Search

Study preregistration has become increasingly popular in psychology, but its potential to restrict researcher degrees of freedom has not yet been empirically verified. We used an extensive protocol to assess the producibility (i.e., the degree to which a study can be properly conducted based on the available information) of preregistrations and the consistency between preregistrations and their corresponding papers for 300 psychology studies. We found that preregistrations often lack methodological details and that undisclosed deviations from preregistered plans are frequent. These results highlight that biases due to researcher degrees of freedom remain possible in many preregistered studies. More comprehensive registration templates typically yielded more producible preregistrations. We did not find that the producibility and consistency of preregistrations differed over time or between original and replication studies. Furthermore, we found that operationalizations of variables were generally preregistered more producible and consistently than other study parts. Inconsistencies between preregistrations and published studies were mainly encountered for data collection procedures, statistical models, and exclusion criteria. Our results indicate that, to unlock the full potential of preregistration, researchers in psychology should aim to write more producible preregistrations, adhere to these preregistrations more faithfully, and more transparently report any deviations from their preregistrations. This could be facilitated by training and education to improve preregistration skills, as well as the development of more comprehensive templates. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Preregistration has gained traction as one of the most promising solutions to improve the replicability of scientific effects. In this project, we compared 193 psychology studies that earned a Preregistration Challenge prize or preregistration badge to 193 related studies that were not preregistered. In contrast to our theoretical expectations and prior research, we did not find that preregistered studies had a lower proportion of positive results (Hypothesis 1), smaller effect sizes (Hypothesis 2), or fewer statistical errors (Hypothesis 3) than non-preregistered studies. Supporting our Hypotheses 4 and 5, we found that preregistered studies more often contained power analyses and typically had larger sample sizes than non-preregistered studies. Finally, concerns about the publishability and impact of preregistered studies seem unwarranted, as preregistered studies did not take longer to publish and scored better on several impact measures. Overall, our data indicate that preregistration has beneficial effects in the realm of statistical power and impact, but we did not find robust evidence that preregistration prevents p-hacking and HARKing (Hypothesizing After the Results are Known)., (© 2023. The Author(s).)

Objective: Type D personality, a joint tendency toward negative affectivity and social inhibition, has been linked to adverse events in patients with heart disease, although with inconsistent findings. Here, we apply an individual patient-data meta-analysis to data from 19 prospective cohort studies ( N = 11,151) to investigate the prediction of adverse outcomes by type D personality in patients with acquired cardiovascular disease., Method: For each outcome (all-cause mortality, cardiac mortality, myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, major adverse cardiac event, any adverse event), we estimated type D's prognostic influence and the moderation by age, sex, and disease type., Results: In patients with cardiovascular disease, evidence for a type D effect in terms of the Bayes factor (BF) was strong for major adverse cardiac event (BF = 42.5; odds ratio [OR] = 1.14) and any adverse event (BF = 129.4; OR = 1.15). Evidence for the null hypothesis was found for all-cause mortality (BF = 45.9; OR = 1.03), cardiac mortality (BF = 23.7; OR = 0.99), and myocardial infarction (BF = 16.9; OR = 1.12), suggesting that type D had no effect on these outcomes. This evidence was similar in the subset of patients with coronary artery disease (CAD), but inconclusive for patients with heart failure (HF). Positive effects were found for negative affectivity on cardiac and all-cause mortality, with the latter being more pronounced in male than female patients., Conclusion: Across 19 prospective cohort studies, type D predicts adverse events in patients with CAD, whereas evidence in patients with HF was inconclusive. In both patients with CAD and HF, we found evidence for a null effect of type D on cardiac and all-cause mortality., (Copyright © 2023 by the American Psychosomatic Society.)

Outcome reporting bias (ORB) refers to the biasing effect caused by researchers selectively reporting outcomes within a study based on their statistical significance. ORB leads to inflated effect size estimates in meta-analysis if only the outcome with the largest effect size is reported due to ORB. We propose a new method (CORB) to correct for ORB that includes an estimate of the variability of the outcomes' effect size as a moderator in a meta-regression model. An estimate of the variability of the outcomes' effect size can be computed by assuming a correlation among the outcomes. Results of a Monte-Carlo simulation study showed that the effect size in meta-analyses may be severely overestimated without correcting for ORB. Estimates of CORB are close to the true effect size when overestimation caused by ORB is the largest. Applying the method to a meta-analysis on the effect of playing violent video games on aggression showed that the effect size estimate decreased when correcting for ORB. We recommend to routinely apply methods to correct for ORB in any meta-analysis. We provide annotated R code and functions to help researchers apply the CORB method., (© 2023. The Author(s).)

