CATAcode Overview

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Introduction to CATAcode

Check-all-that-apply (CATA) items present numerous methodological challenges that can hinder the validity of survey research. In particular, accurately measuring, reporting, interpreting, and evaluating participants’ identities is essential.

CATAcode is an R package designed to assist researchers in exploring CATA responses for summary descriptives and and preparing CATA items for statistical modeling. Applying this tool to cross-sectional and longitudinal data can help enhance the generalizability, transparency, and reproducibility of your research.

In surveys, a CATA item can also be structured as a series of forced choice dichotomous items (e.g., Yes/No). For instance, for an evaluation of their program graduate students were asked, “Have you experienced any of these barriers to conducting research?”, with 5 options of: lack of funding, lack of mentorship, lack of research infrastructure (e.g., software), lack of time capacity, and other barriers. The survey could either instruct responses to CATA or to explicitly select Yes or No to each option. The CATAcode package is suitable for analyzing data from both CATA and forced choice formats.

This vignette demonstrates how to use the CATAcode package to:

The CATAcode Workflow

  1. Import & wrangle raw wider format data into longer format using the cata_prep() function.
  2. Explore all response combinations or category counts to understand complexity.
  3. Code new variables with principled strategies (multiple, priority, mode).
  4. Document & export metadata, tables, and optional visualizations.

1. Import and Wrangle Data

You can install the released version of CATAcode from CRAN with:

install.packages("CATAcode")

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Or the development version from GitHub:

devtools::install_github("knickodem/CATAcode")

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Once installed, load the package:

library(CATAcode)

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Data Preparation

Before using the main cata_code() function, the data must be prepared. The cata_prep() function helps reshape your data from wider to longer format.

Requirements

Your dataset should include:

  • An ID variable
  • A set of variables (i.e., columns) indicating the check-all-that-apply categories to examine. All variables are expected to be dichotomous (e.g., 1/0, Yes/No, TRUE/FALSE) where the value signifying endorsement is consistent across all of the variables.
  • For longitudinal data, a time variable (e.g., Wave)

Example Data

The CATAcode package includes a longitudinal dataset comprised of CATA responses to 7 race/ethnicity identities from 6,442 students at four time points. For each identity category/column, 1 = Selected and NA = Not selected. To load and view the first few rows of the dataset:

data("sources_race")
head(sources_race)
#> # A tibble: 6 × 9
#>   ID     Wave Black Native_American Asian Hispanic Multiracial Pacific_Islander
#>   <chr> <dbl> <dbl>           <dbl> <dbl>    <dbl>       <dbl>            <dbl>
#> 1 10002     1    NA              NA    NA        1          NA               NA
#> 2 10005     1    NA              NA    NA        1          NA               NA
#> 3 10006     1    NA              NA    NA        1          NA               NA
#> 4 10007     1    NA              NA    NA        1          NA               NA
#> 5 10009     1    NA              NA    NA        1          NA               NA
#> 6 10010     1    NA              NA    NA       NA          NA               NA
#> # ℹ 1 more variable: White <dbl>

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Let’s also create some example cross-sectional (i.e., single timepoint) data based on our earlier question to graduate students: “Have you experienced any of these barriers to conducting research?” For each category, students provide a “Yes” or “No” response.


# Creating a cross-sectional dataset (N = 1000)
set.seed(123)  

n_cross = 1000

cross = data.frame(
  ID               = 1:n_cross,
  Funding          = sample(c("No", "Yes"), n_cross, replace = TRUE, prob = c(.15, .85)),
  Mentorship       = sample(c("No", "Yes"), n_cross, replace = TRUE, prob = c(.10, .90)),
  Infrastructure   = sample(c("No", "Yes"), n_cross, replace = TRUE, prob = c(.45, .55)),
  Time_Capacity    = sample(c("No", "Yes"), n_cross, replace = TRUE, prob = c(.25, .75)),
  Other_Barrier    = sample(c("No", "Yes"), n_cross, replace = TRUE, prob = c(.80, .20))
  )

# Display the first few rows of the dataset
head(cross)
#>   ID Funding Mentorship Infrastructure Time_Capacity Other_Barrier
#> 1  1     Yes        Yes            Yes           Yes            No
#> 2  2     Yes        Yes            Yes            No           Yes
#> 3  3     Yes        Yes            Yes           Yes            No
#> 4  4      No        Yes            Yes           Yes           Yes
#> 5  5      No        Yes            Yes           Yes            No
#> 6  6     Yes        Yes             No           Yes            No

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Using cata_prep

cata_prep() is the gateway function for every workflow in cata_code().

