Introduction: Survey Non-response Bias
Surveys allow institutional researchers to collect and measure valuable information from students/constituents for assessment (eg, satisfaction and perception). They are especially useful when all individuals invited to participate actually respond. However, in reality, the response rate is typically very low, especially when the survey is administered by email.
When only a subset of individuals respond, surveys can suffer from non-response bias, meaning results can be skewed since responders may be very different than non-responders. For example, when conducting a survey by email at a community college, responders may tend to be younger students (eg, those more tech savvy), where older students from the community may be less likely to respond since they don’t check their email as often.
When reporting survey results, it is customary to quantify the survey response rate to communicate the possibility of non-response bias. A careful analyst could caution of this potential non-response bias and suggest that the results hold for the responders, and may or may not hold for the larger target population of interest. For surveys where the stakes are higher, the analyst could compare the demographics of the responders to non-responders or to the entire target population to understand which groups are under- or over-represented.
One way to address non-response bias is to increase the response rate, either by incentivizing the response with a prize or administering the survey in a way that ensures a response (eg, using the survey to take attendance for a required workshop). A data-driven alternative is to build a model for survey response (yes or no), produce survey weights based on this model, and re-weight observations when analyzing the results (eg, cases with lower likelihood of response would be weighted higher). This approach addresses non-response bias only if the non-response is independent of the outcome (responses to questions in the survey). If whether or not an individual responds to the survey depends on how they would respond to the survey questions, then a weighted analysis will not fix this bias (there is no way to correct for the bias as the actual responses are unknown by definition of non-response, making it impossible to correctly model response). Also, a weighted analysis is useful only if the analyst has access to variables that relate to decisions around participation. That is, demographic information alone may not be enough if the decision to participate is more intricate.
In this vignette, we illustrate how one might leverage the twang package in R
to model survey response using gradient boosted trees and derive survey
weights. The twang package was designed to estimate
propensity scores using gradient boosted trees, but can be re-purposed
for survey non-response as described in the “Non-response weights”
section in the twang vignette.
Modeling survey response using gradient boosted trees from
twang allows one to predict survey response without
investing a lot of time and effort to arrive at a reasonable model. For
those not familiar, gradient boosted trees is a popular and
well-established machine learning algorithm that arrives at good
predictions without too much manual tweaking from the analyst’s
perspective. The trade-off compared to a semi-parametric regression
model is that the underlying structure and relationships between the
covariates and outcome are not transparent, which is acceptable in this
application since the goal is to arrive at non-response weights to
reduce non-respose bias, not to draw inference on survey response. The
structure of the survey response model is not of primary interest.
Note that this vignette does not suggest adjusting for survey non-response using weights in all surveys. The purpose of the vignette is to illustrate how one might accomplish this task in R. In practicality, the author usually reports survey results as is, with the understanding that the results hold for the responders, and employ survey weights in very rare, high stakes surveys.
Toy Data Set: Student Satisfaction at a Community College
To assist with demonstrating the estimation of non-response weights,
we employ the survey_response and
survey_results data sets from the IRexamples
package. First, let’s load the data sets.
library(IRexamples) # package with data
# Load data from package
data(survey_response)
data(survey_results)
# ?survey_response or ?survey_results for descriptionFor a description of these two data sets, type
?survey_response and ?survey_results in the R
console.
Briefly, a student satisfaction survey was administered in a
particular year at community college. The survey_response
data set contains a list of responders and non-responders. It also
contains demographic information of all students that were invited to
participate in the survey. Actual survey results for the responders are
stored in the survey_results data set.
Let’s briefly explore the two data sets.