Self-report scales are widely used in psychology to compare means in latent constructs across groups, experimental conditions, or time points. However, for these comparisons to be meaningful and unbiased, the scales must demonstrate measurement invariance (MI) across compared time points or (experimental) groups. MI testing determines whether the latent constructs are measured equivalently across groups or time, which is essential for meaningful comparisons. We conducted a systematic review of 426 psychology articles with openly available data, to (a) examine common practices in conducting and reporting of MI testing, (b) assess whether we could reproduce the reported MI results, and (c) conduct MI tests for the comparisons that enabled sufficiently powerful MI testing. We identified 96 articles that contained a total of 929 comparisons. Results showed that only 4% of the 929 comparisons underwent MI testing, and the tests were generally poorly reported. None of the reported MI tests were reproducible, and only 26% of the 174 newly performed MI tests reached sufficient (scalar) invariance, with MI failing completely in 58% of tests. Exploratory analyses suggested that in nearly half of the comparisons where configural invariance was rejected, the number of factors differed between groups. These results indicate that MI tests are rarely conducted and poorly reported in psychological studies. We observed frequent violations of MI, suggesting that reported differences between (experimental) groups may not be solely attributed to group differences in the latent constructs. We offer recommendations aimed at improving reporting and computational reproducibility practices in psychology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The COVID-19 outbreak has led to an exponential increase of publications and preprints about the virus, its causes, consequences, and possible cures. COVID-19 research has been conducted under high time pressure and has been subject to financial and societal interests. Doing research under such pressure may influence the scrutiny with which researchers perform and write up their studies. Either researchers become more diligent, because of the high-stakes nature of the research, or the time pressure may lead to cutting corners and lower quality output. In this study, we conducted a natural experiment to compare the prevalence of incorrectly reported statistics in a stratified random sample of COVID-19 preprints and a matched sample of non-COVID-19 preprints. Our results show that the overall prevalence of incorrectly reported statistics is 9-10%, but frequentist as well as Bayesian hypothesis tests show no difference in the number of statistical inconsistencies between COVID-19 and non-COVID-19 preprints. In conclusion, the literature suggests that COVID-19 research may on average have more methodological problems than non-COVID-19 research, but our results show that there is no difference in the statistical reporting quality., Competing Interests: We declare we have no competing interests., (© 2023 The Authors.)

Employing two vignette studies, we examined how psychology researchers interpret the results of a set of four experiments that all test a given theory. In both studies, we found that participants' belief in the theory increased with the number of statistically significant results, and that the result of a direct replication had a stronger effect on belief in the theory than the result of a conceptual replication. In Study 2, we additionally found that participants' belief in the theory was lower when they assumed the presence of p-hacking, but that belief in the theory did not differ between preregistered and non-preregistered replication studies. In analyses of individual participant data from both studies, we examined the heuristics academics use to interpret the results of four experiments. Only a small proportion (Study 1: 1.6%; Study 2: 2.2%) of participants used the normative method of Bayesian inference, whereas many of the participants' responses were in line with generally dismissed and problematic vote-counting approaches. Our studies demonstrate that many psychology researchers overestimate the evidence in favor of a theory if one or more results from a set of replication studies are statistically significant, highlighting the need for better statistical education., (© 2023. The Author(s).)

When cognitive and educational tests are administered under time limits, tests may become speeded and this may affect the reliability and validity of the resulting test scores. Prior research has shown that time limits may create or enlarge gender gaps in cognitive and academic testing. On average, women complete fewer items than men when a test is administered with a strict time limit, whereas gender gaps are frequently reduced when time limits are relaxed. In this study, we propose that gender differences in test strategy might inflate gender gaps favoring men, and relate test strategy to stereotype threat effects under which women underperform due to the pressure of negative stereotypes about their performance. First, we applied a Bayesian two-dimensional item response theory (IRT) model to data obtained from two registered reports that investigated stereotype threat in mathematics, and estimated the latent correlation between underlying test strategy (here, completion factor, a proxy for working speed) and mathematics ability. Second, we tested the gender gap and assessed potential effects of stereotype threat on female test performance. We found a positive correlation between the completion factor and mathematics ability, such that more able participants dropped out later in the test. We did not observe a stereotype threat effect but found larger gender differences on the latent completion factor than on latent mathematical ability, suggesting that test strategies affect the gender gap in timed mathematics performance. We argue that if the effect of time limits on tests is not taken into account, this may lead to test unfairness and biased group comparisons, and urge researchers to consider these effects in either their analyses or study planning., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Background: Empirical evidence indicates that both HIV infection and stunting impede cognitive functions of school-going children. However, there is less evidence on how these two risk factors amplify each other's negative effects. This study aimed to examine the direct effects of stunting on cognitive outcomes and the extent to which stunting (partially) mediates the effects of HIV, age, and gender on cognitive outcomes., Methodology: We applied structural equation modelling to cross-sectional data from 328 children living with HIV and 260 children living without HIV aged 6-14 years from Nairobi, Kenya to test the mediating effect of stunting and predictive effects of HIV, age, and gender on cognitive latent variables flexibility, fluency, reasoning, and verbal memory., Results: The model predicting the cognitive outcomes fitted well (RMSEA = 0.041, CFI = 0.966, χ 2  = 154.29, DF = 77, p  < 0.001). Height-for-age (a continuous indicator of stunting) predicted fluency ( β  = 0.14) and reasoning ( β  = 0.16). HIV predicted height-for-age ( β  = -0.24) and showed direct effects on reasoning ( β  = -0.66), fluency ( β  = -0.34), flexibility ( β  = 0.26), and verbal memory ( β  = -0.22), highlighting that the effect of HIV on cognitive variables was partly mediated by height-for-age., Conclusion: In this study, we found evidence that stunting partly explains the effects of HIV on cognitive outcomes. The model suggests there is urgency to develop targeted preventative and rehabilitative nutritional interventions for school children with HIV as part of a comprehensive set of interventions to improve cognitive functioning in this high-risk group of children. Being infected or having been born to a mother who is HIV positive poses a risk to normal child development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Maina, He, Abubakar, Perez-Garcia, Kumar and Wicherts.)

