A review of scientific visualization software for the Macintosh computer

Many programs are available for the Macintosh that provide advanced visualization tools. Some of the tools are individual applications, and some are part of mathematical packages. This paper reviews the capabilities, functionality, and ease of use of visualization tools within six software packages: Mathematica, Spyglass Transform/View, Spyglass Dicer, JMP, MacSpin, and SYSTAT.     

Software for generating psychological experiments

This review compares four software packages for generating and running psychological computer experiments. It aims to inform researchers' decisions about which software to choose for their lab. Four widely used general purpose commercial packages available for the Windows platform are considered: DirectRT, E-Prime, Inquisit, and SuperLab. An overview of each package is given, and the implementation of two test experiments is described. Packages are evaluated with regard to the amount of complexity reduction they provide for the task of programming an experiment and the variety of experimental designs they can accommodate.     

Clustering, seriation, and subset extraction of confusion data

The study of confusion data is a well established practice in psychology. Although many types of analytical approaches for confusion data are available, among the most common methods are the extraction of 1 or more subsets of stimuli, the partitioning of the complete stimulus set into distinct groups, and the ordering of the stimulus set. Although standard commercial software packages can sometimes facilitate these types of analyses, they are not guaranteed to produce optimal solutions. The authors present a MATLAB *.m file for preprocessing confusion matrices, which includes fitting of the similarity-choice model. Two additional MATLAB programs are available for optimally clustering stimuli on the basis of confusion data. The authors also developed programs for optimally ordering stimuli and extracting subsets of stimuli using information from confusion matrices. Together, these programs provide several pragmatic alternatives for the applied researcher when analyzing confusion data. Although the programs are described within the context of confusion data, they are also amenable to other types of proximity data.     

Software for advanced ANOVA courses: A survey

In response to a survey of faculty regarding their commercial software preferences for advanced analysis of variance courses, it was found that the most frequently used packages were SAS, SPSS^x, and BMDP, all originally mainframe packages. The fourth choice, SYSTAT, was written for microcomputers but is currently also available for other host computers. Difficulty for students was the most frequently cited reason fornot using one of these four packages. The most important criteria for choosing software packages were variety of designs, accuracy, and ease of use. Cost was a factor only for microcomputer licenses. Packages using the general linear model approach were paramount to some, while others would not consider such packages. Instructors decried the lack of the “ideal program,” but the diversity of their desires makes it clear that no program could be ideal for all of them. Many faculty seemed unaware of newer software packages or of the extent to which older packages (particularly Minitab) have been modified; some had their choices constrained by departmental limitations. Better dissemination of information about statistical software is needed, whether it be from software publishers or through the professional literature.     

Bayesian Thurstonian models for ranking data using JAGS

A Thurstonian model for ranking data assumes that observed rankings are consistent with those of a set of underlying continuous variables. This model is appealing since it renders ranking data amenable to familiar models for continuous response variables—namely, linear regression models. To date, however, the use of Thurstonian models for ranking data has been very rare in practice. One reason for this may be that inferences based on these models require specialized technical methods. These methods have been developed to address computational challenges involved in these models but are not easy to implement without considerable technical expertise and are not widely available in software packages. To address this limitation, we show that Bayesian Thurstonian models for ranking data can be very easily implemented with the JAGS software package. We provide JAGS model files for Thurstonian ranking models for general use, discuss their implementation, and illustrate their use in analyses.     

结构方程模型中调节效应的标准化估计

回归分析和结构方程分析中,标准化估计对解释模型和比较效应大小有重要作用。对于调节效应模型（或交互效应模型）,通常的标准化估计没有意义。虽然显变量的调节效应模型标准化估计问题已经解决,但潜变量的调节效应模型标准化估计问题复杂得多。本文先介绍回归分析中显变量调节效应模型的标准化估计,然后提出了一种通过参数的原始估计和通常标准化估计来计算潜变量调节效应模型的“标准化”估计的方法,得到的“标准化”估计是尺度不变的,说明可以用“标准化”估计来解释和比较主效应和调节效应     

Iterative least squares estimates of communality: Initial estimate need not affect stabilized value

A common criticism of iterative least squares estimates of communality is that method of initial estimation may influence stabilized values. As little systematic research on this topic has been performed, the criticism appears to be based on cumulated experience with empirical data sets. In the present paper, two studies are reported in which four types of initial estimate (unities, squared multiple correlations, highestr, and zeroes) and four levels of convergence criterion were employed using four widely available computer packages (BMDP, SAS, SPSS, and SOUPAC). The results suggest that initial estimates have no effect on stabilized communality estimates when a stringent criterion for convergence is used, whereas initial estimates appear to affect stabilized values employing rather gross convergence criteria. There were no differences among the four computer packages for matrices without Heywood cases.     

