From 6b86502b19f2f5670f0023d73e87c184d4d13803 Mon Sep 17 00:00:00 2001 From: Rebecca Powell Date: Sun, 3 Mar 2024 13:29:48 -0500 Subject: [PATCH] Missing data chapter (#96) * Update missing data in C03, PH in C11 * Beginning of missing data chapter * Adding naniar to renv, bib, and in text in set-up chapter. * Updating draft of missing data chapter. * Remove old data package reference * Missing chapter edits SZ --------- Co-authored-by: Stephanie Zimmer --- 01-introduction.Rmd | 61 ++- ...nderstanding-survey-data-documentation.Rmd | 73 ++- 04-set-up.Rmd | 2 +- 10-specifying-sample-designs.Rmd | 20 +- 11-missing-data.Rmd | 419 ++++++++++++++++++ _output.yml | 15 +- book.bib | 11 + images/codebook-ncvs-weapon-handgun.jpg | Bin 0 -> 45656 bytes images/codebook-ncvs-weapon-li.jpg | Bin 0 -> 99432 bytes images/questionnaire-ncvs-weapon.jpg | Bin 0 -> 75617 bytes renv.lock | 114 +++++ 11 files changed, 660 insertions(+), 55 deletions(-) create mode 100644 images/codebook-ncvs-weapon-handgun.jpg create mode 100644 images/codebook-ncvs-weapon-li.jpg create mode 100644 images/questionnaire-ncvs-weapon.jpg diff --git a/01-introduction.Rmd b/01-introduction.Rmd index b0905946..be3a4f9d 100644 --- a/01-introduction.Rmd +++ b/01-introduction.Rmd @@ -40,4 +40,63 @@ This book will cover many aspects of survey design and analysis, from understand - **Chapter \@ref(c13-ncvs-vignette)**: National Crime Victimization Survey Vignette. A vignette on how to analyze data from the NCVS, a survey in the U.S. that collects information on crimes and their characteristics. This illustrates an analysis that requires multiple files to calculate victimization rates. - **Chapter \@ref(c14-ambarom-vignette)**: AmericasBarometer Vignette. A vignette on how to analyze data from the AmericasBarometer, a survey of attitudes, evaluations, experiences, and behavior in countries in the Western Hemisphere. This includes how to make choropleth maps with survey estimates. -In most chapters, you'll find code that you can follow. Each of these chapters starts with a "setup" section. The setup section includes the code needed to load the necessary packages and datasets in the chapter. We then provide the main idea of the chapter and examples on how to use the functions. Most chapters end with exercises to work through. Solutions to the exercises can be found in the Appendix. \ No newline at end of file +<<<<<<< HEAD +In most chapters, you'll find code that you can follow. Each of these chapters starts with a "set-up" section. This section will include the code needed to load the necessary packages and datasets in the chapter. We then provide the main idea of the chapter and examples on how to use the functions. Most chapters end with exercises to work through. Solutions to the exercises can be found in the Appendix. + +## Datasets used in this book + +We work with two key datasets throughout the book: the Residential Energy Consumption Survey [RECS -- @recs-2020-tech] and the American National Election Studies [ANES -- @debell]. To ensure that all readers can follow the examples, we have provided analytic datasets in an R package, {srvyrexploR}. Install the package from GitHub using the {remotes} package. + +```r +remotes::install_github("tidy-survey-r/srvyrexploR") +``` + +To explore the provided datasets in the package, access the documentation usng the `help()` command. + +```r +help(package="srvyrexploR") +``` +To load the RECS and ANES datasets, start by running `library(srvyrexploR)` to load the package. Then, use the `data()` command to load the datasets into the environment. + +```{r} +#| label: intro-setup +#| error: FALSE +#| warning: FALSE +#| message: FALSE +library(tidyverse) +library(survey) +library(srvyr) +library(srvyrexploR) +``` + +```{r} +#| label: intro-setup-readin +#| error: FALSE +#| warning: FALSE +#| message: FALSE + +data(recs_2020) +data(anes_2020) +``` + +RECS is a study that provides energy consumption and expenditures data in American households. The Energy Information Administration funds RECS and has been fielded 15 times between 1950 and 2020. The survey has two components - the household survey and the energy supplier survey. In 2020, the household survey was collected by web and paper questionnaires and included questions about appliances, electronics, heating, air conditioning (A/C), temperatures, water heating, lighting, respondent demographics, and energy assistance. The energy supplier survey consists of components relating to energy consumption and energy expenditure. Below is an overview of the `recs_2020` data: + +```{r} +#| label: intro-recs +recs_2020 %>% select(-starts_with("NWEIGHT")) +recs_2020 %>% select(starts_with("NWEIGHT")) +``` + +From this output, we can see that there are `r nrow(recs_2020) %>% formatC(big.mark = ",")` rows and `r ncol(recs_2020) %>% formatC(big.mark = ",")` variables. We can see that there are variables containing an ID (`DOEID`), geographic information (e.g., `Region`, `state_postal`, `Urbanicity`), along with information about the house, including the type of house (`HousingUnitType`) and when the house was built (`YearMade`). Additionally, there is a long list of weighting variables that we will use in the analysis (e.g., `NWEIGHT`, `NWEIGHT1`, ..., `NWEIGHT60`). We will discuss using these weighting variables in Chapter \@ref(c10-specifying-sample-designs). For a more detailed codebook, see Appendix \@ref(recs-cb). + +The ANES is a series study that has collected data from election surveys since 1948. These surveys contain data on public opinion and voting behavior in U.S. presidential elections. The 2020 survey (the data we will be using) was fielded to individuals over the web, through live video interviewing, or over with computer-assisted telephone interviewing (CATI). The survey includes questions on party affiliation, voting choice, and