Recoding a forced choice item with a text field

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