Audit Network Survey Nonresponse
Separate unknown dyads from observed zeros, map who is missing, and report how coverage can distort centrality, cohesion, and subgroup comparisons.
Method sources
By the end of this tutorial
- 1Define network nonresponse and missing-tie sensitivity from an explicit node, tie, boundary, direction, weight, and observation-window specification.
- 2Apply and audit this procedure: Build a response-flow table, compare respondents and nonrespondents, mark unknown adjacency cells, calculate coverage by subgroup, bound key metrics under plausible missing ties, and repeat conclusions after inverse-probability or imputation sensitivity when justified.
- 3Interpret the result with a sensitivity check and the following evidence boundary: Coding nonresponse as no tie manufactures isolates and can bias density, reciprocity, centrality, communities, and group mixing in unequal ways.
SNA
Network specification
Analysis scenario
A teacher advice survey reaches 72 percent of a district roster, with lower response among temporary staff and one remote school.
Nodes
Every eligible district teacher on the roster, carrying response status, school, contract type, and observable opportunity attributes.
Ties
Directed advice nominations from respondents, with outgoing ties for nonrespondents unknown and incoming nominations retained when observed.
Network type
Partially observed directed whole network with actor-level nonresponse, asymmetric knowledge of dyads, retained roster nodes, and one survey wave.
Step-by-step tutorial
Freeze the relational question
Write the decision the analysis must inform, then lock the eligible node roster, tie-generating event, direction, weight, self-tie rule, observation window, and missing-data code. Preserve a read-only source copy and record why this specification represents the stated question.
Checkpoint
A second analyst can reconstruct the same node set and edge table from the written rules without guessing what an absent record means.
Compute the focal structure
Work on a versioned analysis copy and carry out the focal method exactly as specified: Build a response-flow table, compare respondents and nonrespondents, mark unknown adjacency cells, calculate coverage by subgroup, bound key metrics under plausible missing ties, and repeat conclusions after inverse-probability or imputation sensitivity when justified. Save software and package versions, every threshold, normalization, seed, and intermediate count needed to reproduce the result.
Checkpoint
The output is tied to one named data version and includes the denominator, parameter settings, and a reproducible calculation record.
Run a structural sensitivity check
Repeat the analysis under at least one defensible alternative boundary, missingness rule, tie threshold, weight transformation, or model setting. Compare membership and substantive conclusions, not only a single coefficient, and investigate every change large enough to alter a decision.
Checkpoint
The audit states which patterns persist, which actors or groups change classification, and which conclusion depends on an analyst choice.
Report for responsible action
Pair the numerical result with a table or structure-preserving visual, document excluded and missing actors, and explain uncertainty in plain language. Convert the finding into a reversible support question, not an automatic ranking, while stating this boundary: Coding nonresponse as no tie manufactures isolates and can bias density, reciprocity, centrality, communities, and group mixing in unequal ways.
Checkpoint
The final note contains the question, specification, result, sensitivity evidence, uncertainty, privacy controls, and a proportionate next step.
Interpret with care
network nonresponse and missing-tie sensitivity describes a property of the specified relation and network boundary. It does not transfer automatically to another relation, time period, class, platform, or population.
Coding nonresponse as no tie manufactures isolates and can bias density, reciprocity, centrality, communities, and group mixing in unequal ways. Compare the result with raw counts, missingness, plausible alternative specifications, and contextual evidence before acting.