level of trust with the government. Here is an overview of the `anes_2020` data. First, we show the variables starting with "V" followed by a number; these are the original variables. Then, we show you the remaining variables that we created based on the original data: + +```{r} +#| label: intro-anes +anes_2020 %>% select(matches("^V\\d")) +anes_2020 %>% select(-matches("^V\\d")) +``` + +From this output we can see that there are `r nrow(anes_2020) %>% formatC(big.mark = ",")` rows and `r ncol(anes_2020) %>% formatC(big.mark = ",")` variables. Most of the variables start with V20, so referencing the documentation for survey will be crucial to not get lost (see Chapter \@ref(c03-understanding-survey-data-documentation)). We have created some more descriptive variables for you to use throughout this book, such as the age (`Age`) and gender (`Gender`) of the respondent, along with variables that represent their party affiliation (`PartyID`). Additionally, we need the variables `Weight` and `Stratum` to analyze this data accurately. We will discuss how to use these weighting variables in Chapters \@ref(c10-specifying-sample-designs) and \@ref(c03-understanding-survey-data-documentation). For a more detailed codebook, see Appendix \@ref(anes-cb). + +In most chapters, you'll find code that you can follow. Each of these chapters starts with a "setup" section. The setup section includes the code needed to load the necessary packages and datasets in the chapter. We then provide the main idea of the chapter and examples on how to use the functions. Most chapters end with exercises to work through. Solutions to the exercises can be found in the Appendix. diff --git a/03-understanding-survey-data-documentation.Rmd b/03-understanding-survey-data-documentation.Rmd index 388a098c..9263f9af 100644 --- a/03-understanding-survey-data-documentation.Rmd +++ b/03-understanding-survey-data-documentation.Rmd @@ -88,70 +88,47 @@ The 2004 ANES dataset released an erratum, notifying analysts to remove a specif Survey documentation may include additional material, such as interviewer instructions or "show cards" provided to respondents during interviewer-administered surveys to help respondents answer questions. Explore the survey website to find out what resources were used and in what contexts. -## Working with Missing Data +## Missing data coding -Missing data in surveys refers to situations where participants do not provide complete responses to survey questions. Respondents may not have seen a question by design. Or, they may not respond to a question for various other reasons, such as not wanting to answer a particular question, not understanding the question, or simply forgetting to answer. +For some observations in a dataset, there may be missing data. This can be by design or from nonresponse, and these concepts are detailed in Chapter \@ref(c11-missing-data). In that chapter, we also discuss how to analyze data with missing data. In this section, we discuss how to understand documentation related to missing data. -Missing data can be a significant problem in survey analysis, as it can introduce bias and reduce the representativeness of the data. Missing data typically falls into two main categories: missing by design or unintentional missing data. +The survey documentation, often the codebook, represents the missing data with a code. The codebook may list different codes depending on why certain data is missing. In the example of variable `V202066` from the ANES (Figure \@ref(fig:understand-codebook-examp)), `-9` represents "Refused," `-7` means that the response was deleted due to an incomplete interview, `-6` means that there is no response because there was no follow-up interview, and `-1` means "Inapplicable" (due to the designed skip pattern). -1. **Missing by design/questionnaire skip logic**: This type of missingness occurs when certain respondents are intentionally directed to skip specific questions based on their previous responses or characteristics. For example, in a survey about employment, if a respondent indicates that they are not employed, they may be directed to skip questions related to their job responsibilities. Additionally, some surveys randomize questions or modules so that not all participants respond to all questions. In these instances, respondents would have missing data for the modules not randomly assigned to them. - -2. **Unintentional missing data**: This type of missingness occurs when researchers do not intend for there to be missing data on a particular question, for example, if respondents did not finish the survey or refused to answer individual questions. There are three main types of unintentional missing data that each should be considered and handled differently [@mack; @Schafer2002]: - - a. **Missing completely at random (MCAR)**: The missing data are unrelated to both observed and unobserved data, and the probability of being missing is the same across all cases. For example, if a respondent missed a question because they had to leave the survey early due to an emergency. - - b. **Missing at random (MAR)**: The missing data are related to observed data but not unobserved data, and the probability of being missing is the same within groups. For example, if older respondents choose not to answer specific questions but younger respondents do answer them and we know the respondent's age. - - c. **Missing not at random (MNAR)**: The missing data are related to unobserved data, and the probability of being missing varies for reasons we are not measuring. For example, if respondents with depression do not answer a question about depression severity. - -The survey documentation, often the codebook, represents the missing data with a code. For example, a survey may have "Yes" responses coded to `1`, "No" responses coded to `2`, and missing responses coded