observational data: An overview and tutorial.

Statistical properties of the ability level estimate ( ) in item response theory (IRT) were investigated through a Monte Carlo investigation, based on data generated with a four cases multifactor design. Dichotomous items and the logistic two-parameter IRT model in a one-dimensional setting have been chosen. The estimation procedure was the marginalized Bayesian item parameters estimation and EAP estimation for . The property of invariance is discussed. Results show that estimation of is intrinsically biased, is constrained by the number of items and that it performs better when the number of items and the number of examinees increase. Furthermore, IRT parameters do not seem to perform better nor give more information than those used in classical test theory.

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software.

When running an experiment, precision is essential to ensure results are as exact as possible.Thus, computers, which offer endless accuracy, have become an inevitable tool to design experiments.To avoid programming from scratch for each new situation, a program, E-Prime, has been created to ease the conception of experiments.E-Prime, developed by PSTNet, offers a user-friendly interface that makes typical experiments easy to create.This paper shows how to effortlessly create an experiment with E-Prime, followed by a simple example.E-Prime ( 2004) is a program designed to facilitate the conception of any experiment that uses a computer as an interface between the subject and the experimenter.It can also run the experiment, collect the results, do some basic data analysis and export said data.These options are parsed through the five programs that constitute E-Prime: E-Studio, E-Run, E-DataAid, E-Merge and E-Recovery. E-StudioE-Studio is the subprogram of E-Prime dedicated to the creation of experiments.

The present work is an introduction to Latent Class Growth Modelling (LCGM). LCGM is a semi‐parametric statistical technique used to analyze longitudinal data. It is used when the data follows a pattern of change in which both the strength and the direction of the relationship between the independent and dependent variables differ across cases. The analysis identifies distinct subgroups of individuals following a distinct pattern of change over age or time on a variable of interest. The aim of the present tutorial is to introduce readers to LCGM and provide a concrete example of how the analysis can be performed using a real‐world data set and the SAS software package with accompanying PROC TRAJ application. The advantages and limitations of this technique are also discussed.

The purpose of this paper is to promote statistical power analysis in the behavioral sciences by introducing the easy to use GPower software. GPower is a free general power analysis program available in two essentially equivalent versions, one designed for Macintosh OS/OS X and the other for MS‐DOS/Windows platforms. Psychological research examples are presented to illustrate the various features of the GPower software. In particular, a priori, post‐hoc, and compromise power analyses for t‐tests, F‐tests, and χ2‐tests will be demonstrated. For all examples, the underlying statistical concepts as well as the implementation in GPower will be described. In the behavioral sciences, we routinely apply statistical tests, but control of statistical power cannot be taken for granted. However, neglecting statistical power—the probability of rejecting false null hypotheses—can have severe consequences. For example, without control of statistical power it is very difficult to interpret nonsignificant results. Statistical tests can produce nonsignificant results because (a) the null hypothesis (H0) holds and is retained correctly or (b) the alternative hypothesis (H1) holds but the test has not been powerful enough to detect the deviations from H0. Obviously, there is no reasonable way to decide between interpretations (a) and

When investigating the deficits of a single patient, psychologists usually compare his/her performance in one or more tests to the performance of a control group. This can be done for any kind of variables, provided (i) that the design does not require the investigation of interactions between two or more factors, (ii) that the comparison between two or more individuals is not desired, and (iii) that the collection of the control data is possible. Yet, researchers are constantly interested in assessing interactions in the performance of an individual, and in the comparison of two or more individuals for investigating double dissociations or the efficiency of different methods of therapy, etc. They also may desire to investigate cases where only extremely simple and easy tasks can be performed, where ceiling effects are observed in the performance of the controls, and thus the case-controls comparison is impossible. The available statistical tools for the analysis of intra-individual or inter-individual performance (mainly with proportions) do not offer the possibility to assess interaction, they are not appropriate when some cells may contain 0 or 1 proportions, and when the sample size is small. Here, we present the Q’ test which may be used to test the hypothesis of equal proportions and proportion differences in 2 × K designs, offering therefore the possibility for researchers to investigate the main effects and interaction. This test can be used for any sample size and even when the data contains extreme proportions. Finally, a procedure of multiple comparisons described in this paper may be used to locate statistically significant sources of variance and differences.

Estimating parameters describing response time distributions is difficult. The most commonly used method for parameter estimation is the maximum likelihood method (ML). However, this method applied on the three-parameter Weibull distribution returns biased estimates and the amount of bias is unknown. A recent method, that we call nested maximum likelihood, was proposed by Gourdin, Hansen and Jaumard (1994). Due to its complexity, it has never been used and tested systematically. Here I compare it to the maximum likelihood method. The results shows that nested maximum likelihood is slightly better than ML. Although the gains are marginal, the method has important implications for future research in parameter estimation.