Bayesian inference for psychology,part III: Parameter estimation in nonstandard models

We demonstrate the use of three popular Bayesian software packages that enable researchers to estimate parameters in a broad class of models that are commonly used in psychological research. We focus on WinBUGS, JAGS, and Stan, and show how they can be interfaced from R and MATLAB. We illustrate the use of the packages through two fully worked examples; the examples involve a simple univariate linear regression and fitting a multinomial processing tree model to data from a classic false-memory experiment. We conclude with a comparison of the strengths and weaknesses of the packages. Our example code, data, and this text are available via https://osf.io/ucmaz/.     

Logistic regression

Logistic regression has probably been underutilized in clinical investigations of personality because of its relatively recent development (dictated by the need for computer programs to obtain maximum likelihood estimates), and the fact that use has been largely confined to the fields of biostatistics, epidemiology, and economics Its use should be given serious consideration when the outcome of interest is dichotomous (or polychotomous) in nature and the predictors of interest may be categorical or continuous. The logit transformation is quite tractable mathematically, and it embodies the notion of threshold, which may have relevance for many of the variables that are of interest to investigators in the field of personality. Furthermore, investigators with experience in multiple linear regression or contingency table analysis should have little trouble in transitioning to logistic regression. Logistic regression programs are readily available in the major statistical packages, all of which provide fairly standard output.     

Logistic Regression

Logistic regression has probably been underutilized in clinical investigations of personality because of its relatively recent development (dictated by the need for computer programs to obtain maximum likelihood estimates), and the fact that use has been largely confined to the fields of biostatistics, epidemiology, and economics Its use should be given serious consideration when the outcome of interest is dichotomous (or polychotomous) in nature and the predictors of interest may be categorical or continuous. The logit transformation is quite tractable mathematically, and it embodies the notion of threshold, which may have relevance for many of the variables that are of interest to investigators in the field of personality. Furthermore, investigators with experience in multiple linear regression or contingency table analysis should have little trouble in transitioning to logistic regression. Logistic regression programs are readily available in the major statistical packages, all of which provide fairly standard output.     

Psynteract: A flexible,cross-platform,open framework for interactive experiments

We introduce a novel platform for interactive studies, that is, any form of study in which participants’ experiences depend not only on their own responses, but also on those of other participants who complete the same study in parallel, for example a prisoner’s dilemma or an ultimatum game. The software thus especially serves the rapidly growing field of strategic interaction research within psychology and behavioral economics. In contrast to all available software packages, our platform does not handle stimulus display and response collection itself. Instead, we provide a mechanism to extend existing experimental software to incorporate interactive functionality. This approach allows us to draw upon the capabilities already available, such as accuracy of temporal measurement, integration with auxiliary hardware such as eye-trackers or (neuro-)physiological apparatus, and recent advances in experimental software, for example capturing response dynamics through mouse-tracking. Through integration with OpenSesame, an open-source graphical experiment builder, studies can be assembled via a drag-and-drop interface requiring little or no further programming skills. In addition, by using the same communication mechanism across software packages, we also enable interoperability between systems. Our source code, which provides support for all major operating systems and several popular experimental packages, can be freely used and distributed under an open source license. The communication protocols underlying its functionality are also well documented and easily adapted to further platforms. Code and documentation are available at https://github.com/psynteract/.     

Analysis and visualization of complex psychophysiological data: Coordinated use of multiple applications on a microcomputer

Personal computers have become ubiquitous for data analysis and visualization. Complex analyses and visualizations of large data sets prove challenging for microcomputer environments and highlight a tradeoff between customized programming to enhance specificity and processing speed and the use of commercially available software packages. We frame this choice in terms of a continuum that exists from low-level programming languages to higher-level languages, and beyond this to object-oriented programming environments and other advanced software products; we argue that there is no sharp conceptual distinction between programming and not programming. In this light, we discuss the pros and cons of using several commercial software packages in concert on a single platform to facilitate complex research tasks and discuss the implications of this choice for innovative research.     

Using digitized auditory stimuli on the Macintosh computer

Hardware/software packages that digitize sound on the Apple Macintosh can help researchers prepare and present auditory materials needed for their experiments. Common features and benefits of commercially available sound digitizing packages are discussed in terms of some possible applications to cognitive psychology experiments.     

