Meta-Analysis in Social Science Research: Applications and Considerations

Meta-analysis is a statistical approach that combines results from multiple independent studies to produce a pooled estimate of an effect or association. In social science research, meta-analysis serves a distinct purpose: it transforms a body of individual studies into a quantitative synthesis that can reveal patterns too small or too inconsistent for any single study to detect. For researchers in psychology, education, economics, and related life-science fields, meta-analysis offers a systematic method for summarizing evidence, testing moderators, and identifying gaps in the literature. This article explains how meta-analysis is applied across social science disciplines, examines the special considerations that arise when synthesizing human behavioral data, and provides a practical checklist for conducting a meta-analysis in social science contexts.

What Meta-Analysis Does in Social Science Research

Meta-analysis in social science research addresses a fundamental problem: individual studies often produce conflicting or imprecise results because of small samples, measurement differences, and contextual variation. By pooling data across studies, meta-analysis increases statistical power and provides a more precise estimate of the true effect. The method is not simply averaging numbers. It involves a structured process of defining a research question, systematically searching for studies, extracting data, computing effect sizes, assessing heterogeneity, and interpreting results in light of study quality and publication bias.

The application of meta-analysis in social sciences has grown substantially over the past two decades. Systematic reviews of prevalence studies are now conducted across medicine, ecology, psychology, and social sciences, using methods designed to pool single proportions and compare proportions between groups [11]. This expansion reflects a broader recognition that cumulative knowledge requires transparent and reproducible synthesis methods.

Meta-analysis in social science differs from meta-analysis in biomedical research in several important ways. Social science outcomes are often measured with self-report instruments instead of biological markers. Effect sizes are frequently expressed as correlations or standardized mean differences instead of risk ratios. And the populations studied are typically defined by social or psychological characteristics instead of clinical diagnoses. These differences shape every stage of the meta-analytic process, from search strategy to statistical modeling.

Core Principles of Social Science Meta-Analysis

Effect Size Selection and Computation

The choice of effect size metric is foundational to any meta-analysis. In social science research, common effect sizes include Pearson's r for correlational studies, Hedges' g or Cohen's d for group comparisons, and odds ratios for categorical outcomes. The selected metric must be comparable across studies, which requires that primary studies report sufficient statistical information.

Meta-analysis of proportions has become a popular application in social science fields, particularly for prevalence studies. Classic approaches based on the inverse variance method and generalized linear mixed models that account for the binary structure of the data are both used, and transformations of proportions and their back-transformations must be handled carefully both for individual studies and in the meta-analysis setting [11].

For studies reporting correlational data, Pearson's r is often the natural effect size. A meta-analysis examining the relationship between cultural identity and substance use among Indigenous youth used Pearson's r as the effect size and found no significant pooled association, with high heterogeneity and possible publication bias [18]. This example illustrates that even well-conducted meta-analyses can produce null findings, and that reporting the full pattern of results is essential.

Heterogeneity Assessment

Heterogeneity refers to the variability in effect sizes across studies beyond what would be expected by chance alone. In social science meta-analyses, heterogeneity is typically high because studies differ in samples, measures, settings, and time periods. Quantifying heterogeneity is not a limitation to be eliminated but a finding to be explained.

The I² statistic is commonly reported to describe the percentage of total variability attributable to between-study differences. In the cultural identity and substance use meta-analysis, heterogeneity was high at 72%, and the authors examined potential moderators including age, sex, measurement approach, and living on a reservation, none of which significantly moderated the overall effect [18]. This outcome demonstrates that high heterogeneity can persist even after testing plausible moderators, and that reporting this limitation is part of responsible synthesis.

Fixed Effects and Random Effects Models

The choice between fixed effects and random effects models depends on assumptions about the sources of variation. A fixed effects model assumes that all studies estimate a single true effect and that observed differences are due to sampling error alone. A random effects model assumes that studies estimate different true effects that vary around an overall mean, and it incorporates both within-study and between-study variance.

In social science applications, random effects models are generally more appropriate because studies rarely replicate identical conditions. A meta-analysis of transcranial direct current stimulation effects on psychological factors in athletes used either random or fixed effects models depending on the outcome, computing Hedges' g effect sizes with 95% confidence intervals [15]. The choice of model affects the width of confidence intervals and the interpretation of pooled estimates.