nrow(survey_response)
## [1] 12379
nrow(survey_results)
## [1] 1423
head(survey_response)
## Email_Masked Responded Age Female Ethnicity K12_Student
## 1 email_00001@college.edu 0 21 0 White 0
## 2 email_00002@college.edu 0 41 1 Asian 0
## 3 email_00003@college.edu 0 18 1 White 0
## 4 email_00004@college.edu 0 18 1 Asian 0
## 5 email_00005@college.edu 0 26 0 Hispanic / Latino 0
## 6 email_00006@college.edu 0 18 0 Two or More Races 0
## First_Time_College_Student Veteran DSPS EOPS FinAidEver Honors International
## 1 0 0 0 0 1 0 0
## 2 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0
## 4 1 0 0 0 1 0 0
## 5 0 0 0 0 1 0 0
## 6 1 0 0 0 1 0 0
## ESL Athlete GPA_Beg_Of_Term Units_Attempted_Beg_Of_Term Units
## 1 0 0 2.791667 48 9
## 2 1 0 0.000000 0 8
## 3 0 0 0.000000 0 9
## 4 0 0 0.000000 0 10
## 5 0 0 0.000000 14 4
## 6 0 0 0.000000 0 14
## Noncredit_Student Part_Time_Student Full_Time_Student Any_Online All_Online
## 1 0 1 0 0 0
## 2 0 1 0 0 0
## 3 0 1 0 0 0
## 4 0 1 0 0 0
## 5 0 1 0 1 1
## 6 0 0 1 0 0
length(unique(survey_response$Email_Masked))
## [1] 12379
head(survey_results)
## Email_Masked Overall Satisfaction Classroom learning experience
## 1 email_06316@college.edu Somewhat Satisfied Very Satisfied
## 2 email_05103@college.edu Very Dissatisfied Somewhat Dissatisfied
## 3 email_09762@college.edu Very Satisfied Somewhat Satisfied
## 4 email_00473@college.edu Very Satisfied Very Satisfied
## 5 email_03027@college.edu Somewhat Satisfied Very Satisfied
## 6 email_06955@college.edu Somewhat Satisfied Somewhat Satisfied
## Your ability to register for classes you want/need Variety of courses offered
## 1 Somewhat Dissatisfied Somewhat Satisfied
## 2 Somewhat Satisfied Somewhat Satisfied
## 3 Somewhat Satisfied Very Satisfied
## 4 Somewhat Satisfied Somewhat Satisfied
## 5 Somewhat Dissatisfied Somewhat Satisfied
## 6 Very Satisfied Very Satisfied
## Class size (number of students in a class) Campus safety/security
## 1 Somewhat Satisfied Somewhat Satisfied
## 2 Somewhat Satisfied Very Satisfied
## 3 Very Satisfied Very Satisfied
## 4 Very Satisfied Somewhat Satisfied
## 5 Very Satisfied Very Satisfied
## 6 Very Satisfied Very Satisfied
## Financial Aid Office Tutoring Services Selecting a major Canvas
## 1 <NA> <NA> <NA> Very Satisfied
## 2 <NA> Very Dissatisfied Somewhat Easy Very Dissatisfied
## 3 <NA> <NA> <NA> Very Satisfied
## 4 Very Satisfied <NA> <NA> Very Satisfied
## 5 <NA> Very Satisfied Somewhat Easy Very Satisfied
## 6 <NA> <NA> Very Easy <NA>
## Have you heard about the new California Initiative called Guided Pathways?
## 1 No
## 2 No
## 3 No
## 4 No
## 5 No
## 6 No
length(unique(survey_results$Email_Masked))
## [1] 1423
## Response rate
mean(survey_response$Responded)
## [1] 0.1149527Based on this exploration, Email_Masked is the common
key that could be used to join the two data sets. The response rate for
the survey is 11%.
Modeling Survey Response with twang
To model survey response, we leverage the ps function
from twang to build a gradient boosted tree model on
Responded as an outcome from the
survey_response data. The survey_response data
set also contains demographic and background information on students
that could be used to model survey response.
