JMASM is an independent print and electronic journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, quantitative or qualitative evaluators, and methodologists.
Work appearing in Regular Articles, Brief Reports, and Emerging Scholars are externally peer reviewed, with input from the Editorial Board; in Statistical Software Applications and Review and JMASM Algorithms and Code are internally reviewed by the Editorial Board.
Three areas are appropriate for JMASM:
- Development or study of new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods
- Development or study of nonparametric, robust, permutation, exact, and approximate randomization methods
- Applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.
Elegant derivations, as well as articles with no take-home message to practitioners, have low priority. Articles based on Monte Carlo (and other computer-intensive) methods designed to evaluate new or existing techniques or practices, particularly as they relate to novel applications of modern methods to everyday data analysis problems, have high priority. Learn more about JMASM
The Journal of Modern Applied Statistical Methods (JMASM) is lead by its founder and editor Professor Sawilowsky. He is the author of two books on statistical methods (Statistics via Monte Carlo Simulation with Fortran, and Real Data Analysis), and has published over one hundred peer-reviewed book chapters, articles, and encyclopedia entries on social and behavioral science statistical methods, psychometrics and testing, quantitative program evaluation, and research and experimental design.