiple imputation of all values. Perform the analysis using this imputed data set. What are your results?

```{r, results='hide'}

hw8imp <- mice(data = hw8data_imputed[, c("enjoy_school", "overall_grades", "time_absent", "times_participated", "hours_hw_perweek", "parent_interaction", "PPSU", "PSTRATUM", "FPWT")], seed = 13, m = 10)

```

```{r}

print(hw8imp)

plot(hw8imp)

summary(hw8imp)

hw8dataimp <- complete (hw8imp, action = 1)

head(hw8dataimp)

```

```{r, results="hide", message=FALSE}

options(survey.lonely.psu = "adjust")

hw8design_imp <- svydesign(ids = ~PPSU,
                       strata = ~PSTRATUM,
                       weights = ~FPWT,
                       data = hw8dataimp,
                       nest = TRUE)

fit.imp <- svyglm (enjoy_school ~ overall_grades + time_absent + times_participated + hours_hw_perweek + parent_interaction,
                 hw8design_imp,
                 family = quasibinomial(link="logit"))
```

```{r}

summary(fit.imp)

```

## 5) Were the results similar between the mean/modal and multiple imputed data sets? How do the results compare to the results from the model fit with the data source with missing values?

```{r, results="hide", message=FALSE}

options(survey.lonely.psu = "adjust")

hw8design <- svydesign(ids = ~PPSU,
                       strata = ~PSTRATUM,
                       weights = ~FPWT,
                       data = hw8data_complete,
                       nest = TRUE)

fit.complete <- svyglm (enjoy_school ~ overall_grades + time_absent + times_participated + hours_hw_perweek + parent_interaction,
                 hw8design,
                 family = quasibinomial(link="logit"))

```

```{r}

summary(fit.complete)

```
