# Best Software Tools for Your Literature Review Matrix Literature review matrix software spans everything from a plain spreadsheet you fully control to dedicated review platforms that manage screening and extraction for you, and the right choice depends on how much structure and automation you want imposed on your notes. The rule of thumb is simple: a single-author narrative review with a few dozen papers usually runs fine in a spreadsheet or a reference manager, while a team-based systematic or scoping review benefits from a dedicated screening platform connected to a shared extraction matrix. This article defines what a literature review matrix is, shows where it sits in the process of how to write a literature review, compares the main software options with their pros and cons, and walks through worked scenarios and common mistakes so you can pick a tool and start extracting data the same day. ## What a Literature Review Matrix Actually Is A literature review matrix is a structured table in which each row is one source and each column is a piece of information you plan to compare across sources. Typical columns include author and year, study design, population or sample, intervention or exposure, key findings, limitations, and the theme or theme codes the paper contributes to your synthesis. Some writers add a column for direct quotations with page numbers and a column for their own critical comments. The matrix does two jobs at once. First, it forces you to extract comparable information from every paper, which prevents the common failure of summarizing each study in isolation and then struggling to synthesize anything. Second, it becomes the raw material for your written synthesis, because patterns, contradictions, and gaps become visible when comparable rows sit next to each other. Published reviews use this device openly. A scoping review of obstetric violence in the United States extracted data from the six included articles into a literature matrix and then synthesized the data into emergent themes such as epistemic and identity-based violence and erosion of maternal autonomy [1]. A scoping review of integrated health campaigns for immunization extracted data following a predefined matrix and analyzed it deductively before presenting a narrative synthesis [2]. A structured integrative review of algorithmic management used a thematic coding matrix to classify algorithmic practices, autonomy facets, psychological mediators, outcome categories, moderators, evidence type, and level of analysis [3]. In each case, the matrix is not a note-taking habit. It is the analytic instrument of the review. The matrix concept also appears outside research synthesis whenever someone must compare software options against fixed criteria. A university library choosing virtual reference software in the early 2000s documented its selection process with a list of essential deal-breaker features, a list of useful but noncritical features, and a comparison matrix included in the article appendix [4]. That same logic, deciding your columns before you look at your options, is exactly how you should build a literature matrix and, fittingly, how you should evaluate the software you run it in. ## Where the Matrix Fits in How to Write a Literature Review ![Five-step flow from defining the question through search, screening, extraction into the matrix, and synthesis, with a screening count chip.](/article-images/illustrations/literature-review-matrix-software-matrix-build-order.webp) A literature review has a predictable sequence of stages, and the matrix sits in the middle of it. Knowing the sequence clarifies which software you need at each point. 1. **Define the question and protocol.** For systematic and scoping reviews, you write the question, inclusion criteria, and analysis plan before searching. Protocol-driven reviews state this explicitly, such as a systematic review protocol for economic evaluations of cognitive behavioral therapy for insomnia that predefined which economic evaluation methods would count as eligible evidence [5]. Diagnostic accuracy reviews often register protocols in advance and follow the PRISMA-DTA checklist, as one oral cancer biomarker review did under a PROSPERO registration [6]. 2. **Run the systematic literature search.** You search multiple databases with a documented strategy. Reviews commonly combine large indexes such as PubMed, Embase, Scopus, Web of Science, and the Cochrane Library with field-specific databases such as CINAHL, PsycINFO, or CNKI. One bone graft meta-analysis searched eight databases from database inception to a stated cutoff date [7]. A behavioral intervention review searched eleven bibliographic databases, screened reference lists of included studies, and contacted experts [8]. Building the search string itself often starts with a small pilot search, as in the insomnia protocol where the team ran a preliminary search in MEDLINE, Google Scholar, and dissertation databases to develop the search terms before running the full strategy [5]. 3. **Screen titles, abstracts, and full texts.** Screening removes records that fail the inclusion criteria. The volume matters here. A scoping review of financial toxicity in head and neck cancer screened 1,466 studies before 29 met the eligibility criteria [9]. An integrative review of algorithmic management identified 358 records, retained 276 after de-duplication, assessed 94 full texts, and produced a final corpus of 41 sources [3]. Screening at this scale is where dedicated software earns its keep. 4. **Extract data into the matrix.