Cover crops (CCs) are widely promoted for their multifunctional roles in sustainable agriculture, including improving soil health, enhancing crop productivity, and suppressing weeds. This meta-analysis quantitatively assessed the effects of CCs on three key outcomes: soil organic carbon (SOC), successor crop yield, and weed biomass, based on data from multiple independent studies. Weighted random-effects models and log response ratios (lnRR) were used to synthesize results. CCs significantly increased SOC (mean lnRR = 0.390), corresponding to an estimated 47.7% gain compared to controls, although substantial heterogeneity was observed (I2 = 97%), indicating context-dependent responses across systems. Successor crop yields showed an overall neutral response (mean lnRR = 0.052), with high between-study variability (I² = 90.5%), suggesting that positive or negative outcomes depend on site-specific factors. Weed biomass was consistently reduced across all studies (mean lnRR = –1.759), corresponding to an average 82.8% suppression, although variability remained high (I² = 99.2%). Complementary economic analysis indicated that while CCs involve initial establishment costs (∼USD 150/ha), these are often offset by savings in agrochemical use, improved weed and fertility management, and long-term gains in land value. Altogether, the results highlight the potential of CCs as a sustainable agronomic practice, offering multiple ecosystem services and economic co-benefits. Optimizing species selection, management timing, and system integration will be key to maximizing outcomes under diverse agronomic conditions.

Systematic reviews are often characterized as being inherently replicable, but several studies have challenged this claim. The objective of the study was to investigate the variation in results following independent replication of literature searches and meta-analyses of systematic reviews. We included 10 systematic reviews of the effects of health interventions published in November 2020. Two information specialists repeated the original database search strategies. Two experienced review authors screened full-text articles, extracted data, and calculated the results for the first reported meta-analysis. All replicators were initially blinded to the results of the original review. A meta-analysis was considered not ‘fully replicable’ if the original and replicated summary estimate or confidence interval width differed by more than 10%, and meaningfully different if there was a difference in the direction or statistical significance. The difference between the number of records retrieved by the original reviewers and the information specialists exceeded 10% in 25/43 (58%) searches for the first replicator and 21/43 (49%) searches for the second. Eight meta-analyses (80%, 95% CI: 49–96) were initially classified as not fully replicable. After screening and data discrepancies were addressed, the number of meta-analyses classified as not fully replicable decreased to five (50%, 95% CI: 24–76). Differences were classified as meaningful in one blinded replication (10%, 95% CI: 1–40) and none of the unblinded replications (0%, 95% CI: 0–28). The results of systematic review processes were not always consistent when their reported methods were repeated. However, these inconsistencies seldom affected summary estimates from meta-analyses in a meaningful way.

Random-effects meta-analysis is a widely applied methodology to synthesize research findings of studies related to a specific scientific question. Besides estimating the mean effect, an important aim of the meta-analysis is to summarize the heterogeneity, that is, the variation in the underlying effects caused by the differences in study circumstances. The prediction interval is frequently used for this purpose: a 95% prediction interval contains the true effect of a similar new study in 95% of the cases when it is constructed, or in other words, it covers 95% of the true effects distribution on average in repeated sampling. In this article, after providing a clear mathematical background, we present an extensive simulation investigating the performance of all frequentist prediction interval methods published to date. The work focuses on the distribution of the coverage probabilities and how these distributions change depending on the amount of heterogeneity and the number of involved studies. Although the single requirement that a prediction interval has to fulfill is to keep a nominal coverage probability on average, we demonstrate why the distribution of coverages should not be disregarded. We show that for meta-analyses with small number of studies, this distribution has an unideal, asymmetric shape. We argue that assessing only the mean coverage can easily lead to misunderstanding and misinterpretation. The length of the intervals and the robustness of the methods concerning the non-normality of the true effects are also investigated.

