SPSS alternative

Switching from SPSS: what’s covered, what isn’t.

Open .sav, .zsav and .por files with their labels and run 115 SPSS procedures in your browser. Free, no account, and the file never leaves your device.

Open your .sav file See what's covered

All 59 SPSS command families covered · 115 Statistics procedures plus crosstabs · 117 procedure guides · Updated 26 October 2026

FROM .SAV TO CROSSTABSYNTHETIC DATA

1 · Labels read from the file

nps_grpNPS group1Promoter2Passive3Detractor
segmentCustomer segment1Prospect2New Customer3Loyal Customer4At Risk

2 · NPS group by Customer segment, column %

NPS group	Prospect(A)	New(B)	Loyal(C)	At risk(D)
Promoter	26%	35%D	56%ABD	19%
Passive	39%CD	30%	25%	23%
Detractor	35%C	34%C	19%	58%ABC
Base n=	140	164	188	108

Letters mark a column percentage significantly higher than the lettered column (95%, Bonferroni-adjusted). Example data: 600 synthetic responses.

Checked against SciPy, statsmodels and R

See the validation evidence

statistic families checked against independent references: 15
reference cases from SciPy, R, mpmath, NumPy, statsmodels, and Python: 48
largest absolute difference from the reference values: 5.0 × 10⁻¹³
within the manifest tolerance: 15/15

Computed when this page is built: the browser engine reruns every case in the public reference manifest. Tolerance 1.0 × 10⁻¹⁴ absolute or 1.0 × 10⁻¹⁰ relative.

Procedure by procedure

SPSS procedures in Crosstabs

Everything listed works in the browser today. Open a group, tap a row for its full description, or follow Open to its guide.

Open data files5

GET FILE (.sav)
Open an SPSS .sav data file. Opens compressed and uncompressed .sav files in the browser with variable labels, value labels and numeric user-missing codes and ranges.
In SPSS: Open an SPSS .sav data file.
Open: GET FILE (.sav)
GET FILE / IMPORT (.zsav, .por)
Open zlib-compressed .zsav files and portable .por files. Reads .zsav and .por files with labels and missing-value definitions.
In SPSS: Open zlib-compressed .zsav files and portable .por files.
Open: GET FILE / IMPORT (.zsav, .por)
GET DATA / GET STATA / GET SAS
Read Excel, delimited text, Stata and SAS data. Reads CSV/TSV, Excel (.xlsx, .xlsm, .xls, .xlsb), OpenDocument .ods/.fods, JSON, Stata .dta, SAS .sas7bdat/.xpt, R .rds/.RData, Parquet, Arrow/Feather and SQLite database files.
In SPSS: Read Excel, delimited text, Stata and SAS data.
Open: GET DATA / GET STATA / GET SAS
SAVE OUTFILE (.sav)
Save the active dataset as an SPSS .sav file. Data › Export data writes the session's data, transformations and recodes included, as a byte-code compressed or uncompressed .sav file with variable and value labels, measurement levels, user-missing codes, an optional case weight and multiple-dichotomy sets, in the browser.
In SPSS: Save the active dataset as an SPSS .sav file.
Open: SAVE OUTFILE (.sav)
RANK / OPTIMAL BINNING / SAMPLE
Ranks and normal scores, optimal binning and random case samples. Workspace Statistics mode: eight rank types with four tie rules and normal scores, supervised MDLP or equal-frequency binning, and seeded case samples, all as previews and downloads that never change your data.
In SPSS: Ranks and normal scores, optimal binning and random case samples.
Open: RANK / OPTIMAL BINNING / SAMPLE

