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.]

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...

https://tandfonline.com/doi/pdf/10.1080/13854046.2016.1205136

The importance of measurement invariance in neurocognitive ability testing.

Around their 18th birthday, basically all Dutch males born between 1934 and 1964 unknowingly took part in a study of the malleability of intelligence. When these young men appeared before the Dutch military draft board, they took a non-verbal IQ test based on Raven’s (1960) Progressive Matrices. With a little help from Piet Vroon, James Flynn (1987) discovered that those born in 1934 (cohort of 1952) scored on average 20 IQ points lower on the test than those born in 1964 (cohort of 1982). This suggested that in only 30 years, the Dutch male population had shown an increase of more than one standard deviation in average IQ. Flynn (1987) also documented this gain in average IQ in 13 other countries over the course of the 20th century and the effect is now commonly known as the Flynn effect. The Flynn effect raises many questions: How can IQ be substantially heritable, yet show such strong gains that appear to be due to environmental factors? Were Dutch males in 1982 so much smarter than Dutch males in 1952?

[Review of: J.R. Flynn (2007) What is intelligence? Beyond the Flynn effect]

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.

https://www.mdpi.com/2076-2615/7/12/90/pdf

The Weak Spots in Contemporary Science (and How to Fix Them).

An increasing number of schools offer bilingual programs, where lessons are taught in more than one language. Several theories state that bilinguals have greater metalinguistic awareness than monolinguals. We investigated whether this greater metalinguistic awareness is also related to an increased ability to understand an unknown language. To measure metalinguistic awareness and the ability to understand text written in an unknown language, we designed the Indonesian Language Test (ILT). The ILT consists of items regarding a story in Indonesian. Dutch high school students from monolingual and bilingual classes were administered the ILT, a Dutch Language Test, an English Language Test, and a general intelligence test. The ILT showed promising psychometric properties. Bilingual students scored significantly higher on the ILT than monolingual students. Multi-group confirmatory factor analyses showed (i) that ILT measures the ability to understand an unknown language, and (ii) that bilingual students score significantly higher than monolingual students on this ability. Both observations support the notion that bilingual education increases metalinguistic awareness and therefore the ability to understand an unknown language.

Bilingual Education, Metalinguistic Awareness, and the Understanding of an Unknown Language.

Reasoning 10.42 2.96 6.80 3.33 9.24 3.34 7.34 2.83 Verbal Reasoning 7.27 3.01 5.37 2.82 6.65 3.47 5.56 2.70 The values for univariate skewness and kurtosis in the four groups are in an acceptable range (i.e., [-0.89 0.88]), suggesting no large deviations from normality. Therefore, the use of Maximum Likelihood for estimating the factor models is justified. Table 3.2 contains means and standard deviations of the three subtests for both ethnic groups in the two conditions. First, we provide the analysis of variance results. Box's M test suggests some covariance heterogeneity over groups (F(18, 28483) = 1.787, p .30). The multivariate interaction effect between condition and ethnicity is significant (F(3, 289) = 2.642, p = .050). The only significant univariate interaction effect is found on the Abstract Reasoning subtest (F(1, 291) = 5.56, p .30). All multivariate and univariate simple effects for condition within the minority group are non-significant (all Ps > .30), which is opposite to what one would expect from stereotype threat theory. Whereas, the minority group scored significantly lower than the majority group, these ANOVA results indicate that on average the minority students in the stereotype threat condition did not score lower than the minority students in the control condition. However, it is important to stress that the sample may be expected to be heterogeneous with respect to domain-identification, considered an important moderator of stereotype threat effects (e.g., Steele, 1997). For instance, Aronson et al. (1999) found that test-takers that identified strongly with the domain of interest (i.e., mathematics) were more susceptible to stereotype threat, whereas test-takers who moderately identified with the domain performed better under stereotype threat conditions than under control STEREOTYPE THREAT AND MEASUREMENT INVARIANCE 51 conditions. This suggests that within heterogeneous samples that contain both highly identified and moderately identified test-takers, effects of stereotype threat may differ substantively over persons. In such samples, positive and negative effects may cancel out, resulting in no, or only a small effect on the mean. However, the absence of a mean effect does not necessarily mean the absence of an effect. To investigate the possibility that covariance structure was affected by the stereotype threat induction, we tested for measurement invariance with respect to the four groups. The results of the multi-group confirmatory factor analyses are reported in Table 3.3. Table 3.3 Fit measures of steps towards strict factorial invariance (Study 1) Step Restrictions DF χ2 p ∆DF ∆χ2 p RMSEA CFI 1 0 0 1.000 0.000 1.000 2 Λ 6 14.73* 0.023 6 14.73* 0.023 0.145 0.942 2a Λ 5 4.74 0.449 (-) 1 9.99** 0.002 0.000 1.000 3 Λ,Θ 14 23.68 0.050 9 18.94* 0.026 0.097 0.936 3a Λ,Θ 13 16.45 0.226 (-) 1 7.23** 0.007 0.058 0.977 4 Λ,Θ, Ψcon 15 16.91 0.324 2 0.46 0.795 0.040 0.987 5 Λ,Θ, τ, Ψcon 20 31.78* 0.046 5 14.87* 0.011 0.089 0.922 5a Λ,Θ, τ, Ψcon 19 27.43 0.095 (-) 1 4.35* 0.037 0.079 0.944 5b Λ,Θ, τ, Ψcon 18 23.70 0.165 (-) 1 3.73 0.053 0.065 0.962 6 Λ,Θ, τ, Ψcon, αcon 20 24.44 0.224 2 0.74 0.691 0.056 0.971 Note: Underlined restrictions are tested by likelihood ratio test ∆χ2; *p .05), whereas in the other groups this factor loading is significantly greater than zero (λ1 = 0.92, SE = 0.22, Z = 4.19, p< .01). In Step 3, the residual variances are restricted to be invariant over the four groups. This, again, leads to a significant deterioration in model fit, as can be seen by the significant increase in chi-square, increase in RMSEA, and lowering of CFI. Not surprisingly, the misfit in this step is mainly due to the residual variance of the Numerical Ability subtest of the minority group in the stereotype threat condition (MI = 7). Freeing this parameter leads to a significant improvement in model fit (Step 3a). The residual variance of Numerical Ability is larger in the minority group, stereotype threat condition (6.33, SE = 1.06), than in the other groups (3.47, SE = 0.61). In the fourth step,

/pdf/group-differences-in-intelligence-test-performance-21jxktg0ts.pdf

Jelte M. Wicherts

Papers

The importance of measurement invariance in neurocognitive ability testing.

[Review of: J.R. Flynn (2007) What is intelligence? Beyond the Flynn effect]

The Weak Spots in Contemporary Science (and How to Fix Them).

Bilingual Education, Metalinguistic Awareness, and the Understanding of an Unknown Language.

Group differences in intelligence test performance