Statistical software packages for PCs — A market survey

The increase of statistical software applications for PCs is caused by decreasing hardware costs and dramatically enhanced PC performance. Whereas in the past the domain of statistical computing has been reserved to mainframe solutions, a great number of new software packages for PCs have come out in the last five years. Therefore, the producers of established mainframe software were also forced to offer PC-based solutions. By limiting a market analysis to products with a medium set of well known statistical methods, the immense number of available products is reduced to about fifty systems. We ordered an evaluation copy of these systems to test the numerical quality, the system speed, and the performance of several procedures. Seventeen packages were made available for an extensive examination. This paper will (1) discuss the problems and the solutions of obtaining a complete and correct datamatrix that describes the entire market and (2) present the results of a comparative market analysis.     

Aspects of Statistical Computing: What Packages for the 1980's Ought to Do

A wish list of desirable statistical computing capabilities is presented. This may help one question which of these capabilities can be satisfied by existing packages, which might be met through reasonable extensions to these packages, which might require substantial new development, and which ought to be supplied by the computing environment rather than the packages. These questions are explored, taking into account the nature of the statistical work and the choices presented by technology. Attention is given to the barriers to be overcome if future statistical packages are to take full advantage of new technology.     

Statistical Computing Packages: Some Words of Caution

Three situations are cited when caution is needed in using statistical computing packages: (a) when analyzing data and having insufficient statistical knowledge to completely understand the output; (b) when teaching the use of packages in a statistics course, to the exclusion of teaching statistics; and (c) when using packages in subject-matter teaching, without teaching the statistical methods underlying the packages.     

Statistical Microcomputing—Like it is

A statistical software package is a collaborative effort between a program's authors and users. When statistical analysis took place exclusively on mainframe computers, the entire statistical community was served by some three to six major packages, which helped to ensure that program errors would be quickly uncovered and corrected. The current trend toward performing statistical analysis on microcomputers has resulted in an explosion of software of varying quality, with more than 200 packages for the IBM PC alone. Since all of these programs are competing for the same base of knowledgeable users, the number of sophisticated users per package is dramatically less than for mainframe packages; the net result is that problems in any particular package are more likely to go unnoticed and uncorrected. For example, the most widely used shareware package contains major errors that should cause it to be rejected out of hand, and three best-selling packages analyze unbalanced two-factor experiments using an approximate technique originally developed for hand calculation. Several strategies are offered to help author and user reveal any problems that might be present in their software.     

Key Attributes of a Modern Statistical Computing Tool

ABSTRACT

In the 1990s, statisticians began thinking in a principled way about how computation could better support the learning and doing of statistics. Since then, the pace of software development has accelerated, advancements in computing and data science have moved the goalposts, and it is time to reassess. Software continues to be developed to help do and learn statistics, but there is little critical evaluation of the resulting tools, and no accepted framework with which to critique them. This article presents a set of attributes necessary for a modern statistical computing tool. The framework was designed to be broadly applicable to both novice and expert users, with a particular focus on making more supportive statistical computing environments. A modern statistical computing tool should be accessible, provide easy entry, privilege data as a first-order object, support exploratory and confirmatory analysis, allow for flexible plot creation, support randomization, be interactive, include inherent documentation, support narrative, publishing, and reproducibility, and be flexible to extensions. Ideally, all these attributes could be incorporated into one tool, supporting users at all levels, but a more reasonable goal is for tools designed for novices and professionals to “reach across the gap,” taking inspiration from each others’ strengths.     

An Examination of Five Statistical Software Packages for Epidemiology

Five statistical software packages for epidemiology and clinical trials are reviewed. The five packages are EPI INFO, EPICURE, EPILOG PLUS, STATA, and TRUE EPI-STAT. Only DOS versions of these packages are compared and rated (Windows versions are discussed but not rated). Although the packages differ in their target audiences, interfaces, capabilities, and approaches, they are examined according to criteria that are of most interest to epidemiologists, biostatisticians, and others involved in epidemiologic and clinical research. A general discussion with recommendations follows the review of the statistical packages.     

A Review of Eight Statistics Software Packages for General Use

Eight statistical software packages for general use by non-statisticians are reviewed. The packages are GraphPad Prism, InStat, ISP, NCSS, SigmaStat, Statistix, Statmost, and Winks. Summary tables of statistical capabilities and “usability” features are followed by discussions of each package. Discussions include system requirements, data import capabilities, statistical capabilities, and user interface. Recommendations, based on user needs and sophistication, are presented following the reviews.     

An Examination of Discrepancies in Multiple Imputation Procedures Between SAS® and SPSS®

Multiple imputation (MI) has become a feasible method to replace missing data due to the rapid development of computer technology over the past three decades. Nonetheless, a unique issue with MI hinges on the fact that different software packages can give different results. Even when one begins with the same random number seed, conflicting findings can be obtained from the same data under an identical imputation model between SAS® and SPSS®. Consequently, as illustrated in this article, a predictor variable can be claimed both significant and not significant depending on the software being used. Based on the considerations of multiple imputation steps, including result pooling, default selection, and different numbers of imputations, practical suggestions are provided to minimize the discrepancies in the results obtained when using MI. Features of Stata® are briefly reviewed in the Discussion section to broaden the comparison of MI computing across widely used software packages.     

