A computer program for determining optimal data transformations minimizing skew

A program is presented that solves for an optimal transformation of data that minimizes skew. Various possible transformations are represented by a function that depends upon a single parameter, λ. A process of iterative approximations is employed to find a value of λ that will transform the data so as to produce a third moment equal to zero. The transformation found by this process may be used as computed or used to indicate which of the commonly employed transformations will most adequately counteract skew. The use of this program in processing data before analysis of variance is discussed.     

Statistical power analysis for one-way analysis of variance: A computer program

To facilitate the computation of statistical power for analysis of variance, Cohen developed the index of effect sizef, defined as theSD between groups divided by theSD within groups. A microcomputer program for statistical power allows the user to compute the value off in any of several ways: by specifying the mean andSD for every cell in the ANOVA; by specifying the mean value for the two extreme cells and the pattern of dispersion for the remaining cells; by estimating the proportion of variance in the dependent variable that will be explained by group membership; and/or with reference to conventions for small, medium, and large effects. The program will compute power for any single set of parameters; it will also allow the user to generate tables and graphs showing how power will vary as a function of effect size, sample size, andα.     

A statistical analysis system (SAS) computer program for Markov chain analysis

A computer program for Markov chain analysis is presented and discussed. The program is written in the language of the Statistical Analysis System (SAS) but detailed knowledge of SAS is not required for its use. The program tests hypotheses about the goodness of fit of first- and second-order Markov models. It also tests if transition probabilities are homogeneous between the first and the second half of each sequence.Presented, in part, at the Association for the Advancement of Behavior Therapy Convention, November 1984.     

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.     

A spectral analysis program for the processing of neuro-electric data

The Spectral Analysis Program is designed to perform the following kinds of spectral analysis, using the Cooley-Tukey Fast Fourier Transform Algorithm: cross correlation, cross power spectrum, Fourier transform, inverse Fourier transform, and double Fourier transform (the process of transforming two real functions simultaneously). These operations are performed on two blocks of core that can be filled through time-sequential sampling of an external analog signal or from previously sampled data that have been stored on DECtape. Through the use of nine Teletype commands, the user can manipulate input and output and select a particular type of spectral analysis. Output may be displayed on a 30D scope stored on DECtape or turned into hard copy by a Calcomp plotter used for processing neuroelectric data at the Stanley Cobb Laboratories for Psychiatric Research.     

multiTree: A computer program for the analysis of multinomial processing tree models

Multinomial processing tree (MPT) models are a family of stochastic models for psychology and related sciences that can be used to model observed categorical frequencies as a function of a sequence of latent states. For the analysis of such models, the present article presents a platform-independent computer program called multiTree, which simplifies the creation and the analysis of MPT models. This makes them more convenient to implement and analyze. Also, multiTree offers advanced modeling features. It provides estimates of the parameters and their variability, goodness-of-fit statistics, hypothesis testing, checks for identifiability, parametric and nonparametric bootstrapping, and power analyses. In this article, the algorithms underlying multiTree are given, and a user guide is provided. The multiTree program can be downloaded from http://psycho3.uni-mannheim.de/multitree.