Introduction: Culturally validated neurocognitive measures for children in Low- and Middle-Income Countries are important in the timely and correct identification of neurocognitive impairments. Such measures can inform development of interventions for children exposed to additional vulnerabilities like HIV infection. The Battery for Neuropsychological Evaluation of Children (BENCI) is an openly available, computerized neuropsychological battery specifically developed to evaluate neurocognitive impairment. This study adapted the BENCI and evaluated its reliability and validity in Kenya., Methodology: The BENCI was adapted using translation and back-translation from Spanish to English. The psychometric properties were evaluated in a case-control study of 328 children (aged 6 - 14 years) living with HIV and 260 children not living with HIV in Kenya. We assessed reliability, factor structure, and measurement invariance with respect to HIV. Additionally, we examined convergent validity of the BENCI using tests from the Kilifi Toolkit., Results: Internal consistencies (0.49 < α < 0.97) and test-retest reliabilities (-.34 to .81) were sufficient-to-good for most of the subtests. Convergent validity was supported by significant correlations between the BENCI's Verbal memory and Kilifi's Verbal List Learning (r = .41), the BENCI's Visual memory and Kilifi's Verbal List Learning (r = .32) and the BENCI's Planning total time test and Kilifi's Tower Test (r = -.21) and the BENCI's Abstract Reasoning test and Kilifi's Raven's Progressive Matrix (r = .21). The BENCI subtests highlighted meaningful differences between children living with HIV and those not living with HIV. After some minor adaptions, a confirmatory four-factor model consisting of flexibility, fluency, reasoning and working memory fitted well (χ 2  = 135.57, DF = 51, N = 604, p < .001, RMSEA = .052, CFI = .944, TLI = .914) and was partially scalar invariant between HIV positive and negative groups., Conclusion: The English version of the BENCI formally translated for use in Kenya can be further adapted and integrated in clinical and research settings as a valid and reliable cognitive test battery., (© 2023. The Author(s).)

Increased execution of replication studies contributes to the effort to restore credibility of empirical research. However, a second generation of problems arises: the number of potential replication targets is at a serious mismatch with available resources. Given limited resources, replication target selection should be well-justified, systematic and transparently communicated. At present the discussion on what to consider when selecting a replication target is limited to theoretical discussion, self-reported justifications and a few formalized suggestions. In this Registered Report, we proposed a study involving the scientific community to create a list of considerations for consultation when selecting a replication target in psychology. We employed a modified Delphi approach. First, we constructed a preliminary list of considerations. Second, we surveyed psychologists who previously selected a replication target with regards to their considerations. Third, we incorporated the results into the preliminary list of considerations and sent the updated list to a group of individuals knowledgeable about concerns regarding replication target selection. Over the course of several rounds, we established consensus regarding what to consider when selecting a replication target. The resulting checklist can be used for transparently communicating the rationale for selecting studies for replication., (© 2023 The Authors.)

Effect misestimations plague Psychological Science, but advances in the identification of dissemination biases in general and publication bias in particular have helped in dealing with biased effects in the literature. However, the application of publication bias detection methods appears to be not equally prevalent across subdisciplines. It has been suggested that particularly in I/O Psychology, appropriate publication bias detection methods are underused. In this meta-meta-analysis, we present prevalence estimates, predictors, and time trends of publication bias in 128 meta-analyses that were published in the Journal of Applied Psychology (7,263 effect sizes, 3,000,000 + participants). Moreover, we reanalyzed data of 87 meta-analyses and applied nine standard and more modern publication bias detection methods. We show that (a) the bias detection method applications are underused (only 41% of meta-analyses use at least one method) but have increased in recent years, (b) those meta-analyses that apply such methods now use more, but mostly inappropriate methods, and (c) the prevalence of potential publication bias is concerning but mostly remains undetected. Although our results indicate somewhat of a trend toward higher bias awareness, they substantiate concerns about potential publication bias in I/O Psychology, warranting increased researcher awareness about appropriate and state-of-the-art bias detection and triangulation. Embracing open science practices such as data sharing or study preregistration is needed to raise reproducibility and ultimately strengthen Psychological Science in general and I/O Psychology in particular. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Background: Traditionally, research integrity studies have focused on research misbehaviors and their explanations. Over time, attention has shifted towards preventing questionable research practices and promoting responsible ones. However, data on the prevalence of responsible research practices, especially open methods, open codes and open data and their underlying associative factors, remains scarce. Methods: We conducted a web-based anonymized questionnaire, targeting all academic researchers working at or affiliated to a university or university medical center in The Netherlands, to investigate the prevalence and potential explanatory factors of 11 responsible research practices. Results: A total of 6,813 academics completed the survey, the results of which show that prevalence of responsible practices differs substantially across disciplines and ranks, with 99 percent avoiding plagiarism in their work but less than 50 percent pre-registering a research protocol. Arts and humanities scholars as well as PhD candidates and junior researchers engaged less often in responsible research practices. Publication pressure negatively affected responsible practices, while mentoring, scientific norms subscription and funding pressure stimulated them. Conclusions: Understanding the prevalence of responsible research practices across disciplines and ranks, as well as their associated explanatory factors, can help to systematically address disciplinary- and academic rank-specific obstacles, and thereby facilitate responsible conduct of research., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Gopalakrishna G et al.)

