Collins, L. M., & Lanza, S. T. (2009). Latent Class and Latent Transition Analysis (1st ed.). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470567333

“Latent Class and Latent Transition Analysis is a comprehensive guide for identifying unobserved subgroups within a population using categorical data. Designed for researchers and students in social, behavioral, and health sciences, the book covers latent class and latent transition analysis techniques, which are used to infer hidden patterns within groups based on survey responses or other observed variables. It begins with foundational concepts and progresses to advanced topics like longitudinal latent class models, parameter restrictions, and multi-group analysis. Each method is presented with both theoretical background and practical applications, including examples of real-world data analysis. Chapters conclude with key takeaways, and the book offers online resources with data sets and specialized software tools (Proc LCA and Proc LTA in SAS) to help readers apply and experiment with the methods discussed. This resource is ideal for advanced coursework in categorical data analysis or for researchers using latent variable modeling.“

Hagenaars, J. A., & McCutcheon, A. L. (2002). Applied Latent Class Analysis. Cambridge University Press. https://www.cambridge.org/core/books/applied-latent-class-analysis/30C364913C52083262DD7CE5A2E05685

“Applied Latent Class Analysis is a hands-on guide for researchers seeking practical applications of latent class modeling to uncover hidden subgroups within data. This book is structured to support applied work, moving beyond theoretical explanations to focus on implementing latent class analysis (LCA) for real-world research challenges. The chapters cover essential methods, such as clustering and measurement models, and extend to sophisticated applications, including causal analysis, dynamic models, and handling missing data. Each section combines theoretical insights with concrete examples and step-by-step instructions on conducting analyses in SAS, providing readers with a toolkit for navigating complex datasets. The book is particularly valuable for social and behavioral researchers who need guidance on translating latent class techniques into empirical insights, with each chapter including practical case studies and applications that demonstrate the versatility of LCA in various research contexts. With empirical examples and tips on best practices, Applied Latent Class Analysis is an essential resource for those aiming to enhance their methodological skill set and apply LCA effectively in their own work. The book is also a tribute to Clifford C. Clogg, whose work laid foundational principles in the field.”

A great literature base or reference library is vital to help with writing and developing ideas; however, getting started in a new area or developing interdisciplinary / transdisciplinary work can be challenging. A literature base is the first step in achieving a comprehensive review, systematic review, or meta-analysis. Literature reviews are essential for constructing an introduction section of a manuscript, discussion section, or covering information within a book chapter. Sometimes finding the various sources and filtering out articles to synthesize can be a daunting task. Now there are several tools to build a solid literature base. The first step is to get started with a comprehensive literature review or even a systematic literature review and meta-analysis.

There are many bibliographic research tools available out there. Many use simple co-citation analysis to identify a network of relevant articles, while others use more powerful algorithms to generate and visualize their networks. Below, is a selection of some of the more unique tools, representing a slice of tools available. These can be used to build a base reference library. They are user-friendly and many have re-occurring updates. You can play around with these features to figure out which tool works best for you.

*This list is in alphabetical order, not by ranking. All videos provided by websites cited.

CoCites: www.cocites.com
CoCites uses a browser-based extension for Chrome and Firefox which adds an information box for papers in PubMed. It provides information about the numbers of times a paper is cited and its co-citated articles. The tool uses a co-citation similarity network to rank relevant papers, currently limited to 100 most recently published. This has been found to be effective in evaluation studies of the tool.

Connected Papers: www.connectedpapers.com
Connected Papers is a literature visualization tool, with a search engine connected to multiple databases including PubMed, Semantic Scholar and arXiv. Other features include:
i. A graph that details the lead author, year, and strength of the connection based on co-citation and bibliographic coupling to the original paper
ii. Recommends relevant articles, identifying articles that are brand new and articles that do not cite each other but may be related as it does not rely on a co-citation mechanism
iii. Once the graph developed, the articles in the graph can be downloaded, and the prior papers (likely to be most critical in the field) and derivative works (likely to be reviews) tab can also be downloaded
The demonstration below (image provided by Connected Papers) shows the website’s recent collaboration with arXiv, where every paper in arXiv.org links to a Connected Papers Graph.

Inciteful: inciteful.xyz
Inciteful.xyz is an academic article network finder with a fast, user friendly interface. It features various ways to engage with the “seed papers” users’ input. The tool’s powerful seeding mechanism allows for further refining the network of papers that are most relevant in multiple rounds. It also allows for filtering of the network by keywords, by distance and by year. As well, for adding additional papers to the network manually. The tool provides a range of metrics useful in analyzing the publication landscape including:
i. Similar papers
ii. Most important papers in the network as identified by pagerank
iii. Recent papers by the top 100 authors
iv. Most important recent paper with a ranking
v. Top authors in the area for the papers identified
vi. Institutions most published in the network
vii. Top journals for the research area

JSTOR Labs Text Analyzer: jstor.org/analyze
JSTOR Text Analyzer is a program that extracts and analyzes text in an article. It can help build better key terms for a systematic review and papers related to the example article analyzed. Following submission, the tool analyzes terms that are explicit or implied in the text and highlights relevant topics. It then generates a list of recommended topics that can be filtered by year, type, and accessibility.

Litmaps: www.litmaps.co
Litmaps website develops maps based on key terms and articles, which can be selected on the site or uploaded from a citation manager. There are multiple ways to create a map based on the articles selected or key terms, which shows the selected articles’ relatedness. The maps tab features allow users to develop their maps. The explore tab allows users to find articles based on the original map. The systematic tab is a new feature coming soon to develop a systematic search process.

Open Knowledge Maps: www.openknowledgemaps.org
Open Knowledge Maps promotes research discoverability by showing an overview of topics and relevant concepts using the 100 relevant papers. Retrieving literature with high meta-data quality, the maps cluster papers based on key terms used in the field.

Vosviewer: www.vosviewer.com
VOSViewer is a text mining and article network software that must be downloaded. The software allows users to build various maps based on authors, journals, universities, conferences, and key terms. These maps also have functions for customized clustering, how the maps can be visualized, and an analysis function. The tool has been extensively reviewed, validated, and is highly cited in literature. It features:
i. Rich range of data sourcesfrom Web of Science and Pubmed to OpenCitations and WikiData
ii. Mapping tool and visualization network with labeling
iii. Clustering of networks based on co-authorship, co-citations, and bibliographic coupling
iv.

Researchrabbit: www.researchrabbit.ai
ResearchRabbit is a newly-developed literature discovery tool which provides a personalized experience for users. Currently early access is available by request. The features include:
i. Finding earlier, latest, and similar works based on key article(s) provided to build a collection that can be saved if you need to come back to it
ii. A literature map based on the data provided
iii. A list of references from selected papers can also be added to the collection
iv. Ability to export the collection to a reference manager
v. Email updates based on your collections about articles that may be of interest