# Load packages
library(twang) # ps function
library(dplyr) # data manipulation
library(forcats) # maniuplate cateogorical/factor variables
library(ggplot2) # plotting
library(ggthemes) # themes
# Potential predictors: use all variables that are included
pred_vars <- names(survey_response)[3:ncol(survey_response)]
pred_vars
## [1] "Age" "Female"
## [3] "Ethnicity" "K12_Student"
## [5] "First_Time_College_Student" "Veteran"
## [7] "DSPS" "EOPS"
## [9] "FinAidEver" "Honors"
## [11] "International" "ESL"
## [13] "Athlete" "GPA_Beg_Of_Term"
## [15] "Units_Attempted_Beg_Of_Term" "Units"
## [17] "Noncredit_Student" "Part_Time_Student"
## [19] "Full_Time_Student" "Any_Online"
## [21] "All_Online"
# Summary
summary(survey_response %>% select(Responded, one_of(pred_vars)))
## Responded Age Female Ethnicity
## Min. :0.000 Min. :10.00 Min. :0.0000 Length:12379
## 1st Qu.:0.000 1st Qu.:19.00 1st Qu.:0.0000 Class :character
## Median :0.000 Median :21.00 Median :0.0000 Mode :character
## Mean :0.115 Mean :24.58 Mean :0.4957
## 3rd Qu.:0.000 3rd Qu.:26.00 3rd Qu.:1.0000
## Max. :1.000 Max. :81.00 Max. :1.0000
## K12_Student First_Time_College_Student Veteran
## Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.0429 Mean :0.1761 Mean :0.01656
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.00000
## DSPS EOPS FinAidEver Honors
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.07658 Mean :0.07795 Mean :0.4771 Mean :0.04702
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## International ESL Athlete GPA_Beg_Of_Term
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :2.526
## Mean :0.0618 Mean :0.1494 Mean :0.02779 Mean :2.083
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:3.389
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :4.000
## Units_Attempted_Beg_Of_Term Units Noncredit_Student
## Min. : 0.00 Min. : 0.500 Min. :0
## 1st Qu.: 0.00 1st Qu.: 5.000 1st Qu.:0
## Median : 18.00 Median :10.000 Median :0
## Mean : 26.19 Mean : 9.763 Mean :0
## 3rd Qu.: 42.00 3rd Qu.:14.000 3rd Qu.:0
## Max. :256.00 Max. :28.000 Max. :0
## Part_Time_Student Full_Time_Student Any_Online All_Online
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :1.0000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.5626 Mean :0.4374 Mean :0.3343 Mean :0.07957
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000Before we start modeling Responded, we first convert
variables with a character data type to a
factor data type in R. That way, modeling functions (eg,
ps) will know to treat it as a group variable.
# Convert character variables to factor
char_vars <- lapply(survey_response, class) == 'character'
survey_response[, char_vars] <- lapply(survey_response[, char_vars], as.factor)Next, we model survey response using ps from
twang.
# Propensity Score on Responded variable
ps_fit <- ps(Responded ~ . ## Use all variables
, data=survey_response %>% select(Responded, one_of(pred_vars)) ## Select just the response and specified predictors
, estimand='ATE' ## ATE: Generalize to all those invited for the survey, not just responders
, verbose=FALSE
)
## Warning in gbm.fit(x = x, y = y, offset = offset, distribution = distribution, :
## variable 17: Noncredit_Student has no variation.
length(ps_fit)
## [1] 18
names(ps_fit)
## [1] "gbm.obj" "treat" "treat.var" "desc" "ps"
## [6] "w" "sampw" "estimand" "version" "datestamp"
## [11] "parameters" "alerts" "iters" "balance.ks" "balance.es"
## [16] "balance" "n.trees" "data"We specify the estimand to be 'ATE' as we want weights
that generalize to all students invited to participate in the survey,
not just those that responded. We return the results of ps
to ps_fit. The object ps_fit contains many
data elements and objects that helps the user select the gradient tree
model that could be used to derive survey weights. In general, there are
two tree models worth considering, one labeled es.mean.ATE
and one labeled ks.mean.ATE. In the vast majority of cases,
the two are quite similar. For simplicity, choose one of the two and
stick with it in any analysis. For this vignette, we choose the
es.mean.ATE model. For a brief description on these two
models, see the vignette on
propensity score weights.
In this next set of code, we extract the weights from the
es.mean.ATE model.
# summary(ps_fit$gbm.obj, n.trees=ps_fit$desc$es.mean.ATE$n.trees)
# plot(ps_fit, plots=3)
# bal.table(ps_fit)$es.mean.ATE
# Get weights
survey_response$weight <- get.weights(ps_fit, stop.method="es.mean")
summary(survey_response$weight)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.012 1.073 1.109 1.880 1.193 45.005Recall that in a propensity score analysis, the hope is that the
distribution of the covariates are balanced between the two comparison
groups, treatment and control, when the observations are weighted. For
survey non-response, the hope is that the weighted observations from the
responders are similar to that of the full
sample that includes both responders and non-responders. To do
this assessment, we construct a synthetic data set with synthetic
treatment and weight variables to “trick” the functions from
twang to compare the full sample (first group) to that of
the responders (second group). More precisely, we take the following
steps:
- Create a new data set that combines (stacks) two data sets:
- The full data set that includes responders and non-responders.
- The responders data set.