Applications Across Social Science Disciplines

Psychology and Mental Health

Meta-analysis has become a standard tool in psychology for synthesizing intervention effects and testing theoretical models. The scope of applications ranges from clinical interventions to cognitive processes.

A systematic review and meta-analysis examined the effectiveness of cognitive behavioral therapy based natural language processing enabled artificial intelligence conversational agents for mental health intervention. The analysis included 15 randomized controlled trials with 1,737 participants and found small to moderate effects on depressive symptoms and small effects on negative affect, while effects on generalized anxiety, stress, and positive affect were not significant after adjusting for publication bias [16]. Subgroup analyses suggested that multi-modal conversational agents may be more effective than single-modality agents for depressive symptoms, and meta-regression revealed that higher-quality studies reported larger effect sizes [16].

This example highlights several considerations specific to social science meta-analysis. Publication bias adjustment can change conclusions, quality ratings can correlate with effect sizes, and subgroup analyses can generate hypotheses even when overall effects are modest.

Another application in psychology involves meta-analytic structural equation modeling, which extends traditional meta-analysis by testing complex models of relationships. A systematic review and meta-analytic structural equation modeling study examined the longitudinal relationship between co-rumination and depressive symptoms in adolescents. The analysis included 46 datasets comprising 18,222 participants and found that co-rumination prospectively predicted depressive symptoms, while the reverse pathway was smaller and less consistent [19]. Measurement instruments significantly moderated the cross-lagged paths, while gender, age, time lag, and study quality did not [19].

The co-rumination example demonstrates how meta-analysis can address questions of temporal directionality that individual studies cannot resolve. It also illustrates the importance of testing moderators systematically and reporting sensitivity analyses.

Education and Learning

In education research, meta-analysis is used to evaluate instructional interventions, learning technologies, and assessment methods. The challenges of educational meta-analysis include variability in outcome measures, differences in implementation fidelity, and the nested structure of educational data.

A systematic review and three-level meta-analysis examined the irrelevant speech effect in reading, where readers are exposed to background speech. The analysis included 30 studies yielding 606 effect sizes and found that both intelligible and unintelligible speech slowed reading, but intelligible speech exerted a stronger disruptive effect [14]. The three-level meta-analytic approach allowed the authors to account for multiple effect sizes within studies, a common situation in educational research where one study may report outcomes for multiple conditions or time points.

The irrelevant speech effect study also illustrates how meta-analysis can adjudicate between competing theoretical accounts. The results suggested that the phonological and semantic interference hypotheses provided only partial explanations, while the process interference hypothesis offered a more comprehensive account [14]. This theoretical contribution is a distinctive value of meta-analysis in social science.

Economics and Business

Meta-analysis in economics and business research synthesizes evidence on topics ranging from consumer behavior to organizational performance. The application of meta-analysis in these fields often involves observational data, which introduces additional sources of heterogeneity and bias.

A meta-analysis investigating the impact of network science and social network analysis in business contexts drew from 89 peer-reviewed studies published between 2005 and 2023. The analysis examined how network-based methodologies influence decision-making, communication strategies, and operational efficiency, with subgroup analyses indicating significant variations based on platform type, network size, and industry verticals [22]. This example shows how meta-analysis can synthesize evidence across a diverse body of applied research.

Another business-related meta-analysis examined disparities between ordinal and multinomial logistic regression, drawing on 60 peer-reviewed studies published between 2015 and 2025. The review found that the choice between these models was often driven more by researcher preference and software defaults than by rigorous diagnostic testing, and it identified a persistent gap in assumption testing [23]. This application demonstrates that meta-analysis can be used to examine methodological practices themselves, also substantive findings.

Health Behavior and Public Health

Social science meta-analysis frequently intersects with public health research, particularly for behavioral and psychosocial risk factors. These applications require careful attention to measurement and to the distinction between risk factors and outcomes.

A systematic review, meta-analysis, and bibliometric investigation examined the impact of social isolation and loneliness on cardiovascular disease risk factors. The analysis included six studies involving 104,511 patients and found that poor social relationships were associated with a 16% increase in the risk of incident cardiovascular disease [7]. The prevalence of loneliness ranged from 5 to 65.3% and social isolation from 2 to 56.5% across studies, illustrating the wide variation in measurement and population characteristics [7].

This example demonstrates the importance of reporting the range of exposure prevalence and the implications for generalizability. It also shows how meta-analysis can combine with bibliometric analysis to map the structure of a research field.

