eatGADS (version 1.1.1)

collapseMultiMC_Text: Recode multiple choice variable with multiple variables.

Description

Recode multiple variables (representing a single multiple choice item) based on multiple character variables (representing a text field).

Usage

collapseMultiMC_Text(
  GADSdat,
  mc_vars,
  text_vars,
  mc_var_4text,
  var_suffix = "_r",
  label_suffix = "(recoded)",
  invalid_miss_code = -98,
  invalid_miss_label = "Missing: Invalid response",
  notext_miss_code = -99,
  notext_miss_label = "Missing: By intention"
)

Value

Returns a GADSdat containing the newly computed variables.

Arguments

GADSdat

A GADSdat object.

mc_vars

A character vector with the variable names of the multiple choice variable. Names of the character vector are the corresponding values that are represented by the individual variables. Creation by matchValues_varLabels is recommended.

text_vars

A character vector with the names of the text variables which should be collapsed.

mc_var_4text

The name of the multiple choice variable that signals that information from the text variable should be used. This variable is recoded according to the final status of the text variables.

var_suffix

Variable suffix for the newly created GADSdat. If an empty character, the existing variables are overwritten.

label_suffix

Suffix added to variable label for the newly created or modified variables in the GADSdat.

invalid_miss_code

Missing code which is given to new character variables if all text entries where recoded into the dichotomous variables.

invalid_miss_label

Value label for invalid_miss_code.

notext_miss_code

Missing code which is given to empty character variables.

notext_miss_label

Value label for notext_miss_code.

Details

If a multiple choice item can be answered with ticking multiple boxes, multiple variables in the data set are necessary to represent this item. In this case, an additional text field for further answers can also contain multiple values at once. However, some of the answers in the text field might be redundant to the dummy variables. collapseMultiMC_Text allows to recode multiple MC items of this kind based on multiple text variables. The recoding can be prepared by expanding the single text variable (createLookup and applyLookup_expandVar) and by matching the dummy variables to its underlying values stored in variable labels (matchValues_varLabels).

The function recodes the dummy variables according to the character variables. Additionally, the mc_var_4text variable is recoded according to the final status of the text_vars (exception: if the text variables were originally NA, mc_var_4text is left as it was).

Missing values in the character variables can be represented either by NAs or by empty characters. The multiple choice variables specified with mc_vars can only contain the values 0, 1 and missing codes. The value 1 must always represent "this category applies". If necessary, use recodeGADS for recoding.

For cases for which the text_vars contain only values that can be recoded into the mc_vars, all new text_vars are given specific missing codes (see invalid_miss_code and invalid_miss_label). All remaining NAs on the character variables are given a specific missing code (notext_miss_code).

Examples

# Prepare example data
mt2 <- data.frame(ID = 1:4, mc1 = c(1, 0, 0, 0), mc2 = c(0, 0, 0, 0), mc3 = c(0, 1, 1, 0),
                  text1 = c(NA, "Eng", "Aus", "Aus2"), text2 = c(NA, "Franz", NA, "Ger"),
                  stringsAsFactors = FALSE)
mt2_gads <- import_DF(mt2)
mt3_gads <- changeVarLabels(mt2_gads, varName = c("mc1", "mc2", "mc3"),
                            varLabel = c("Lang: Eng", "Aus spoken", "other"))

## All operations (see also respective help pages of functions for further explanations)
mc_vars <- matchValues_varLabels(mt3_gads, mc_vars = c("mc1", "mc2", "mc3"),
            values = c("Aus", "Eng", "Eng"), label_by_hand = c("other" = "mc3"))

out_gads <- collapseMultiMC_Text(mt3_gads, mc_vars = mc_vars,
             text_vars = c("text1", "text2"), mc_var_4text = "mc3")

out_gads2 <- multiChar2fac(out_gads, vars = c("text1_r", "text2_r"))

final_gads <- remove2NAchar(out_gads2, vars = c("text1_r_r", "text2_r_r"),
                              max_num = 1, na_value = -99, na_label = "missing: excessive answers")