Prevalence of research misconduct, questionable research practices (QRPs) and their associations with a range of explanatory factors has not been studied sufficiently among academic researchers. The National Survey on Research Integrity targeted all disciplinary fields and academic ranks in the Netherlands. It included questions about engagement in fabrication, falsification and 11 QRPs over the previous three years, and 12 explanatory factor scales. We ensured strict identity protection and used the randomized response method for questions on research misconduct. 6,813 respondents completed the survey. Prevalence of fabrication was 4.3% (95% CI: 2.9, 5.7) and of falsification 4.2% (95% CI: 2.8, 5.6). Prevalence of QRPs ranged from 0.6% (95% CI: 0.5, 0.9) to 17.5% (95% CI: 16.4, 18.7) with 51.3% (95% CI: 50.1, 52.5) of respondents engaging frequently in at least one QRP. Being a PhD candidate or junior researcher increased the odds of frequently engaging in at least one QRP, as did being male. Scientific norm subscription (odds ratio (OR) 0.79; 95% CI: 0.63, 1.00) and perceived likelihood of detection by reviewers (OR 0.62, 95% CI: 0.44, 0.88) were associated with engaging in less research misconduct. Publication pressure was associated with more often engaging in one or more QRPs frequently (OR 1.22, 95% CI: 1.14, 1.30). We found higher prevalence of misconduct than earlier surveys. Our results suggest that greater emphasis on scientific norm subscription, strengthening reviewers in their role as gatekeepers of research quality and curbing the "publish or perish" incentive system promotes research integrity., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Type D personality, operationalized as high scores on negative affectivity (NA) and social inhibition (SI), has been associated with various medical and psychosocial outcomes. The recent failure to replicate several earlier findings could result from the various methods used to assess the Type D effect. Despite recommendations to analyze the continuous NA and SI scores, a popular approach groups people as having Type D personality or not. This method does not adequately detect a Type D effect as it is also sensitive to main effects of NA or SI only, suggesting the literature contains false positive Type D effects. Here, we systematically assess the extent of this problem., Method: We conducted a systematic review including 44 published studies assessing a Type D effect with both a continuous and dichotomous operationalization., Results: The dichotomous method showed poor agreement with the continuous Type D effect. Of the 89 significant dichotomous method effects, 37 (41.6%) were Type D effects according to the continuous method. The remaining 52 (58.4%) are therefore likely not Type D effects based on the continuous method, as 42 (47.2%) were main effects of NA or SI only., Conclusion: Half of the published Type D effect according to the dichotomous method may be false positives, with only NA or SI driving the outcome., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Researchers face many, often seemingly arbitrary, choices in formulating hypotheses, designing protocols, collecting data, analyzing data, and reporting results. Opportunistic use of "researcher degrees of freedom" aimed at obtaining statistical significance increases the likelihood of obtaining and publishing false-positive results and overestimated effect sizes. Preregistration is a mechanism for reducing such degrees of freedom by specifying designs and analysis plans before observing the research outcomes. The effectiveness of preregistration may depend, in part, on whether the process facilitates sufficiently specific articulation of such plans. In this preregistered study, we compared 2 formats of preregistration available on the OSF: Standard Pre-Data Collection Registration and Prereg Challenge Registration (now called "OSF Preregistration," http://osf.io/prereg/). The Prereg Challenge format was a "structured" workflow with detailed instructions and an independent review to confirm completeness; the "Standard" format was "unstructured" with minimal direct guidance to give researchers flexibility for what to prespecify. Results of comparing random samples of 53 preregistrations from each format indicate that the "structured" format restricted the opportunistic use of researcher degrees of freedom better (Cliff's Delta = 0.49) than the "unstructured" format, but neither eliminated all researcher degrees of freedom. We also observed very low concordance among coders about the number of hypotheses (14%), indicating that they are often not clearly stated. We conclude that effective preregistration is challenging, and registration formats that provide effective guidance may improve the quality of research., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: DM, CS, and BN are employees of the Center for Open Science (COS), a non-profit technology and culture change company with a mission to increase openness, integrity, and reproducibility of research. COS operates the OSF and the Prereg Challenge.

In this meta-study, we analyzed 2442 effect sizes from 131 meta-analyses in intelligence research, published from 1984 to 2014, to estimate the average effect size, median power, and evidence for bias. We found that the average effect size in intelligence research was a Pearson's correlation of 0.26, and the median sample size was 60. Furthermore, across primary studies, we found a median power of 11.9% to detect a small effect, 54.5% to detect a medium effect, and 93.9% to detect a large effect. We documented differences in average effect size and median estimated power between different types of intelligence studies (correlational studies, studies of group differences, experiments, toxicology, and behavior genetics). On average, across all meta-analyses (but not in every meta-analysis), we found evidence for small-study effects, potentially indicating publication bias and overestimated effects. We found no differences in small-study effects between different study types. We also found no convincing evidence for the decline effect, US effect, or citation bias across meta-analyses. We concluded that intelligence research does show signs of low power and publication bias, but that these problems seem less severe than in many other scientific fields.