to `-9`. Or, the codebook may list different codes depending on why certain data are missing. In the example of variable `V202066` from the ANES (Figure \@ref(fig:understand-codebook-examp)), `-9` represents "Refused," `-7` means that the response was deleted due to an incomplete interview, `-6` means that there is no response because there was no follow-up interview, and `-1` means "Inapplicable" (due to the designed skip pattern). - -When running analysis in R, we must handle missing responses as missing data (i.e., `NA`) and not numeric data. If missing responses are treated as zeros or arbitrary values, they can artificially alter summary statistics or introduce spurious patterns in the analysis. Recoding these values to `NA` will allow us to handle missing data in different ways in R, such as using functions like `na.omit()`, `complete.cases()`, or specialized packages like {tidyimpute} or {mice}. These tools allow us to treat missing responses as missing data to conduct our analysis accurately and obtain valid results. - -Visualizing the missing data can also inform the types of missing data that are present. The {naniar} package provides many valuable missing data visualizations, such as using `gg_miss_var()` to see the count or percent of missing data points by variable or `gg_miss_fct()` to see relationships in missing data across levels of a factor variable. Investigating the relationships and nature of the missing data before running models can ensure that the missing data are accurately accounted for. - -### Accounting for Questionnaire Skip Patterns - -Questionnaires may include skip patterns, in which specific questions are skipped based on the respondent's answers to earlier questions. For example, if a respondent answers "no" to a question on whether they voted in the last election, they may be instructed to skip a series of questions related to that election. - -Skip patterns are used in surveys to streamline the data collection process and avoid asking irrelevant questions to certain respondents. However, they also result in missing data, as respondents cannot respond to questions they were instructed to skip. Analyzing the data missing by design requires understanding the underlying reasons for the skip patterns. Our survey analysis must properly account for skip patterns to ensure unbiased and accurate population parameters. - -Dealing with missing data due to skip patterns requires careful consideration. We can treat skipped questions as missing data. Or, we can run an analysis that accounts for the conditional dependence between the skipped and answered questions. The appropriate method depends on the nature and extent of the skip patterns, the research questions, and the methodology. For example, if we wanted to know what proportion of eligible voters voted for a particular candidate, the denominator would be all eligible voters, while if we wanted to know what proportion voted for a specific candidate among those who voted, the denominator would be those who voted. We include or exclude missing values depending on our research question. - -### Accounting for Unintentional Missing Data - -When dealing with missing data that is MCAR, MAR, or MNAR, we must consider the implications of how we handle these missing data and avoid introducing more sources of bias. For instance, we can analyze only the respondents who answered all questions by performing listwise deletion, which drops all rows from a data frame with a missing value in any column. For example, let's say we have a dataset `dat` with one complete case and two cases with some missing data. We can use the function `tidyr::drop_na()` for listwise deletion. +As another example, there may be a summary variable that describes the missingness of a set of variables - particularly with "select all that apply" or "multiple response" questions. In the National Crime Victimization Survey (NCVS), respondents who are victims of a crime and saw the offender are asked if the offender have a weapon and then asked what the type of weapon was. This part of the questionnaire from 2021 is shown in Figure \@ref(fig:understand-ncvs-weapon-q). ```{r} -#| label: understand-dropna-example1 -dat <- tibble::tribble(~ col1, ~ col2, ~ col3, - "a", "d", "e", - "b", NA, NA, - "c", NA, "f") +#| label: understand-ncvs-weapon-q +#| echo: false +#| fig.cap: Excerpt from the NCVS 2020-2021 Crime Incident Report - Weapon Type +#| fig.alt: Questions 22 and 23a from the NCVS 2020-2021 Crime Incident Report, see https://bjs.ojp.gov/content/pub/pdf/ncvs20_cir.pdf -dat +knitr::include_graphics(path="images/questionnaire-ncvs-weapon.jpg") ``` -If we use the `tidyr::drop_na()` function, only the first case will remain, as the other two cases have at least one missing value. + + +The NCVS codebook includes coding for all multiple response variables of a "lead in" variable that summarizes the individual options. For question 23a on the weapon type, the lead in variable is V4050 which is shown in \@ref(fig:understand-ncvs-weapon-cb). This variable is then followed by a set of variables for each weapon type. An example of one of the individual variables from the codebook, the handgun, is shown in \@ref(fig:understand-ncvs-weapon-cb-hg). We will dive in more to this example in Chapter \@ref(c11-missing-data) of how to analyze this variable. + ```{r} -#| label: understand-dropna-example2 -dat %>% - tidyr::drop_na() +#| label: understand-ncvs-weapon-cb +#| echo: false +#| fig.cap: Excerpt from the NCVS 2021 Codebook for V4050 - LI WHAT WAS WEAPON +#| fig.alt: Codebook includes location of variable (files and columns), variable type (numeric), question (What was the weapon? Anything else?), and the coding of this lead in variable +knitr::include_graphics(path="images/codebook-ncvs-weapon-li.jpg") ``` -However, if we want to only remove rows that