Crosstabs and custom tables8

CROSSTABS /CELLS
Two-way tables with counts, percentages, expected counts and residuals. Counts, row, column and total percentages, expected counts and adjusted standardized residuals.
In SPSS: Two-way tables with counts, percentages, expected counts and residuals.
Open: CROSSTABS /CELLS
CROSSTABS /STATISTICS=CHISQ
Chi-square tests of independence. Pearson and likelihood-ratio chi-square, continuity correction, linear-by-linear association, and Fisher's exact test (one- and two-sided for 2×2, exact Fisher–Freeman–Halton for larger tables).
In SPSS: Chi-square tests of independence.
Open: CROSSTABS /STATISTICS=CHISQ
CROSSTABS /STATISTICS (measures)
Nominal and ordinal association measures. Phi, Cramér's V, contingency coefficient, lambda, Goodman–Kruskal tau, uncertainty coefficient, gamma, Kendall's tau-b and tau-c, Somers' d and kappa, each with asymptotic standard error and significance in the workspace All statistics panel; quick calculators for single measures.
In SPSS: Nominal and ordinal association measures.
Open: CROSSTABS /STATISTICS (measures)
CROSSTABS /STATISTICS (full set)
Every CROSSTABS statistic, with asymptotic standard errors and approximate significance. Workspace Table mode, All statistics panel: all CROSSTABS statistics: Pearson, Yates, likelihood ratio, Fisher (one- and two-sided, and exact R×C), linear-by-linear; phi, V, C, lambda, Goodman–Kruskal tau and uncertainty coefficient; gamma, tau-b, tau-c and Somers d with ASE and p; Pearson r, Spearman and eta; kappa, McNemar and McNemar–Bowker; risk estimates; Cochran–Mantel–Haenszel, Breslow–Day and Tarone with a layer variable; and raw, standardized and adjusted residuals.
In SPSS: Every CROSSTABS statistic, with asymptotic standard errors and approximate significance.
Open: CROSSTABS /STATISTICS (full set)
CTABLES (nesting, stacking, nets)
Custom tables with nested or stacked rows and banners, nets and subtotals. Tab books with nested or stacked rows and banners, nets and subtotals, saved and re-run in one place.
In SPSS: Custom tables with nested or stacked rows and banners, nets and subtotals.
Open: CTABLES (nesting, stacking, nets)
CTABLES /COMPARETEST (proportions)
Column proportions tests shown as letters. Column-proportion z-tests as significance letters, with confidence tiers, a minimum base and multiple-comparison correction.
In SPSS: Column proportions tests shown as letters.
Open: CTABLES /COMPARETEST (proportions)
CTABLES summary statistics
Means, medians and column means tests inside custom tables. Adds scale summary rows under any table or saved tab-book table — mean, median, mode, standard deviation, variance, standard error, minimum, maximum, range, sum, row % of sum, valid and total N and percentiles per banner column — with an SPSS-style column means test (pooled or pairwise variance, Bonferroni, Holm or Benjamini–Hochberg) shown as letters and kept in every export.
In SPSS: Means, medians and column means tests inside custom tables.
Open: CTABLES summary statistics
MULT RESPONSE
Multiple-response sets in frequency and crosstab tables. Multi-select questions as sets, with percentages of respondents or of responses kept distinct.
In SPSS: Multiple-response sets in frequency and crosstab tables.
Open: MULT RESPONSE

Describe variables5

FREQUENCIES
Frequency tables with valid and cumulative percentages, percentiles and summary statistics. Workspace Statistics mode: frequency tables with user-missing rows, valid and cumulative percent, five percentile methods, grouped percentiles, moments with standard errors, bar charts and histograms.
In SPSS: Frequency tables with valid and cumulative percentages, percentiles and summary statistics.
Open: FREQUENCIES
DESCRIPTIVES
Mean, standard deviation, range, skewness and kurtosis. Workspace Statistics mode: N, minimum, maximum, mean with standard error, SD, variance, skewness and kurtosis with standard errors, and listwise valid N.
In SPSS: Mean, standard deviation, range, skewness and kurtosis.
Open: DESCRIPTIVES
EXAMINE
Explore: robust statistics, normality tests and outlier checks. Workspace Statistics mode: descriptives with confidence intervals, 5% trimmed mean, four M-estimators, extreme values, Shapiro–Wilk and Lilliefors tests, four Levene variants and boxplot outlier rules.
In SPSS: Explore: robust statistics, normality tests and outlier checks.
Open: EXAMINE
MEANS
Means and other statistics by group. Workspace Statistics mode: all 19 SPSS cell statistics in up to three crossed layers, with the ANOVA table, eta and the test for linearity.
In SPSS: Means and other statistics by group.
Open: MEANS
MVA / MULTIPLE IMPUTATION / BOOTSTRAP
Missing value analysis, multiple imputation with pooling, and bootstrapping. Workspace Statistics mode: MVA with EM estimates and Little's MCAR test, pattern analysis, multiple imputation (FCS or monotone, regression, PMM or logistic) with Rubin's rules pooling of descriptives and linear regression, RMV previews, and seeded bootstrap percentile and BCa intervals.
In SPSS: Missing value analysis, multiple imputation with pooling, and bootstrapping.
Open: MVA / MULTIPLE IMPUTATION / BOOTSTRAP

