--- name: revedres-literature-synthesis description: Use when executing the systematic search, screening to a PRISMA flow, and extracting data for a Review of Educational Research (RER) review or meta-analysis. Builds the documented evidence corpus; it does not impose the conceptual spine (revedres-organizing-framework) or run robustness/risk-of-bias appraisal (revedres-comprehensiveness-and-balance). --- # Systematic Search & Synthesis (revedres-literature-synthesis) ## When to trigger - The protocol is fixed and it is time to search the literature exhaustively - Searching feels ad hoc; you cannot yet report a reproducible PRISMA flow - You have hundreds of records and need a defensible screening trail - A reviewer at RER is likely to ask "why did you omit study X / database Y?" ## Search to a documented PRISMA flow, not to memory An RER systematic review's credibility rests on a reader's belief that you found **everything that meets your criteria** — and can prove it. Execute the protocol, logging every number for the **PRISMA flow diagram** (identification → screening → eligibility → included). 1. **Run the registered search.** Search every database in the protocol (ERIC, PsycINFO, Education Source, Web of Science, Scopus, ProQuest Dissertations) with the recorded strings, plus grey-literature and hand-searches of key journals. Record hits per source and the search date. 2. **Deduplicate and log.** Report records identified, duplicates removed, and records screened — exact counts. 3. **Dual independent screening.** Two screeners at title/abstract, then full-text, against the eligibility criteria; record exclusions **with reasons** at full-text (required by PRISMA). Report inter-rater reliability (Cohen's κ or % agreement) and how conflicts were resolved. 4. **Supplement to saturation.** Backward (reference lists of included studies) and forward (who cites them) snowballing; ask whether new searches still surface eligible studies. Document where they stop. 5. **Extract into a structured dataset.** Apply the codebook to every included study — this is the raw material for the framework, the tables, and the meta-analysis. ## From extraction to synthesis (not summary) Summarizing is restating each study; **synthesizing** is making the studies answer your question together. Maintain a coding dataset as you extract: | Column | What to capture | |--------|-----------------| | Study | author–year; the included report (watch for multiple reports of one sample) | | Sample/context | learners, setting, grade/level, country — for moderator analysis and scope claims | | Design | RCT / quasi-experiment / correlational / qualitative — for risk-of-bias and weighting | | Construct/measure | exactly what was measured (so non-commensurable outcomes are not pooled) | | Effect / finding | effect size + variance (meta-analysis) or coded finding (narrative synthesis) | | Risk of bias | your appraisal on the a-priori tool (you do not re-run the study; you judge it) | | Dependencies | shared samples / multiple effects per study (drives the variance model) | This dataset feeds the organizing framework, the forest plot and coding tables, and the even-handed treatment of conflicting evidence. You **appraise** the primary studies (you are the field's reviewer-of-record); you do **not** re-collect their data. ## Education-specific search hazards The education literature is scattered across disciplines and document types, which creates predictable holes: - **Cross-disciplinary indexing.** Relevant work hides in psychology (PsycINFO), economics (EconLit/NBER), sociology, and policy databases — searching only ERIC misses it. Map your constructs to each field's vocabulary. - **Grey literature is large and consequential.** Dissertations (ProQuest), technical and foundation reports, and What Works Clearinghouse / IES products carry many null and small-sample results; omitting them biases pooled effects upward. - **Terminology drift.** The same construct is named differently across eras and subfields (e.g. "self-regulation" vs. "metacognition" vs. "executive function") — build a thesaurus of synonyms into the search string. - **Multiple reports of one study.** Program evaluations spawn several papers on the same sample; collapse them to one unit or model the dependency, or you double-count. Document how you handled each, so a reviewer sees the gaps were anticipated, not missed. ## Checklist - [ ] Every protocol database searched with recorded strings + search date - [ ] Records identified / duplicates / screened / excluded-with-reasons / included all counted for PRISMA - [ ] Dual independent screening; inter-rater reliability reported; conflict resolution stated - [ ] Backward + forward snowballing run to saturation and documented - [ ] Grey literature / dissertations handled per protocol (and publication-bias implications noted) - [ ] Codebook applied uniformly; multiple-reports-of-one-sample and dependent effects flagged - [ ] Non-commensurable outcomes flagged (not pooled into a false common effect) - [ ] No eligible study or relevant database an informed reviewer could name as missing ## Anti-patterns - Searching from memory or one database (predictable, fatal coverage gaps at RER) - A PRISMA diagram whose numbers do not reconcile (a red flag reviewers check) - Single-screener inclusion with no reliability statistic - Excluding grey literature without acknowledging the publication-bias risk it creates - Pooling outcomes that measure different constructs into one "effect of X" - Re-analyzing or "correcting" a primary study's raw data — you appraise, you do not re-collect ## Output format ``` 【Databases + date】 【PRISMA counts】identified / dedup / screened / full-text / excluded-w-reasons / included 【Screening reliability】κ or % agreement; conflicts resolved by 【Snowballing】backward + forward to saturation? Y/N 【Grey literature】included? Y/N — publication-bias implication noted? Y/N 【Coding dataset】codebook applied; dependent effects + shared samples flagged? Y/N 【Coverage risks】 【Next step】→ revedres-organizing-framework (impose the conceptual spine on the corpus) ```