# Recoding a forced choice item with a text field

#### Benjamin Becker, Johanna Busse

#### 2024-10-09

Forced choice items are multiple choice items with exclusive answer options (only one option can be chosen). If a forced choice item is administered, sometimes not all possible answers can be covered by predefined response options. In such cases, often an additional response option (e.g. “other option”, “something else”, …) is given accompanied by an open text field. An example of such a multiple choice item is asking for the birthplace of a person:

However, in the resulting data set such an item will often be stored as two separate variables: a numeric variable with value labels (containing the existing response options) and a character variable (containing the answers in the text field). For data analysis, usually a single numerical and labeled variable is desirable. Often the following steps are required:

- recode the character variable manually (e.g. to eliminate spelling mistakes)
- transform open answers which refer to existing response options (e.g. if a test taker overlooked a response option)
- summarize both remaining variables into a single numerical, labeled variable

To illustrate the steps we have implemented a small `SPSS` example data set in this package. The data set can be loaded using the `import_spss()` function. For further information on importing `SPSS` data see `import_spss`: Importing data from ‘SPSS’. Note that the data set is a minimal working example, containing only the required variables for this illustration.

    library(eatGADS)
    data_path <- system.file("extdata", "forcedChoice.sav", package = "eatGADS")
    gads <- import_spss(data_path)

    # Show example data set
    gads
    #> $dat
    #>    ID mcvar stringvar
    #> 1   1     3
    #> 2   2     2     Italy
    #> 3   3     3   England
    #> 4   4     3       Ita
    #> 5   5     3       Eng
    #> 6   6    NA    German
    #> 7   7     1      Germ
    #> 8   8     2
    #> 9   9     3
    #> 10 10     3       Eng
    #>
    #> $labels
    #>     varName varLabel format display_width labeled value valLabel missings
    #> 1        ID     <NA>   F8.0            NA      no    NA     <NA>     <NA>
    #> 2     mcvar     <NA>   F8.2            NA     yes     1  Germany    valid
    #> 3     mcvar     <NA>   F8.2            NA     yes     2   Poland    valid
    #> 4     mcvar     <NA>   F8.2            NA     yes     3    other    valid
    #> 5     mcvar     <NA>   F8.2            NA     yes   -99  missing     miss
    #> 6 stringvar     <NA>     A7            NA      no    NA     <NA>     <NA>
    #>
    #> attr(,"class")
    #> [1] "GADSdat" "list"

The variable names of the data set above are connected to the forced choice question as indicated:

## Preparing the data set

As illustrated, data can be loaded into `R` in the `GADSdat` format via the functions `import_spss()`, `import_DF()` or `import_raw()`. Depending on the original format, omitted responses to open text fields might be stored as empty strings instead of `NAs`. In these cases, the `recode2NA()` function should be used to recode these values to `NA`. Per default, matching strings across all variables in the data set are recoded. Specific variables selection can be specified using the `recodeVars` argument. Note that the function only performs recodings to exact matches of a single, specific value (in our example `""`).

    gads <- recode2NA(gads, value = "")
    #> Recodes in variable ID: 0
    #> Recodes in variable mcvar: 0
    #> Recodes in variable stringvar: 3

## Creating and editing a lookup table

With `createLookup()`, you can create a lookup table which allows recoding one or multiple variables.
You can choose which string variables in a `GADSdat` object you would like to recode by using the `recodeVars` argument. The resulting lookup table is a long format `data.frame` with rows being variable x value pairings. In case you want to sort the output to make recoding easier, the argument `sort_by` can be used. Extra columns can be added to the lookup table by the argument `addCols` (but can also be added later manually e.g. in Excel). The respective column names are irrelevant and just for convenience purpose.

    lookup <- createLookup(GADSdat = gads, recodeVars = "stringvar", sort_by = 'value',
                           addCols = c("new", "new2"))

    lookup
    #>    variable   value new new2
    #> 1 stringvar    <NA>  NA   NA
    #> 2 stringvar     Eng  NA   NA
    #> 3 stringvar England  NA   NA
    #> 4 stringvar    Germ  NA   NA
    #> 5 stringvar  German  NA   NA
    #> 6 stringvar     Ita  NA   NA
    #> 7 stringvar   Italy  NA   NA

Now you have to add the desired values for recoding. You should use (a) the existing value labels of the corresponding numerical, labeled variable and (b) consistent new values that can serve as value labels later. Spelling mistakes within the recoding will result in different values in the output!
To fill in the columns you could use `R` directly to modify the columns. Alternatively, we recommend using `eatAnalysis::write_xlsx()` to create an Excel file in which you can fill in the values.

    # write lookup table to Excel
    eatAnalysis::write_xlsx(lookup, "lookup_forcedChoice.xlsx")

After filling out the Excel sheet the lookup table might look like this:

The Excel file can be read back into `R` via `readxl::read_xlsx()`. Detailed information on how missing values should be recoded is provided in the last section of this vignette.

If you have more than one person working on the variable or if you want to use templates, you may have 2 different possible recode values (in our example: `new` and `new2`) . You can fill in both in the lookup table and then choose which one you want to prioritize later.

