High Heterogeneity in AI Anxiety Research: Meta-Analytic and Bibliometric Evidence Consistent with a Pre-paradigmatic Stage

This deposit contains all materials required to reproduce the analyses reported in the manuscript "High Heterogeneity in AI Anxiety Research: Meta-Analytic and Bibliometric Evidence Consistent with a Pre-paradigmatic Stage" (Frontiers in Psychology, Manuscript ID 1924329). Contents of the upload pack: (1) Data.xlsx — the study-level meta-analytic dataset (11 studies, N = 6,156). Effect sizes are standardized deviations of sample means from scale midpoints (Hedges' g), computed with the one-sample sampling-variance formula. Sheets: Study Data, Summary, Subgroup, Leave-One-Out. (2) R Code.R — the complete meta-analysis code (version 10.1), implementing DerSimonian–Laird random-effects pooling with REML and Hartung–Knapp robustness checks, subgroup tests, exploratory meta-regression, PET-PEESE publication-bias assessment, influence diagnostics, and leave-one-out analyses. (3) Bibliometric Analysis Code.py — the bibliometric pipeline (term extraction with a concept dictionary, NetworkX co-occurrence network, Louvain clustering, Kleinberg two-state burst detection). (4) Bibliometric Document List.xlsx — the manually rescreened bibliometric corpus (n = 337 documents) with per-record screening outcomes.