We examined the evidence for heterogeneity (of effect sizes) when only minor changes to sample population and settings were made between studies and explored the association between heterogeneity and average effect size in a sample of 68 meta-analyses from 13 preregistered multilab direct replication projects in social and cognitive psychology. Among the many examined effects, examples include the Stroop effect, the "verbal overshadowing" effect, and various priming effects such as "anchoring" effects. We found limited heterogeneity; 48/68 (71%) meta-analyses had nonsignificant heterogeneity, and most (49/68; 72%) were most likely to have zero to small heterogeneity. Power to detect small heterogeneity (as defined by Higgins, Thompson, Deeks, & Altman, 2003) was low for all projects (mean 43%), but good to excellent for medium and large heterogeneity. Our findings thus show little evidence of widespread heterogeneity in direct replication studies in social and cognitive psychology, suggesting that minor changes in sample population and settings are unlikely to affect research outcomes in these fields of psychology. We also found strong correlations between observed average effect sizes (standardized mean differences and log odds ratios) and heterogeneity in our sample. Our results suggest that heterogeneity and moderation of effects is unlikely for a 0 average true effect size, but increasingly likely for larger average true effect size. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

In this preregistered study, we investigated whether the statistical power of a study is higher when researchers are asked to make a formal power analysis before collecting data. We compared the sample size descriptions from two sources: (i) a sample of pre-registrations created according to the guidelines for the Center for Open Science Preregistration Challenge (PCRs) and a sample of institutional review board (IRB) proposals from Tilburg School of Behavior and Social Sciences, which both include a recommendation to do a formal power analysis, and (ii) a sample of pre-registrations created according to the guidelines for Open Science Framework Standard Pre-Data Collection Registrations (SPRs) in which no guidance on sample size planning is given. We found that PCRs and IRBs (72%) more often included sample size decisions based on power analyses than the SPRs (45%). However, this did not result in larger planned sample sizes. The determined sample size of the PCRs and IRB proposals (Md = 90.50) was not higher than the determined sample size of the SPRs (Md = 126.00; W = 3389.5, p = 0.936). Typically, power analyses in the registrations were conducted with G*power, assuming a medium effect size, α = .05 and a power of .80. Only 20% of the power analyses contained enough information to fully reproduce the results and only 62% of these power analyses pertained to the main hypothesis test in the pre-registration. Therefore, we see ample room for improvements in the quality of the registrations and we offer several recommendations to do so., Competing Interests: The authors have declared that no competing interests exist. Jelte M. Wicherts is a PLOS ONE Editorial Board member. This does not alter the authors’ adherence to PLOS ONE Editorial policies and criteria.

To determine the reproducibility of psychological meta-analyses, we investigated whether we could reproduce 500 primary study effect sizes drawn from 33 published meta-analyses based on the information given in the meta-analyses, and whether recomputations of primary study effect sizes altered the overall results of the meta-analysis. Results showed that almost half (k = 224) of all sampled primary effect sizes could not be reproduced based on the reported information in the meta-analysis, mostly because of incomplete or missing information on how effect sizes from primary studies were selected and computed. Overall, this led to small discrepancies in the computation of mean effect sizes, confidence intervals and heterogeneity estimates in 13 out of 33 meta-analyses. We provide recommendations to improve transparency in the reporting of the entire meta-analytic process, including the use of preregistration, data and workflow sharing, and explicit coding practices., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Jelte M. Wicherts is a PLOS ONE Editorial Board member. This does not alter the authors’ adherence to PLOS ONE Editorial policies and criteria.

We systematically reviewed observational and Mendelian randomization (MR) articles that evaluated the association between obesity and 17 gastrointestinal (GI) diseases to integrate causal and observational evidence. A total of 594 observational studies from 26 systematic reviews and meta-analyses and nine MR articles were included. For every 5 kg/m 2 increase in body mass index (BMI), there was an increased risk of GI diseases ranging from 2% for rectal cancer (relative risk [RR]: 1.02, 95% confidence interval [CI]: 1.01 to 1.03) to 63% for gallbladder disease (RR: 1.63, 95% CI: 1.50 to 1.77). MR articles indicated that risks of developing GI diseases elevated with each 1 standard deviation increase in genetically predicted BMI, ranging from 11% for Crohn's disease to 189% for nonalcoholic fatty liver disease. Moreover, upper GI conditions were less susceptible, whereas hepatobiliary organs were more vulnerable to increased adiposity. Among the associations between obesity and the 17 GI conditions, causal relationships were inferred from only approximately half (10/17, 59%). This study reveals a substantial gap between observational and causal evidence, indicating that a combined approach is necessary to effectively inform public health policies and guide epidemiological research on obesity and GI diseases., (© 2024 The Author(s). Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.)

Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.

Publication bias is a substantial problem for the credibility of research in general and of meta-analyses in particular, as it yields overestimated effects and may suggest the existence of non-existing effects. Although there is consensus that publication bias exists, how strongly it affects different scientific literatures is currently less well-known. We examined evidence of publication bias in a large-scale data set of primary studies that were included in 83 meta-analyses published in Psychological Bulletin (representing meta-analyses from psychology) and 499 systematic reviews from the Cochrane Database of Systematic Reviews (CDSR; representing meta-analyses from medicine). Publication bias was assessed on all homogeneous subsets (3.8% of all subsets of meta-analyses published in Psychological Bulletin) of primary studies included in meta-analyses, because publication bias methods do not have good statistical properties if the true effect size is heterogeneous. Publication bias tests did not reveal evidence for bias in the homogeneous subsets. Overestimation was minimal but statistically significant, providing evidence of publication bias that appeared to be similar in both fields. However, a Monte-Carlo simulation study revealed that the creation of homogeneous subsets resulted in challenging conditions for publication bias methods since the number of effect sizes in a subset was rather small (median number of effect sizes equaled 6). Our findings are in line with, in its most extreme case, publication bias ranging from no bias until only 5% statistically nonsignificant effect sizes being published. These and other findings, in combination with the small percentages of statistically significant primary effect sizes (28.9% and 18.9% for subsets published in Psychological Bulletin and CDSR), led to the conclusion that evidence for publication bias in the studied homogeneous subsets is weak, but suggestive of mild publication bias in both psychology and medicine., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Jelte M. Wicherts is a PLOS ONE Editorial Board member. This does not alter the authors’ adherence to PLOS ONE Editorial policies and criteria.