A systematic review and meta-analysis of ecological momentary assessment studies examined within-person associations between psychological factors and eating behavior in healthy adults. The review found high diversity in conceptualization and reporting of effect sizes, which limited the ability to meta-analyze all outcomes [20]. Only around half of the studies reported on reliability or validity of the predictors or outcomes, and most referred to adaptations of non-EMA scales with little information about the exact adaptation [20].

This example illustrates a common challenge in social science meta-analysis: inconsistent reporting of measurement properties and effect sizes across primary studies. The authors noted that study adherence ranged between 79 and 90%, but the diversity in methods limited synthesis [20].

Special Considerations for Social Science Meta-Analysis

Measurement Variability

Social science constructs are rarely measured with a single gold standard instrument. Depression, self-esteem, cultural identity, and eating behavior can each be operationalized in multiple ways, and different measures may capture different aspects of the construct. This measurement variability creates challenges for effect size comparability and for interpreting pooled estimates.

A meta-analysis on the relationship between cultural identity and substance use among Indigenous youth noted that the null pooled association likely reflected limitations in how cultural identity is operationalized within quantitative research [18]. The authors described measurement approaches and tribal nation representation, finding that tribal affiliation was largely not reported [18]. This example underscores the need for meta-analysts to code measurement characteristics and to consider them as potential moderators.

Publication Bias and Small Study Effects

Publication bias occurs when studies with statistically significant results are more likely to be published than studies with null or negative results. In social science research, publication bias can distort meta-analytic estimates, particularly when the literature includes many small studies.

The cultural identity and substance use meta-analysis found funnel plot asymmetry suggesting possible publication bias [18]. The conversational agent meta-analysis found that effects on generalized anxiety, stress, and positive affect were not significant after adjusting for publication bias [16]. These examples illustrate that publication bias is not a theoretical concern but an empirical one that can change conclusions.

Meta-analysts should assess publication bias using multiple methods, including funnel plots, trim and fill procedures, and selection models. They should also consider whether the literature base includes unpublished studies, dissertations, and reports from non-English sources.

Quality Assessment

Assessing the quality of primary studies is an essential component of meta-analysis. In social science research, quality assessment may include evaluation of study design, sample size, measurement validity, attrition, and statistical analysis. Quality ratings can be used as moderators or as weights in sensitivity analyses.

The serious games meta-analysis used the Cochrane RoB 2 tool for randomized controlled trials and the ROBINS-I tool for non-randomized studies to assess internal validity [24]. The conversational agent meta-analysis found that higher-quality studies reported larger effect sizes, suggesting that the true efficacy of these interventions may be underestimated in the current literature [16]. This finding illustrates the importance of quality assessment for interpreting meta-analytic results.

Reporting Standards

Transparent reporting is critical for the credibility and usability of meta-analyses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was developed to facilitate transparent and complete reporting of systematic reviews and has been updated to PRISMA 2020 to reflect recent advances in methodology and terminology [8]. The methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings [8].

Systematic reviews should build on a protocol that describes the rationale, hypothesis, and planned methods of the review. Detailed, well-described protocols can facilitate the understanding and appraisal of the review methods, as well as the detection of modifications to methods and selective reporting in completed reviews [6]. The PRISMA for Protocols 2015 statement consists of a 17-item checklist intended to facilitate the preparation and reporting of a robust protocol for the systematic review [6].

For diagnostic test accuracy reviews, the PRISMA diagnostic test accuracy guideline provides a 27-item checklist that reflects the specific requirements for reporting systematic reviews and meta-analyses of diagnostic test accuracy studies [13]. While this guideline was developed for diagnostic tests, its principles apply to social science research that evaluates screening tools or assessment instruments.

At a Glance

Practical Workflow for Conducting a Meta-Analysis

Step 1: Define the Research Question and Protocol

The research question should specify the population, intervention or exposure, comparison, and outcomes of interest. The question should be narrow enough to be answerable but broad enough to capture relevant studies. A protocol should be developed before the search begins, describing the rationale, hypothesis, and planned methods [6]. Protocol registration provides a public record of the planned methods and facilitates the detection of modifications.

Step 2: Develop the Search Strategy

The search strategy should include multiple databases and a combination of keywords and subject headings. The cultural identity meta-analysis used a systematic literature search that identified 18 studies [18]. The co-rumination meta-analysis searched seven databases [19]. The eating behavior meta-analysis searched Ovid MEDLINE, Embase, PsycINFO, and Web of Science [20]. The search should be documented in sufficient detail to allow replication.