Latent variable techniques for categorical data

Two useful statistical methods for generating a latent variable are described and extended to incorporate polytomous data and additional covariates. Item response analysis is not well-known outside its area of application, mainly because the procedures to fit the models are computer intensive and not routinely available within general statistical software packages. The linear score technique is less computer intensive, straightforward to implement and has been proposed as a good approximation to item response analysis. Both methods have been implemented in the standard statistical software package GLIM 4.0, and are compared to determine their effectiveness.     

A Note on “Characteristics of the Membership of the American Statistical Association, 1958”

A second course in statistics, for nonstatisticians who will use packaged statistical software in their work, is outlined. The course is directed toward the wise choice, use, and evaluation of statistical computer packages. The goal of the course is to train educated consumers of statistical programs. Particular attention is paid to computer-based data analysis, interpretation of output, comparison of competing packages, and statistical problems that arise when computers are employed to analyze large data sets.     

Comparisons of Software Packages for Generalized Linear Multilevel Models

We review five software packages that can fit a generalized linear mixed model for data with more than a two-level structure and a multiple number of independent variables. These five packages are MLn, MLwiN, SAS Proc Mixed (Glimmix Macro), HLM, and VARCL. We first discuss the features of each of the five packages. These features include data input and data management, statistical model capabilities, output, and user friendliness. We then compare their performance on several simulated datasets.     

The use of semantic knowledge to enhance statistical software

The shortcomings of conventional statistical packages are discussed to illustrate the need to develop software which is able to exhibit a greater degree of statistical expertise, thereby reducing the misuse of statistical methods by those not well versed in the art of statistical analysis. Up to now the majority of the research into developing knowledge-based statistical software has concentrated on moving away from conventional architectures by adopting what can be termed expert systems approaches. This paper proposes an approach which is based upon the concept of semantic modelling. By representing some of the semantic meaning of data, it is conceived that a system could examine a request to apply a statistical technique and check if the use of the chosen technique was semantically sound, i.e. will the results obtained be meaningful. Current systems, in contrast, can only perform what can be considered as syntactic checks. The prototype system that has been implemented to explore the feasibility of such an approach is presented; the system has been designed as an enhanced variant of a conventional style statistical package.     

A note on a parametric extension of rank tests to the analysis of multifactor experiments within blocks

A general rank test procedure based on an underlying multinomial distribution is suggested for randomized block experiments with multifactor treatment combinations within each block. The Wald statistic for the multinomial is used to test hypotheses about the within–block rankings. This statistic is shown to be related to the one–sample Hotellingt's T² statistic, suggesting a method for computing the test statistic using the standard statistical computer packages.     

The Future of Statistical Computing on Desktop Computers

A class of computing devices known as desktop computers has emerged over the last several years. The International Data Corporation (McGovern 1980) estimates that the number of desktop computers is increasing by approximately 53,000 each month. Because of the projected widespread use of desktop computers and anticipated improvements in hardware, the potential for impressive statistical computing on these devices is exciting. Two features of desktop computers will be particularly important for those doing statistical analyses: (a) the ease-of-use of the computers, and (b) their extensive graphics capabilities. The author suggests that sophisticated statistical software will be available in the near future on many different models of desktop computers. Indeed, several of the manufacturers provide high-quality software at the present time. The implications for statisticians of a rapid growth rate for desktop computers are discussed for data analysis, software development, graphics, and instructional usage.     

A Review of Three Directed Acyclic Graphs Software Packages

This article offers a review of three software packages that estimate directed acyclic graphs (DAGs) from data. The three packages, MIM, Tetrad and WinMine, can help researchers discover underlying causal structure. Although each package uses a different algorithm, the results are to some extent similar. All three packages are free and easy to use. They are likely to be of interest to researchers who do not have strong theory regarding the causal structure in their data. DAG modeling is a powerful analytic tool to consider in conjunction with, or in place of, path analysis, structural equation modeling, and other statistical techniques.     

Fast and Stable Algorithms for Computing and Sampling From the Noncentral Hypergeometric Distribution

Although the noncentral hypergeometric distribution underlies conditional inference for 2 × 2 tables, major statistical packages lack support for this distribution. This article introduces fast and stable algorithms for computing the noncentral hypergeometric distribution and for sampling from it. The algorithms avoid the expensive and explosive combinatorial numbers by using a recursive relation. The algorithms also take advantage of the sharp concentration of the distribution around its mode to save computing time. A modified inverse method substantially reduces the number of searches in generating a random deviate. The algorithms are implemented in a Java class, Hypergeometric, available on the World Wide Web.     

Letters to the Editor

Although several authors have indicated that the median test has low power in small samples, it continues to be presented in many statistical textbooks, included in a number of popular statistical software packages, and used in a variety of application areas. We present results of a power simulation study that shows that the median test has noticeably lower power, even for the double exponential distribution for which it is asymptotically most powerful, than other readily available rank tests. We suggest that the median test be “retired” from routine use and recommend alternative rank tests that have superior power over a relatively large family of symmetric distributions.