Recent studies showing that some outcome variables do not statistically significantly differ between real-stakes and hypothetical-stakes conditions have raised methodological challenges to experimental economics’ disciplinary norm that experimental choices should be incentivized with real stakes. I show that the hypothetical bias measures estimated in these studies do not econometrically identify the hypothetical biases that matter in most modern experiments. Specifically, traditional hypothetical bias measures are fully informative in ‘elicitation experiments’ where the researcher is uninterested in treatment effects (TEs). However, in ‘intervention experiments’ where TEs are of interest, traditional hypothetical bias measures are uninformative; real stakes matter if and only if TEs differ between stakes conditions. I demonstrate that traditional hypothetical bias measures are often misleading estimates of hypothetical bias for intervention experiments, both econometrically and through re-analyses of three recent hypothetical bias experiments. The fact that a given experimental outcome does not statistically significantly differ on average between stakes conditions does not imply that all TEs on that outcome are unaffected by hypothetical stakes. Therefore, the recent hypothetical bias literature does not justify abandoning real stakes in most modern experiments. Maintaining norms that favor completely or probabilistically providing real stakes for experimental choices is useful for ensuring externally valid TEs in experimental economics.

Democratic innovations aim to strengthen citizen participation in democratic decision-making processes. Building on theories of deliberative democracy, participatory democracy and direct democracy, different types of democratic innovations have been developed, ranging from mini-publics, to participatory processes and referendums and citizens’ initiatives. Over the last four decades, an expanding number of scholars have investigated the effects of these democratic innovations on citizens. However, even though a considerable amount of research has been done, there currently exists no overview of the effects of different types of democratic innovations on citizens’ attitudes, behaviour and capabilities. In addition, it is unclear which effects prove robust across studies, and which effects require more investigation.

The aim of this paper is to systematically evaluate what we know and what we do not know yet about the effects of democratic innovations on citizens who participate in them. In order to do so, we conduct a meta-analysis of 100 quantitative empirical studies published between 1980 and 2020. We find, perhaps unsurprisingly, that mini-publics are widely researched for their effects on citizens, whereas studies into the effects of participatory processes and referendums and citizens’ initiatives on participating citizens are much less frequent. We also find that participation in mini-publics changes citizens’ policy attitudes and positively affects citizens’ political attitudes, knowledge, internal efficacy and reasoning skills. For participatory processes, our analyses indicate that they appear to have a positive effect on participants’ political attitudes and knowledge and no effect on participants’ internal efficacy, but there are too few studies to draw robust conclusions. Participation in referendums and citizens’ initiatives appears to have a positive effect on participants’ knowledge and internal efficacy, even though these findings should also be considered preliminary due to the limited number of studies.

This article explores how and to what extent revenue diversification and concentration strategies affect financial performance, particularly financial capacity and vulnerability, in nonprofit organizations. Using a sample collected from a systematic literature search of all major databases, we first conducted a bibliometric analysis of 86 existing studies to visualize the clusters of major topics in this area and to explore the connections between existing studies. We then employed a meta-analysis to quantitatively synthesize 258 effect sizes from 23 existing empirical studies. We found that diversification had little effect on financial vulnerability, but it had a slightly negative effect on financial capacity. The article finally uses a meta-regression to discuss some of the theoretical and practical reasons why there is inconsistency in the results across existing studies and calls for more discussion of the assumptions and effectiveness of revenue diversification among nonprofit scholars and practitioners.

The past two decades have witnessed massive growth in the amount of quantitative research in nonprofit studies. Despite the large number of studies, findings from these studies have not always been consistent and cumulative. The diverse and competing findings constitute a barrier to offering clear, coherent knowledge for both research and practice. To further advance nonprofit studies, some have called for meta-analysis to synthesize inconsistent findings. Although meta-analysis has been increasingly used in nonprofit studies in the past decade, many researchers are still not familiar with the method. This article thus introduces meta-analysis to nonprofit scholars and, through an example demonstration, provides general guidelines for nonprofit scholars with background in statistical methods to conduct meta-analyses, with a focus on various judgement calls throughout the research process. This article could help nonprofit scholars who are interested in using meta-analysis to address some unsolved research questions in the nonprofit literature.

This article examines four lines of scholarly difference in European Union (EU) studies – meta-theoretical, (sub)disciplinary, epistemological and methodological – and whether these are linked to the geographical and institutional affiliations of the authors operating in the field. The study uses a novel dataset based on a quantitative content analysis and human coding of 1597 articles in leading journals dealing with the EU published in the period 2003–2012. The article shows that USA-based scholars score on average – though in many cases, not significantly – higher when it comes to indicators of a comparative politics approach to the EU, use of a rational choice, positivist and statistical vocabulary, and articles coded as quantitative. However, on most of these indicators scholars in some European countries, and especially some institutions, score significantly higher, suggesting that we should disaggregate ‘Europe’ when discussing scholarly differences in the field.

