Welche Analyse soll ich verwenden?

Bibliometric Overview

Headline descriptives: production, growth, top sources, authors and keywords.

Engine: Lacuna native (Python)

Sources Analysis

Source productivity and core journals via Bradford’s Law zones.

Engine: Lacuna native (Python)

Authors Analysis

Author productivity (Lotka’s Law), impact and dominance over time.

Engine: Lacuna native (Python)

Documents Analysis

Most-cited documents and the words that travel with them.

Engine: Lacuna native (Python)

Ready-made Network Analysis

Import a VOSviewer network without discarding its map coordinates, clusters, weights or scores; add structure, centrality and bridge diagnostics.

Engine: Lacuna native (Python)

Intellectual Structure

Co-citation and bibliographic coupling — the intellectual base and research fronts.

Engine: Lacuna native (Python)

Social Structure

Collaboration networks across authors, institutions and countries.

Engine: Lacuna native (Python)

Thematic Map

Bibliographic term co-occurrence mapped into relative motor, basic, niche and emerging/declining themes.

Engine: Lacuna native (Python)

Co-word Analysis & Thematic Map

Keyword co-occurrence clustered into a strategic diagram (centrality × density).

Engine: Lacuna native (Python)

Topic Modeling (LDA / NMF)

Probabilistic or matrix-factorisation topics over the full-text corpus.

Engine: Lacuna native (Python)

Embedding-based Topic Clustering

Documents embedded (TF-IDF by default; transformer/SBERT optional) and clustered into semantically coherent topics.

Engine: Lacuna native (Python)

Factorial Analysis

MCA / correspondence analysis projecting terms into a conceptual map.

Engine: Lacuna native (Python)

Thematic Evolution

How themes split, merge and flow across time slices (Sankey).

Engine: Lacuna native (Python)

Term Trajectories & Attention Concentrations

Terms gaining or losing relative visibility, with periods of concentrated attention.

Engine: Lacuna native (Python)

Univariate Meta-Analysis

Pool one effect size per study into a single summary estimate with heterogeneity diagnostics.

Engine: metafor 5.0.1 (R)

Multilevel Meta-Analysis

Two- or three-level models for dependent effect sizes nested within studies or labs.

Engine: metafor 5.0.1 (R)

GLMM Meta-Analysis

Generalised linear mixed models for binary/count outcomes without normal approximation.

Engine: metafor 5.0.1 (R)

Network Meta-Analysis

Mixed treatment comparison across ≥3 interventions with a connected evidence network.

Engine: netmeta (R)

Diagnostic Test Accuracy

Bivariate sensitivity/specificity model with a summary ROC curve.

Engine: mada (R)

Dose–Response Meta-Analysis

Model the shape of an exposure–outcome relationship across dose levels.

Engine: dosresmeta 2.2.0 (R)

P-Uniform*

Bias-corrected effect estimate robust to selective publication.

Engine: Lacuna native (Python)

P-Curve Analysis

Test the significant p-values for evidential value vs p-hacking via the p-curve's right-skew (Simonsohn et al. 2014).

Engine: Lacuna native (Python)

Trial Sequential Analysis

Adjust cumulative meta-analysis for repeated significance testing — is the evidence conclusive, or is more information needed (Wetterslev et al. 2008)?

Engine: Lacuna native (Python)

MA Power Analysis

Prospective / retrospective power for a meta-analytic design specification.

Engine: Lacuna native (Python)

Cumulative Reading (Time Bridge)

Re-estimate the pooled effect after each study in year order — when the evidence first became conclusive.

Engine: Lacuna native (Python)

Dependency Reading (Author-Network Bridge)

Re-pool with cluster-robust variance for studies produced by the same research team, from an author-network run.

Engine: Lacuna native (Python)

Evidence-Base Diagnosis (Citation Bridge)

Read the body of evidence behind a meta-analysis descriptively, matched to the project's bibliographic records.

Engine: Lacuna native (Python)