Support for labeled missingness

[khusmann]

Presently, missing values (whether specified at a schema-level or field-level) are specified using their physical values.

It is not uncommon for these physical missing values to represent logical reasons for why a given observation might be missing (e.g. "Participant skipped item", "Declined", "Not applicable"). Sometimes these reasons can be inferred from the physical values ("SKIPPED", "DECLINED", "N/A"). Other times these reasons are encoded: (-99, -98, -97).

As extensively discussed here, many software packages (Stata, SPSS, MPlus, etc.) use the concept of "value labels" to map encoded categorical values to logical categorical levels as well as physical missing values to logical missing reasons. The recently proposed categorical field type (#875) would give us the former capability: mappings from physical categorical encodings to logical categorical levels. Here, I want to propose a similar pattern to give us the latter ability as well: mappings from physical missingValues to logical missing reasons.

Below is the proposed type signature (for use in both schema-level and field-level missingValues definitions). Note that in accordance with the V2 spec change rules, it would not interfere with the existing way of specifying missingValues (i.e. an array of strings without labels is still valid):

{
  missingValues: ({ value: string, label?: string } | string)[]
}

Alternatively in RFC language: missingValues MUST be an array where each element is a JSON object or a string. If the array element is an object, it MUST have a value field of type string and optionally a label field, also of type string.

With this definition, the above example would translate to a missingValues spec like this:

{
  "schema": {
    "fields": [...],
    "missingValues": [
      { "value": "-99", "label": "Participant skipped item" },
      { "value": "-98", "label": "Declined" },
      { "value": "-97", "label": "Not applicable" }
    ]
  }
}

This format is also easily extendable with reason-level metadata via user defined fields:

{
  "schema": {
    "fields": [...],
    "missingValues": [
      { "value": "-99", "label": "Participant skipped item" },
      { "value": "-98", "label": "Declined" },
      { "value": "-97", "label": "Not applicable" },
      { "value": "-96", "label": "Special incident 1", "description": "The survey software crashed in our final week of collection causing significant data loss" }
    ]
  }
}

And as mentioned above, unlabelled missingness would still be valid as an array of strings:

{
  "schema": {
    "fields": [...],
    "missingValues": ["-99", "-98", "-97"]
  }
}

Although other software like Pandas / SQL / Polars / R, etc. do not natively support the concept of labeled missingness, or even types of missingness (all missingness values are collapsed into a single Null / NA / None type), it is still possible to emulate the behavior of a "tagged missing" type by loading values and missing reasons into separate columns, as I describe here (for an R implementation). So even when missing reasons are not natively supported by an implementation's environment, implementations still have the potential to make use of these missing labels, if they desire.

Tagging @peterdesmet and @pschumm for their input…