Skills Data Science Inclusion and Coding for Meta-Analysis

Inclusion and Coding for Meta-Analysis

v20260724
psychbull-inclusion-and-coding
This skill details the rigorous methodology for defining study eligibility criteria and coding data for comprehensive meta-analyses. It emphasizes pre-specifying inclusion/exclusion rules in a protocol, developing a detailed codebook, performing double-coding, and calculating inter-rater reliability (e.g., Kappa, ICC) to ensure the reproducibility and trustworthiness of synthesis findings.
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Overview

Inclusion & Coding (psychbull-inclusion-and-coding)

The credibility of a synthesis rests on transparent, pre-specified eligibility and reliable coding. Decisions about which studies are in — and how their features are coded — must be reproducible and made by at least two trained coders with documented agreement. This skill governs eligibility and coding; the statistical model lives in psychbull-meta-analysis-methods.

When to trigger

  • Writing eligibility (inclusion/exclusion) criteria before full-text screening
  • Building the codebook for study features, moderators, and effect-size inputs
  • Setting up double-coding and computing inter-rater reliability
  • A reviewer questions selection or coding decisions

Eligibility criteria

  • Pre-specify inclusion/exclusion rules (population, design, measures, outcome, time window, language, publication type) in the protocol, before screening (see psychbull-open-science-and-transparency).
  • Operationalize each rule so two screeners apply it the same way; pilot on a sample and refine.
  • Two-stage screening: title/abstract → full text, each by two screeners, with a reconciliation log and exclusion reasons feeding the PRISMA flow.

Codebook & double-coding

  1. A written codebook with explicit decision rules for every variable: study descriptors, design, sample, measures, risk-of-bias/quality indicators, candidate moderators, and the statistics needed to compute effect sizes (means, SDs, ns, correlations, ORs, test statistics).
  2. Double-code all (or a substantial, documented subset of) studies; resolve disagreements by discussion or a third coder; record the resolution.
  3. Capture what you need for the effect size, including the direction of effects and any sign-flips, plus enough to handle multiple effect sizes per study (dependency) downstream.

Inter-rater reliability

  • Report a reliability statistic: Cohen's / Fleiss' kappa for categorical codes, ICC for continuous codes; report the value and how disagreements were resolved.
  • For study quality / risk of bias, use a documented appraisal scheme and report it.

Anti-patterns

  • Eligibility criteria invented after seeing which studies give the desired result
  • A single coder, or no reliability statistic reported
  • A codebook so vague that coders silently diverge
  • Dropping "inconvenient" studies without a rule that excludes them
  • Losing the data needed to compute effect sizes (no SDs/ns) — recoverable only by author contact

What referees check on eligibility and coding

At the APA's flagship synthesis journal, eligibility and coding are where a reviewer probes whether the study pool is principled and the data are trustworthy. The bar they apply:

Referee expectation Pass Major-revision / reject trigger
Pre-specified criteria Eligibility fixed in the protocol before screening Rules that shift to admit favorable studies
Two-stage screening Title/abstract → full text, two screeners, reconciliation log One screener, no audit trail
Codebook completeness Covers moderators and every effect-size input Vague codes; SDs/ns not captured
Reliability reported κ or ICC with a resolution procedure No agreement statistic at all
Dependency captured Multiple effects per study coded for downstream RVE Effects collapsed silently, losing structure

Worked vignette — coding the intervention pool

Illustrative numbers only. Screening for the self-affirmation synthesis takes 1,640 deduplicated records to 188 full texts to k = 42 included studies. Under this skill's rules:

  • Eligibility (population, randomized design, validated outcome, 1995–2024, English) was fixed in the OSF protocol before any full text was read.
  • Double-coding covered all 42 studies; first-pass agreement was κ = 0.81 on categorical codes and ICC = 0.92 on continuous codes; 14 disagreements went to a third coder and were logged.
  • Effect-size inputs (means, SDs, ns, and 6 test-statistic conversions) were captured with their direction, so g could be recomputed independently.
  • Dependency: 9 studies reported 2–4 effects each, all coded so the analysis can apply RVE rather than treating them as independent.

Referee pushback → venue-specific fix

  • "Your inclusion rules look like they shifted mid-stream." → Show the timestamped protocol; document any amendment with its date and rationale, not a silent edit.
  • "No inter-rater reliability is reported." → Add κ/ICC values and the disagreement-resolution procedure; double-code a documented subset if full coverage is infeasible.
  • "Effect sizes can't be reverified from your table." → Restore the means/SDs/ns or test statistics so each g is independently recomputable.

Output format

【Eligibility】pre-specified in protocol? [Y/N] + key rules
【Screening】two screeners, reconciliation log? [Y/N]
【Codebook】covers moderators + effect-size inputs? [Y/N]
【Double-coding】coverage + disagreement resolution
【Reliability】kappa / ICC value(s)
【Next】psychbull-meta-analysis-methods

Supplementary resources

Info
Category Data Science
Name psychbull-inclusion-and-coding
Version v20260724
Size 5.8KB
Updated At 2026-07-29
Language