Research on money priming typically investigates whether exposure to money-related stimuli can affect people's thoughts, feelings, motivations, and behaviors (for a review, see Vohs, 2015). Our study answers the call for a comprehensive meta-analysis examining the available evidence on money priming (Vadillo, Hardwicke, & Shanks, 2016). By conducting a systematic search of published and unpublished literature on money priming, we sought to achieve three key goals. First, we aimed to assess the presence of biases in the available published literature (e.g., publication bias). Second, in the case of such biases, we sought to derive a more accurate estimate of the effect size after correcting for these biases. Third, we aimed to investigate whether design factors such as prime type and study setting moderated the money priming effects. Our overall meta-analysis included 246 suitable experiments and showed a significant overall effect size estimate (Hedges' g = .31, 95% CI [0.26, 0.36]). However, publication bias and related biases are likely given the asymmetric funnel plots, Egger's test and two other tests for publication bias. Moderator analyses offered insight into the variation of the money priming effect, suggesting for various types of study designs whether the effect was present, absent, or biased. We found the largest money priming effect in lab studies investigating a behavioral dependent measure using a priming technique in which participants actively handled money. Future research should use sufficiently powerful preregistered studies to replicate these findings. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Increasing integrity concerns in medical research have prompted the development of tools to detect untrustworthy studies. Existing tools primarily assess published aggregate data (AD), though scrutiny of individual participant data (IPD) is often required to detect trustworthiness issues. Thus, we developed the IPD Integrity Tool for detecting integrity issues in randomised trials with IPD available. This manuscript describes the development of this tool. We conducted a literature review to collate and map existing integrity items. These were discussed with an expert advisory group; agreed items were included in a standardised tool and automated where possible. We piloted this tool in two IPD meta-analyses (including 116 trials) and conducted preliminary validation checks on 13 datasets with and without known integrity issues. We identified 120 integrity items: 54 could be conducted using AD, 48 required IPD, and 18 were possible with AD, but more comprehensive with IPD. An initial reduced tool was developed through consensus involving 13 advisors, featuring 11 AD items across four domains, and 12 IPD items across eight domains. The tool was iteratively refined throughout piloting and validation. All studies with known integrity issues were accurately identified during validation. The final tool includes seven AD domains with 13 items and eight IPD domains with 18 items. The quality of evidence informing healthcare relies on trustworthy data. We describe the development of a tool to enable researchers, editors, and others to detect integrity issues using IPD. Detailed instructions for its application are published as a complementary manuscript in this issue., (© 2024 The Author(s). Research Synthesis Methods published by John Wiley & Sons Ltd.)

In a recent study, te Nijenhuis et al. (2017) used a version of Jensen's method of correlated vectors to study the nature of ethnic group differences on Raven's Progressive Matrices test. In this comment, the author points out that this method has been shown to be psychometrically inappropriate in studying group differences in performance on dichotomous (correctly or incorrectly scored) items. Specifically, the method uses item statistics like the item-total correlation that necessarily differ across groups differing in ability and employs a linear model to test inherent non-linear relations. Wicherts (2017) showed that this method can provide correlations far exceeding r=0.44 in cases where the group differences cannot possibly be on g because the items measure different traits across the groups. The psychometric problems with their method cast serious doubts on te Nijenhuis et al.'s conclusions concerning the role of g in the studied group difference in cognitive test performance.

In their response to my criticism of their recent article in Journal of Biosocial Science (te Nijenhuis et al., 2017), te Nijenhuis and van den Hoek (2018) raise four points none of which concerns my main point that the method of correlated vectors (MCV) applied to item-level data represents a flawed method. Here, I discuss te Nijenhuis and van den Hoek's four points. First, I argue that my previous application of MCV to item-level data showed that the method can yield nonsensical results. Second, I note that meta-analytic corrections for sampling error, imperfect measures, restriction of range and unreliability of the vectors are futile and cannot help fix the method. Third, I note that even with perfect data, the method can yield negative correlations. Fourth, I highlight the irrelevance of te Nijenhuis and van den Hoek (2018)'s point that my comment had not been published in a peerreviewed journal by referring to my articles in 2009 and 2017 on MCV in peer-reviewed journals.