** This is the step the software choices in this article center on. Two reviewers extracting data independently is standard in systematic reviews, as reported in both a pregnancy weight management review [8] and a dentine pretreatment meta-analysis that applied its quality assessment tool with two independent reviewers [10]. 5. **Synthesize.** Depending on the review type, synthesis means narrative themes, a meta-analysis, or a framework analysis. The matrix feeds all of these. A practical habit from the screening stage is backward and forward citation tracing, meaning you check the reference lists of included papers and search for newer papers that cite them. The algorithmic management review used exactly this approach alongside database searching [3]. Your matrix should have a column recording how each paper entered the review, because tracked citations are treated differently from database hits when you describe your search later. ## Best Literature Review Matrix Software at a Glance The best literature review matrix software is rarely one program. Most working reviewers combine a search layer, a screening layer, and a matrix layer. The table below compares the main literature review matrix software options by what they do best and where they fall short. | Tool category | Examples | Best for | Matrix strengths | Main drawbacks | Typical stage | |--|--|--|--|--|--| | Spreadsheets | Excel, Google Sheets | Solo reviews, small corpora, full control | Unlimited custom columns, filtering, sorting, easy export | No PDF storage, weak deduplication, clunky collaboration at scale | Extraction and synthesis | | Database workspaces | Notion, Airtable | Solo or small teams wanting linked notes and views | Relational links between papers, themes, and authors, multiple views of one dataset | Learning curve, export limitations, no built-in screening | Extraction and synthesis | | Reference managers | Zotero, EndNote | Anyone collecting papers | Built-in citation metadata, PDF library, tagging, note fields | Not designed for synthesis columns, weak cross-study comparison | Search and extraction support | | Screening platforms | Covidence and similar systematic review software | Team-based systematic and scoping reviews | Title and abstract screening, conflict resolution, bulk data extraction | Designed for screening workflows rather than flexible matrix design | Screening and extraction | | Meta-analysis software | RevMan, general statistics packages | Reviews pooling quantitative results | Structured effect size entry, forest plots, heterogeneity statistics | Handles numbers, not narrative matrix columns | Synthesis only | | Bibliometric mapping | VOSviewer | Mapping research trends and networks | Visualizes co-occurrence and collaboration patterns | Describes the literature, does not extract study findings | Scoping and gap identification | Read the table as a pipeline, not a menu. You will likely use one tool from the bottom three rows to find and filter papers and one tool from the top three rows to hold the matrix itself. ## Literature Review Matrix Software Options Explained ### Spreadsheets: Excel and Google Sheets A spreadsheet is the default literature review matrix software for good reason. You define the columns, you see every study on one screen, you can filter by theme or study design in one click, and you can export to almost anything. Google Sheets adds live collaboration and version history at no cost for most academic users, which matters when a coauthor wants to add rows. The pros are real: zero learning curve for basic use, total flexibility in column design, instant sorting, and painless copy-paste into a manuscript. The cons are equally real. Spreadsheets do not store PDFs alongside rows, de-duplicating thousands of search results is manual and error-prone, tracking who changed which cell requires discipline, and a very wide matrix becomes hard to read on a laptop. Spreadsheets also invite a specific failure mode: free-text columns that drift in wording until the synthesis step finds that five different phrases describe the same finding. A reasonable compromise is a hybrid layout. Keep one sheet for the matrix, one sheet for the screening log with reasons for exclusion, and one sheet defining your column headings and allowed values. Reviews that follow reporting standards such as PRISMA need that screening log anyway, and PRISMA-based reviews are expected to document their search and selection process, as the dentine pretreatment review did by following the PRISMA statement and reporting flow from 934 identified studies down to the included set [10]. ### Database Workspaces: Notion and Airtable Database workspaces are spreadsheet-database hybrids. Each paper is a record, columns are typed fields, and you can build linked relations, so a paper record can point to theme records, author records, and project records. You can then flip between a table view that looks like a classic matrix, a board view grouped by theme, and a gallery view showing abstracts. The strengths are structure without a database administrator. If you define a "Study design" field with a fixed set of options, every row must use one of them, which solves the free-text drift problem. The weaknesses are practical. These tools charge for full features beyond a free tier, they are not built for importing thousands of search results or de-duplicating them, and getting your data out in a clean format for publication can require workarounds. They also tend to be single-user or small-team tools, so they fit a graduate student building a matrix for a thesis chapter better than a six-person guideline panel. ### Reference Managers: Zotero