Skip to contents

Create a Statistical Disclosure Control Report

sdc_check.Rd

This function generates a statistical disclosure control (SDC) report, identifying variables with categories that have low absolute frequencies. Such low-frequency categories could potentially lead to statistical data disclosure issues, particularly in data sets involving individual-level data from studies like large-scale assessments. The function currently performs only a uni-variate check, flagging categories with a frequency below a specified threshold.

Usage

sdc_check(fileName, boundary = 5, exclude = NULL, encoding = NULL)

Arguments

fileName

A character string specifying the path to the SPSS file to import as a GADSdat object.

boundary

An integer specifying the frequency threshold for identifying low-frequency categories. Categories with less than or equal to this number of observations will be flagged. The default value is 5.

exclude

An optional character vector containing variable names that should be excluded from the report.

encoding

An optional character string specifying the character encoding for importing the SPSS file. If NULL (default), the encoding specified in the file is used.

Value

A data.frame summarizing categories with low frequencies, including the following columns:

  • variable: The name of the variable with low-frequency categories.

  • varLab: The label for the variable (if present).

  • existVarLab: Whether a variable label exists (TRUE or FALSE).

  • existValLab: Whether value labels exist for the variable (TRUE or FALSE).

  • skala: Information on the variable type/classification.

  • nKatOhneMissings: The total number of non-missing categories.

  • nValid: The total number of valid observations for the variable.

  • nKl5: Indicator for variables with categories flagged as low frequency (TRUE or FALSE).

  • exclude: Whether the variable has been excluded based on the exclude argument.

Examples

# Load an example SPSS file
sav_path <- system.file("extdata", "example_data2.sav", package = "eatFDZ")

# Exclude unique identifier variables from the SDC check
exclude_vars <- c("ID", "ID_name")

# Generate the SDC report
sdc_report <- sdc_check(fileName = sav_path, boundary = 5, exclude = exclude_vars)

# Print the SDC report
print(sdc_report)
#>      variable                  varLab existVarLab existValLab     skala
#> 1          ID          Participant ID        TRUE       FALSE character
#> 2     ID_name                       ä        TRUE       FALSE character
#> 3       infoß           General notes        TRUE       FALSE character
#> 4         sex                     Sex        TRUE        TRUE   numeric
#> 5     subjfav        Favorite subject        TRUE       FALSE character
#> 6         äge                     Age        TRUE       FALSE   numeric
#> 7    siblings      Number of siblings        TRUE       FALSE   numeric
#> 8        home      Place of residence        TRUE        TRUE   numeric
#> 9       birth                Birthday        TRUE       FALSE character
#> 10      books Number of books at home        TRUE        TRUE   numeric
#> 11     school                    <NA>       FALSE        TRUE   numeric
#> 12 grade_math       Grade mathematics        TRUE       FALSE   numeric
#> 13 grade_germ            Grade German        TRUE       FALSE   numeric
#> 14  grade_eng           Grade English        TRUE       FALSE   numeric
#>    nKatOhneMissings nValid nKl5 exclude
#> 1                10     11 TRUE    TRUE
#> 2                11     12 TRUE    TRUE
#> 3                10     12 TRUE   FALSE
#> 4                 2     12 TRUE   FALSE
#> 5                 8     12 TRUE   FALSE
#> 6                 4     12 TRUE   FALSE
#> 7                 6     12 TRUE   FALSE
#> 8                 3     12 TRUE   FALSE
#> 9                10     12 TRUE   FALSE
#> 10                7     11 TRUE   FALSE
#> 11                6     12 TRUE   FALSE
#> 12                6     12 TRUE   FALSE
#> 13                6     12 TRUE   FALSE
#> 14                6     12 TRUE   FALSE