This systematic review and meta-analyses provide the first synthesis of the literature on trait mindfulness and psychotic-like experiences (PLEs). Theoretical models suggest a protective function of mindfulness and it is important to understand any potential role of mindfulness in the prevention and treatment of PLEs. We examined the following: (1) What is the relationship between trait mindfulness and PLEs in nonclinical populations?; and (2) What is the effect of mindfulness-based interventions (MBIs) on PLEs in nonclinical populations? Five databases were searched, and effect sizes were extracted for each study. Seventeen papers were included in the review. Eleven papers explored the relationship between mindfulness and PLEs, and the meta-regression found a small negative association between PLEs and mindfulness (k = 8; pooled correlation r = −0.25; 95% confidence interval [CI]: −0.37, −0.13, p < .001). Eight studies investigated the effect of MBIs on PLEs and the summary effect was not significant in the meta-analysis (k = 5; pooled standard mean difference = .09; 95% CI: −0.61, 0.79, p = 0.80). Overall, the findings suggest that higher levels of mindfulness are associated with reduced PLEs, with no evidence for the effectiveness of MBIs in reducing PLEs. Findings should be interpreted cautiously given the small number of studies and high heterogeneity in the meta-analyses. Future studies are needed to determine whether MBIs might prevent the transition to psychosis or an at-risk mental state and might usefully measure a broader range of clinically relevant outcomes.

A meta-analysis of diagnostic test accuracy (DTA) studies typically synthesizes study-specific test sensitivity ($Se$) and specificity ($Sp$) to quantify the accuracy of an index test of interest. The bivariate linear mixed effects model with logit transformation of $Se$ and $Sp$ (BLMM-Logit) is commonly used to make statistical inferences, but may lead to misleading results due to the need for Haldane–Anscombe correction and an approximate estimation of variance within the study. Alternative models based on the arcsine square root and Freeman–Tukey double arcsine transformation have been proposed to address these issues; however, they still rely on approximate variance estimation, which is suitable only for large sample sizes. The bivariate generalized linear mixed effects model (BGLMM) is another option, but it faces convergence issues with small meta-analyses or sparse primary studies. To address these limitations, we proposed an exact within-study variance calculation method that does not require Haldane–Anscombe correction and is applicable regardless of the transformation used or the number of studies and participants. We evaluated this method against existing approaches using real-life and simulated DTA meta-analyses. The methods were comparable for large meta-analyses. However, BLMM-Logit demonstrated substantial negative bias in estimating variances between studies and consistently underestimated summary $Se$ and $Sp$ in all simulation scenarios. In contrast, the proposed exact methods (Exact-Logit, Exact-ASR, and Exact-FTDA) and BGLMM had minimal bias and better performance metrics, particularly for meta-analyses with sparse primary studies. Thus, the proposed exact methods should be preferred for DTA meta-analyses with small or sparse studies.

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.