scholarly journals When learning from animations is more successful than learning from static pictures: learning the specifics of change

2021 ◽  
Author(s):  
Rolf Ploetzner ◽  
Sandra Berney ◽  
Mireille Bétrancourt
Keyword(s):  
Experimental Study ◽  
University Students ◽  
Effect Sizes ◽  
Gear Mechanism ◽  
Static Pictures ◽  
Meta Analyses

AbstractThe results of three meta-analyses show that the effectiveness of learning from animations, when compared to learning from static pictures, is rather limited. A recent re-analysis of one of these meta-analyses, however, supports that learning from animations is considerably more effective than learning from static pictures if the specifics of the displayed changes need to be learned. In order to further validate this finding as well as to clarify the educational strengths and weaknesses of animations and static pictures, an experimental study with three groups was conducted. Overall, 88 university students participated in the study. One group of learners (n = 30) watched a single picture of a gear mechanism, one group of learners (n = 28) watched four pictures, and one group of learners (n = 30) watched an animation. All groups had to identify specific motions and spatial arrangements covered by the gear mechanism. While learners who watched the animation exhibited the best performance with respect to the identification of motions, learners who watched the pictures showed the best performance with respect to the identification of spatial arrangements. The effect sizes are large. The results of the study help to clarify when animations and when static pictures are most suitable for learning.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

Sleep and cognition

2018 ◽  
Author(s):  
Donald L. Bliwise ◽  
Michael K. Scullin
Keyword(s):  
Cognitive Function ◽  
Natural History ◽  
Population Health ◽  
Effect Sizes ◽  
Biological Plausibility ◽  
Vast Array ◽  
Neurobiological Substrates ◽  
Meta Analyses ◽  
Sleep And Cognition ◽  
Future Work

Possible associations between sleep and cognition are provocative across different domains and hold the promise of prevention or reversibility. A vast array of studies has been reported. Evidence is suggestive but hardly definitive. We provide an overview of this literature, adopting the framework of Hill’s perspective on epidemiological causation. With rare exception, formal meta-analyses have yet to appear. Apparent consistency of findings suggests relationships, but the diversity of findings involving specific components of cognitive function raises interpretative caution. Large effect sizes have been noted, but small-to-moderate effects predominate. Natural history data are similarly enticing, and studies of biological plausibility and gradient indicate likely neurobiological substrates. Perhaps the ultimate population-health criterion, demonstration of reversibility of impairment, remains elusive at best. This area offers an exciting topic for future work.

Career Counselling Effectiveness and Contributing Factors

2020 ◽  
Author(s):  
Susan C. Whiston
Keyword(s):  
Career Choice ◽  
Significant Influence ◽  
Empirical Support ◽  
Effect Sizes ◽  
Career Counselling ◽  
Contributing Factors ◽  
Considerable Evidence ◽  
Effective Strategies ◽  
The World ◽  
Meta Analyses

This chapter explores the research related to whether career counselling is effective for individuals with vocational issues. In particular, there is considerable empirical support for career counselling related to career choice issues and searching for employment. Hence, practitioners can use this evidence to convince administrators, policymakers, parents, students, and other constituencies of the worth of career counselling. In addition, the chapter provides empirical evidence that practitioners can use to improve their effectiveness in working with people with career issues. This discussion mainly focuses on the results from older and newer meta-analyses regarding the ingredients that have a significant influence on effect sizes or the critical ingredients in career counselling. For example, there is considerable evidence that support from individuals, including the counsellor, may play an important role in the effectiveness of career counselling. Other factors that contribute to effective practice are also identified and discussed. The chapter further explores the need for additional research that addresses the most effective methods for providing career counselling. As the world of work becomes increasingly complex, it is important that researchers continue to explore the most effective strategies for assisting people in finding satisfying, meaningful, and productive work.

scholarly journals A protocol for the systematic review and meta-analysis of thigmotactic behaviour in the open field test in rodent models associated with persistent pain

2021 ◽  
Vol 5 (1) ◽  
pp. e100135
Author(s):  
Xue Ying Zhang ◽  
Jan Vollert ◽  
Emily S Sena ◽  
Andrew SC Rice ◽  
Nadia Soliman
Keyword(s):  
Systematic Review ◽  
Outcome Measure ◽  
Meta Analysis ◽  
Persistent Pain ◽  
Effect Sizes ◽  
Bias Assessment ◽  
Animal Pain ◽  
Trim And Fill ◽  
Meta Analyses ◽  
The Impact

