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Cross-Case Evidence Matrix Tool

Data-assembly tool — instantiates Structured Comparative Case Design

Assembles a cases-by-variables grid — one row per case, one column per factor — filled with comparably-coded, sourced values so patterns can be read across cases.

Before any tool can adjudicate or configure, the evidence has to sit in one place, in one shape. The Cross-Case Evidence Matrix Tool lays the study's evidence into a rectangular matrix — rows are cases, columns are the outcome and the candidate explanatory variables, and each cell holds a comparably-coded, individually-sourced value — so that cross-case patterns become legible and every later mechanism draws from a single shared substrate. Its distinguishing role is that it is the data layer, defined by the outcome-and-explanatory-variable map as its columns: it assembles and displays, it does not judge. The rival table and the truth table both read from this grid; this tool builds it.

Example

A conservation scientist compares 12 national parks, some of which curbed elephant poaching over a decade and some of which did not. The matrix tool builds the grid: 12 rows (parks), and columns for the outcome (poaching trend) and the candidate drivers — ranger density, community revenue-sharing, fence coverage, prosecution rate, border length. Each cell is filled from a stated source and coded on a common scale, and where a park's prosecution data is missing it is marked as missing, not quietly guessed.

Laid out this way, a pattern surfaces for the eye and for the tools downstream: every park that curbed poaching shows both revenue-sharing and above-median ranger density, while fence coverage varies freely across winners and losers. The tool declares no cause. It makes the pattern legible and hands the identical grid to the rival table and the truth table, so the argument that follows is over one dataset rather than five private ones.

How it works

  • Fix the columns from the outcome-and-explanatory-variable map — the same variables, in the same order, for every case.
  • Fill each cell with a comparably-coded, individually-sourced value, and mark missing data as missing rather than imputing a convenient number.
  • Keep it descriptive: cells carry values and provenance, never verdicts.
  • Serve as the shared substrate the adjudicating and configurational tools read from, so they cannot silently disagree about the facts.

Tuning parameters

  • Column set — which variables become columns; more columns catch more but invite fishing and multiply empty cells.
  • Coding scheme — raw values versus ordinal grades versus binary marks; coarser coding is more comparable but discards detail.
  • Sourcing stringency — how much provenance each cell must carry; stricter is auditable but slower to populate.
  • Missing-data convention — blank, estimated, or case-excluded; how gaps are shown quietly shapes which patterns look real.

When it helps, and when it misleads

Its strength is that it forces one consistent variable set across every case and makes cross-case patterns legible at a glance, giving each downstream mechanism a common, sourced footing instead of private notes. Its failure mode is that a tidy, fully-filled grid implies a comparability it may not possess — identical column headers can hide non-equivalent measures beneath them — and the choice of columns quietly bounds what any later analysis can possibly find. The classic misuse is reading a cell pattern straight off as a causal finding when the matrix only describes. The discipline that guards against this is to keep the matrix strictly descriptive, and to hand equivalence-of-measures to a dedicated audit and causal verdicts to the adjudicating tools.[1]

How it implements the components

  • cross_case_evidence_matrix — the tool is the matrix: the assembled cases-by-variables grid of comparable, sourced evidence.
  • outcome_and_explanatory_variable_map — it operationalises that map as the matrix's columns, fixing one outcome and one candidate-variable set for every case.

It assembles and displays but does not verify that the measures in its cells mean the same thing across cases (that's the measurement equivalence audit), nor adjudicate what the pattern means (that's Rival Explanation Elimination Table and Configurational Comparison Truth Table).

Notes

Because every downstream mechanism reads from this one grid, its column choices and coding conventions silently constrain the entire design: a variable never entered as a column can never be found by the rival table or the truth table. Treat the matrix's schema as a design decision to be reviewed, not a clerical step to be rushed.

References

[1] Meta-matrix (Miles & Huberman) — a master case-by-variable display that stacks all cases in one common format so cross-case patterns can be read systematically. It organises evidence for comparison; it does not itself establish causation. ↩