and EndNote Reference managers solve the metadata problem. When you import a paper, the citation fields arrive filled in, the PDF is attached, and you can tag and search the library. That makes them strong partners to the matrix rather than replacements for it. A common workflow imports everything from the systematic literature search into the reference manager, tags records during screening, and then copies verified citations into the matrix so author, year, and journal are always correct. The limitation is analytical. Reference managers store one notes field per paper with no inherent comparison across papers, so the cross-study pattern-finding that makes a matrix valuable does not happen inside the tool. Treat them as the intake system and the source of accurate citations, and keep your synthesis columns elsewhere. ### Screening and Review Platforms Dedicated review platforms automate the screening stages that precede extraction. Covidence is a documented example from the published literature. The obstetric violence scoping review completed a two-reviewer screening process using Covidence systematic review software, then moved the six included articles into a literature matrix for extraction [1]. That sequence, platform for screening, matrix for extraction, is the standard pattern. The pros are efficiency and audit trails at scale. When you face hundreds or thousands of records, automated de-duplication, blinded dual screening with conflict resolution, and one-click PRISMA flow counts save enormous time and reduce error. Reviews following Joanna Briggs Institute guidance, such as the financial toxicity scoping review that conducted its review in accordance with JBI guidelines and screened over a thousand records [9], or the aflatoxin detection review conducted under JBI guidelines and reported per the PRISMA framework [11], are the natural users of this category. The cons are cost, rigidity, and matrix fit. Most platforms in this category are commercial products aimed at teams doing formal reviews, and their extraction modules are designed for a predetermined extraction form rather than an evolving exploratory matrix. If your review is exploratory and your columns will change as you read, you may find yourself exporting out of the platform into a spreadsheet anyway. ### Meta-Analysis Software When your review pools quantitative results, a second synthesis tool enters the picture. Cochrane's RevMan is the most widely cited example in the published record. A dentine pretreatment meta-analysis used RevMan version 5.4 with a random-effects model to generate mean differences with 95 percent [confidence intervals](/blog/data-analysis/confidence-interval-formula-examples), and used the Q-test and I-squared test for heterogeneity [10]. A pregnancy weight management review used RevMan for its meta-analyses alongside thematic analysis of [qualitative data](/blog/guides/qualitative-data-analysis-a-practical-guide-for-life-scientists) [8]. A breast reconstruction meta-analysis performed its analysis using Cochrane RevMan and IBM SPSS software [12]. The pros are purpose-built statistics: pooled effect sizes, forest plots, and heterogeneity measures computed correctly. The cons are scope. These packages handle numbers from your matrix, not the matrix itself, and they carry a real learning curve in statistics as well as software. Most reviewers extract effect data into the matrix, verify it, and then import into the analysis package. ### Bibliometric Mapping Tools Bibliometric tools answer a different question than a matrix does. VOSviewer, used in the financial toxicity scoping review to complement its thematic matrix with a bibliometric analysis [9], maps patterns such as keyword co-occurrence, author collaboration, and citation networks across a corpus. For a scoping review trying to describe the shape of a young research field, this kind of map identifies clusters and gaps that then guide which papers deserve full matrix rows. The pros are speed and visual insight across hundreds of records. The cons are that a co-occurrence map tells you nothing about study quality, effect direction, or findings, so it supplements a literature matrix rather than replacing one. ## How to Choose: The Criteria That Decide Real Choices ![Comparison of spreadsheets, database workspaces, and screening platforms across matrix flexibility, screening support, export, and best team size.](/article-images/illustrations/literature-review-matrix-software-tool-category-fit.webp) Generic feature lists hide the decisions that actually matter. Borrow the method the university library used when selecting its virtual reference software: name your deal-breakers first, then your useful features, then the nice-to-haves, and let the deal-breakers eliminate options before you compare the survivors [4]. Applied to literature review matrix software, five criteria usually decide the outcome. **Corpus size and screening load.** If your systematic literature search will retrieve more records than you can eyeball, screening platforms stop being optional. Reviews reporting screening counts in the hundreds or over a thousand records [3] [9] could not have been managed in a spreadsheet alone. **Team size and reviewer independence.** Dual independent screening and extraction is standard practice in systematic reviews [10] [1] [8]. If you need two reviewers to work independently and a third to resolve conflicts, choose software with built-in reviewer roles and conflict reporting. If you work alone, this requirement disappears and simpler tools win. **Matrix flexibility.