Its jobs are to:

  1. Reshape data from wide to tidy‑long format so the downstream cata_code() function can iterate over one row per person‑category (or person‑time‑category).
  2. Standardize column names (id, Category, Response, time) and stores them as attributes, eliminating repetitive arguments. You tell cata_prep() which columns hold the IDs, which columns hold the categories, and how you want to name the two columns in the long format data that contain the categories and endorsed/not endorsed responses.

cata_prep() function arguments:

  • data = cross
    • Provide the name of the dataset. In our case cross for the cross-sectional dataset.
  • id = ID
    • Supply the column that uniquely identifies each respondent. Must be unique within each time‑point if you also pass time =.
    • If your ID column is named something else, e.g. “participant_id”, write id = participant_id.
  • cols = Funding:Other_Barrier
    • Tell cata_prep() which columns are the dichotomous CATA indicators. In the cross-sectional data, these are the barriers; in the longitudinal data, these are the race/ethnicity identities.
    • You can:
      • Use the tidy‑select range syntax we show here, which grabs every column from Funding through Other_Barrier, inclusive, in the order they appear in the data frame.
      • List them explicitly: cols = c(Black, Native_American, Asian, White, Pacific_Islander, Hispanic, Multiracial)
      • Or use a tidy‑select helper when applicable: cols = starts_with(“race_”)
  • names_to = “Barriers” and values_to = “YN”
    • When cata_prep() transforms the data into long format it needs to name the resulting two columns storing the cateogry labels and participants’ responses to each category, respectively. You have the option of providing the names using the names_to argument for the categories and values_to argument for the responses. By default, cata_prep() uses the names “Categories” and “Responses”, respectively.
  • time = Wave
    • For longitudinal data, provide the column indicating the time so cata_prep() keeps observations from different time points separate.

After this call, the new data will be a tidy long dataframe with three or four standardized columns: id, Category, Response, and time (if supplied).

# Prepare cross-sectional 
datacross_prep <- cata_prep(data = cross, id = ID, cols = Funding:Other_Barrier, names_to = "Barriers", values_to = "YN")

# Prepare longitudinal 
datalong_prep <- cata_prep(data = sources_race, id = ID, cols = c(Asian, Black:White), time = Wave)

# Display the first few rows of the prepared data
head(datacross_prep)
#> # A tibble: 6 × 3
#>      ID Barriers       YN   
#>   <int> <chr>          <chr>
#> 1     1 Funding        Yes  
#> 2     1 Mentorship     Yes  
#> 3     1 Infrastructure Yes  
#> 4     1 Time_Capacity  Yes  
#> 5     1 Other_Barrier  No   
#> 6     2 Funding        Yes
head(datalong_prep)
#> # A tibble: 6 × 4
#>   ID     Wave Category         Response
#>   <chr> <dbl> <chr>               <dbl>
#> 1 10002     1 Asian                  NA
#> 2 10002     1 Black                  NA
#> 3 10002     1 Native_American        NA
#> 4 10002     1 Hispanic                1
#> 5 10002     1 Multiracial            NA
#> 6 10002     1 Pacific_Islander       NA

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2. Explore All Response Combinations

The first step when analyzing CATA data is exploring all combinations of categories present in the data. The cata_code() function with approach = "all" helps identify every unique category combination. For longitudinal data, approach = "counts" provides a summary of how many times each participant endorsed each category across time.

cata_code() function arguments:

Explore all combinations in cross-sectional data

# Explore all combinations in cross-sectional data
cross_all <- cata_code(data = datacross_prep,
                      id = ID,
                      categ = Barriers,
                      resp = YN,
                      approach = "all",
                      endorse = "Yes",
                      new.name = "Combinations",
                      sep = "-")

