Tools

In this page, you can find a comprehensive list of software that can be useful for SLR and Meta-analysis. Our page includes a variety of tools that can help you with your research, from data extraction to statistical analysis.

Concept Mapping: Leximancer generates concept maps that visually represent the key themes and concepts within a text. These maps illustrate how concepts are related to each other.
Automatic Content Analysis: The software automatically extracts meaningful concepts from text data without requiring predefined categories or coding schemes. This is achieved through machine learning and natural language processing (NLP) algorithms that identify patterns and relationships within the text.
Thematic Analysis: Leximancer goes beyond simple word frequency counts to understand the text's thematic structure. It clusters related concepts into broader themes, providing a higher-level view of the data.
Visual Outputs: The software produces interactive visual maps where concepts are displayed as nodes, and the strength of their relationships is shown through the distance and connectivity between nodes.
Quantitative Metrics: Leximancer provides quantitative metrics such as concept co-occurrence frequencies and connectivity scores, which help users to quantitatively evaluate the importance and relationships of concepts.

Goh, E., & Wilk, V. (2022). Showcasing Leximancer in tourism and hospitality research: a review of Leximancer-based research published in tourism and hospitality journals during 2014–2020. Tourism Recreation Research, 1-14.

Khan, S., Rana, S., & Goel, A. (2022). Presence of digital sources in international marketing: A review of literature using Leximancer. International Journal of Technology Marketing, 16(3), 246-274.

Peng, Y., Alias, B. S., & Mansor, A. N. (2024). Application of Stakeholder Theory in Education Management: A Comprehensive Systematic Literature Review (SLR). International Journal of Learning, Teaching and Educational Research, 23(6), 1-31.

Fares, O. H., Butt, I., & Lee, S. H. M. (2023). Utilization of artificial intelligence in the banking sector: A systematic literature review. Journal of Financial Services Marketing, 28(4), 835-852.

Leximancer is a text analytics software designed to analyze and visually represent the relationships and patterns within large bodies of textual data.

Visit Site

Active Learning Algorithms: ASReview employs active learning, a type of machine learning where the model iteratively improves by querying the user (typically a reviewer) for labels on the most informative data points.
User-Friendly Interface: The software provides an intuitive, user-friendly interface that allows researchers to easily import datasets, initiate screening processes, and monitor the progress of the review.
Integration with Data Sources: ASReview can integrate with various bibliographic databases and repositories, allowing users to import references directly into the software.
Customization and Flexibility: Users can customize the active learning model by choosing different algorithms and tuning parameters to better suit their specific review needs.
Visualization and Reporting: ASReview includes tools for visualizing the screening process and generating reports.

Baek, E., Haines, S., Fares, O. H., Huang, Z., Hong, Y., & Lee, S. H. M. (2022). Defining digital fashion: Reshaping the field via a systematic review. Computers in Human Behavior, 137, 107407.

Quan, Y., Tytko, T., & Hui, B. (2024). Utilizing ASReview in screening primary studies for meta-research in SLA: A step-by-step tutorial. Research Methods in Applied Linguistics, 3(1), 100101.

Zhou, Q., Yu, H., Adams, K., Attah-Boakye, R., & Johansson, J. (2024). The impacts and outcomes of sustainable servitisation: A systematic literature review. Journal of Cleaner Production, 141334.

Kempeneer, S., Pirannejad, A., & Wolswinkel, J. (2023). Open government data from a legal perspective: An AI-driven systematic literature review. Government Information Quarterly, 40(3), 101823.

ASReview, short for Active learning for Systematic Reviews, is an open-source software tool designed to facilitate the screening process in systematic reviews using machine learning techniques. ASReview leverages active learning algorithms to prioritize and identify the most relevant studies for inclusion in systematic reviews, thereby significantly reducing the time and effort required for this often labor-intensive task.

Visit Site

HubMeta

Data Aggregation and Management: HubMeta offers tools to collect and manage data from various studies efficiently. Users can import data from different sources, organize it systematically, and keep track of study characteristics, effect sizes, and other relevant metrics.

Automated Data Extraction: The platform includes automated data extraction features that help users quickly and accurately extract relevant data from published studies.

Statistical Analysis Tools: HubMeta provides robust statistical tools specifically designed for meta-analysis. These tools include options for calculating effect sizes, heterogeneity measures, and conducting subgroup analyses and sensitivity analyses.

Visualization and Reporting: The platform generates detailed visualizations, such as forest plots, funnel plots, and other graphical representations that help users interpret and present their findings clearly.
Collaboration and Sharing: HubMeta supports collaborative workflows, allowing multiple users to work on the same project simultaneously.