Step 3: Screen Studies and Extract Data

Title and abstract screening should be conducted by at least two independent reviewers, with disagreements resolved by discussion or a third reviewer. Full-text screening applies the inclusion and exclusion criteria to the remaining records. Data extraction should capture study characteristics, effect sizes, and quality indicators.

Semi-automated approaches to data extraction for systematic reviews and meta-analyses in social sciences are being developed as a living review topic [26]. These tools may reduce the burden of manual extraction, but they require validation and careful monitoring.

Step 4: Compute Effect Sizes and Conduct Statistical Analysis

Effect sizes should be computed from the extracted data using appropriate formulas. The analysis should include a forest plot, heterogeneity statistics, and planned moderator analyses. Sensitivity analyses should examine the robustness of results to decisions about inclusion criteria, effect size computation, and statistical models.

Step 5: Interpret Results and Report Findings

Interpretation should consider the magnitude and precision of the pooled effect, the consistency of findings across studies, the influence of moderators, and the risk of bias. Reporting should follow PRISMA 2020 and include a flow diagram, search details, and a complete description of methods [8].

Records and Measurements

Documentation Requirements

Meta-analysis requires meticulous documentation at every stage. The search strategy should record database names, search dates, and the full search strings. Screening decisions should be documented with reasons for exclusion. Data extraction forms should be piloted and refined. Statistical analyses should be reproducible from the extracted data.

The PRISMA 2020 statement provides a framework for reporting that includes a checklist of items and examples from published reviews [8]. Following this framework ensures that readers can assess the trustworthiness and applicability of the review findings [8].

Data Management

Data management for meta-analysis involves organizing study-level data, effect size data, and moderator data. A structured data extraction form should capture all variables needed for the planned analyses. Data should be double-entered or verified to minimize errors.

The Research Data Framework from the National Institute of Standards and Technology provides guidance on data management practices that apply to research data across disciplines [1]. While not specific to meta-analysis, this framework addresses the broader context of research data stewardship.

Common Failure Patterns in Social Science Meta-Analysis

Inadequate Search Strategies

A meta-analysis is only as good as its search. Searches that rely on a single database, use overly narrow keywords, or exclude non-English studies can miss relevant evidence. The eating behavior meta-analysis found that a systematic search of four databases yielded 2,046 articles for title and abstract screening, of which 208 were screened in full text [20]. This funneling pattern is typical, but it requires a comprehensive initial search.

Insufficient Heterogeneity Exploration

High heterogeneity is common in social science meta-analyses, and failing to explore it is a frequent weakness. The cultural identity meta-analysis tested multiple moderators and reported that none significantly moderated the overall effect [18]. The co-rumination meta-analysis found that only measurement instruments significantly moderated the cross-lagged paths after correction for multiple testing [19]. These examples show that moderator analysis is not always successful, but it should always be attempted and reported.

Overinterpretation of Null or Small Effects

Meta-analyses that find small or null effects require careful interpretation. The self-esteem and externalizing problems meta-analysis found weak negative associations in bivariate analyses, but once baseline levels were controlled in cross-lagged panel models, neither prior self-esteem predicted later externalizing problems nor did prior externalizing problems predict later self-esteem [21]. The authors concluded that educators should exercise caution in targeting self-esteem enhancement as an intervention for externalizing problems [21]. This conclusion follows from the evidence instead of from prior expectations.

Ignoring Measurement Issues

Social science constructs are measured with error, and meta-analyses that ignore measurement issues can produce misleading results. The eating behavior meta-analysis found that only around half of the studies reported on reliability or validity of the predictors or outcomes [20]. The cultural identity meta-analysis noted that the null association likely reflected limitations in how cultural identity is operationalized [18]. Meta-analysts should code measurement characteristics and consider them in interpretation.

Limitations and Professional Escalation Criteria

Limitations of Meta-Analysis in Social Science

Meta-analysis has inherent limitations that researchers should acknowledge. The quality of a meta-analysis depends on the quality of the primary studies. Publication bias can distort pooled estimates. Heterogeneity may remain unexplained even after extensive moderator analysis. And the aggregation of results across studies can obscure important contextual differences.

