LITDISCOVER: Stata module for theory-aware literature review, analysis, and discovery

Nebojsa Davcik

Statistical Software Components from Boston College Department of Economics

Abstract: litdiscover combines latent Dirichlet allocation (LDA) topic modelling of abstract text with a deductive overlay of researcher- coded construct fields (theory, dependent variable, independent variable, moderator, mediator, decision, context, method, journal, year). It produces topic-by-field cross-tabulations, TCCM and ADO framework outputs, network-analytic measures (degree, betweenness, Louvain communities, modularity) over construct co-occurrences, FREX exclusivity scores, per-topic stability diagnostics via Jaccard similarity across random seeds, UMass coherence diagnostics, and a suite of static figures and interactive HTML deliverables (pyLDAvis, pyvis, plotly Sankey). Designed for systematic literature reviews across management, marketing, organisation studies, information systems, education, health policy, and the broader social and behavioural sciences.

Language: Stata
Requires: Stata version 19, heatplot, palettes, colrspace from SSC (q.v.), Python 3.10, Python packages
Keywords: topic modelling; literature (search for similar items in EconPapers)
Date: 2026-05-20
Note: This module should be installed from within Stata by typing "ssc install litdiscover". The module is made available under terms of the MIT license (https://opensource.org/licenses/MIT).
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Downloads: (external link)
http://fmwww.bc.edu/repec/bocode/l/litdiscover.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/litdiscover.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/l/litdi.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/litdiscover.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/litdiscover_net.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/litdiscover_viz.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/litdiscover_example500.dta

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