The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

https://www.bmj.com/content/bmj/372/bmj.n71.full.pdf

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

Behavioral scientists routinely publish broad claims about human psychology and behavior in the world's top journals based on samples drawn entirely from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. Researchers - often implicitly - assume that either there is little variation across human populations, or that these "standard subjects" are as representative of the species as any other population. Are these assumptions justified? Here, our review of the comparative database from across the behavioral sciences suggests both that there is substantial variability in experimental results across populations and that WEIRD subjects are particularly unusual compared with the rest of the species - frequent outliers. The domains reviewed include visual perception, fairness, cooperation, spatial reasoning, categorization and inferential induction, moral reasoning, reasoning styles, self-concepts and related motivations, and the heritability of IQ. The findings suggest that members of WEIRD societies, including young children, are among the least representative populations one could find for generalizing about humans. Many of these findings involve domains that are associated with fundamental aspects of psychology, motivation, and behavior - hence, there are no obvious a priori grounds for claiming that a particular behavioral phenomenon is universal based on sampling from a single subpopulation. Overall, these empirical patterns suggests that we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity. We close by proposing ways to structurally re-organize the behavioral sciences to best tackle these challenges.

/pdf/the-weirdest-people-in-the-world-480wr4m3kb.pdf

The WEIRDest People in the World

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA’s such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.

/pdf/calculating-and-reporting-effect-sizes-to-facilitate-4a429kama3.pdf

Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

/pdf/weird-languages-have-misled-us-too-comment-on-henrich-et-al-57dmcdjt24.pdf

WEIRD languages have misled us, too [Comment on Henrich et al.]

Interesting volume on emotional intelligence, whatever it is [Review of: G. Matthews, M. Zeidner (2007) The science of emotional intelligence: knowns and unknowns]

The Replication Paradox

Type D personality predicts the occurrence of major cardiac events in patients with coronary artery disease : A multi-method analysis

p-Values Less Than 0.05 in Psychology: What is Going on?

This is our commentary on the "What is IQ? Life Beyond "General Intelligence"" paper by Kovacs and Conway (2019- https://doi.org/10.1177/0963721419827275). We advocate for a latent variable approach in experimental research, and illustrate both conceptual and statistical benefits of using this approach in context of research on cognition. Benefits include the availability of more information about the constructs being measured, the ability to check for specific or general effects of covariates, and larger statistical power to detect mean differences between groups.

Jelte M. Wicherts

Papers

Interesting volume on emotional intelligence, whatever it is [Review of: G. Matthews, M. Zeidner (2007) The science of emotional intelligence: knowns and unknowns]

The Replication Paradox

Type D personality predicts the occurrence of major cardiac events in patients with coronary artery disease : A multi-method analysis

p-Values Less Than 0.05 in Psychology: What is Going on?

Distinguishing Specific from General Effects in Cognition Research