The conversational agent meta-analysis found that higher-quality studies reported larger effect sizes, suggesting that the true efficacy of these interventions may be underestimated in the current literature [16]. This finding illustrates that meta-analytic results can be sensitive to study quality and that conclusions should be tempered accordingly.

When to Seek Specialized Assistance

Researchers should consider seeking specialized assistance when their meta-analysis involves advanced statistical methods, such as meta-analytic structural equation modeling, network meta-analysis, or individual participant data meta-analysis. The co-rumination study used one-stage meta-analytic structural equation modeling to estimate cross-lagged panel models from pooled correlation matrices [19]. This method requires specialized software and statistical expertise.

Researchers should also seek assistance when dealing with complex data structures, such as multiple effect sizes per study, clustered data, or longitudinal outcomes. The irrelevant speech effect study used a three-level meta-analysis to account for multiple effect sizes within studies [14]. This approach requires careful specification of the hierarchical structure.

Professional Escalation Criteria

Researchers should escalate to a statistician or meta-analysis methodologist when they encounter any of the following situations:

• The number of eligible studies is very small, requiring specialized methods for sparse data
• The effect sizes are highly heterogeneous and planned moderators do not explain the variability
• The data include complex dependencies, such as multiple outcomes per study or clustered samples
• The research question requires advanced methods, such as meta-analytic structural equation modeling or network meta-analysis
• The results are sensitive to the inclusion or exclusion of specific studies, requiring influence analysis

Quality and Welfare Context

Ethical Considerations

Meta-analysis in social science research raises ethical considerations related to the responsible conduct of research. These include the obligation to report findings accurately, to avoid selective reporting, and to acknowledge limitations. The PRISMA 2020 statement emphasizes that the methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings [8].

Participant Welfare in Primary Studies

While meta-analysis itself does not involve direct contact with participants, the synthesis of results from primary studies carries an obligation to consider participant welfare. Meta-analyses of intervention studies should assess the risk of bias in the primary studies and consider whether the evidence base is sufficient to support practice recommendations. The serious games meta-analysis noted limitations in interpreting results, such as the lack of long-term follow-up, while finding that interventions incorporating biofeedback provided the strongest evidence for generalizability of learned emotion regulation skills to real life [24].

Data Quality and Reproducibility

The credibility of meta-analysis depends on the quality and accessibility of the underlying data. The National Center for Biotechnology Information provides literature resources that support the discovery and retrieval of primary studies [4]. PubMed, maintained by the National Library of Medicine, is a key database for identifying relevant research [5]. The EQUATOR Network provides resources for reporting guidelines that support transparent and complete reporting [2].

The Experimental Design Assistant from the NC3Rs provides guidance on experimental design that can improve the quality of primary studies and, consequently, the quality of the evidence base available for meta-analysis [3]. While developed primarily for animal research, its principles of rigorous design and transparent reporting apply broadly.

Frequently Asked Questions

What is the difference between a systematic review and a meta-analysis?

A systematic review is a structured process of identifying, evaluating, and synthesizing all relevant studies on a research question. A meta-analysis is the statistical combination of results from those studies. A systematic review may or may not include a meta-analysis, but a meta-analysis should always be conducted within the framework of a systematic review to ensure that the included studies are identified and selected without bias.

How do I choose between fixed effects and random effects models?

Fixed effects models assume that all studies estimate a single true effect and that observed differences are due to sampling error alone. Random effects models assume that studies estimate different true effects that vary around an overall mean. In social science research, random effects models are generally more appropriate because studies differ in samples, measures, settings, and time periods. The choice should be justified in the protocol and reported in the methods.

What effect size should I use for my meta-analysis?

The choice of effect size depends on the research question and the types of studies being synthesized. Pearson's r is appropriate for correlational studies, Hedges' g or Cohen's d for group comparisons, and odds ratios for categorical outcomes. Meta-analysis of proportions is used for prevalence studies [11]. The selected metric must be comparable across studies, which may require transforming effect sizes reported in different metrics.

How do I handle studies with multiple effect sizes?

Studies may report multiple effect sizes for different outcomes, different time points, or different subgroups. Options include selecting one effect size per study, averaging effect sizes within studies, or using multilevel meta-analysis that accounts for the dependency structure. The irrelevant speech effect study used a three-level meta-analysis to handle 606 effect sizes from 30 studies [14].

What is publication bias and how do I assess it?