** Narrative and scoping reviews often refine their themes while reading, which means columns change. A predefined extraction matrix fits deductive analyses, as in the immunization campaign scoping review that extracted data following a predefined matrix and analyzed it deductively [2]. Flexible coding matrices that evolve, like the thematic coding matrix in the algorithmic management review [3], favor spreadsheets and database workspaces. **Reporting requirements.** If a journal or supervisor expects PRISMA-compliant reporting, your software must give you a clean record of records identified, screened, excluded with reasons, and included. Platforms produce these counts automatically. Spreadsheets require you to maintain the log manually, which is workable for small reviews and risky for large ones. **Export and longevity.** Your matrix should outlive any single tool. Ask for every option: can I get my data out as a standard CSV file, and will the file still open in ten years? Spreadsheets win outright here. Proprietary platforms and workspace apps vary, and lock-in is a legitimate deal-breaker under the library-style selection method [4]. ## Worked Scenarios **Scenario one: a master's student writing a narrative review chapter.** The student expects to read around 40 papers on a focused question. A practical stack is Zotero or EndNote to hold and tag the papers, Google Sheets for the matrix with columns for author and year, design, population, intervention, findings, limitations, and themes, and no paid tools at all. The systematic literature search across two or three databases is small enough to manage by hand, and the matrix becomes the outline of the synthesis section. **Scenario two: a four-person team conducting a systematic review with a meta-analysis.** This team registers a protocol, searches multiple databases with a documented strategy in the pattern of reviews that searched six or eight databases [7] [13], and expects dual-reviewer screening. The stack is a screening platform for de-duplication and independent screening, a shared matrix with a locked extraction form agreed before full-text review in the predefined-matrix style [2], and RevMan or an equivalent package for the pooled analysis [10] [8] [12]. Deal-breakers from the selection exercise are reviewer independence, PRISMA flow counts, and audit trails [4]. **Scenario three: a scoping review mapping an emerging field.** The question is broad and the corpus is heterogeneous, so themes will emerge rather than being fixed in advance. A reasonable stack is a reference manager for intake, VOSviewer to map the field's clusters and identify where evidence concentrates [9], and a spreadsheet or workspace database holding a coding matrix that can grow new columns as themes emerge, in the spirit of the emergent-theme synthesis used in the obstetric violence review [1] and the evolved coding matrix of the algorithmic management review [3]. ## Common Mix-Ups **Confusing the matrix with the synthesis.** A full matrix is not a literature review. Reviewers who write one paragraph per row produce an annotated bibliography, not a synthesis. The written review organizes by theme or concept, and the matrix is the evidence that the themes exist. **Confusing screening software with matrix software.** Platforms that manage screening are excellent at what reviews use them for, such as the Covidence workflow reported in the obstetric violence review [1], but screening tools and extraction matrices are different jobs. Buying a platform does not automatically give you a good matrix. **Confusing a [confusion matrix](/blog/data-analysis/confusion-matrix-definition-examples) with a literature matrix.** In diagnostic accuracy research, a confusion matrix is a two-by-two table of true positives, false positives, true negatives, and false negatives used to compute sensitivity and specificity, as reported in the large vessel occlusion AI meta-analysis [14]. It shares only the word "matrix" with the literature review matrix described here. **Confusing matrix columns with data extraction forms.** A formal systematic review extraction form is fixed, piloted, and agreed before extraction begins, consistent with the predefined-matrix approach [2]. An exploratory reading matrix is allowed to evolve. Problems start when a team treats an evolving exploratory matrix as if it were a finalized extraction form, or freezes an extraction form too early and discovers mid-review that a needed field was never defined. **Starting the matrix before the inclusion criteria.** Columns such as study design, population, and outcome only make sense relative to a defined question. Reviews that define eligibility criteria and quality assessment tools before screening, as the MMP inhibitor meta-analysis did with an adapted quality assessment tool applied by two independent reviewers [10], produce matrices whose rows are comparable. Reviews that decide criteria while filling rows produce matrices that cannot be synthesized. ## Common Mistakes and Limitations **Too many columns.** A 25-column matrix feels thorough and then goes unused because no one can hold it in view. Start with the smallest column set that answers your question, and add columns only when a real comparison needs them. **Free-text columns that should be coded.** If you plan to count or group anything, use controlled values such as "RCT", "cohort", "cross-sectional" instead of letting every row describe design differently. Deductive analysis against a predefined scheme, as in the immunization campaign review [2], depends on consistent entries. **No reason log for exclusions.