    # read lookup table back to R
    lookup <- readxl::read_xlsx("lookup_forcedChoice.xlsx")
    lookup

    #>    variable   value     new    new2
    #> 1 stringvar    <NA> missing    miss
    #> 2 stringvar     Eng England England
    #> 3 stringvar England    <NA> England
    #> 4 stringvar    Germ Germany    <NA>
    #> 5 stringvar  German Germany Germany
    #> 6 stringvar     Ita    <NA>   Italy
    #> 7 stringvar   Italy   Italy   Italy

We use the `collapseColumns()` function to get the correct layout for the final lookup table. The function merges both columns containing the new values. By using the `prioritize` argument you can decide which column will be preferred. Only if there is an `NA` in the prioritized column, the other column will be used instead.

    lookup_formatted <- collapseColumns(lookup = lookup, recodeVars = c("new", "new2"),
                                        prioritize = "new")
    lookup_formatted
    #>    variable   value value_new
    #> 1 stringvar    <NA>   missing
    #> 2 stringvar     Eng   England
    #> 3 stringvar England   England
    #> 4 stringvar    Germ   Germany
    #> 5 stringvar  German   Germany
    #> 6 stringvar     Ita     Italy
    #> 7 stringvar   Italy     Italy

## Apply lookup to `GADSdat`

You perform the actual data recoding using the `applyLookup()` function. It applies the recodes defined in the lookup table. This means that if the lookup table was created for multiple variables, `applyLookup()` performs recoding for all of these variables simultaneously. If you define a `suffix`, the old variable(s) will not be overwritten.

    gads_string <- applyLookup(GADSdat = gads, lookup = lookup_formatted, suffix = "_r")
    #> No rows removed from meta data.
    #> Adding meta data for the following variables: stringvar_r

    gads_string$dat
    #>    ID mcvar stringvar stringvar_r
    #> 1   1     3      <NA>     missing
    #> 2   2     2     Italy       Italy
    #> 3   3     3   England     England
    #> 4   4     3       Ita       Italy
    #> 5   5     3       Eng     England
    #> 6   6    NA    German     Germany
    #> 7   7     1      Germ     Germany
    #> 8   8     2      <NA>     missing
    #> 9   9     3      <NA>     missing
    #> 10 10     3       Eng     England

## Integrate character and numeric variable

The next step is to integrate the string variable into the integer via the `collapseMC_Text()` function. With `mc_var` and `text_var` we specify the variables used for recoding. With the `mc_code4text` argument we specify the value label of `mc_var` which indicates that `text_var` contains valid information (in our example `"other"`). If the `mc_var` is missing, `text_var` is also utilized (e.g. row 6). If there is a valid value in `mc_var` other than the code for `mc_code4text`, information in `text_var` is ignored (e.g. row 2). New value labels are created for entries in `text_var` without corresponding value labels. The new value labels are ordered alphabetically and inserted after the already existing ones. Additional information on how missings are treated by the function can be found in the last section of the vignette.

Note that in contrast to `createLookup()`, `collapseColumns()` and `applyLookup()` this function only works on a single forced choice variable pair. Integrating multiple variable pairs has to be performed in separate steps.

    gads_final <- collapseMC_Text(GADSdat = gads_string, mc_var = "mcvar",
                                  text_var = "stringvar_r", mc_code4text = "other",
                                  var_suffix = "_r", label_suffix = "(recoded)")
    #> No rows removed from meta data.
    #> Adding meta data for the following variables: mcvar_r

    gads_final$dat
    #>    ID mcvar stringvar stringvar_r mcvar_r
    #> 1   1     3      <NA>     missing     -99
    #> 2   2     2     Italy       Italy       2
    #> 3   3     3   England     England       4
    #> 4   4     3       Ita       Italy       5
    #> 5   5     3       Eng     England       4
    #> 6   6    NA    German     Germany       1
    #> 7   7     1      Germ     Germany       1
    #> 8   8     2      <NA>     missing       2
    #> 9   9     3      <NA>     missing     -99
    #> 10 10     3       Eng     England       4
    extractMeta(gads_final, "mcvar_r")
    #>    varName  varLabel format display_width labeled value valLabel missings
    #> 8  mcvar_r (recoded)   F8.2            NA     yes     1  Germany    valid
    #> 9  mcvar_r (recoded)   F8.2            NA     yes     2   Poland    valid
    #> 10 mcvar_r (recoded)   F8.2            NA     yes     3    other    valid
    #> 11 mcvar_r (recoded)   F8.2            NA     yes   -99  missing     miss
    #> 12 mcvar_r (recoded)   F8.2            NA     yes     4  England    valid
    #> 13 mcvar_r (recoded)   F8.2            NA     yes     5    Italy    valid

`checkMissings()` is a function for automatically setting missing values in a `GADSdat` object. If new values should receive missing codes, `checkMissings()` would be necessary. However, in our example no new values representing missings have been added, therefore the function does not change the `GADSdat` object.