How the price of giving affects charitable donations has been subject to extensive scrutiny in the literature, but the empirical evidence so far has been inconsistent. We conduct a meta-analysis to synthesize the empirical findings on the price-donation relationship, estimate a generalized effect and explore underlying moderators. After combining 386 effect sizes from 52 existing studies, we find that the price of giving generally has a significant, negative association with the level of charitable donations. Further meta-regression analysis suggests that this price effect on charitable donations is moderated by donor type and data year. Overall, donors are sensitive to the price of giving, and the price effect varies under certain circumstances.

Assessing the impact of the nonprofit sector on society has been one of the most fundamental yet challenging questions in public and nonprofit management scholarship. Built on a recent systematic literature review published in VOLUNTAS (Cheng and Choi in Int J Volunt Nonprofit Organ 33:1245–1255, 2022), our meta-analysis synthesizes the existing literature from multiple disciplines and fills this critical knowledge gap. Using 357 effects from 29 studies, our moderation analysis shows that a larger nonprofit sector has a more positive impact on society especially when the impact is political and measured at the city/county level. Studies that used fixed-effects models and quasi-experimental designs also found a more positive societal impact of the nonprofit sector. However, the choice of sector size measure, the selection of impact measure, the use of lagged explanatory variables, publication bias, and publication time seem not to matter.

Do government activities discourage or leverage nonprofit activities? The extant literature has proposed competing lines of arguments, making the net effect ambiguous. The present study conducts a meta-analysis to synthesize extant studies concerning the relationship between the level of government activities and the level of nonprofit activities within a locality and explore potential moderating effects. Through systematically reviewing 30 extant studies, the study finds a mostly positive association between the level of government activities and the level of nonprofit activities, but this relationship is generally weak and sometimes statistically insignificant. In addition, the moderator analysis concludes that data structure, unit of analysis, and field of activity significantly moderate effect size estimates across extant studies. Overall, the net relationship between the level of government activities and the level of nonprofit activities within a locality ranges from null to slight positive. Government activities generally seem not to discourage nonprofit activities, but may slightly leverage them.

Evaluation of clinical prediction models across multiple clusters, whether centers or datasets, is becoming increasingly common. A comprehensive evaluation includes an assessment of the agreement between the estimated risks and the observed outcomes, also known as calibration. Calibration is of utmost importance for clinical decision making with prediction models, and it often varies between clusters. We present three approaches to take clustering into account when evaluating calibration: (1) clustered group calibration (CG-C), (2) two-stage meta-analysis calibration (2MA-C), and (3) mixed model calibration (MIX-C), which can obtain flexible calibration plots with random effects modeling and provide confidence interval (CI) and prediction interval (PI). As a case example, we externally validate a model to estimate the risk that an ovarian tumor is malignant in multiple centers (N = 2489). We also conduct a simulation study and a synthetic data study generated from a true clustered dataset to evaluate the methods. In the simulation study, MIX-C and 2MA-C (splines) gave estimated curves closest to the true overall curve. In the synthetic data study, MIX-C produced cluster-specific curves closest to the truth. Coverage of the PI across the plot was best for 2MA-C with splines. We recommend using 2MA-C with splines to estimate the overall curve and 95% PI and MIX-C for cluster-specific curves, especially when the sample size per cluster is limited. We provide ready-to-use code to construct summary flexible calibration curves, with CI and PI to assess heterogeneity in calibration across datasets or centers.

Coenzyme Q10 (CoQ10) is biologically plausible as an ergogenic aid through roles in mitochondrial energy production and antioxidant defence, yet findings from randomised trials are inconsistent. This review included 24 studies from 6 databases published up to November 2025, assessing effects of CoQ10 on exercise performance, subjective fatigue, and circulating CoQ10 levels in healthy adults. Randomised trials comparing CoQ10 with placebo were synthesised using a three-level model. Risk of bias was assessed with RoB2 and certainty judged with GRADE.