Background: Many psychosocial and behavioral interventions have been developed for informal dementia caregivers. Because existing meta-analyses only focused on a limited number of interventions and outcomes, how effective these interventions are overall and which interventions components are associated with larger effects has yet to be explored., Objective: To provide a comprehensive meta-analysis of the effectiveness of psychosocial and behavioral interventions on burden, depression, anxiety, quality of life, stress, and sense of competence in informal dementia caregivers. In addition, we examined if interventions which utilized more sessions and/or were delivered personally (face-to-face) had larger effect sizes. In exploratory meta-regressions, we examined seven additional moderators., Methods: The protocol was registered with PROSPERO, number CRD42017062555. We systematically searched the literature to identify controlled trials assessing the effect of psychosocial and behavioral interventions on the six outcome measures, for informal dementia caregivers. We performed six random effects meta-analyses, to assess the pooled effect sizes of the interventions. In addition, we performed separate meta-regressions, for each outcome, for each moderator., Results: The sample consisted of 60 studies. For all outcomes except anxiety, the pooled effects were small and in favor of the intervention group. No moderator was found to systematically predict these effects. There were no indications for publication bias or selection bias based on significance., Conclusion: Overall, the interventions yield significant (small) effects, independent of intervention characteristics. Future research should explore options to enhance the effectiveness of interventions aimed at assisting informal caregivers.

In determining the need to directly replicate, it is crucial to first verify the original results through independent reanalysis of the data. Original results that appear erroneous and that cannot be reproduced by reanalysis offer little evidence to begin with, thereby diminishing the need to replicate. Sharing data and scripts is essential to ensure reproducibility.

In this review, the author discusses several of the weak spots in contemporary science, including scientific misconduct, the problems of post hoc hypothesizing (HARKing), outcome switching, theoretical bloopers in formulating research questions and hypotheses, selective reading of the literature, selective citing of previous results, improper blinding and other design failures, p-hacking or researchers' tendency to analyze data in many different ways to find positive (typically significant) results, errors and biases in the reporting of results, and publication bias. The author presents some empirical results highlighting problems that lower the trustworthiness of reported results in scientific literatures, including that of animal welfare studies. Some of the underlying causes of these biases are discussed based on the notion that researchers are only human and hence are not immune to confirmation bias, hindsight bias, and minor ethical transgressions. The author discusses solutions in the form of enhanced transparency, sharing of data and materials, (post-publication) peer review, pre-registration, registered reports, improved training, reporting guidelines, replication, dealing with publication bias, alternative inferential techniques, power, and other statistical tools., Competing Interests: The author declares no conflicts of interest.

A survey in the United States revealed that an alarmingly large percentage of university psychologists admitted having used questionable research practices that can contaminate the research literature with false positive and biased findings. We conducted a replication of this study among Italian research psychologists to investigate whether these findings generalize to other countries. All the original materials were translated into Italian, and members of the Italian Association of Psychology were invited to participate via an online survey. The percentages of Italian psychologists who admitted to having used ten questionable research practices were similar to the results obtained in the United States although there were small but significant differences in self-admission rates for some QRPs. Nearly all researchers (88%) admitted using at least one of the practices, and researchers generally considered a practice possibly defensible if they admitted using it, but Italian researchers were much less likely than US researchers to consider a practice defensible. Participants' estimates of the percentage of researchers who have used these practices were greater than the self-admission rates, and participants estimated that researchers would be unlikely to admit it. In written responses, participants argued that some of these practices are not questionable and they have used some practices because reviewers and journals demand it. The similarity of results obtained in the United States, this study, and a related study conducted in Germany suggest that adoption of these practices is an international phenomenon and is likely due to systemic features of the international research and publication processes.

The syntax or codes used to fit Structural Equation Models (SEMs) convey valuable information on model specifications and the manner in which SEMs are estimated. We requested SEM syntaxes from a random sample of 229 articles (published in 1998-2013) that ran SEMs using LISREL, AMOS, or Mplus. After exchanging over 500 emails, we ended up obtaining a meagre 57 syntaxes used in these articles (24.9% of syntaxes we requested). Results considering the 129 (corresponding) authors who replied to our request showed that the odds of the syntax being lost increased by 21% per year passed since publication of the article, while the odds of actually obtaining a syntax dropped by 13% per year. So SEM syntaxes that are crucial for reproducibility and for correcting errors in the running and reporting of SEMs are often unavailable and get lost rapidly. The preferred solution is mandatory sharing of SEM syntaxes alongside articles or in data repositories.

Do lay people and scientists themselves recognize that scientists are human and therefore prone to human fallibilities such as error, bias, and even dishonesty? In a series of three experimental studies and one correlational study (total N = 3,278) we found that the "storybook image of the scientist" is pervasive: American lay people and scientists from over 60 countries attributed considerably more objectivity, rationality, open-mindedness, intelligence, integrity, and communality to scientists than to other highly-educated people. Moreover, scientists perceived even larger differences than lay people did. Some groups of scientists also differentiated between different categories of scientists: established scientists attributed higher levels of the scientific traits to established scientists than to early-career scientists and Ph.D. students, and higher levels to Ph.D. students than to early-career scientists. Female scientists attributed considerably higher levels of the scientific traits to female scientists than to male scientists. A strong belief in the storybook image and the (human) tendency to attribute higher levels of desirable traits to people in one's own group than to people in other groups may decrease scientists' willingness to adopt recently proposed practices to reduce error, bias and dishonesty in science.

