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The nature of religious change and the future of religion have been central questions of social science since its inception. But empirical research on this question has been quite American-centric, encouraged by the conventional wisdom that the United States is an outlier of religiosity in the developed world, and, more pragmatically, by the availability of survey data. The dramatic growth in the number and reach of cross-national surveys over the past two decades has offered a corrective. These data have allowed research on religious trends in the United States, Canada, and Europe, putting American trends into comparative relief. This research synthesis reviews the past quarter century of cross-national comparative survey research on religious behavior, focusing on religious service attendance as a commonly measured behavior that is arguably more equivalent across societies and cultures than other measures of religiosity. The lack of evidence for religious revival is highlighted, noting instead declining rates of attendance in the United States and Canada, and either declining rates or low \"bottomed-out\" stability in Western Europe, most of Eastern Europe, and Australia and New Zealand. Finally, countries in Latin America, Africa, and Asia are discussed to the extent that research allows, before a call for future research—in these places in particular—is made in order to correct for the Western and Christian focus of much of the research on cross-national religious trends.

The present article reports on a study which examined research synthetic studies over a period of 24 years from 1998 to 2021 collected from 30 representative applied linguistic journals. The study aimed to investigate the adequacy of reporting and adherence to standards of rigor and transparency in all steps of the meta-analyses. Following a synthetic approach, we synthesized 90 meta-analyses in applied linguistics (AL) journals. The results revealed strengths in all steps and a number of weaknesses in some steps of the process. AL meta-analysts did fairly well in locating the candidate studies by using different databases and supplementary search strategies. In their data analysis phase, they used random effects models more than a combination of random and fixed effects models. To interpret the effect sizes, almost half of the studies used Cohen’s benchmark and almost one third of them used Plonsky and Oswald’s field-specific benchmark. The areas which need to be addressed in future synthetic studies were identified as: more precise identification of moderator variables; more reliable data collection by paying adequate attention to coding sheet piloting and coder qualifications and training; and more reliance on measures other than Q-statistics. The implications of these results are discussed.

Herbert Simon’s (1956) concept of satisficing provides an intuitive explanation for the reasons why respondents to surveys sometimes adopt response strategies that can lead to a reduction in data quality. As such, the concept rapidly gained popularity among researchers after it was first introduced to the field of survey methodology by Krosnick and Alwin (1987), and it has become a widely cited buzzword linked to different forms of response error. In this article, we present the findings of a systematic review involving a content analysis of journal articles published in English-language journals between 1987 and 2015 that have drawn on the satisficing concept to evaluate survey data quality. Based on extensive searches of online databases, and an initial screening exercise to apply the study’s inclusion criteria, 141 relevant articles were identified. Guided by the theory of survey satisficing described by Krosnick (1991), the methodological features of the shortlisted articles were coded, including the indicators of satisficing analyzed, the main predictors of satisficing, and the presence of main or interaction effects on the prevalence of satisficing involving indicators of task difficulty, respondent ability, and respondent motivation. Our analysis sheds light on potential differences in the extent to which satisficing theory holds for different types of response error, and highlights a number of avenues for future research.

Background Over 120 million mice and rats are used annually in research, conventionally housed in shoebox-sized cages that restrict natural behaviours (e.g. nesting and burrowing). This can reduce physical fitness, impair thermoregulation and reduce welfare (e.g. inducing abnormal stereotypic behaviours). In humans, chronic stress has biological costs, increasing disease risks and potentially shortening life. Using a pre-registered protocol ( https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17955 ), this meta-analysis therefore tested the hypothesis that, compared to rodents in ‘enriched’ housing that better meets their needs, conventional housing increases stress-related morbidity and all-cause mortality. Results Comprehensive searches (via Ovid, CABI, Web of Science, Proquest and SCOPUS on May 24 2020) yielded 10,094 publications. Screening for inclusion criteria (published in English, using mice or rats and providing ‘enrichments’ in long-term housing) yielded 214 studies (within 165 articles, using 6495 animals: 59.1% mice; 68.2% male; 31.8% isolation-housed), and data on all-cause mortality plus five experimentally induced stress-sensitive diseases: anxiety, cancer, cardiovascular disease, depression and stroke. The Systematic Review Center for Laboratory animal Experimentation (SYRCLE) tool assessed individual studies’ risks of bias. Random-effects meta-analyses supported the hypothesis: conventional housing significantly exacerbated disease severity with medium to large effect sizes: cancer (SMD = 0.71, 95% CI = 0.54–0.88); cardiovascular disease (SMD = 0.72, 95% CI = 0.35–1.09); stroke (SMD = 0.87, 95% CI = 0.59–1.15); signs of anxiety (SMD = 0.91, 95% CI = 0.56–1.25); signs of depression (SMD = 1.24, 95% CI = 0.98–1.49). It also increased mortality rates (hazard ratio = 1.48, 95% CI = 1.25–1.74; relative median survival = 0.91, 95% CI = 0.89–0.94). Meta-regressions indicated that such housing effects were ubiquitous across species and sexes, but could not identify the most impactful improvements to conventional housing. Data variability (assessed via coefficient of variation) was also not increased by ‘enriched’ housing. Conclusions Conventional housing appears sufficiently distressing to compromise rodent health, raising ethical concerns. Results also add to previous work to show that research rodents are typically CRAMPED (cold, rotund, abnormal, male-biased, poorly surviving, enclosed and distressed), raising questions about the validity and generalisability of the data they generate. This research was funded by NSERC, Canada.