Supplementation consistently increased blood CoQ10, indicating robust biochemical responsiveness. In contrast, performance effects were small and inconsistent. In primary analyses, chronic supplementation showed a small benefit, whereas acute supplementation showed no benefit. After excluding outliers, the chronic effect was no longer stable and the acute effect remained trivial. All subgroup analyses were restricted to chronic supplementation. Within this context, aerobic endurance was significant in primary analyses but became borderline after outlier exclusion, while anaerobic and strength outcomes showed little change. Evidence for reduced subjective fatigue was suggestive and became more consistent after outlier exclusion. Benefits in trained individuals were unstable and became consistent only after outlier exclusion. No stable dose–response pattern emerged for supplementation dosage or duration. Heterogeneity and moderate-to-high risk of bias reduced certainty.

Collectively, CoQ10 reliably elevates circulating levels but provides at most modest and context-dependent benefits for exercise performance, largely under chronic use. Overall certainty is very low to low. Well-controlled randomised trials that standardise formulation, dose, and duration and examine sex-specific and endurance-related responses are needed.

Autobiographical memory (AM) dysfunction has been proposed as a neurocognitive mechanism underlying the development and maintenance of depression. However, case–control neuroimaging studies investigating the neural correlates of AM in depression have yielded inconsistent findings. The present study utilized neuroimaging meta-analyses to identify robust neural markers of AM dysfunction in depression and characterize the associated behavioral and network-level mechanisms. A preregistered neuroimaging meta-analysis (https://osf.io/35xtf) was conducted, incorporating data from 341 patients with unipolar depression, 82 individuals at risk of depression, and 261 healthy controls across case–control functional magnetic resonance imaging studies examining AM processing. Meta-analytic network-level and behavioral decoding analyses were performed to aid interpretation of the findings. Compared with controls, the depression group displayed increased activation in the right paracingulate cortex (dorsal anterior cingulate [dACC]) and precuneus, and decreased activation in the anterior insula during AM recall. Exploratory valence-specific analyses revealed that negative AM recall was associated with increased activity the dACC and precuneus. Meta-analytic decoding linked the dACC to the salience network and to domains related to negative affect and executive control, while the precuneus was associated with the default mode network and with processes related to social cognition and AM. Findings do not support prevailing models emphasizing altered amygdala and hippocampal function in AM deficits in depression. Instead, they highlight the involvement of core regions within the salience and default mode networks as key neural substrates of AM dysfunction. These regions may contribute to affective, social-cognitive, and mnemonic disturbances that shape the valence-specific nature of AM deficits in depression.

The I-squared index was proposed in 2002 as a measure to help understand the consistency of study results in a meta-analysis. It was developed to overcome some of the limitations of existing measures, principally the chi-squared test for heterogeneity and the between-study variance as estimated in a random-effects meta-analysis. I-squared measures approximately the proportion of total variability in results that is due to true heterogeneity rather than random error; it is also conveniently interpreted as a measure of inconsistency in the results of the studies. The index has become extremely widely used, although it is often misinterpreted as an absolute measure of the amount of heterogeneity, which it is not. Here, we discuss the I-squared index and the different ways it can be defined, computed, and interpreted. We introduce a new interpretation of I-squared as a weighted sum of squares, which we propose may be helpful when setting up simulation studies. We discuss some of the extensions and repurposes that have been proposed for I-squared and offer some recommendations on the appropriate use of the index in practice.

This study systematically evaluates the effects of probiotic interventions on gut microbiota and clinical outcomes in diabetic patients to determine the optimal target population and conditions for effective use, with an emphasis on precision treatment. A comprehensive search was performed across PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Internet (CNKI), and Wanfang databases until April 2024. Randomized controlled trials (RCTs) assessing probiotics as adjunctive therapy for diabetes were included. The control group received standard care, and the intervention group received probiotics alongside standard care. Data were managed with Endnote and Excel, and analyses were conducted using Revman 5.3 and Stata 16. Twelve RCTs involving 1,113 participants were included. Probiotics significantly increased fecal Lactobacillus (standardized mean difference (SMD) 1.42, P < 0.0001, I2 = 95%) and Bifidobacterium levels (SMD 1.27, P < 0.0001, I² = 90%) and reduced fasting plasma glucose (SMD -0.35, P = 0.004). Subgroup analysis showed that shorter intervention durations (≤3 months) improved FPG, HbA1c, and Bifidobacterium levels, while younger patients (≤60 years) experienced the most significant improvements in Bifidobacterium levels. In conclusion, probiotics improve gut microbiota and clinical outcomes in diabetic patients, with intervention duration and patient age as key factors influencing treatment effectiveness.