While it is known that speakers tend to use more reduced forms for more predictable words or phrases, it is unclear whether the same happens at the referential level: the influence of referent predictability on pronoun production remains a contentious issue, with divergent findings reported in the literature. To address this inconsistency, we carried out a Bayesian meta-analysis of the current literature on the relationship between referent predictability and pronoun production. Our meta-analysis covers twenty primary peer-reviewed studies, encompassing twenty-six experiments across eight languages. We find stronger evidence for a small positive effect of referent predictability on pronoun usage, as opposed to the alternative hypothesis of no effect or a negative effect. As the first comprehensive synthesis of available evidence on this topic, our study offers insights into pronoun production and identifies promising avenues for future research: focus on typologically diverse languages, on conditions where a variety of referring expressions are expected or where the effect of predictability is more likely to appear, and others. Finally, we also advocate for the use of meta-analysis as a tool for theoretical linguistics.

This meta-analysis investigates the contributions of viewing audiovisual input on second language (L2) learning. We calculated 75 effect sizes from 56 experiments (n = 1954) and assessed the effects of audiovisual input on language learning using a within-group (pre-post) meta-analytic approach. Fifteen moderator variables were included in the analysis. Results showed that a) there was a small effect of audiovisual input on L2 learning (g = .89); b) no differences were found between the effects of viewing audiovisual input on different areas of L2 learning (vocabulary, grammar, pronunciation, speaking, listening proficiency); and c) video category had a significant impact on L2 learning with entertainment-focused videos (e.g., TV series, movies, and mixed videos) yielding lower effects than educational videos (e.g., TED Talks, documentaries, and language-focused).

1. Meta-analyses are a reliable method for a quantitative research synthesis. They are, however, prone to specific biases that can be introduced in the process. Such a bias could exist if primary literature produces similar results if coming from the same authors. Authorship network bias is the non-independence of effect sizes introduced by the overlap of authors of primary studies. If not accounted for, it can severely impact the quality of meta-analysis and the conclusions drawn from it.

2. To account for such non-independence, multilevel models with author clusters as an additional hierarchy level were recently suggested. We propose a new method for the detection of non-independent effect sizes based on authorship networks and for their correction.

3. An analysis of simulated data demonstrates the effectiveness of the here-suggested new method. We further applied our new method to nine exemplary meta-analyses.

4. Our new method for detection and effective correction can be easily integrated in existing meta-analysis workflows, using the functionality already offered by R’s metafor package.

5. Our goal is to enhance the reliability of meta-analyses by highlighting potential authorship network bias and offering a method to address this often-overlooked bias.

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.

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.