Data Analysis & Evidence (asq-data-analysis)

When to trigger

• You have data but the path from data to theory is opaque
• Qualitative: your quotes are decorative, not evidentiary; coding is undocumented
• Quantitative: main results exist but robustness/alternative explanations are thin
• Reviewers ask "how did you get from your data to these constructs?"

Branch A — Qualitative analysis (the data-to-theory link)

ASQ expects readers to see how raw data became theory — its guidelines stress that helping readers understand how the research was performed and ensuring the trustworthiness of published work are explicit aims (verify at journals.sagepub.com/author-instructions/asq). Qualitative rigor is judged on its own terms here, not held to a quantitative yardstick. Make the analytic ladder visible.

• Transparent coding. Describe first-order codes (informant terms), second-order themes (researcher constructs), and aggregate dimensions — the Gioia-style data structure — or an equivalent (Eisenhardt cross-case, Langley process bracketing). State who coded, how disagreements were resolved, and how iteration proceeded.
• Data-to-theory table. Provide a table linking representative raw evidence → codes → constructs, so the inference is auditable (see asq-tables-figures).
• Power quotes vs. proof quotes. Use a few vivid "power quotes" in the body; place corroborating "proof quotes" in tables/appendix. Quotes must carry the claim, not illustrate it after the fact.
• Evidence for each construct. Every theoretical construct should be backed by patterned evidence across informants/cases, with counts or prevalence where appropriate.
• Negative cases. Report disconfirming instances and how they refined the theory.
• Process display. For process theory, show the temporal/event structure (timeline, phase model, visual mapping) — as Barley (1986, ASQ) did in tracing how CT scanners restructured radiology departments over time.