Adaptive Visual Analog Scales (AVAS): A modifiable software program for the creation,administration, and scoring of visual analog scales

The Adaptive Visual Analog Scales is a freely available computer software package designed to be a flexible tool for the creation, administration, and automated scoring of both continuous and discrete visual analog scale formats. The continuous format is a series of individual items that are rated along a solid line and scored as a percentage of distance from one of the two anchors of the rating line. The discrete format is a series of individual items that use a specific number of ordinal choices for rating each item. This software offers separate options for the creation and use of standardized instructions, practice sessions, and rating administration, all of which can be customized by the investigator. A unique participant/patient ID is used to store scores for each item, and individual data from each administration are automatically appended to that scale’s data storage file. This software provides flexible, time-saving access for data management and/or importing data into statistical packages. This tool can be adapted so as to gather ratings for a wide range of clinical and research uses and is freely available at www.nrlc-group.net.     

无均值结构的潜变量交互效应模型的标准化估计

潜变量交互效应建模研究近年来有两项重要进展, 一是提出了潜变量交互效应模型的标准化估计及其计算公式; 二是发现无均值结构模型可以取代传统的有均值结构模型, 建模大为简化。但标准化估计是在传统的有均值结构模型中建立的, 在简化的模型中同样适用吗？本文在无均值结构模型的框架内, 给出了潜变量交互效应模型的标准化形式、计算公式和建模步骤, 并通过模拟研究比较了极大似然和广义最小二乘两种估计方法、配对乘积指标和全部乘积指标两种指标类型, 结果表明, 在计算交互效应的标准化估计时, 应当使用配对乘积指标建模, 并且首选极大似然估计。     

An evaluation of statistical software for research and instruction

A variety of microcomputer statistics packages were evaluated. The packages were compared on a number of dimensions, including error handling, documentation, statistical capability, and accuracy. Results indicated that there are some very good packages available both for instruction and for analyzing research data. In general, the microcomputer packages were easier to learn and to use than were mainframe packages. Furthermore, output of mainframe packages was found to be less accurate than output of some of the microcomputer packages.     

SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test

Popular statistical software packages do not have the proper procedures for determining the number of components in factor and principal components analyses. Parallel analysis and Velicer’s minimum average partial (MAP) test are validated procedures, recommended widely by statisticians. However, many researchers continue to use alternative, simpler, but flawed procedures, such as the eigenvaluesgreater-than-one rule. Use of the proper procedures might be increased if these procedures could be conducted within familiar software environments. This paper describes brief and efficient programs for using SPSS and SAS to conduct parallel analyses and the MAP test.     

Abstract: A Meta-Analysis of the Autocorrelation in Single Case Designs

Single case design (SCD) experiments in the behavioral sciences utilize just one participant from whom data is collected over time. This design permits causal inferences to be made regarding various intervention effects, often in clinical or educational settings, and is especially valuable when between-participant designs are not feasible or when interest lies in the effects of an individualized treatment. Regression techniques are the most common quantitative practice for analyzing time series data and provide parameter estimates for both treatment and trend effects. However, the presence of serially correlated residuals, known as autocorrelation, can severely bias inferences made regarding these parameter estimates. Despite the severity of the issue, few researchers test or correct for the autocorrelation in their analyses.

Shadish and Sullivan (in press) recently conducted a meta-analysis of over 100 studies in order to assess the prevalence of the autocorrelation in the SCD literature. Although they found that the meta-analytic weighted average of the autocorrelation was close to zero, the distribution of autocorrelations was found to be highly heterogeneous. Using the same set of SCDs, the current study investigates various factors that may be related to the variation in autocorrelation estimates (e.g., study and outcome characteristics). Multiple moderator variables were coded for each study and then used in a metaregression in order to estimate the impact these predictor variables have on the autocorrelation.

This current study investigates the autocorrelation using a multilevel meta-analytic framework. Although meta-analyses involve nested data structures (e.g., effect sizes nested within studies nested within journals), there are few instances of meta-analysts utilizing multilevel frameworks with more than two levels. This is likely attributable to the fact that very few software packages allow for meta-analyses to be conducted with more than two levels and those that do allow this provide sparse documentation on how to implement these models. The proposed presentation discusses methods for carrying out a multilevel meta-analysis. The presentation also discusses the findings from the metaregression on the autocorrelation and the implications these findings have on SCDs.     

Reporting structural equation modeling results in Psychology and Aging: some proposed guidelines.

Structural equation modeling (SEM) is now widely used in social and behavioral science research. SEM provides the possibility of fitting, and evaluating the fit, of well-specified, theoretical models to empirical data--more generally, of testing elaborated psychological theories. The options available to users of these approaches are many and varied. Popular SEM computational software packages, such as LISREL and EQS, provide a large amount of information, and there is some uncertainty as to what should be routinely reported. A series of guidelines are proposed for reporting SEM results in articles submitted to Psychology and Aging. The suggested guidelines ask authors using SEM methodology to provide important analysis information that will enable readers to evaluate the findings.