# Display the result
head(cross_all)
#> # A tibble: 6 × 2
#>      ID Combinations                                         
#>   <int> <chr>                                                
#> 1     1 Funding-Mentorship-Infrastructure-Time_Capacity      
#> 2     2 Funding-Mentorship-Infrastructure-Other_Barrier      
#> 3     3 Funding-Mentorship-Infrastructure-Time_Capacity      
#> 4     4 Mentorship-Infrastructure-Time_Capacity-Other_Barrier
#> 5     5 Mentorship-Infrastructure-Time_Capacity              
#> 6     6 Funding-Mentorship-Time_Capacity
# 
# Count the frequency of each combination
table(cross_all$Combinations)
#> 
#>                                                       Funding 
#>                                                            12 
#>                                        Funding-Infrastructure 
#>                                                            11 
#>                          Funding-Infrastructure-Other_Barrier 
#>                                                             1 
#>                          Funding-Infrastructure-Time_Capacity 
#>                                                            18 
#>            Funding-Infrastructure-Time_Capacity-Other_Barrier 
#>                                                             5 
#>                                            Funding-Mentorship 
#>                                                            72 
#>                             Funding-Mentorship-Infrastructure 
#>                                                            97 
#>               Funding-Mentorship-Infrastructure-Other_Barrier 
#>                                                            27 
#>               Funding-Mentorship-Infrastructure-Time_Capacity 
#>                                                           259 
#> Funding-Mentorship-Infrastructure-Time_Capacity-Other_Barrier 
#>                                                            57 
#>                              Funding-Mentorship-Other_Barrier 
#>                                                             7 
#>                              Funding-Mentorship-Time_Capacity 
#>                                                           213 
#>                Funding-Mentorship-Time_Capacity-Other_Barrier 
#>                                                            51 
#>                                         Funding-Other_Barrier 
#>                                                             1 
#>                                         Funding-Time_Capacity 
#>                                                            20 
#>                           Funding-Time_Capacity-Other_Barrier 
#>                                                             2 
#>                                                Infrastructure 
#>                                                             3 
#>                                  Infrastructure-Other_Barrier 
#>                                                             1 
#>                                  Infrastructure-Time_Capacity 
#>                                                             5 
#>                                                    Mentorship 
#>                                                            12 
#>                                     Mentorship-Infrastructure 
#>                                                            13 
#>                       Mentorship-Infrastructure-Other_Barrier 
#>                                                             5 
#>                       Mentorship-Infrastructure-Time_Capacity 
#>                                                            34 
#>         Mentorship-Infrastructure-Time_Capacity-Other_Barrier 
#>                                                             6 
#>                                      Mentorship-Time_Capacity 
#>                                                            53 
#>                        Mentorship-Time_Capacity-Other_Barrier 
#>                                                             9 
#>                                                 Time_Capacity 
#>                                                             4 
#>                                   Time_Capacity-Other_Barrier 
#>                                                             1

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Explore endorsement counts over time in longitudinal data

# Explore all combinations in cross-sectional data
# Get counts across waves
long_counts <- cata_code(data = datalong_prep,
                         id = ID,
                         categ = Category,
                         resp = Response,
                         approach = "counts",
                         endorse = 1)

# Display the result
head(long_counts)
#> # A tibble: 6 × 3
#> # Groups:   ID [5]
#>   ID     Category        n_time
#>   <chr>  <chr>            <int>
#> 1 100003 White                4
#> 2 100006 White                1
#> 3 100009 Asian                1
#> 4 100010 White                2
#> 5 100011 Native_American      2
#> 6 100011 White                1

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3. Coding A New Variable for Statistical Analysis

CATAcode offers several approaches with the cata_code() function to prepare CATA data for statistical modeling:

The “multiple” Approach

The “multiple” approach automatically combines individuals who have reported two or more categories into the same group.

A new argument to name the new category:

# Apply the "multiple" approach
cross_multiple <- cata_code(data = datacross_prep,
                            id = ID,
                            categ = Barriers,
                            resp = YN,
                            approach = "multiple",
                            endorse = "Yes",
                            new.name = "Barrier",
                            multi.name = "Multiple")

# Display the results
table(cross_multiple$Barrier)
#> 
#>        Funding Infrastructure     Mentorship       Multiple  Time_Capacity 
#>             12              3             12            968              4

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The “priority” Approach

In our example the vast majority of students selected two or more categories and were combined into the Multiple category. Although this informs the graduate program that most students experience multiple barriers to conducting research, it provides little information for actionable changes. In contrast, the “priority” approach allows us to prioritize specific categories of interest. For instance, the graduate program might have a particular interest improving mentorship of graduate researchers and investing in research infrastructure.

A new argument to list the priority categories:

# Apply the "priority" approach
cross_priority <- cata_code(data = datacross_prep,
                            id = ID,
                            categ = Barriers,
                            resp = YN,
                            approach = "priority",
                            endorse = "Yes",
                            new.name = "Barrier",
                            multi.name = "Multiple",
                            priority = c("Mentorship", "Infrastructure"))

# Display the results
table(cross_priority$Barrier)
#> 
#>        Funding Infrastructure     Mentorship       Multiple  Time_Capacity 
#>             12             44            915             24              4

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The “mode” Approach for Longitudinal Data

The “mode” approach is designed for longitudinal data, placing individuals into the category they endorsed most often across time points.