have missing values in `col3`, we can specify this as an argument in `drop_na()` as follows: ```{r} -#| label: understand-dropna-example3 -dat %>% - tidyr::drop_na(col3) +#| label: understand-ncvs-weapon-cb-hg +#| echo: false +#| fig.cap: "Excerpt from the NCVS 2021 Codebook for V4051 - C WEAPON: HAND GUN" +#| fig.alt: Codebook includes location of variable (files and columns), variable type (numeric), question (What was the weapon? Anything else?), and the coding of this categorical variable +knitr::include_graphics(path="images/codebook-ncvs-weapon-handgun.jpg") ``` -The `drop_na()` function works on `tbl_svy` objects as well and should only be applied after creating the design object. - -If the data are not missing completely at random (MCAR), then listwise deletion may produce biased estimates if there is a pattern of respondents who do not respond to specific questions. In these circumstances, we should explore other options, such as multiple imputation or weighted estimation. However, imputation is not always appropriate and can introduce its own sources of bias. See @allison for more details. +When data is read into R, some values may be system missing, that is they are coded as `NA` even if that is not evident in a codebook. We will discuss in Chapter \@ref(c11-missing-data) how to analyze data with `NA` values and review how R handles missing data in calculations. -In summary, we need to deeply understand the types and reasons for missing data in our survey before running any analysis. The survey documentation is an important resource for understanding how to deal with missing data. Carefully review the documentation for guidance from the researchers. + ## Example: American National Election Studies (ANES) 2020 Survey Documentation diff --git a/04-set-up.Rmd b/04-set-up.Rmd index 936eb902..0019aa12 100644 --- a/04-set-up.Rmd +++ b/04-set-up.Rmd @@ -72,7 +72,7 @@ Once this package is installed, load it using the `library()` function: library(censusapi) ``` -Additional packages are used in the vignettes. We list them in the Prerequisite boxes at the beginning of the chapters. When working through those chapters, please ensure you pay attention to the setup box at the beginning of the chapter and install all necessary packages. +Additional packages are used in the Real Life Data and Vignettes sections of the book. We list them in the Prerequisite boxes at the beginning of the chapters. When working through those chapters, please ensure you pay attention to this prerequisite box at the beginning of the chapter and load all necessary packages and data. ## Data diff --git a/10-specifying-sample-designs.Rmd b/10-specifying-sample-designs.Rmd index edd541d1..2463f1cb 100644 --- a/10-specifying-sample-designs.Rmd +++ b/10-specifying-sample-designs.Rmd @@ -584,7 +584,6 @@ To specify this design, use the following syntax: #| echo: FALSE #| warning: FALSE #| message: FALSE -#| cache: TRUE data(recs_2015) ``` @@ -657,6 +656,7 @@ To specify this design, use the following syntax: ```{r} #| label: samp-des-recs2020-des + recs_des <- recs_2020 %>% as_survey_rep( weights = NWEIGHT, @@ -672,6 +672,24 @@ recs_des summary(recs_des) ``` +```{r} +#| label: samp-des-recs2020-des-save +#| echo: false + +recs_des <- recs_2020 %>% + as_survey_rep( + weights = NWEIGHT, + repweights = NWEIGHT1:NWEIGHT60, + type = "JK1", + scale = 59/60, + mse = TRUE + ) + + + +``` + + When printing the design object or looking at the summary, the replicate weight type is re-iterated as `Unstratified cluster jacknife (JK1) with 60 replicates and MSE variances`, and the variables are included. No weight or probability summary is included. ### Bootstrap Method diff --git a/11-missing-data.Rmd b/11-missing-data.Rmd index 17a2fb02..0a7045bc 100644 --- a/11-missing-data.Rmd +++ b/11-missing-data.Rmd @@ -1 +1,420 @@ # Missing data {#c11-missing-data} + +```{r} +#| label: missing-styler +#| include: false +knitr::opts_chunk$set(tidy = 'styler') +``` + +::: {.prereqbox-header} +`r if (knitr:::is_html_output()) '### Prerequisites {- #prereq11}'` +::: + +::: {.prereqbox data-latex="{Prerequisites}"} +For this chapter, load the following packages: +```{r} +#| label: missing-setup +#| error: FALSE +#| warning: FALSE +#| message: FALSE +library(tidyverse) +library(survey) +library(srvyr) +library(srvyrexploR) +library(naniar) +library(haven) +library(gt) +``` + +We will be using data from ANES and RECS. Here is the code to create the design objects for each to use throughout this chapter. For ANES, we need to adjust the weight so it sums to the population instead of the sample (see the ANES documentation and Chapter \@ref(c03-understanding-survey-data-documentation) for more information). +```{r} +#| label: missing-anes-des +#| eval: FALSE +targetpop <- 231592693 +data(anes_2020) + +anes_adjwgt <- anes_2020 %>% + mutate(Weight = Weight / sum(Weight) * targetpop) + +anes_des <- anes_adjwgt %>% + as_survey_design( + weights = Weight, + strata = Stratum, + ids = VarUnit, + nest = TRUE + ) +``` + +For RECS, details are included in the RECS documentation and Chapter \@ref(c10-specifying-sample-designs). + +```{r} +#| label: missing-recs-des +#| eval: FALSE +data(recs_2020) + +recs_des <- recs_2020 %>% + as_survey_rep( + weights = NWEIGHT, + repweights = NWEIGHT1:NWEIGHT60, + type = "JK1", + scale = 59/60, + mse = TRUE + ) +``` +::: + +## Introduction + +Missing data in surveys refers to situations where participants do not provide complete responses to survey questions. Respondents may not have seen a question by design. Or, they may not respond to a question for various other reasons, such as not wanting to answer a particular question, not understanding the question, or simply forgetting to answer. Missing data is important to consider and account for, as it can introduce bias and reduce the representativeness of the data. This