Collaborative efforts to directly replicate empirical studies in the medical and social sciences have revealed alarmingly low rates of replicability, a phenomenon dubbed the ‘replication crisis’. Poor replicability has spurred cultural changes targeted at improving reliability in these disciplines. Given the absence of equivalent replication projects in ecology and evolutionary biology, two inter-related indicators offer the opportunity to retrospectively assess replicability: publication bias and statistical power. This registered report assesses the prevalence and severity of small-study (i.e., smaller studies reporting larger effect sizes) and decline effects (i.e., effect sizes decreasing over time) across ecology and evolutionary biology using 87 meta-analyses comprising 4,250 primary studies and 17,638 effect sizes. Further, we estimate how publication bias might distort the estimation of effect sizes, statistical power, and errors in magnitude (Type M or exaggeration ratio) and sign (Type S). We show strong evidence for the pervasiveness of both small-study and decline effects in ecology and evolution. There was widespread prevalence of publication bias that resulted in meta-analytic means being over-estimated by (at least) 0.12 standard deviations. The prevalence of publication bias distorted confidence in meta-analytic results, with 66% of initially statistically significant meta-analytic means becoming non-significant after correcting for publication bias. Ecological and evolutionary studies consistently had low statistical power (15%) with a 4-fold exaggeration of effects on average (Type M error rates = 4.4). Notably, publication bias reduced power from 23% to 15% and increased type M error rates from 2.7 to 4.4 because it creates a non-random sample of effect size evidence. The sign errors of effect sizes (Type S error) increased from 5% to 8% because of publication bias. Our research provides clear evidence that many published ecological and evolutionary findings are inflated. Our results highlight the importance of designing high-power empirical studies (e.g., via collaborative team science), promoting and encouraging replication studies, testing and correcting for publication bias in meta-analyses, and adopting open and transparent research practices, such as (pre)registration, data- and code-sharing, and transparent reporting.

Unreliable research programmes waste funds, time, and even the lives of the organisms we seek to help and understand. Reducing this waste and increasing the value of scientific evidence require changing the actions of both individual researchers and the institutions they depend on for employment and promotion. While ecologists and evolutionary biologists have somewhat improved research transparency over the past decade (e.g. more data sharing), major obstacles remain. In this commentary, we lift our gaze to the horizon to imagine how researchers and institutions can clear the path towards more credible and effective research programmes.

Research synthesis is the process of bringing together findings and attributes from different publications, for example, to give a more complete description of phenomena than is usually possible in a single work. We bring the Research Synthesis Series to BMC Biology to promote meta-analyses, other research syntheses including meta-research studies, and research synthesis methodologies in biology, facilitating their dissemination to broader communities.

Related Content: Vangen (PAR March/April 2017)

Background Circadian rhythms are important for all aspects of biology; virtually every aspect of biological function varies according to time of day. Although this is well known, variation across the day is also often ignored in the design and reporting of research. For this review, we analyzed the top 50 cited papers across 10 major domains of the biological sciences in the calendar year 2015. We repeated this analysis for the year 2019, hypothesizing that the awarding of a Nobel Prize in 2017 for achievements in the field of circadian biology would highlight the importance of circadian rhythms for scientists across many disciplines, and improve time-of-day reporting. Results Our analyses of these 1000 empirical papers, however, revealed that most failed to include sufficient temporal details when describing experimental methods and that few systematic differences in time-of-day reporting existed between 2015 and 2019. Overall, only 6.1% of reports included time-of-day information about experimental measures and manipulations sufficient to permit replication. Conclusions Circadian rhythms are a defining feature of biological systems, and knowing when in the circadian day these systems are evaluated is fundamentally important information. Failing to account for time of day hampers reproducibility across laboratories, complicates interpretation of results, and reduces the value of data based predominantly on nocturnal animals when extrapolating to diurnal humans.

Background Meta-analysis is often used to make generalisations across all available evidence at the global scale. But how can these global generalisations be used for evidence-based decision making at the local scale, if the global evidence is not perceived to be relevant to local decisions? We show how an interactive method of meta-analysis—dynamic meta-analysis—can be used to assess the local relevance of global evidence. Results We developed Metadataset ( www.metadataset.com ) as a proof-of-concept for dynamic meta-analysis. Using Metadataset, we show how evidence can be filtered and weighted, and results can be recalculated, using dynamic methods of subgroup analysis, meta-regression, and recalibration. With an example from agroecology, we show how dynamic meta-analysis could lead to different conclusions for different subsets of the global evidence. Dynamic meta-analysis could also lead to a rebalancing of power and responsibility in evidence synthesis, since evidence users would be able to make decisions that are typically made by systematic reviewers—decisions about which studies to include (e.g. critical appraisal) and how to handle missing or poorly reported data (e.g. sensitivity analysis). Conclusions In this study, we show how dynamic meta-analysis can meet an important challenge in evidence-based decision making—the challenge of using global evidence for local decisions. We suggest that dynamic meta-analysis can be used for subject-wide evidence synthesis in several scientific disciplines, including agroecology and conservation biology. Future studies should develop standardised classification systems for the metadata that are used to filter and weight the evidence. Future studies should also develop standardised software packages, so that researchers can efficiently publish dynamic versions of their meta-analyses and keep them up-to-date as living systematic reviews. Metadataset is a proof-of-concept for this type of software, and it is open source. Future studies should improve the user experience, scale the software architecture, agree on standards for data and metadata storage and processing, and develop protocols for responsible evidence use.