    gads_final <- checkMissings(GADSdat = gads_final, missingLabel = "missing",
                                addMissingCode = TRUE, addMissingLabel = FALSE)
    extractMeta(gads_final, "mcvar_r")
    #>    varName  varLabel format display_width labeled value valLabel missings
    #> 8  mcvar_r (recoded)   F8.2            NA     yes     1  Germany    valid
    #> 9  mcvar_r (recoded)   F8.2            NA     yes     2   Poland    valid
    #> 10 mcvar_r (recoded)   F8.2            NA     yes     3    other    valid
    #> 11 mcvar_r (recoded)   F8.2            NA     yes   -99  missing     miss
    #> 12 mcvar_r (recoded)   F8.2            NA     yes     4  England    valid
    #> 13 mcvar_r (recoded)   F8.2            NA     yes     5    Italy    valid

## Remove variables from `GADSdat`

In a last step you can remove intermediate or superfluous variables from the `GADSdat` object by using the function `removeVars()`.

    gads_final <- removeVars(GADSdat = gads_final, vars = c("mcvar", "stringvar_r"))
    #> Removing the following rows from meta data: mcvar, stringvar_r
    #> No rows added to meta data.
    gads_final$dat
    #>    ID stringvar mcvar_r
    #> 1   1      <NA>     -99
    #> 2   2     Italy       2
    #> 3   3   England       4
    #> 4   4       Ita       5
    #> 5   5       Eng       4
    #> 6   6    German       1
    #> 7   7      Germ       1
    #> 8   8      <NA>       2
    #> 9   9      <NA>     -99
    #> 10 10       Eng       4

## Missing value codes

In some scenarios, there might be conceptual differences between missing codes in the data (e.g. invalid responses, item not administered, omission). These conceptual differences might require different integration of the two variables (numerical & labeled, character) depending on the type of missing. In this section, we illustrate how `collapseMC_Text()` behaves depending on how missings are defined in the data.

To illustrate the described behavior, we have included an additional `SPSS` data set in the package with a forced choice variable pair including all possible value and missing combinations. The possible values in the string variable are `new valid`, indicating an arbitrary valid entry, `NA` indicating for example an omission and `special missing`, indicating for example an invalid entry.

    data_path_miss <- system.file("extdata", "forcedChoice_missings.sav", package = "eatGADS")
    gads_miss <- import_spss(data_path_miss)
    gads_miss <- recode2NA(gads_miss, value = "")
    #> Recodes in variable ID: 0
    #> Recodes in variable mc: 0
    #> Recodes in variable string: 4

    # Show example data set
    gads_miss
    #> $dat
    #>    ID  mc          string
    #> 1   1   2       new valid
    #> 2   2   1       new valid
    #> 3   3 -99       new valid
    #> 4   4 -98       new valid
    #> 5   5   2            <NA>
    #> 6   6   1            <NA>
    #> 7   7 -99            <NA>
    #> 8   8 -98            <NA>
    #> 9   9   2 special missing
    #> 10 10   1 special missing
    #> 11 11 -99 special missing
    #> 12 12 -98 special missing
    #>
    #> $labels
    #>   varName varLabel format display_width labeled value        valLabel missings
    #> 1      ID     <NA>   F8.0            NA      no    NA            <NA>     <NA>
    #> 2      mc     <NA>   F8.2            NA     yes     1           valid    valid
    #> 3      mc     <NA>   F8.2            NA     yes     2           other    valid
    #> 4      mc     <NA>   F8.2            NA     yes   -99 missing omitted     miss
    #> 5      mc     <NA>   F8.2            NA     yes   -98 special missing     miss
    #> 6  string     <NA>    A15            NA      no    NA            <NA>     <NA>
    #>
    #> attr(,"class")
    #> [1] "GADSdat" "list"

If both variables have valid but contradicting entries, `collapseMC_Text()` prefers information from the numerical, labeled variable (e.g. row 2). If both entries are missing, the behavior of `collapseMC_Text()` depends on the missing type in the character variable. If the missing is indicated via an explicit missing definition (`special missing` in the example), this missing code is preferred to missing codes from the numerical, labeled variable (e.g. row 11). If the missing is indicated via an actual `NA` in the character variable, the information from the numerical, labeled variable is preferred (e.g. row 7).

    # summarize numerical, labeled variable and character variable
    gads <- collapseMC_Text(gads_miss, "mc", "string", mc_code4text = "other", "_r", "recoded")
    #> No rows removed from meta data.
    #> Adding meta data for the following variables: mc_r
    gads$dat
    #>    ID  mc          string mc_r
    #> 1   1   2       new valid    3
    #> 2   2   1       new valid    1
    #> 3   3 -99       new valid    3
    #> 4   4 -98       new valid    3
    #> 5   5   2            <NA>    2
    #> 6   6   1            <NA>    1
    #> 7   7 -99            <NA>  -99
    #> 8   8 -98            <NA>  -98
    #> 9   9   2 special missing  -98
    #> 10 10   1 special missing    1
    #> 11 11 -99 special missing  -98
    #> 12 12 -98 special missing  -98