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- W755066608 abstract "Preamble: This journal, focusing on Psychological Test and Assessment Modeling since 2010, has received great attention from researchers interested in psychologyspecific statistical methods and problems, general psychometrics, and psychological assessment in theory and practice. That is, a journal with deep methodical approaches enlarges scene. Of course, journals of methods in psychology (and educational science) suffer from lack of high impact factors, on average, and from lack of willing competent reviewers. While matter of impact factors is actually encouraging, though fluctuating - according to Kubinger, Heuberger, and Poinstingl (2010) selfevaluated impact factor is 2010: 0.565, 2011: 0.525, 2012: 0.783, 2013: 0.420 -, reviewers are rare. Nevertheless, we aim for a very quick processing of submitted papers, giving precise reviews of how to improve quality of a paper - if worthwhile. In following editorial we once again outline scope of journal, but primarily give hints on how to manage research work in order to contribute to concerning area by a very high methodical standard.Statistical standardsWithin psychology there are several misuses of statistical analyses, at least some improper traditions. Rasch, Kubinger, and Yanagida (2011), for instance - if reader prefers German, see Kubinger, Rasch, and Yanagida (2011) - elucidate many of them.First of all, it is to be emphasized that type-I-error (the significance level) must be established in advance, but not when some p-value has indeed been calculated: For instance also in predecessor of this journal, Rasch, Kubinger, Schmidtke, and Hausler (2004, p. 232) outlined, that the 'practice of asterisks' always implies highest ?from all ?-levels that one would ever accept: If a researcher decides, according to result, what level of ? he/she applies (in order to get a significant result that might be even just at ?=.05), then general ?-level is one that would suffice even in worst case. Claiming a-posteriori that a lower ?applies, would merely disclose researcher's self-deception.In second instant, obviously a proper approach would be to argue in detail, why a certain type-I-risk (?) is established, all above with respect to practical consequences of an eventual type-I-error in relation to an eventual type-II-error. It would be even better to plan study in advance regarding necessary sample size: Given a certain type-Iand type-II-risk as well as a relevant effect size, pertinent computer programs, especially R-routine OPDOE (OPtimal Design Of Experiments); Rasch, Pilz, Verdooren, & Gebhardt, 2011) calculate sample size so that only relevant effects will result in significance, but such relevant effects will not be detected only with a probability of type-II-risk settled. Admittedly, planning a study in such a way is currently only at a researcher's disposal for parametric tests such as two-sample t-test and analysis of variance, and moreover almost only for univariate analyses - Planning study as concerns non-parametric methods generally results in problems because alternative hypothesis is hard to quantify. (Rasch, Kubinger, & Yanagida, 2011, p. 216). However, planning according to some parametric test at least, although a non-parametric homologous test is aimed-for, rather meets statistical state-of-the-art than fixing sample size arbitrarily with consequence, that often significant, but irrelevant effect sizes result or other way round, that no significance occurs although estimated effect size is indeed of practical relevance. Concerning multivariate analyses in general, Rasch, Kubinger, and Yanagida (2011, p. 418) emphasize: Planning a study according to a multivariate analysis of variance happens either with regard to an in some way 'most important' [variable]; or researcher calculates necessary sample size for each [variable] on its own - given certain precision requirements - and then decides for largest one. …" @default.
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- W755066608 date "2014-04-01" @default.
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- W755066608 title "Editorial: Toward Essential Contributions for Psychological Test and Assessment Modeling" @default.
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