PSAAL: A LabVIEW 3 program for data acquisition and analysis in psychophysiological experiments

PSAAL (Physiological Signals Acquisition and Analysis in LabVIEW language) is a group of seven programs, written in LabVIEW 3.0 language and specialized for acquisition, analysis, reduction, and display of peripheral and central physiological data. The main characteristics of the programs are the wide range of use for most short-duration psychophysiological paradigms and the easy-to-use graphic interface. In addition, two noteworthy features of LabVIEW are included: the portability of the programs on Macintosh, PowerMac, IBM, HP-UX, and SUN computers, and the capability of handling acquisition on many National Instruments A/D boards, including the low-cost ones. PSAAL performs acquisition and simple reduction of peripheral physiological responses with 12- or 16-bit resolution according to the A/D board used. Analysis includes conversion of electrocardiogram into heart rate, the elaboration of the main parameters of skin conductance responses, electromyogram, startle reflex, and averaging of evoked potentials or other event-related physiological responses, under different experimental conditions. The programs allow the user to perform the organization of the data in a spreadsheet ready for most statistical packages and without the use of other spreadsheet applications.     

RSCORE-J: Pooled rating-method data: A computer program for analyzing pooled ROC curves

RSCORE-J, a computer program for a signal-detection analysis of pooled rating-method data, is listed and described. RSCORE-J computes jackknife estimates of ROC parameters and their standard errors from rating-method data pooled over a group of observers.     

C-quence: A tool for analyzing qualitative sequential data

C-quence is a software application that matches sequential patterns of qualitative data specified by the user and calculates the rate of occurrence of these patterns in a data set. Although it was designed to facilitate analyses of face-to-face interaction, it is applicable to any data set involving categorical data and sequential information. C-quence queries are constructed using a graphical user interface. The program does not limit the complexity of the sequential patterns specified by the user.     

ALICE: A system for manipulating and analyzing multidimensional data

The computer program ALICE solves the two major problems of data manipulation and analysis. First, ALICE allows the user to treat data from an experiment in the form they are generated. Second, mathematical calculations and statistical analyses are included as an intrinsic part of the multidimensional approach to data handling. ALICE accepts raw data in the form and order they were collected; reorganizes, partitions, or selects any subset of them (including a single entry), and arithmetically combines, transforms, or evaluates any formula involving them. Furthermore, learning to use ALICE is simple, even for those who are naive to both computers and data analysis.     

Comparing patterns of component loadings: Principal Component Analysis (PCA) versus Independent Component Analysis (ICA) in analyzing multivariate non-normal data

Principal component analysis identifies uncorrelated components from correlated variables, and a few of these uncorrelated components usually account for most of the information in the input variables. Researchers interpret each component as a separate entity representing a latent trait or profile in a population. However, the components are guaranteed to be independent and uncorrelated only when the multivariate normality of the variables is assumed. If the normality assumption does not hold, components are guaranteed to be uncorrelated, but not independent. If the independence assumption is violated, each component cannot be uniquely interpreted because of contamination by other components. Therefore, in the present study, we introduced independent component analysis, whose components are uncorrelated and independent even when the multivariate normality assumption is violated, and each component carries unique information.     

The Chemnitz LogAnalyzer: A tool for analyzing data from hypertext navigation research

Computer-based studies usually produce log files as raw data. These data cannot be analyzed adequately with conventional statistical software. The Chemnitz LogAnalyzer provides tools for quick and comfortable visualization and analyses of hypertext navigation behavior by individual users and for aggregated data. In addition, it supports analogous analyses of questionnaire data and reanalysis with respect to several predefined orders of nodes of the same hypertext. As an illustration of how to use the Chemnitz LogAnalyzer, we give an account of one study on learning with hypertext. Participants either searched for specific details or read a hypertext document to familiarize themselves with its content. The tool helped identify navigation strategies affected by these two processing goals and provided comparisons, for example, of processing times and visited sites. Altogether, the Chemnitz LogAnalyzer fills the gap between log files as raw data of Web-based studies and conventional statistical software.     

A practical method for analyzing factorial designs with heteroscedastic data

The Type I error rates and powers of three recent tests for analyzing nonorthogonal factorial designs under departures from the assumptions of homogeneity and normality were evaluated using Monte Carlo simulation. Specifically, this work compared the performance of the modified Brown-Forsythe procedure, the generalization of Box's method proposed by Brunner, Dette, and Munk, and the mixed-model procedure adjusted by the Kenward-Roger solution available in the SAS statistical package. With regard to robustness, the three approaches adequately controlled Type I error when the data were generated from symmetric distributions; however, this study's results indicate that, when the data were extracted from asymmetric distributions, the modified Brown-Forsythe approach controlled the Type I error slightly better than the other procedures. With regard to sensitivity, the higher power rates were obtained when the analyses were done with the MIXED procedure of the SAS program. Furthermore, results also identified that, when the data were generated from symmetric distributions, little power was sacrificed by using the generalization of Box's method in place of the modified Brown-Forsythe procedure.