CSCW Empirical Work

"Experiments" is the wrong word for most CSCW evidence, and that is the point. The venue is deliberately methods-pluralist: interview studies, ethnography, large-scale trace analysis, surveys, field deployments, controlled experiments, and mixed designs all publish here — each judged by its own tradition's standard of rigor, not by a quantitative default. The commonest reviewing disaster is a paper that borrows a method without its discipline.

Rigor, by method family

| Method | What rigor means here | What reviewers flag | | --- | --- | --- | | Interviews | Purposeful sampling with a rationale; saturation or a defended stopping rule; a described analysis process (coding approach, memoing, disagreement handling) | "We interviewed 12 people and themes emerged" with no analytic trail | | Ethnography / field observation | Duration and depth of engagement; researcher's relationship to the setting; thick description that earns the interpretation | Drive-by observation labeled ethnography | | Trace / log analysis | Construct validity (does the log field measure the practice claimed?); an identification strategy for any causal wording; robustness to platform quirks (bots, deleted content, API sampling) | Correlational results narrated causally; metrics inherited from the platform unexamined | | Surveys | Instrument provenance or validation; sampling frame vs. claimed population; nonresponse handling | Convenience sample generalized to "users" | | Deployments / experiments | Genuine group-level conditions; power analysis where inference is statistical; contamination between conditions addressed | N = groups treated as N = individuals | | Mixed methods | An explicit integration logic — which strand leads, which bounds, where they may disagree | Two mini-studies stapled together, each too thin to stand |

Two pluralism rules cut across all rows:

• Do not apply one tradition's checklist to another's method. Demanding

inter-rater reliability statistics from an interpretivist analysis, or accepting vibes in place of identification from a causal claim, are the same category of error. State which tradition your analysis works in, then meet that tradition fully.

• Qualitative sample sizes are justified by purpose, not by envy. Twenty-four

well-chosen moderators can ground a concept; two million log rows cannot rescue a construct that measures the wrong thing.

Group-level design decisions

• Unit of analysis and unit of observation must be named separately. You may

observe individuals (interviewees, accounts) while claiming about collectives (teams, communities); the analysis section must say how the aggregation is licensed.

• Sample communities, not just people. For multi-community studies, describe

how communities were chosen and what variation the set covers — community selection is the qualitative analogue of a sampling frame.

• Time matters. Cooperative practices are rhythms (shift rotations, release

cycles, norm renegotiations). A snapshot design should say what it cannot see.

Ethics as design, not paperwork

CSCW evidence usually comes from real communities with stakes in the findings. Reviewers read the ethics description as part of the method:

• State IRB/ethics-review status and the consent posture for each data source —

including whether "public" trace data was treated as fair game and why that is defensible for this community.

• Plan quote handling at design time: verbatim quotes from small or hostile-scrutiny

communities can be reverse-searched; commit to paraphrase or alteration policies and disclose them.

• Consider the community's exposure, not only the individual's: naming a small

community can harm it even with every user anonymized (see cscw-artifact-evaluation for release-time handling).

Evidence-plan skeleton

text
[Claim]        <the group-level finding this study must support>
[Tradition]    interpretivist / positivist / computational / mixed (lead strand: ___)
[Observation]  who or what is observed, at what unit and timescale
[Aggregation]  how individual observations license collective claims
[Validity]     the ONE threat most likely to sink this design + mitigation
[Ethics]       consent posture per data source; quote policy; community exposure
[Stop rule]    what tells you data collection is done

Draft this skeleton before collecting anything; paste the filled version into the methods section as its outline. Under Revise and Resubmit, new data collection is often infeasible — a design that anticipates the obvious objection is the cheapest insurance the journal model offers.

Method norms are stable venue culture; submission-mechanics facts elsewhere in this pack carry the 2026-07-08 access date and should be re-verified independently.