Publication bias occurs when studies with statistically significant results are more likely to be published than studies with null or negative results. Assessment methods include funnel plots, trim and fill procedures, and selection models. The cultural identity meta-analysis found funnel plot asymmetry suggesting possible publication bias [18]. Multiple methods should be used because each has limitations.

How do I assess the quality of primary studies?

Quality assessment should use validated tools appropriate to the study designs being evaluated. The serious games meta-analysis used the Cochrane RoB 2 tool for randomized controlled trials and the ROBINS-I tool for non-randomized studies [24]. Quality ratings can be used as moderators or in sensitivity analyses. The conversational agent meta-analysis found that higher-quality studies reported larger effect sizes [16].

What should I do if heterogeneity is high?

High heterogeneity should be explored through planned moderator analyses and meta-regression. If heterogeneity remains unexplained, this should be reported as a limitation. The cultural identity meta-analysis found high heterogeneity at 72% and tested multiple moderators, none of which significantly moderated the overall effect [18]. The co-rumination meta-analysis found that only measurement instruments significantly moderated the cross-lagged paths after correction for multiple testing [19].

Do I need to register my meta-analysis protocol?

Protocol registration is strongly recommended. Systematic reviews should build on a protocol that describes the rationale, hypothesis, and planned methods [6]. The PRISMA for Protocols 2015 statement provides a 17-item checklist for protocol reporting [6]. Funders and those commissioning reviews might consider mandating the use of the checklist to facilitate the submission of relevant protocol information in funding applications [6].

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References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement.. Systematic reviews, 2015.
• The impact of social isolation and loneliness on cardiovascular disease risk factors: a systematic review, meta-analysis, and bibliometric investigation.. Scientific reports, 2024.
• PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews.. BMJ (Clinical research ed.), 2021.
• The effects of kangaroo mother care on physiological parameters of premature neonates in neonatal intensive care unit: A systematic review.. Journal of pediatric nursing, 2023.
• The genetics of rheumatoid arthritis.. Rheumatology (Oxford, England), 2020.
• Meta-Analysis of Proportions.. Methods in molecular biology (Clifton, N.J.), 2022.
• SARC-F and other screening tests for sarcopenia.. Current opinion in clinical nutrition and metabolic care, 2022.
• Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement.. JAMA, 2018.
• The mechanism of the irrelevant speech effect in reading: a systematic review and three-level meta-analysis.. 2026.
• The effects of anodal transcranial direct current stimulation on psychological factors in athletes: a systematic review and meta-analysis.. 2026.
• The effectiveness of CBT-based NLP-enabled AI conversational agents for mental health intervention: a systematic review and meta-analysis.. 2026.
• How visual attention underpins reading: Converging evidence from a meta-analysis of fMRI studies.. 2026.
• A meta-analysis on the relationship between cultural identity and substance use among indigenous youth.. 2026.
• The longitudinal relationship between co-rumination and depressive symptoms in adolescents: A systematic review and meta-analytic structural equation modeling.. 2026.
• Within-person associations of psychological factors with healthy or unhealthy diet in EMA studies in general adult populations: a systematic review and meta-analysis.. 2026.
• No Robust Evidence for Cross-Lagged Effects Between Self-Esteem and Externalizing Problems: A Longitudinal Meta-Analysis.. 2026.
• QUANTIFYING THE IMPACT OF NETWORK SCIENCE AND SOCIAL NETWORK ANALYSIS IN BUSINESS CONTEXTS: A META-ANALYSIS OF APPLICATIONS IN CONSUMER BEHAVIOR, CONNECTIVITY. International Journal of Scientific Interdisciplinary Research, 2024.
• Disparities in Methodology, Assumptions and Applications between Ordinal and Multinomial Logistic Regression: A Meta-Analysis. Mbeya University of Science and Technology Journal of Research and Development, 2025.
• Serious games to support emotional regulation strategies in educational intervention programs with children and adolescents. Systematic review and meta-analysis. Heliyon, 2025.
• Review of Application and Evolution of Meta-Analysis in Social Sciences. Data Analysis and Knowledge Discovery, 2021.
• (Semi)automated approaches to data extraction for systematic reviews and meta-analyses in social sciences: A living review. F1000research, 2024.
• Realizing the full potential of psychometric meta-analysis for a cumulative science and practice of human resource management. Human Resource Management Review, 2017.
• Innovations in meta-analysis and social impact analysis relevant for tech mining. Technological Forecasting and Social Change, 2006.
• Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.