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Analyze Multiple Response Sets

Source: R/multiple_response.R
multiple_response.Rd

multiple_response() analyzes "check all that apply" survey questions — the SPSS MULT RESPONSE procedure. It takes a set of 0/1 indicator variables (one per answer option) and produces the two tables SPSS users know:

  • Without by: the frequencies table — number of mentions per option, percent of responses (sums to 100%), and percent of cases (sums above 100%, because respondents can tick several options).

  • With by: the crosstab of the set against a categorical variable — mentions and case-based column percentages per group.

Usage

multiple_response(data, ..., by = NULL, counted = 1, weights = NULL)

Arguments

data

Your survey data (a data frame or tibble). If grouped (via dplyr::group_by()), separate tables are produced per group (SPSS SPLIT FILE).

...

The indicator variables of the set (unquoted, supports tidyselect, e.g. starts_with("info_")). Each is checked against counted.

by

Optional categorical variable (unquoted) to cross the set against (SPSS MULT RESPONSE ... BY factor).

counted

The value that counts as a mention (default 1, matching SPSS dichotomy sets with counted value 1). For factor or character indicators give the level that marks a mention, e.g. counted = "yes"; a value that does not occur among their levels is an error.

weights

Optional survey weights, treated as frequency weights matching SPSS WEIGHT BY. Give a column name (unquoted or as a string), an expression such as sampling_weight * 2, or a numeric vector with one weight per row.

Value

An object of class "multiple_response" whose $results tibble holds one row per answer option (and group combination) with:

Option

Variable name of the option

Label

The option's variable label (falls back to the name)

n

Number of mentions (weighted sum when weighted)

pct_responses

Share of all mentions — sums to 100%

pct_cases

Share of valid cases mentioning the option — can sum above 100%

With by, $by_results additionally holds the long-form crosstab (columns by_level, Option, Label, n, pct_cases). $n_cases is the number of valid cases (at least one non-missing indicator), $n_responses the total number of mentions.

Details

Understanding the Output

The two percentage columns answer different questions:

  • Percent of responses: "Of all boxes ticked, how many were this option?" — describes the mix of answers.

  • Percent of cases: "What share of respondents ticked this option?" — usually the number reports need. It sums above 100% whenever respondents tick more than one box.

Case handling

Following SPSS MULT RESPONSE, a case is valid if it has at least one non-missing indicator in the set; cases missing on all indicators are excluded and reported as missing. Within a valid case, missing indicators simply contribute no mention. With by, cases missing on the by variable are excluded too.

Technical Details

Weighted analyses count mentions and cases as unrounded sums of weights; displayed Ns are rounded. With weights == 1 the weighted table reduces exactly to the unweighted one. Only dichotomy sets (indicator + counted value) are supported; SPSS's category-range sets are not.

The counts and percentages are checked by direct computation from the indicator matrix and are not compared with SPSS output (see the SPSS compatibility vignette).

See also

frequency for single-variable frequency tables.

crosstab for ordinary two-variable crosstabs.

summary.multiple_response for detailed output.

Other descriptive: crosstab(), describe(), frequency(), normality_test()

Examples

library(dplyr)
data(survey_data)

# Build an example set: which institutions does a respondent
# trust highly (rating of 4 or 5)?
trust <- survey_data %>%
  mutate(
    gov     = as.integer(trust_government >= 4),
    media   = as.integer(trust_media >= 4),
    science = as.integer(trust_science >= 4)
  )

# Frequencies table: % of responses vs. % of cases
multiple_response(trust, gov, media, science)
#> 
#> Multiple Response Results
#> -------------------------
#> - Set: gov, media, science
#> - Counted value: 1
#> 
#> Frequencies
#>   ---------------------------------------------
#>   Option   Responses n  Responses %  % of Cases
#>   ---------------------------------------------
#>   gov              583         23.0        23.3
#>   media            470         18.6        18.8
#>   science         1479         58.4        59.2
#>   ---------------------------------------------
#>   Total           2532        100.0       101.3
#>   ---------------------------------------------
#>   Valid cases: 2500 | Total responses: 2532 | Excluded (all missing): 0
#>   % of Cases can sum above 100% (multiple mentions per case).

# Crossed against gender, with weights
multiple_response(trust, gov, media, science,
                  by = gender, weights = sampling_weight)
#> 
#> Weighted Multiple Response Results
#> ----------------------------------
#> - Set: gov, media, science
#> - Counted value: 1
#> - Weights Variable: sampling_weight
#> - By: gender
#> 
#> Frequencies
#>   ---------------------------------------------
#>   Option   Responses n  Responses %  % of Cases
#>   ---------------------------------------------
#>   gov              586         22.9        23.3
#>   media            477         18.7        18.9
#>   science         1490         58.4        59.2
#>   ---------------------------------------------
#>   Total           2553        100.0       101.5
#>   ---------------------------------------------
#>   Valid cases: 2516 | Total responses: 2553 | Excluded (all missing): 0
#>   % of Cases can sum above 100% (multiple mentions per case).
#> 
#> Crosstab: set BY gender (% of cases per column)
#>   -----------------------------------------------------
#>   Option                Male       Female         Total
#>   -----------------------------------------------------
#>   gov            280 (23.4%)  306 (23.2%)   586 (23.3%)
#>   media          214 (17.9%)  262 (19.8%)   477 (18.9%)
#>   science        694 (58.1%)  797 (60.3%)  1490 (59.2%)
#>   -----------------------------------------------------
#>   Total (cases)         1195         1321          2516
#>   -----------------------------------------------------
#>   Percentages and totals are based on cases (respondents).

# --- Three-layer output ---
result <- multiple_response(trust, gov, media, science)
result              # full tables
#> 
#> Multiple Response Results
#> -------------------------
#> - Set: gov, media, science
#> - Counted value: 1
#> 
#> Frequencies
#>   ---------------------------------------------
#>   Option   Responses n  Responses %  % of Cases
#>   ---------------------------------------------
#>   gov              583         23.0        23.3
#>   media            470         18.6        18.8
#>   science         1479         58.4        59.2
#>   ---------------------------------------------
#>   Total           2532        100.0       101.3
#>   ---------------------------------------------
#>   Valid cases: 2500 | Total responses: 2532 | Excluded (all missing): 0
#>   % of Cases can sum above 100% (multiple mentions per case).
summary(result)     # same tables, with section toggles
#> 
#> Multiple Response Results
#> -------------------------
#> - Set: gov, media, science
#> - Counted value: 1
#> 
#> Frequencies
#>   ---------------------------------------------
#>   Option   Responses n  Responses %  % of Cases
#>   ---------------------------------------------
#>   gov              583         23.0        23.3
#>   media            470         18.6        18.8
#>   science         1479         58.4        59.2
#>   ---------------------------------------------
#>   Total           2532        100.0       101.3
#>   ---------------------------------------------
#>   Valid cases: 2500 | Total responses: 2532 | Excluded (all missing): 0
#>   % of Cases can sum above 100% (multiple mentions per case).