chapter provides an overview of the types of missing data, how to assess missing data in surveys, and how to conduct analysis when missing data is present. Understanding this complex topic can help ensure accurate reporting of survey results and can provide insight into potential changes to the survey design for the future. + +## Missing data mechanisms + +There are two main categories that missing data typically fall into: missing by design or unintentional missing data. Missing by design is part of the survey plan and can be more easily incorporated into weights and analyses. Unintentional missing data on the other hand, can lead to bias in survey estimates if not correctly accounted for. Below we provide more information on the types of missing data. + +1. **Missing by design/questionnaire skip logic**: This type of missingness occurs when certain respondents are intentionally directed to skip specific questions based on their previous responses or characteristics. For example, in a survey about employment, if a respondent indicates that they are not employed, they may be directed to skip questions related to their job responsibilities. Additionally, some surveys randomize questions or modules so that not all participants respond to all questions. In these instances, respondents would have missing data for the modules not randomly assigned to them. + +2. **Unintentional missing data**: This type of missingness occurs when researchers do not intend for there to be missing data on a particular question, for example, if respondents did not finish the survey or refused to answer individual questions. There are three main types of unintentional missing data that each should be considered and handled differently [@mack; @Schafer2002]: + + a. **Missing completely at random (MCAR)**: The missing data is unrelated to both observed and unobserved data, and the probability of being missing is the same across all cases. For example, if a respondent missed a question because they had to leave the survey early due to an emergency. + + b. **Missing at random (MAR)**: The missing data is related to observed data but not unobserved data, and the probability of being missing is the same within groups. For example, if older respondents choose not to answer specific questions but younger respondents do answer them and we know the respondent's age. + + c. **Missing not at random (MNAR)**: The missing data is related to unobserved data, and the probability of being missing varies for reasons we are not measuring. For example, if respondents with depression do not answer a question about depression severity. + + +## Assessing missing data + +Before beginning analysis, we should explore the data to determine if there is missing data and what types of missing data are present. Conducting this descriptive analysis can help with analysis and reporting of survey data (see Section \@ref(c12-pitfalls)), and can inform the survey design in future studies. For example, large amounts of unexpected missing data may indicate the questions were unclear or difficult to recall. There are several ways to explore missing data which we walk through below. When assessing the missing data, we recommend using a data.frame object and not the survey object as most of the analysis is about patterns of records and weights are not necessary. + +### Summarize data + +A very rudimentary first exploration is to use the `summary()` function to summarize the data which will illuminate `NA` values in the data. Let's look at a few analytic variables on the ANES 2020 data using `summary()`: + +```{r} +#| label: missing-anes-summary + +anes_2020 %>% + select(V202051:EarlyVote2020) %>% + summary() +``` + +We see that there are NA values in several of the derived variables (those not beginning with "V") and negative values in the original variables (those beginning with "V"). We can also use the `count()` function to get an understanding of the different types of missing data on the original variables. For example, let's look at the count of data for `V202072`, which corresponds to our `VotedPres2020` variable. + +```{r} +#| label: missing-anes-count + +anes_2020 %>% + count(VotedPres2020,V202072) +``` + +Here we can see that there are three types of missing data, and that the majority of them fall under the "Inapplicable" category. This is usually a term associated with data missing due to skip patterns and is considered to be missing data by design. Based on the documentation from ANES [@debell], we can see that this question was only asked to respondents who voted in the election. + +### Visualization of missing data + +It can be challenging to look at tables for every variable, and instead may be more efficient to view missing data in a graphical format to help narrow in on patterns or unique variables. The {naniar} package is very useful in exploring missing data visually. It provides quick graphics to explore the missingness patterns in the data. We can use the `vis_miss()` function available in both {visdat} and {naniar} packages to view the amount of missing data by variable. + +```{r} +#| label: missing-anes-vismiss +#| warning: FALSE +#| message: FALSE +#| fig.cap: "Visual depiction of missing data in the ANES 2020 data" +#| fig.alt: "This chart shows a the missingness of the selected variables where missing is highlighted in a dark color. Each row of the plot is an observation and each column is a variable. There are some patterns observed such as a large block of missing for `VotedPres2016_selection` and many of the same respondents also having missing for `VotedPres2020_selection`." + +anes_2020_derived<-anes_2020 %>% + select(!starts_with("V2"),-CaseID,-InterviewMode,-Weight,-Stratum,-VarUnit) + +anes_2020_derived %>% + vis_miss(cluster= TRUE, show_perc = FALSE) + + scale_fill_manual(values = book_colors[c(3,1)], + labels = c("Present","Missing"), + name = "") + +``` + +From this visualization, we can start to get a picture of what questions may be related to each other in terms of missing data. Even if we did not have the informative variable names, we could be able to deduce that `VotedPres2020`, `VotedPres2020_selection`, and `EarlyVote2020` are likely related since their missing data patterns are similar. + +Additionally, we can also look at `VotedPres2016_selection` and see that there is a lot of missing data in that variable. Most likely this is due to a skip pattern, and we can look at further graphics to see how it might be related to other variables. The {naniar} package has multiple visualization functions that can help dive deeper such as the `gg_miss_fct()` function which looks at missing data for all variables by levels of another variable. + +```{r} +#| lable: missing-anes-ggmissfct +#| warning: FALSE +#| fig.cap: "Missingness in variables for each level of `VotedPres2016` in the ANES 2020 data" +#| fig.alt: "This chart has x-axis 'Voted for President in 2016' with labels Yes, No and NA and has y-axis 'Variable' with labels Age, AgeGroup, CampaignInterest, EarlyVote2020, Education, Gender, Income, Income7, PartyID, RaceEth, TrustGovernment, TrustPeople, VotedPres2016_selection, VotedPres2020 and VotedPres2020_selection. There is a legend indicating fill is used to show pct_miss, ranging from 0 represented by fill very pale blue to 100 shown as fill dark blue. Among those that voted for president in 2016, they had little missing for other variables (light color) but those that did not vote have more missing data in their 2020 voting patterns and their 2016 president selection." + +anes_2020_derived %>% + gg_miss_fct(VotedPres2016) + + scale_fill_gradientn( + guide = "colorbar", + name = "% Miss", + colors = book_colors[c(3, 2, 1)] + ) + + ylab("Variable") + + xlab("Voted for President in 2016") +``` + +In this case, we can see that if they did not vote for president in 2016 or did not answer that question, then they were not asked about who they voted for in 2016 (the percentage of missing data if 100%). Additionally, we can see with this graphic, that there is more missing data across all questions if they did not provide an answer to `VotedPres2016`. + +There are other graphics that work well with numeric data. For example, in the RECS 2020 data we can plot two continuous variables and the missing data associated with it to see if there are any patterns to the missingness. To do this, we can use the `bind_shadow()` function from the {naniar} package. This creates a **nabular** (combination of "na" with "tabular"), which features the original columns followed by the same number of columns with a specific `NA` format. These `NA` columns are indicators of if the value in the original data is missing or not. The example printed below shows how most levels of `HeatingBehavior` are not missing `!NA` in the NA variable of `HeatingBehavior_NA`, but those missing in `HeatingBehavior` are also missing in `HeatingBehavior_NA`. + +```{r} +#| label: missing-recs-shadow + +recs_2020_shadow <- recs_2020 %>% + bind_shadow() + +ncol(recs_2020) +ncol(recs_2020_shadow) + +recs_2020_shadow %>% + count(HeatingBehavior,HeatingBehavior_NA) +``` + +We can then use these new variables to plot the missing data along side the actual data. For example, let's plot a histogram of the total electric bill grouped by those that are missing and not missing by heating behavior. + +```{r} +#| label: missing-recs-hist +#| fig.cap: "Histogram of Energy Cost by Heating Behavior Missing Data" +#| fig.alt: "This chart has title 'Histogram of Energy Cost by Heating Behavior Missing Data'. It has x-axis 'Total Energy Cost (Truncated at $5000)' with labels 0, 1000, 2000, 3000, 4000 and 5000. It has y-axis 'Number of Households' with labels 0, 500, 1000 and 1500. There is a legend indicating fill is used to show HeatingBehavior_NA, with 2 levels: !NA shown as very pale blue fill and NA shown as dark blue fill. The chart is a bar chart with 30 vertical bars. These are stacked, as sorted by HeatingBehavior_NA." + +recs_2020_shadow %>% + filter(TOTALDOL < 5000) %>% + ggplot(aes(x=TOTALDOL,fill=HeatingBehavior_NA)) + + geom_histogram() + + scale_fill_manual(values = book_colors[c(3, 1)], + labels = c("Present", "Missing"), + name = "Heating Behavior") + + theme_minimal() + + xlab("Total Energy Cost (Truncated at $5000)") + + ylab("Number of Households") + + labs(title = "Histogram of Energy Cost by Heating Behavior Missing Data") +``` + +This plot indicates that respondents who did not provide a response for the heating behavior question may have a different distribution of total energy cost compared to respondents who did provide a response. This view of the raw data and missingness could indicate some bias in the data. Researchers take these different bias aspects into account when calculating weights and we need to make sure that the weights are incorporated when analyzing the data. + +There are many other visualizations that can be helpful in reviewing the data, and we recommend reviewing the {naniar} documentation for more information [@naniar]. + + +## Analysis with missing data + +Once we understand the types of missingness, we can begin the analysis of the data. Different missingness types may be handled in different ways. In most publicly available datasets, researchers will have already calculated weights and imputed missing values if deemed necessary. Those interested in learning more about how to calculate weights and impute data for different missing data mechanisms, we recommended @Kim2021 and @Valliant2018weights. + +Even with weights and imputation, missing data will still most likely exist in the data and need to be accounted for in analysis. This section provides an overview on how to recode