A new argument to list the name of the wave:

# Apply the "mode" approach
long_mode <- cata_code(data = datalong_prep,
                       id = ID,
                       categ = Category,
                       resp = Response,
                       approach = "mode",
                       endorse = 1,
                       time = Wave,
                       new.name = "Race_Ethnicity",
                       multi.name = "Multiracial")

# Display the results
table(long_mode$Race_Ethnicity)
#> 
#>            Asian            Black         Hispanic      Multiracial 
#>              152              110             2882              777 
#>  Native_American Pacific_Islander            White 
#>               65               22             2434

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Combining the “mode” and “priority” Approaches for Longitudinal Data

# Combining "mode" with "priority"
long_mode_priority <- cata_code(data = datalong_prep,
                                id = ID,
                                categ = Category,
                                resp = Response,
                                approach = "mode",
                                endorse = 1,
                                time = Wave,
                                new.name = "Race_Ethnicity",
                                multi.name = "Multiracial",
                                priority = c("Black", "Native_American"))

# Display the results
table(long_mode_priority$Race_Ethnicity)
#> 
#>            Asian            Black         Hispanic      Multiracial 
#>              146              459             2732              423 
#>  Native_American Pacific_Islander            White 
#>              347               22             2313

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4. Document & Export metadata, tables, and optional visualizations.

In addition to comparing the frequency tables with the table() function, visualizing the distribution of categories can help researchers make informed decisions about coding strategies. The next version of CATAcode will include expanded functionality for creating publication ready tables and figures.

Let’s compare how the response frequencies change with the multiple approach and prioritizing mentorship and infrastructure in the cross-sectional data.

library(ggplot2)

# Get counts from the coded data frames created earlier
counts_multiple = cross_multiple |>
  count(Barrier, name = "Count") |>
  mutate(Approach = "Multiple")

counts_priority = cross_priority |>
  count(Barrier, name = "Count") |>
  mutate(Approach = "Priority")

# Display in a figure
cross_plot = bind_rows(counts_multiple, counts_priority) |>
  ggplot(aes(x = reorder(Barrier, -Count), y = Count,
             fill = Approach)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = c(Multiple = "#1F78B4",
                               Priority  = "#FB9A99")) +
  labs(x = "Barrier", y = "Count",
       title = "Comparing Coding Approaches") +
  theme_minimal(base_size = 11) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "top")
cross_plot

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We can also compare the mode approach and the mode with priority approach in the longitudinal data.

library(ggplot2)

# Get counts from the coded data frames created earlier
counts_mode = long_mode|>
  count(Race_Ethnicity, name = "Count") |>
  mutate(Approach = "Mode")

counts_mwp = long_mode_priority |>
  count(Race_Ethnicity, name = "Count") |>
  mutate(Approach = "Mode with Priority")

# Display in a figure
long_plot = bind_rows(counts_mode, counts_mwp) |>
  ggplot(aes(x = reorder(Race_Ethnicity, -Count), y = Count,
             fill = Approach)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = c(Mode = "#1F78B4",
                               `Mode with Priority`  = "#FB9A99")) +
  labs(x = "Race/Ethnicity", y = "Count",
       title = "Comparing Coding Approaches") +
  theme_minimal(base_size = 11) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "top")
long_plot

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Recommendations for Using CATAcode

  1. Start by exploring all combinations using the "all" and "counts" approaches.
  2. Retain as much identity nuance as possible where sample size allows.
  3. Document and justify all subjective decisions for merging or prioritizing categories.
  4. Include supplemental tables with all category combinations to describe the complete demographic picture.
  5. Choose coding approaches based on research questions and sample characteristics.

When to Use Each Approach

Approach Best for Limitations
multiple Quickly grouping multi‑identity cases Obscures data when many participants report multiple identities
priority Preserving often‑overlooked identities Can hide additional endorsed identities
mode Longitudinal data where identity fluctuates over time Can mask short‑term identity changes

Conclusion

CATAcode provides a structured approach to handling CATA survey items in a transparent and principled manner. By enhancing the precision and inclusivity of data, this package supports more robust health and social science research that better reflects the lived experiences and health needs of diverse communities. For additional information, see the package documentation by typing ?CATAcode::cata_prep or ?CATAcode::cata_code in your R console.