- Create a synthetic treatment variable
trt_trick, where the observations in the first data set have valuetrt_trick=1and the observations in the second data set have valuetrt_trick=0. Similarly, create a synthetic weight variableweight_trick, where the observations in the first data set have valueweight_trick=1and the observations in the second data set have valueweight_trick=weight, whereweightcontains weights from the survey response model (derived fromps). - With this new data set and with the synthetic
trt_trickandweight_trickvariables, we could re-purpose thedx.wtsfunction fromtwangto compare the covariates between two groups, the unweighted full sample vs. the weighted responders. For this to work, we have to setestimand='ATT'when callingdx.wts(generalizing to those withtrt_trick=1, which corresponds to the full sample).
survey_trick <- rbind(
data.frame(survey_response, trt_trick=1, weight_trick=1) # full sample
, data.frame(survey_response, trt_trick=0, weight_trick=survey_response$weight) %>%
filter(Responded==1) # Responders, weighted
) # Trick dx.wts function to summarize this data set to compare if weighted responders are similar to the full sample (ATT since full sample has trt_trick=1 as trick treatment)
# Summary object
survey_trick_summ <- dx.wts(x=survey_trick$weight_trick
, data=survey_trick
, estimand='ATT' ## Need this to compare unweighted full sample and the weighted responders
, vars=pred_vars
, treat.var='trt_trick'
)
# unweighted full sample
bal.table(survey_trick_summ)[[1]][, c('tx.mn', 'ct.mn', 'std.eff.sz', 'ks')]
## tx.mn ct.mn std.eff.sz ks
## Age 24.579 27.836 -0.328 0.127
## Female 0.496 0.606 -0.220 0.110
## Ethnicity:American Indian or Alaska Native 0.001 0.001 -0.025 0.001
## Ethnicity:Asian 0.332 0.323 0.021 0.010
## Ethnicity:Black or African American 0.021 0.023 -0.016 0.002
## Ethnicity:Hispanic / Latino 0.247 0.218 0.068 0.029
## Ethnicity:Native Hawaiian or Pacific Islander 0.003 0.003 -0.001 0.000
## Ethnicity:Two or More Races 0.053 0.044 0.039 0.009
## Ethnicity:Unreported or Unknown 0.020 0.018 0.015 0.002
## Ethnicity:White 0.323 0.370 -0.100 0.047
## K12_Student 0.043 0.030 0.063 0.013
## First_Time_College_Student 0.176 0.189 -0.034 0.013
## Veteran 0.017 0.021 -0.035 0.005
## DSPS 0.077 0.102 -0.095 0.025
## EOPS 0.078 0.093 -0.055 0.015
## FinAidEver 0.477 0.494 -0.034 0.017
## Honors 0.047 0.079 -0.150 0.032
## International 0.062 0.048 0.055 0.013
## ESL 0.149 0.178 -0.080 0.028
## Athlete 0.028 0.022 0.032 0.005
## GPA_Beg_Of_Term 2.083 2.299 -0.143 0.107
## Units_Attempted_Beg_Of_Term 26.190 26.001 0.007 0.022
## Units 9.763 9.887 -0.025 0.023
## Noncredit_Student 0.000 0.000 NA 0.000
## Part_Time_Student 0.563 0.548 0.029 0.015
## Full_Time_Student 0.437 0.452 -0.029 0.015
## Any_Online 0.334 0.275 0.126 0.060
## All_Online 0.080 0.054 0.094 0.025
# weighted responders
bal.table(survey_trick_summ)[[2]][, c('tx.mn', 'ct.mn', 'std.eff.sz', 'ks')]