ObjectiveThigmotaxis is an innate predator avoidance behaviour of rodents and is enhanced when animals are under stress. It is characterised by the preference of a rodent to seek shelter, rather than expose itself to the aversive open area. The behaviour has been proposed to be a measurable construct that can address the impact of pain on rodent behaviour. This systematic review will assess whether thigmotaxis can be influenced by experimental persistent pain and attenuated by pharmacological interventions in rodents.Search strategyWe will conduct search on three electronic databases to identify studies in which thigmotaxis was used as an outcome measure contextualised to a rodent model associated with persistent pain. All studies published until the date of the search will be considered.Screening and annotationTwo independent reviewers will screen studies based on the order of (1) titles and abstracts, and (2) full texts.Data management and reportingFor meta-analysis, we will extract thigmotactic behavioural data and calculate effect sizes. Effect sizes will be combined using a random-effects model. We will assess heterogeneity and identify sources of heterogeneity. A risk-of-bias assessment will be conducted to evaluate study quality. Publication bias will be assessed using funnel plots, Egger’s regression and trim-and-fill analysis. We will also extract stimulus-evoked limb withdrawal data to assess its correlation with thigmotaxis in the same animals. The evidence obtained will provide a comprehensive understanding of the strengths and limitations of using thigmotactic outcome measure in animal pain research so that future experimental designs can be optimised. We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines and disseminate the review findings through publication and conference presentation.

scholarly journals Pre-post effect sizes should be avoided in meta-analyses

2016 ◽  
Vol 26 (4) ◽  
pp. 364-368 ◽  
Author(s):  
P. Cuijpers ◽  
E. Weitz ◽  
I. A. Cristea ◽  
J. Twisk
Keyword(s):  
Comparison Group ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Behaviour Therapy ◽  
Natural Processes ◽  
Cognitive Behaviour ◽  
Post Test ◽  
The Difference ◽  
Meta Analyses ◽  
Highly Cited

AimsThe standardised mean difference (SMD) is one of the most used effect sizes to indicate the effects of treatments. It indicates the difference between a treatment and comparison group after treatment has ended, in terms of standard deviations. Some meta-analyses, including several highly cited and influential ones, use the pre-post SMD, indicating the difference between baseline and post-test within one (treatment group).MethodsIn this paper, we argue that these pre-post SMDs should be avoided in meta-analyses and we describe the arguments why pre-post SMDs can result in biased outcomes.ResultsOne important reason why pre-post SMDs should be avoided is that the scores on baseline and post-test are not independent of each other. The value for the correlation should be used in the calculation of the SMD, while this value is typically not known. We used data from an ‘individual patient data’ meta-analysis of trials comparing cognitive behaviour therapy and anti-depressive medication, to show that this problem can lead to considerable errors in the estimation of the SMDs. Another even more important reason why pre-post SMDs should be avoided in meta-analyses is that they are influenced by natural processes and characteristics of the patients and settings, and these cannot be discerned from the effects of the intervention. Between-group SMDs are much better because they control for such variables and these variables only affect the between group SMD when they are related to the effects of the intervention.ConclusionsWe conclude that pre-post SMDs should be avoided in meta-analyses as using them probably results in biased outcomes.

scholarly journals Experimental Study of Developing Creativity of University Students

2016 ◽  
Vol 217 ◽  
pp. 407-413 ◽  
Author(s):  
Aigerim Mynbayeva ◽  
Anastassiya Vishnevskay ◽  
Zukhra Sadvakassova
Keyword(s):  
Experimental Study ◽  
University Students

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

Abstract P060: Major Dietary Risk Factors for Chronic Diseases: A Systematic Review of the Current Evidence for Causal Effects and Effect Sizes

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Shahab Khatibzadeh ◽  
Renata Micha ◽  
Ashkan Afshin ◽  
Mayuree Rao ◽  
Mohammad Y Yakoob ◽  
...  
Keyword(s):  
Risk Factors ◽  
Dose Response ◽  
Convincing Evidence ◽  
Dietary Factors ◽  
Causal Effects ◽  
Effect Sizes ◽  
Current Evidence ◽  
Dietary Risk Factors ◽  
Dietary Risk ◽  
Meta Analyses

Background: Diet habits contribute to development of CVD and diabetes. Estimating the impact of diet on these diseases requires identification and quantification of causal effects of dietary factors. Objectives: To assess major dietary risk factors for CVD and diabetes, evaluate current evidence for causal effects, and identify the best unbiased effect estimates on risk. Methods: For multiple dietary risk factors, we evaluated WHO and similar criteria as part of the Global Burden of Diseases (GBD) study to assess probable or convincing evidence for causal effects, including consistency, dose-response, plausibility, and temporality. We performed systematic searches of online databases from 2008 to 2011, including hand-searches of references and author contacts, to identify systematic reviews and meta-analyses of well-designed observational or interventional studies. Meta-analyses were evaluated based on number of studies, design, definition of diet factors and outcomes, sample size, number of events, length of follow-up, statistical methods, evidence of bias, and control for confounders. Meta-analyses with largest numbers of studies and events and least evidence for bias were identified. Effect sizes and uncertainty were quantified per defined units of exposure, including pooling of categorical dose-response estimates using fixed-effects generalized least squares for trend estimation (GLST). Results: We identified 15 dietary risk factors having probable or convincing evidence of causal effects on CVD or diabetes. For 13, data were identified to provide the best pooled unbiased effect size on disease (Table). Conclusions: This systematic evaluation provides the best evidence-based quantitative estimates of the effects of major dietary factors on CVD and diabetes. These findings enable estimation of quantitative impacts on diseases burdens of suboptimal intakes of these factors in specific populations, and also highlight gaps in knowledge related to causality or effect sizes of other dietary factors.

Sign in / Sign up

Export Citation Format

Share Document