Anyone tried PSPP (free alternative to SPSS)?

#2
Foud it somewere in Wikipedia... Quite some time ago. I downloaded for window but I didn't really like it. A bit limited and, er, ugly. But, of course, it's free, and it'a one hell of a good point. A good alternative.
 
#4
PSPP and other free software

I have also tried PSPP 0.6.1 and like it's SPSS feel but agree that it is very limited. I have downloaded OpenStat http://www.statpages.org/miller/openstat/ and it seems to be fully-featured and comparable to SPSS. The creator also updates it monthly. My question is if anyone has used this software.

I am a PhD student in allied health sciences and do not have a strong background in mathematics. I have attempted to use R and also with its linux GUI interface (RKWard) but am not comfortable with it. Does anyone have any experience with OpenStat? How important is it to double-check my statistical results with commercial packages such as SPSS or SigmaStat?
 
#7
Alnternative to Quantum

Guys!!

I tried installing PSPP but no luck !! It was a worse experience though. Curently I am using Quantum (an SPSS product), but I need a replacement for this which should be a freeware. Can sombody help me out in this regard.
 

gianmarco

TS Contributor
#8
Hi!
I would point out PAST (http://folk.uio.no/ohammer/past/).
It is not comparable to statistica or SPSS, but it is rather complete and very easy to use.

I believe that the scholar who made it (and who continuously update it) is a real "benefactor".


Pasting from the its site:

"PAST is a free, easy-to-use data analysis package originally aimed at paleontology but now also popular in ecology and other fields. It includes common statistical, plotting and modelling functions:

# A spreadsheet-type data entry form
# Both interactive user interface and scripting
# Graph, scatter, 3D scatter, bubble, histogram, kernel density estimation, box, percentile, ternary, survivorship, spindle, matrix, surface and normal probability plots
# Curve fitting: Linear (Standard and Reduced Major Axis) with bootstrapping and permutation, lin-log (exponential), log-log (allometric), polynomial, logistic, von Bertalanffy, sum-of-sines, B-splines, Gaussian (species packing), multiple regression.
# F, t, permutation t, Chi-squared w. permutation test, Fisher's exact, Kolmogorov-Smirnov, Mann-Whitney, Shapiro-Wilk, Jarque-Bera, Spearman's Rho and Kendall's Tau tests with permutation, correlation, covariance, contingency tables, one-way and two-way ANOVA, one-way ANCOVA, Kruskal-Wallis test, sign test, Wilcoxon signed rank test with permutation, Fligner-Killeen test for coefficients of variation, mixture analysis, survival analysis (Kaplan-Meier curves, logrank and other tests).
# Diversity indices with bootstrapping and permutation, individual- and sample-based rarefaction. Capture-recapture richness estimators. Renyi diversity profiles, SHE analysis, beta diversity.
# Abundance model fitting: Geometric, log-series, log-normal, broken stick.
# Multivariate statistics: Principal Components (with Minimal Spanning Tree, bootstrapping etc.), Principal Coordinates (19 distance measures), Non-metric Multidimensional Scaling (19 distance measures), Detrended Correspondence Analysis, Canonical Correspondence Analysis, Cluster analysis (UPGMA, single linkage, Ward's method and neighbour joining, 19 distance measures, two-way clustering, bootstrapping), k-means clustering, seriation, discriminant analysis, one-way MANOVA, one-way and two-way ANOSIM, one-way NPMANOVA, Hotelling's T2, paired Hotelling's T2, Mahalanobis-distance permutation, Mardia's multivariate normality, Box's M, Canonical Variates Analysis, multivariate allometry with bootstrapping, Mantel test, SIMPER, Imbrie & Kipp factor analysis, Modern Analog Technique, two-block Partial Least Squares.
# Time series analysis: Spectral analysis, autocorrelation, cross-correlation, wavelet transform, Walsh transform, runs test. Mantel correlogram and periodogram. ARMA, Box-Jenkins intervention analysis. Solar forcing model.
# Geometrical analysis: Directional statistics (Rayleigh, Rao, chi-squared, Watson-Williams, circular kernel density estimation, angular mean with CI, rose plots, circular correlation), kernel density estimation of point density, point distribution statistics (nearest neighbour and Ripley's K), Fourier shape analysis, elliptic Fourier shape analysis, eigenshapes, landmark analysis with Bookstein and Procrustes fitting (2D and 3D), thin-plate spline transformation grids with expansions and principal strains, partial warps and scores, relative warps and scores, centroid size from landmarks, size removal by Burnaby's method.
# Parsimony analysis (cladistics): Exhaustive, branch-and-bound and heuristic algorithms, Wagner, Fitch and Dollo characters. Bootstrap, strict and majority rule consensus trees. Consistency and retention indices. Three stratigraphic congruency indices with permutation tests. Cladograms and phylograms.
# Biostratigraphy with the methods of Unitary Associations, Ranking-Scaling (RASC), Appearence Event Ordination and Constrained Optimization (CONOP). Confidence intervals on stratigraphic ranges.
# Gridding (spatial interpolation): Moving average, thin-plate spline and kriging with three semivariogram models."