Qualitative Analysis of Child Eyewitness Interviews

Traditional qualitative summaries treat an identification statement as a single datum. MIL reframes identification as a small time-series of categorical choices. That lets analysts quantify consistency across features and map patterns to likelihood bins (low / medium / high).

Because the study covers ages 6–11, qualitative coding should include age-band segmentation (e.g., 6–7, 8–9, 10–11). Analysts can compare selection patterns, confidence language, and narrative detail across these bands to test whether MIL thresholds hold or shift by age.

Jurors often equate confidence with accuracy. Presenting a simple MIL-derived probability (with clear caveats) is a defensible way to translate qualitative consistency into an interpretable metric that complements forensic evidence.

Collect interview transcripts, lineup response logs, and metadata (age, exposure time, retention interval).

Use accurate transcription (custom dictionary for names/phrases, PII redaction). For multilingual corpora, run translation with a custom lexicon to preserve feature labels.

Code each lineup as a binary decision per feature (face=1/0, body=1/0, voice=1/0, etc.). Timestamp and link codes to the original quote for auditability.

Sum the binary selections to produce an MIL score per case. Map scores to likelihood bands using the study thresholds as priors (e.g., face+2 → high).

Compare MIL scores by age band, stress level, exposure context, and interviewer style to surface moderators of reliability.

Pull thematic excerpts (confidence cues, descriptive richness, hesitation) and co-analyze with MIL scores to see which narrative markers predict consistency.

Generate reproducible reports that show raw codes, MIL mappings, and confidence intervals; include methodological caveats for court or policy use.

Investigations combine audio, video, paper logs, and spreadsheets. Evidano ingests transcripts, lineup spreadsheets, and interview audio so you can centralize the corpus quickly (www.evidano.com).

Solution: AI-assisted coding templates for MIL. Import a codebook (face/body/voice/object), run batch coding across transcripts and logs, then review and approve suggestions, saving hours on manual tagging.

Solution: automated cross-segment analysis. Evidano computes MIL scores, compares them by age band or exposure, and produces exportable tables and visualizations (co-occurrence networks, hierarchies) for expert review.

Solution: encrypted storage, PII redaction in transcripts, and an auditable export of source quote links and timestamps. Evidano does not use customer data to train third-party models.

Solution: AI avatar interviewers for standardized follow-ups (consent-first), useful when additional feature checks are required without re-traumatizing a child witness.

A: No. MIL provides probabilistic evidence about consistency across features. It increases inferential confidence but should be combined with corroborating evidence and expert interpretation.

A: The study results are promising (n>900) but authors recommend further validation in field conditions before routine forensic adoption.

A: Use trauma-informed interviewing, minimize repetitions, get informed consent, and treat outputs as research-supportive rather than diagnostic. (Research context only.)

A: AI accelerates transcription, consistent coding, and cross-case comparison, reducing human error in data handling while keeping analyst oversight.