missing data in R, and how to account for skip patterns in analysis. + +### Recoding missing data + +Even within a variable, there can be different reasons for missing data. In publicly released data negative values are often present to provide different meaning for values. For example, in the ANES 2020 data they have the following negative values to represent different types of missing data: + * -9: Refused + * -8: Don't Know + * -7: No post-election data, deleted due to incomplete interview + * -6: No post-election interview + * -5: Interview breakoff (sufficient partial IW) + * -4: Technical error + * -3: Restricted + * -2: Other missing reason (question specific) + * -1: Inapplicable + +When we created the derived variables for use in this book, we coded all negative values as `NA` and proceeded to analyze the data. For most cases this is an appropriate approach as long as you filter the data appropriately to account for skip patterns (see next section). However, the {nanair} package does have the option to code special missing values. For example, if we wanted to have two `NA` values, one that indicated the question was missing by design (e.g., due to skip patterns) and one for the other missing categories we can use the `nabular` format to incorporate these with the `recode_shadow()` function. + + +```{r} +#| label: missing-anes-shadow-recode + +anes_2020_shadow<-anes_2020 %>% + select(starts_with("V2")) %>% + mutate(across(everything(),~case_when(.x < -1 ~ NA, + TRUE~.x))) %>% + bind_shadow() %>% + recode_shadow(V201103 = .where(V201103==-1~"skip")) + +anes_2020_shadow %>% + count(V201103,V201103_NA) +``` + +However it is important to note that at the time of publication, there is no easy way to implement `recode_shadow()` to multiple variables at once (e.g., we cannot use the tidyverse feature of `across()`). The example code above only implements this for a single variable, so this would have to be done to all variables of interest manually or in a loop. + + +### Accounting for skip patterns + +When questions are skipped by design in a survey, it is meaningful that the data is later missing. For example the RECS survey asks people how they control the heat in their home in the winter (`HeatingBehavior`). This is only among those who have heat in their home (`SpaceHeatingUsed`). If no there is no heating equipment used, the value of `HeatingBehavior` is missing. One has several choices when analyzing this data which include 1) only including those with a valid value of `HeatingBehavior` and specifying the universe as those with heat or 2) including those who do not have heat. It is important to specify what population an analysis generalizes to. + +Here is example code where we only include those with a valid value of `HeatingBehavior` (choice 1). Note that we use the design object (`recs_des`) then filter to those that are not missing on `HeatingBehavior`. + +```{r} +#| label: missing-recs-heatcc + +heat_cntl_1 <- recs_des %>% + filter(!is.na(HeatingBehavior)) %>% + group_by(HeatingBehavior) %>% + summarize( + p=survey_prop() + ) + +heat_cntl_1 +``` + +Here is example code where we include those that do not have heat (choice 2). To help understand what we are looking at we have included the output to show both variables `SpaceHeatingUsed` and `HeatingBehavior`. + +```{r} +#| label: missing-recs-heatpop + +heat_cntl_2 <- recs_des %>% + group_by(interact(SpaceHeatingUsed, HeatingBehavior)) %>% + summarize( + p=survey_prop() + ) + +heat_cntl_2 +``` + +```{r} +#| label: missing-recs-heattext +#| echo: FALSE + +pct_1 <- heat_cntl_1 %>% + filter(str_detect(HeatingBehavior, "Program")) %>% + mutate(p=round(p*100, 1)) %>% + pull(p) + +pct_2 <- heat_cntl_2 %>% + filter(str_detect(HeatingBehavior, "Program")) %>% + mutate(p=round(p*100, 1)) %>% + pull(p) + +``` + +If we ran the first analysis, we would say that `r pct_1`% **of households with heat** use a programmable or smart thermostat for the heating of their home. While if we used the results from the second analysis, we could say that `r pct_2`% of households use a programmable or smart thermostat for the heating of their home. The distinction of the two statements is bolded for emphasis. Skip patterns often change the universe that we are talking about and need to be carefully examined. + +Filtering to the correct universe is important when handling these types of missing data. The `nabular` we created above can also help with this. If we have `NA_skip` values in the shadow, we can make sure that we filter out all of these values and only include relevant missing. To do this with survey data we could first create the `nabular`, then create the design object on that data, and then use the shadow variables to assist with filtering the data. Let's use the `nabular` we created above for ANES 2020 (`anes_2020_shadow`) to create the design object. + +```{r} +#| label: missing-anes-shadow-des +#| warning: FALSE + +anes_adjwgt_shadow <- anes_2020_shadow %>% + mutate(V200010b = V200010b/sum(V200010b)*targetpop) + +anes_des_shadow <- anes_adjwgt_shadow %>% + as_survey_design( + weights = V200010b, + strata = V200010d, + ids = V200010c, + nest = TRUE + ) +``` + +Then we can use this design object to look at the percent of the population that voted for each candidate in 2016 (`V201103`). First, let's look at the percentages without removing any cases: + +```{r} +#| label: missing-anes-shadow-ex1 + +pres16_select1<-anes_des_shadow %>% + group_by(V201103) %>% + summarize( + All_Missing=survey_prop() + ) + +pres16_select1 +``` + +Next, we will look at the percentages removing only those that were missing due to skip patterns (i.e., they did not receive this question). + +```{r} +#| label: missing-anes-shadow-ex2 + +pres16_select2<-anes_des_shadow %>% + filter(V201103_NA!