This study documents reporting errors in a sample of over 250,000 p-values reported in eight major psychology journals from 1985 until 2013, using the new R package "statcheck." statcheck retrieved null-hypothesis significance testing (NHST) results from over half of the articles from this period. In line with earlier research, we found that half of all published psychology papers that use NHST contained at least one p-value that was inconsistent with its test statistic and degrees of freedom. One in eight papers contained a grossly inconsistent p-value that may have affected the statistical conclusion. In contrast to earlier findings, we found that the average prevalence of inconsistent p-values has been stable over the years or has declined. The prevalence of gross inconsistencies was higher in p-values reported as significant than in p-values reported as nonsignificant. This could indicate a systematic bias in favor of significant results. Possible solutions for the high prevalence of reporting inconsistencies could be to encourage sharing data, to let co-authors check results in a so-called "co-pilot model," and to use statcheck to flag possible inconsistencies in one's own manuscript or during the review process.

The designing, collecting, analyzing, and reporting of psychological studies entail many choices that are often arbitrary. The opportunistic use of these so-called researcher degrees of freedom aimed at obtaining statistically significant results is problematic because it enhances the chances of false positive results and may inflate effect size estimates. In this review article, we present an extensive list of 34 degrees of freedom that researchers have in formulating hypotheses, and in designing, running, analyzing, and reporting of psychological research. The list can be used in research methods education, and as a checklist to assess the quality of preregistrations and to determine the potential for bias due to (arbitrary) choices in unregistered studies.

Objective: Neurocognitive test batteries such as recent editions of the Wechsler's Adult Intelligence Scale (WAIS-III/WAIS-IV) typically use nation-level population-based norms. The question is whether these batteries function in the same manner across different subgroups based on gender, age, educational background, socioeconomic status, ethnicity, mother tongue, or race. Here, the author argues that measurement invariance is a core issue in determining whether population-based norms are valid for different subgroups., Method: The author introduces measurement invariance, argues why it is an important topic of study, discusses why invariance might fail in cognitive ability testing, and reviews a dozen studies of invariance of commonly used neurocognitive test batteries., Results: In over half of the reviewed studies, IQ batteries were not found to be measurement invariant across groups based on ethnicity, gender, educational background, cohort, or age. Apart from age and cohort, test manuals do not take such lack of invariance into account in computing full-scale IQ scores or normed domain scores., Conclusions: Measurement invariance is crucial for valid use of neurocognitive tests in clinical, educational, and professional practice. The appropriateness of population-based norms to particular subgroups should depend also on whether measurement invariance holds with respect to important subgroups.

Because of overwhelming evidence of publication bias in psychology, techniques to correct meta-analytic estimates for such bias are greatly needed. The methodology on which the p-uniform and p-curve methods are based has great promise for providing accurate meta-analytic estimates in the presence of publication bias. However, in this article, we show that in some situations, p-curve behaves erratically, whereas p-uniform may yield implausible estimates of negative effect size. Moreover, we show that (and explain why) p-curve and p-uniform result in overestimation of effect size under moderate-to-large heterogeneity and may yield unpredictable bias when researchers employ p-hacking. We offer hands-on recommendations on applying and interpreting results of meta-analyses in general and p-uniform and p-curve in particular. Both methods as well as traditional methods are applied to a meta-analysis on the effect of weight on judgments of importance. We offer guidance for applying p-uniform or p-curve using R and a user-friendly web application for applying p-uniform., Competing Interests: The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article., (© The Author(s) 2016.)

Many psychology studies are statistically underpowered. In part, this may be because many researchers rely on intuition, rules of thumb, and prior practice (along with practical considerations) to determine the number of subjects to test. In Study 1, we surveyed 291 published research psychologists and found large discrepancies between their reports of their preferred amount of power and the actual power of their studies (calculated from their reported typical cell size, typical effect size, and acceptable alpha). Furthermore, in Study 2, 89% of the 214 respondents overestimated the power of specific research designs with a small expected effect size, and 95% underestimated the sample size needed to obtain .80 power for detecting a small effect. Neither researchers' experience nor their knowledge predicted the bias in their self-reported power intuitions. Because many respondents reported that they based their sample sizes on rules of thumb or common practice in the field, we recommend that researchers conduct and report formal power analyses for their studies., (© The Author(s) 2016.)

Previous studies provided mixed findings on pecularities in p-value distributions in psychology. This paper examined 258,050 test results across 30,710 articles from eight high impact journals to investigate the existence of a peculiar prevalence of p-values just below .05 (i.e., a bump) in the psychological literature, and a potential increase thereof over time. We indeed found evidence for a bump just below .05 in the distribution of exactly reported p-values in the journals Developmental Psychology, Journal of Applied Psychology, and Journal of Personality and Social Psychology, but the bump did not increase over the years and disappeared when using recalculated p-values. We found clear and direct evidence for the QRP "incorrect rounding of p-value" (John, Loewenstein & Prelec, 2012) in all psychology journals. Finally, we also investigated monotonic excess of p-values, an effect of certain QRPs that has been neglected in previous research, and developed two measures to detect this by modeling the distributions of statistically significant p-values. Using simulations and applying the two measures to the retrieved test results, we argue that, although one of the measures suggests the use of QRPs in psychology, it is difficult to draw general conclusions concerning QRPs based on modeling of p-value distributions.

We respond to the commentaries Waldman and Lilienfeld (Psychometrika, 2015) and Wigboldus and Dotch (Psychometrika, 2015) provided in response to Sijtsma's (Sijtsma in Psychometrika, 2015) discussion article on questionable research practices. Specifically, we discuss the fear of an increased dichotomy between substantive and statistical aspects of research that may arise when the latter aspects are laid entirely in the hands of a statistician, remedies for false positives and replication failure, and the status of data exploration, and we provide a re-definition of the concept of questionable research practices.