The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the minimum ratio of sample size to the number of variables, is invariant across studies. In fact, n...

Acoustic identification is increasingly being used as a non-invasive method for identifying individuals within avian populations. However, most previous studies have utilized small samples of individuals (<30). The feasibility of using acoustic identification of individuals in larger avian populations has never been seriously tested. In this paper, we assess the feasibility of using distinct ac...

The prediction of events is of substantial interest in many research areas. To evaluate the performance of prediction methods, the statistical validation of these methods is of utmost importance. Here, we compare an analytical validation method to numerical approaches that are based on Monte Carlo simulations. The comparison is performed in the field of the prediction of epileptic seizures. In ...

Anderson, Lindsay, and Bushman (1999) compared effect sizes from laboratory and field studies of 38 research topics compiled in 21 meta-analyses and concluded that psychological laboratories produced externally valid results. A replication and extension of Anderson et al. (1999) using 217 lab-field comparisons from 82 meta-analyses found that the external validity of laboratory research differe...

We proposed a test for assessing multivariate normality of the high-dimensional data which the dimension is larger than the sample size. The classical tests based on the sample measures of multivariate skewness and kurtosis defined by Mardia (1970) or Srivastava (1984) do not work for the high-dimensional case. The proposed test does not require explicit conditions on the relationship between t...

BACKGROUND The DerSimonian and Laird approach (DL) is widely used for random effects meta-analysis, but this often results in inappropriate type I error rates. The method described by Hartung, Knapp, Sidik and Jonkman (HKSJ) is known to perform better when trials of similar size are combined. However evidence in realistic situations, where one trial might be much larger than the other trials, i...