="NA_skip") %>% + group_by(V201103) %>% + summarize( + No_Skip_Missing=survey_prop() + ) + +pres16_select2 +``` + +Finally, we will look at the percentages removing all missing values both due to skip patterns and due to those who refused to answer the question. + +```{r} +#| label: missing-anes-shadow-ex3 + +pres16_select3<-anes_des_shadow %>% + filter(V201103_NA=="!NA") %>% + group_by(V201103) %>% + summarize( + No_Missing=survey_prop() + ) + +pres16_select3 +``` + + +```{r} +#| label: missing-anes-shadow-gt +#| echo: FALSE + +pres16_select_gt<-pres16_select1 %>% + full_join(pres16_select2,by="V201103") %>% + full_join(pres16_select3,by="V201103") %>% + mutate(Candidate=case_when(V201103==-1~"Did not Vote for President in 2016", + V201103==1~"Hillary Clinton", + V201103==2~"Donald Trump", + V201103==5~"Other Candidate", + TRUE~"Missing")) %>% + select(Candidate,everything()) %>% + select(-V201103) %>% + gt() %>% + cols_label(All_Missing = "%", + All_Missing_se = "s.e. (%)", + No_Skip_Missing = "%", + No_Skip_Missing_se = "s.e. (%)", + No_Missing = "%", + No_Missing_se = "s.e. (%)") %>% + tab_spanner(label = "Including All Missing Data", + columns = c(All_Missing, All_Missing_se)) %>% + tab_spanner(label = "Removing Skip Patterns Only", + columns = c(No_Skip_Missing, No_Skip_Missing_se)) %>% + tab_spanner(label = "Removing All Missing Data", + columns = c(No_Missing, No_Missing_se)) %>% + fmt_percent(decimals = 1) +``` + +(ref:missing-anes-shadow-tab) Percentage of Votes by Candidate for Different Missing Data Inclusions + +```{r} +#| label: missing-anes-shadow-tab +#| echo: FALSE +#| warning: FALSE + +pres16_select_gt %>% + print_gt_book(knitr::opts_current$get()[["label"]]) +``` + +```{r} +#| label: missing-anes-shadow-tab-text +#| echo: FALSE +#| warning: FALSE + +pres16_select1_1<-pres16_select1 %>% + filter(V201103==1) %>% + pull(All_Missing) + +pres16_select1_2<-pres16_select1 %>% + filter(V201103==2) %>% + pull(All_Missing) + +pres16_select1_out<-round(pres16_select1_1*100-pres16_select1_2*100,1) + + +pres16_select2_1<-pres16_select2 %>% + filter(V201103==1) %>% + pull(No_Skip_Missing) + +pres16_select2_2<-pres16_select2 %>% + filter(V201103==2) %>% + pull(No_Skip_Missing) + +pres16_select2_out<-round(pres16_select2_1*100-pres16_select2_2*100,1) + +``` + + +As Table \@ref(tab:missing-anes-shadow-tab) shows, the results can vary greatly depending on which type of missing data that are removed. If we remove only the skip patterns the margin between the Clinton and Trump is `r pres16_select2_out` percentage points, but if we include all data even including those that did not vote in 2016, the margin is `r pres16_select1_out` percentage points. How we handle the different types of missing values is important for interpretation of the data. diff --git a/_output.yml b/_output.yml index af58868e..23336aeb 100644 --- a/_output.yml +++ b/_output.yml @@ -9,11 +9,18 @@ bookdown::gitbook:
  • Exploring Complex Survey Data Analysis in R
  • after: |
  • Published with bookdown
  • - download: null - edit: https://github.com/tidy-survey-r/tidy-survey-book/%s + download: no + view: https://github.com/tidy-survey-r/tidy-survey-book/blob/main/%s sharing: - github: true facebook: false + github: true + twitter: true + linkedin: true + weibo: false + instapaper: false + vk: false + whatsapp: false + all: ['facebook', 'twitter', 'linkedin', 'github', 'weibo', 'instapaper'] bookdown::pdf_book: includes: in_header: latex/preamble.tex @@ -25,7 +32,7 @@ bookdown::pdf_book: citation_package: natbib template: null pandoc_args: --top-level-division=chapter - toc_depth: 3 + toc_depth: 2 toc_unnumbered: false toc_appendix: true quote_footer: ["\\VA{", "}{}"] diff --git a/book.bib b/book.bib index a84cbbdd..784794f1 100644 --- a/book.bib +++ b/book.bib @@ -445,3 +445,14 @@ @book{git-w-R year = {2023}, howpublished = {\url{https://happygitwithr.com/}} } + +@Article{naniar, + title = {Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations}, + author = {Nicholas Tierney and Dianne Cook}, + journal = {Journal of Statistical Software}, + year = {2023}, + volume = {105}, + number = {7}, + pages = {1--31}, + doi = {10.18637/jss.v105.i07}, +} \ No newline at end of file diff --git a/images/codebook-ncvs-weapon-handgun.jpg b/images/codebook-ncvs-weapon-handgun.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c5ab7b8168f13679a9aeab85e6d54b4ea6321cea GIT binary patch literal 45656 zcmeFYXIN9+7A_h=r3*+0AxIOYDN>{>N)r+3B|tu<6GNm22t+}8Q}_e{1(jYxq=Zf) zy@Pb=BsA#>H9~-seeZqFInUnb-m~w0?w@m>y|PyFBWtZW#++k}Ip#awdH(Bs0l@ZH z*FYC=;lc&LJL(5;J`H#XprxVt$4NadQqN2Dmo8nrc!`mY?lL_yBQrA-BNG$Lm8)zl zSJ8ad9!T^6+wV@?PcS;{3-;F3?h+xp;};(j^8?7A6+X|JTR)4*)y; zg&#EXv=>AGH0&2>*)N>80e}F&g-cXv{~_@IbzGpK%6OTMo`I2xdIN?HKy!hXmgXYu zKcuGK9YXydaFPAe)$4a2T;?!&PAB5UDgWmES9($H@@6j6A)?sb7v5nEjNClDeEi}P zHzaS~QczSDuo=H3|^ zS09+rJ@?`ek$*$asr~+Ic{79PT~i|03-2LDZZQSCIO!j%{U4hBdy0kqf70x~D)!&> 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"Package": "norm", + "Version": "1.0-11.1", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "stats" + ], + "Hash": "82d9d75b78bc83553c034cd5c7ed2b3c" + }, "numDeriv": { "Package": "numDeriv", "Version": "2016.8-1.1", @@ -1398,6 +1466,17 @@ ], "Hash": "01f28d4278f15c76cddbea05899c5d6f" }, + "plyr": { + "Package": "plyr", + "Version": "1.8.8", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "Rcpp" + ], + "Hash": "d744387aef9047b0b48be2933d78e862" + }, "png": { "Package": "png", "Version": "0.1-8", @@ -2194,6 +2273,19 @@ ], "Hash": "c03fa420630029418f7e6da3667aac4a" }, + "viridis": { + "Package": "viridis", + "Version": "0.6.3", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "ggplot2", + "gridExtra", + "viridisLite" + ], + "Hash": "0d774f552202add033efc43a30293b3f" + }, "viridisLite": { "Package": "viridisLite", "Version": "0.4.2", @@ -2204,6 +2296,28 @@ ], "Hash": "c826c7c4241b6fc89ff55aaea3fa7491" }, 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