## tx.mn ct.mn std.eff.sz ks
## Age 24.579 24.511 0.007 0.008
## Female 0.496 0.507 -0.024 0.012
## Ethnicity:American Indian or Alaska Native 0.001 0.000 0.011 0.000
## Ethnicity:Asian 0.332 0.324 0.018 0.009
## Ethnicity:Black or African American 0.021 0.019 0.016 0.002
## Ethnicity:Hispanic / Latino 0.247 0.262 -0.036 0.015
## Ethnicity:Native Hawaiian or Pacific Islander 0.003 0.001 0.028 0.001
## Ethnicity:Two or More Races 0.053 0.051 0.008 0.002
## Ethnicity:Unreported or Unknown 0.020 0.018 0.015 0.002
## Ethnicity:White 0.323 0.324 -0.003 0.001
## K12_Student 0.043 0.036 0.035 0.007
## First_Time_College_Student 0.176 0.189 -0.034 0.013
## Veteran 0.017 0.016 0.002 0.000
## DSPS 0.077 0.077 -0.003 0.001
## EOPS 0.078 0.080 -0.006 0.002
## FinAidEver 0.477 0.495 -0.037 0.018
## Honors 0.047 0.049 -0.009 0.002
## International 0.062 0.062 0.000 0.000
## ESL 0.149 0.145 0.012 0.004
## Athlete 0.028 0.021 0.042 0.007
## GPA_Beg_Of_Term 2.083 2.050 0.022 0.016
## Units_Attempted_Beg_Of_Term 26.190 25.214 0.034 0.021
## Units 9.763 9.831 -0.014 0.015
## Noncredit_Student 0.000 0.000 NA 0.000
## Part_Time_Student 0.563 0.550 0.026 0.013
## Full_Time_Student 0.437 0.450 -0.026 0.013
## Any_Online 0.334 0.314 0.043 0.020
## All_Online 0.080 0.067 0.048 0.013
d_demo_summ_trick <- bind_rows(
bal.table(survey_trick_summ)[[1]] %>%
mutate(Comparison_Type='Unadj.', Variable=rownames(bal.table(ps_fit)$unw))
, bal.table(survey_trick_summ)[[2]] %>%
mutate(Comparison_Type='Adj.', Variable=rownames(bal.table(ps_fit)$es.mean.ATE))
) %>%
# filter(!(Variable %in% c('NA'))) %>%
filter(!is.na(std.eff.sz)) %>%
select(Comparison_Type, Variable, std.eff.sz)# Visualize
d_demo_summ_trick %>%
arrange(desc(Comparison_Type), desc(std.eff.sz)) %>%
group_by(Variable) %>%
mutate(std.eff.sz0=std.eff.sz[Comparison_Type=='Unadj.']) %>%
ungroup %>%
# Originally full sample vs. responders, let's reverse it.
mutate(std.eff.sz0=-1 * std.eff.sz0
, std.eff.sz=-1 * std.eff.sz
) %>%
ggplot(data=., mapping=aes(x=fct_reorder(factor(Variable), std.eff.sz0), y=std.eff.sz, group=Variable)) +
geom_point(mapping=aes(size=ifelse(Comparison_Type=='Unadj.', 1, 2))) +
scale_size_continuous(range = c(1,4)) +
geom_line() +
geom_hline(yintercept=0, lty=2) +
coord_flip() +
ylab('Standardized Difference') +
xlab('') +
# ggtitle('Responded to Survey vs. Not') +
ggtitle('Responders vs. Full Sample') +
theme_tufte(base_family='Arial Narrow') +
theme(text=element_text(size=14)) +
# theme_ipsum(grid=FALSE) +
# scale_color_manual(values=c('slategrey', ivc_color), guide=FALSE) +
theme(legend.position="none") +
labs(caption='Small dots: unweighted difference\nLarge dots: weighted difference')
#dev.copy(png, filename='survey_plot_balance.png', width=9, height=5, units='in', res=150)
#dev.off()
Let’s interpret the previous visualization. Prior to applying the
survey weights, responders tend to be older compared to the full sample
(top row, small dot). After incoporating survey weights, the responders
have a similar age distribution compared to the full sample as expressed
by the mean (top row, large dot). Similarly, prior to applying the
survey weights, responders have a lower rate of students taking any
online course compared to the full sample that has a higher online
course rate (last row, small dot). After incorporating the survey
weights, the difference in online course rate is closer to 0. Overall,
there was imbalance on a few variables (Age,
Female, GPA, and Ethnicity:White)
prior to the use of survey weights. The use of survey weights reduced
the imbalance across all variables (absolute standardized difference
less than 0.1).
Statistical Inference with Survey Weights
With the survey weights (survey_response$weight), one
could now summarize the responses accounting for survey non-response.
First, let’s combine the survey weights with the survey results.