Davie, C. A. (2023). A Multilevel Meta-Analytical Review of Job-Organizational Attraction during Recruitment.

Steel, P., Beugelsdijk, S., & Aguinis, H. (2021). The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews. Journal of International Business Studies, 52, 23-44.

Tehrani, S. A., Keshtkar, A., Ramasamy, A., & Fadaei, M. (2021). The worldwide prevalence of burnout syndrome among bank employees: a systematic review and meta-analysis protocol. Systematic Reviews, 10, 1-6.

de Alencar Neto, J. N., Scheffer, M. K., Correia, B. P., Franchini, K. G., Felicioni, S. P., & De Marchi, M. F. N. (2024). Systematic review and meta-analysis of diagnostic test accuracy of ST-segment elevation for acute coronary occlusion. International Journal of Cardiology, 131889.

HubMeta is a platform designed to support researchers and professionals in the process of conducting meta-analyses. Meta-analysis is a statistical method that combines the results of multiple scientific studies to arrive at a more comprehensive understanding of a particular research question or topic.

Visit Site

LIWC

Dictionary-Based Analysis: LIWC uses a comprehensive dictionary of words that are categorized into various linguistic, psychological, and social dimensions. The software scans texts and counts the frequency of words in each category, providing a quantitative measure of the linguistic and psychological characteristics of the text.
Psychological and Linguistic Categories: The LIWC dictionary includes categories such as: Linguistic dimensions, Psychological processes, Personal concerns, Social processes, and Emotion and Sentiment Analysis.
Temporal Orientation: The software can assess the temporal orientation of a text by categorizing words related to the past, present, and future.
Output and Reporting: LIWC provides detailed output reports that include the frequency of words in each category, summary variables, and graphical representations of the data.

Singh, S., & Singh, W. (2024). AI-based personality prediction for human well-being from text data: a systematic review. Multimedia Tools and Applications, 83(15), 46325-46368.

Feizi-Derakhshi, A. R., Feizi-Derakhshi, M. R., Ramezani, M., Nikzad-Khasmakhi, N., Asgari-Chenaghlu, M., Akan, T., ... & Jahanbakhsh-Naghadeh, Z. (2022). Text-based automatic personality prediction: a bibliographic review. Journal of Computational Social Science, 5(2), 1555-1593.

Sert, B., & Ülker, S. V. (2023). A Review of LIWC and Machine Learning Approaches On Mental Health Diagnosis. Social Review of Technology and Change, 1(2), 71-92.

Linguistic Inquiry and Word Count (LIWC) is a text analysis software tool designed to analyze the psychological, emotional, and cognitive aspects of written or spoken language.

Visit Site

User-Friendly Interface: CMA is designed to be accessible to users with varying levels of statistical expertise. It provides a straightforward interface that guides users through the process of entering data, performing analyses, and interpreting results.
Versatile Data Input: Users can input data in multiple formats, including raw data, summary data (e.g., means, standard deviations), and effect sizes (e.g., odds ratios, risk ratios).
Wide Range of Effect Sizes: CMA can compute a variety of effect sizes, including standardized mean differences, correlations, odds ratios, risk ratios, and hazard ratios.
Advanced Statistical Analyses: The software supports sophisticated statistical techniques such as fixed-effect and random-effects models, subgroup analyses, meta-regression, and publication bias tests (e.g., funnel plots, Egger’s test).
Graphical Displays: CMA provides numerous graphical displays to help users visualize their data and results. These include forest plots, funnel plots, and Galbraith plots, which aid in understanding the distribution and impact of study results.
Comprehensive Reporting: The software generates detailed output reports that include statistical summaries, heterogeneity statistics, and confidence intervals.

Borenstein, M. (2022). Comprehensive meta‐analysis software. Systematic reviews in health research: meta‐analysis in context, 535-548.

Khoury, B., Lecomte, T., Fortin, G., Masse, M., Therien, P., Bouchard, V., ... & Hofmann, S. G. (2013). Mindfulness-based therapy: a comprehensive meta-analysis. Clinical psychology review, 33(6), 763-771.

Sahu, K. C., Khan, M. N., & Gupta, K. D. (2022). Customer Experience and Its Outcome Measures: A Meta-Analytic Approach. Academy of Marketing Studies Journal, 26(3).

Chang, C. C., & Huang, M. H. (2020). Antecedents predicting health information seeking: a systematic review and meta-analysis. International Journal of Information Management, 54, 102115.

Comprehensive Meta-Analysis (CMA) is a statistical software package specifically designed for conducting meta-analyses. Meta-analysis is a method used to systematically combine results from multiple studies to develop a single conclusion that has greater statistical power.

Visit Site