** PRISMA-based reporting requires reasons for exclusion at full text [10] [11]. Reviewers who skip the log reconstruct it months later from memory, which is unreliable. **Ignoring the grey literature and citation tracing.** Database searching alone misses material. Reviews deliberately added institutional websites for grey literature [2], dissertation databases during search development [5], and backward and forward citation tracing [3]. Plan where these records enter your matrix and label their provenance. **Assuming software guarantees rigor.** Software enforces workflow, not judgment. The reviews cited here achieved their quality through predefined protocols, independent dual review, and validated assessment tools [10] [5] [8], with software supporting each step. A tool cannot fix a vague question or inconsistent inclusion criteria. **A note on scope.** The tool categories described here are established in the research methods literature and illustrated by the published reviews cited throughout, but individual products change features and pricing over time, so verify current capabilities against the developer's documentation before committing a team to one. ## Frequently Asked Questions ### What is literature review matrix software? It is any software used to build and maintain the structured table where each row is a source and each column is a study characteristic you compare across sources. That ranges from Excel and Google Sheets to database workspaces, reference managers, and dedicated review platforms. ### Do I need special software, or is a spreadsheet enough? For a solo narrative review reading a modest number of papers, a spreadsheet is enough and is the most common choice. Dedicated platforms become worthwhile when you need automated de-duplication, independent dual screening, conflict resolution, and PRISMA flow counts at scale. ### What columns should my literature review matrix include? At minimum, citation details, study design, population or sample, intervention or exposure, key findings, and limitations. Add theme codes, direct quotations with page numbers, and your own critical comments, plus a column recording how each paper entered the review, whether by database search or citation tracing. ### How is a literature matrix different from a systematic review data extraction form? A matrix is built for cross-study comparison and may evolve as themes emerge, especially in narrative and scoping reviews. A systematic review extraction form is fixed and piloted before extraction begins, because the protocol defines in advance which data will be collected. ### Can one tool handle the whole review from search to synthesis? No single tool covers the full pipeline well. Most published reviews combine database searching, a screening platform for record management, a matrix layer for extraction, and a statistics package such as RevMan when pooling quantitative results. ### How do I choose between the literature review matrix software options? List your deal-breakers first, in the style of documented software selection exercises, then match them to the criteria that decide real choices: corpus size, team size and reviewer independence, matrix flexibility, reporting requirements, and export. Options that fail a deal-breaker are out regardless of their other features. ## Sources 1. [A Scoping Review of Women's Experiences of Obstetric Violence in the United States.](https://pubmed.ncbi.nlm.nih.gov/42225150/) 2. [A scoping review on integrated health campaigns for immunization in low- and middle-income countries.](https://pubmed.ncbi.nlm.nih.gov/37699072/) 3. [The algorithmic management paradox: a structured integrative review of perceived autonomy on performance in digital workplaces.](https://pubmed.ncbi.nlm.nih.gov/42643206/) 4. [One library's experience with review and selection of chat software for reference.](https://pubmed.ncbi.nlm.nih.gov/12723817/) 5. [Economic evaluation of cognitive behavioural therapy for insomnia (CBT-I) for improving health outcomes in adult population: a systematic review protocol.](https://pubmed.ncbi.nlm.nih.gov/31699744/) 6. [Diagnostic Efficacy of Serum Biomarkers for Oral Cancer: An Updated Systematic Review and Meta-Analysis.](https://pubmed.ncbi.nlm.nih.gov/41945936/) 7. [Comparison of the osteogenic effectiveness of an autogenous demineralised dentin matrix and Bio-Oss® in bone augmentation: a systematic review and meta-analysis.](https://pubmed.ncbi.nlm.nih.gov/35568576/) 8. [Behavioural interventions for weight management in pregnancy: a systematic review of quantitative and qualitative data.](https://pubmed.ncbi.nlm.nih.gov/21696589/) 9. [Financial toxicity in patients with head and neck cancer: a scoping review.](https://pubmed.ncbi.nlm.nih.gov/43000311/) 10. [Impact of Dentine Pretreatment with Matrix Metalloproteinase Inhibitors on Bond Strength of Coronal Composite Restorations: A Systematic Review and Meta-analysis of In Vitro Studies.](https://pubmed.ncbi.nlm.nih.gov/36400108/) 11. [Revolutionizing Food Safety: A Systematic Review of Nanotechnology-Based Aflatoxin Detection (2010-2023).](https://pubmed.ncbi.nlm.nih.gov/41867317/) 12. [A systematic review and meta-analysis on the prepectoral and partial subpectoral immediate single-stage Implant-Based Breast Reconstruction Using ADM.](https://pubmed.ncbi.nlm.nih.gov/41836237/) 13. [Association of peripheral and CSF zinc levels with Parkinson's disease: A systematic review and meta-analysis.](https://pubmed.ncbi.nlm.nih.gov/41783106/) 14. [Diagnostic Test Accuracy of Artificial Intelligence in Large Vessel Occlusion: A Systematic Review and Meta-Analysis.](https://pubmed.ncbi.nlm.nih.gov/42028301/)