# Append weight to results data
survey_results_w <- survey_results %>%
left_join(survey_response %>%
select(Email_Masked, weight)
)
sum(survey_results_w$weight)
## [1] 10957.57Next, let’s explore some results.
table(survey_results_w$`Overall Satisfaction`)
##
## Somewhat Dissatisfied Somewhat Satisfied Very Dissatisfied
## 103 554 80
## Very Satisfied
## 671
survey_results_w %>%
## filter(!is.na(`Overall Satisfaction`)) %>%
group_by(`Overall Satisfaction`) %>%
summarize(N_Raw=n()
, N_Wt=sum(weight)
) %>%
ungroup %>%
mutate(Pct_Raw=N_Raw/sum(N_Raw)
, Pct_Wt=N_Wt/sum(N_Wt)
)
## # A tibble: 5 x 5
## `Overall Satisfaction` N_Raw N_Wt Pct_Raw Pct_Wt
## <chr> <int> <dbl> <dbl> <dbl>
## 1 Somewhat Dissatisfied 103 848. 0.0724 0.0774
## 2 Somewhat Satisfied 554 4486. 0.389 0.409
## 3 Very Dissatisfied 80 628. 0.0562 0.0573
## 4 Very Satisfied 671 4865. 0.472 0.444
## 5 <NA> 15 131. 0.0105 0.0119In the previous results, the Raw suffix refers to
unweighted results and the Wt suffix refers to weighted
results. The weighted results and unweighted results (the columns with
Pct prefix) aren’t too different for
Overall Satisfaction. This isn’t too surprising since there
wasn’t a lot of imbalance between survey responders and the full sample
to begin with. In surveys where responders are highly not representative
of the full sample, the weighted results may have a more substantial
difference.
Recap
As mentioned at the beginning of this vignette, there are a few options when it comes to reporting survey results when a good portion of the invitees do not respond:
- Report the raw survey results for the responders, and acknowledge that the results hold for just the responders. It may or may not hold for the target population.
- Examine and compare the demographics of the responders to non-responders. If the demographics are similar, then report the raw survey results and mention that responders look to be similar to non-responders, so one could make an assumption that the results based on the responders hold true for the target population. The responders may differ than non-responders in other ways that are not measurable, and hence, cannot be tested.
- Construct a survey response model as illustrated in this vignettte, and report the weighted results. Assuming the survey response model reasonably captures participants’ decision to respond or not, the results could be generalized for the target population.
Appendix: R and R Package Versions
This vignette was generated using an R session with the following packages. There may be some discrepancies when the reader replicates the code caused by version mismatch.
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17763)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggthemes_4.2.4 ggplot2_3.3.3 forcats_0.5.1
## [4] dplyr_1.0.6 twang_2.3 IRexamples_0.0.1
## [7] RevoUtils_11.0.2 RevoUtilsMath_11.0.0
##
## loaded via a namespace (and not attached):
## [1] gbm_2.1.8 tidyselect_1.1.1 xfun_0.23
## [4] bslib_0.2.5.1 purrr_0.3.4 mitools_2.4
## [7] splines_4.0.2 lattice_0.20-44 colorspace_2.0-1
## [10] vctrs_0.3.8 generics_0.1.0 htmltools_0.5.1.1
## [13] yaml_2.2.1 utf8_1.2.1 survival_3.2-11
## [16] rlang_0.4.11 jquerylib_0.1.4 pillar_1.6.1
## [19] withr_2.4.2 glue_1.4.2 DBI_1.1.1
## [22] xgboost_1.4.1.1 RColorBrewer_1.1-2 jpeg_0.1-8.1
## [25] lifecycle_1.0.0 stringr_1.4.0 MatrixModels_0.5-0
## [28] munsell_0.5.0 gtable_0.3.0 evaluate_0.14
## [31] labeling_0.4.2 latticeExtra_0.6-29 knitr_1.33
## [34] fansi_0.5.0 highr_0.9 xtable_1.8-4
## [37] scales_1.1.1 jsonlite_1.7.2 farver_2.1.0
## [40] png_0.1-7 digest_0.6.27 stringi_1.4.6
## [43] survey_4.0 grid_4.0.2 cli_2.5.0
## [46] tools_4.0.2 magrittr_2.0.1 sass_0.4.0
## [49] tibble_3.1.2 crayon_1.4.1 pkgconfig_2.0.3
## [52] ellipsis_0.3.2 Matrix_1.3-4 data.table_1.14.0
## [55] rstudioapi_0.13 rmarkdown_2.9 R6_2.5.0
## [58] compiler_4.0.2