CHI Experiments and Studies

"Experiments" at CHI means human evidence: controlled lab studies, field deployments, interview and diary studies, surveys, log analyses, and mixtures of these. Two of the four assisted desk-reject rubric grounds CHI now screens with — ADR-Data (grossly insufficient data for the claims) and ADR-Method (grossly insufficient methodological detail or transparency) — are study-design judgments made before full review. Evidence design is therefore survival, not polish.

Match the evidence to the claim, not to habit

| Claim shape | Evidence that convinces CHI reviewers | Chronic mismatch seen in reviews | |---|---|---| | "Technique X outperforms Y" | Controlled comparison, counterbalanced, powered, effect sizes | Underpowered n=12 with p-values only | | "Users experience/need Z" | Interviews or diary study to saturation, systematic analysis | Cherry-picked quotes, no analysis method stated | | "System S is usable/useful in practice" | Field deployment with real tasks over time | One-hour lab walkthrough of a demo | | "Population P interacts differently" | Sampling strategy that can reach P, comparative design | Convenience sample of students standing in for P | | "Design guideline G holds" | Multiple probes/instantiations, triangulated methods | Single prototype, single context, universal claim | | "Measure M captures construct C" | Validation study: reliability, convergent validity | New questionnaire used, never validated |

Mixed methods are a CHI signature: a quantitative result explains that, the paired qualitative strand explains why. If you run both, integrate them in the analysis — a qualitative section bolted after the ANOVA reads as decoration.

Quantitative discipline

• Power before running. Decide the smallest effect worth detecting, then size the

study; report the analysis. Post-hoc power excuses convince nobody.

python
# a priori sample size for a within-subjects comparison (paired t-test)
from statsmodels.stats.power import TTestPower
n = TTestPower().solve_power(effect_size=0.5, alpha=0.05, power=0.8,
                             alternative="two-sided")
print(round(n))   # ≈ 34 participants for d=0.5 — n=12 detects only d≈0.88

• Report effect sizes with confidence intervals alongside test statistics; CHI's

methods community has campaigned against naked p-values for a decade.

• State the analysis plan's provenance: preregistered, planned-but-unregistered, or

exploratory. Label exploratory findings as such instead of promoting them.

• Check assumptions (normality, sphericity) and name the corrections used; Likert

and time data routinely need non-parametric or transformed treatment.

• Counterbalance and report order effects for within-subjects interaction studies.

Qualitative discipline

Qualitative work at CHI is judged on rigor, not sample size. What reviewers audit:

• The named analysis method actually followed — reflexive thematic analysis,

grounded-theory procedures, interaction analysis — with its own reporting conventions honored (e.g., do not report inter-rater reliability for reflexive TA while claiming a codebook emerged from consensus; pick a coherent paradigm).

• Recruitment, who the participants are, and what they were paid, in a table.
• Enough quote evidence per theme to show the theme is in the data, attributed

with participant IDs (P1–Pn), balanced across participants.

• Researcher positionality where the topic makes the researcher's standpoint

analytically relevant (common in accessibility, health, and marginalized-community work) — a norm in parts of CHI, not a universal requirement.

Participants and ethics are results-page material

CHI reviewers read the participants section as evidence, and screening cites it:

1. Recruitment channel and criteria; compensation and its local adequacy.
2. Ethics approval (IRB or equivalent) named, or the honest statement of why the

jurisdiction requires none — plus consent procedure for data, recordings, and any footage reused in the video figure (chi-supplementary).

1. Demographics reported to the level the claims need: an accessibility claim needs

disability descriptions; a cross-cultural claim needs more than "US and EU".

1. Risks and mitigations for sensitive topics; deception disclosed and debriefed.
2. Data handling: anonymization, storage, deletion timeline.

Deployment and AI-system studies

For field deployments, report duration, retention, and usage telemetry honestly — attrition is data. For AI-infused interfaces, evaluate both the model and the human experience: state model version, prompts/configurations, and failure behavior during the study window, because "users trusted the system" is uninterpretable without knowing how often the system was wrong. Pin model versions; a study run on a moving API is unreplicable by construction (chi-reproducibility).

Pre-submission evidence audit

Walk each headline claim backwards: which figure/table/theme supports it, from which data, collected from whom, analyzed how? Any claim that dead-ends is either cut, scoped down ("in our lab task, for our participants..."), or flagged as future work. This single pass defuses most ADR-Data exposure.

Output format

text
[Contribution type] <from chi-topic-selection>
[Evidence inventory] <study 1: design, n, analysis> · <study 2: ...>
[Claim-evidence dead ends] <claims without support, or none>
[Quant status] power: <basis> / effect sizes+CIs: yes/no / plan provenance: prereg|planned|exploratory
[Qual status] method named+followed: yes/no / quotes balanced: yes/no
[Ethics] approval: <body or n/a+reason> / compensation: <amount> / consent for footage: yes/no
[ADR exposure] Data: low/med/high · Method: low/med/high — <weakest point>