Compare groups6

T-TEST
Independent-samples, paired and one-sample t-tests. Workspace Statistics mode: one-sample, independent-samples (Levene, pooled and Welch rows) and paired-samples t tests with Cohen d, Hedges g and Glass delta and their confidence intervals.
In SPSS: Independent-samples, paired and one-sample t-tests.
Open: T-TEST
ONEWAY (post hoc)
One-way ANOVA with robust tests and post hoc comparisons. Workspace Statistics mode: ANOVA with Levene, Welch and Brown–Forsythe tests, eta², epsilon² and omega² with confidence intervals, polynomial contrasts, Tukey, Bonferroni, Šidák, Scheffé, LSD, Games–Howell, Dunnett, Hochberg's GT2, Gabriel, Tamhane's T2 and Dunnett's T3 and C post hoc tests, and homogeneous subsets for Tukey, S-N-K, Tukey's-b, Duncan, R-E-G-W F and Q and the Waller–Duncan k-ratio t test.
In SPSS: One-way ANOVA with robust tests and post hoc comparisons.
Open: ONEWAY (post hoc)
NPAR TESTS /CHISQUARE
One-sample chi-square goodness-of-fit test. Goodness-of-fit chi-square against equal or custom expected proportions.
In SPSS: One-sample chi-square goodness-of-fit test.
Open: NPAR TESTS /CHISQUARE
NPAR TESTS /MCNEMAR
McNemar test for paired binary outcomes. McNemar test for a paired 2×2 table.
In SPSS: McNemar test for paired binary outcomes.
Open: NPAR TESTS /MCNEMAR
NPAR TESTS (rank tests)
Mann–Whitney, Kruskal–Wallis, Wilcoxon, Friedman and related tests. Workspace Statistics mode: chi-square goodness of fit, binomial, runs, one- and two-sample Kolmogorov–Smirnov, Mann–Whitney U, Moses, Wald–Wolfowitz, Kruskal–Wallis with Dunn pairwise comparisons, median test, Jonckheere–Terpstra, Wilcoxon, sign, McNemar, Friedman, Kendall W and Cochran Q, with exact p-values where SPSS gives them.
In SPSS: Mann–Whitney, Kruskal–Wallis, Wilcoxon, Friedman and related tests.
Open: NPAR TESTS (rank tests)
BAYES procedures
Bayesian one-sample, independent and related t tests, ANOVA, correlation, regression, loglinear and repeated measures. Workspace Statistics mode: Bayes factors and posterior distributions with credible intervals for one-sample (normal, binomial, Poisson), paired and independent samples, Pearson correlation, one-way ANOVA, linear regression, contingency tables and repeated measures.
In SPSS: Bayesian one-sample, independent and related t tests, ANOVA, correlation, regression, loglinear and repeated measures.
Open: BAYES procedures

Association and scales7

CORRELATIONS / NONPAR CORR
Pearson, Spearman and Kendall correlation matrices. Workspace Statistics mode: Pearson (with Fisher-z confidence intervals), Spearman and Kendall tau-b matrices with one- or two-tailed significance, flags, pairwise or listwise deletion, and partial correlations.
In SPSS: Pearson, Spearman and Kendall correlation matrices.
Open: CORRELATIONS / NONPAR CORR
RELIABILITY
Scale reliability: Cronbach's alpha and item-total statistics. Workspace Statistics mode: Cronbach and standardized alpha, split-half, Guttman λ1–λ6, parallel and strictly parallel models, McDonald omega, item-total statistics, ANOVA with Friedman or Cochran, Tukey nonadditivity, Hotelling T² and intraclass correlations.
In SPSS: Scale reliability: Cronbach's alpha and item-total statistics.
Open: RELIABILITY
FACTOR
Principal components and factor analysis with rotation. Workspace Statistics mode: principal components, principal axis and maximum-likelihood extraction, varimax, quartimax, equamax, oblimin and promax rotation, KMO and Bartlett, anti-image matrices, communalities, variance explained and scree plot, plus regression, Bartlett and Anderson–Rubin factor scores saved as new variables (FACTOR /SAVE).
In SPSS: Principal components and factor analysis with rotation.
Open: FACTOR
CORRESPONDENCE / ANACOR
Correspondence analysis and perceptual maps of a two-way table. Workspace Statistics mode: inertia and chi-square, row and column point tables with contributions, five normalizations and a labelled perceptual map you can export.
In SPSS: Correspondence analysis and perceptual maps of a two-way table.
Open: CORRESPONDENCE / ANACOR
MULTIPLE CORRESPONDENCE / HOMALS
Homogeneity analysis of several nominal variables. Workspace Statistics mode: model summary with Cronbach alpha, discrimination measures, category points, object scores and a joint plot.
In SPSS: Homogeneity analysis of several nominal variables.
Open: MULTIPLE CORRESPONDENCE / HOMALS
PROXSCAL / ALSCAL (MDS)
Multidimensional scaling of proximities. Workspace Statistics mode: PROXSCAL with ratio, interval or ordinal transformations, the six stress and fit measures, Shepard diagram and classical (Torgerson) scaling. Also ALSCAL (S-stress alternating least squares on squared distances, ratio or ordinal).
In SPSS: Multidimensional scaling of proximities.
Open: PROXSCAL / ALSCAL (MDS)
ROC / WEIGHTED KAPPA / Meta Analysis
ROC curves, weighted and Fleiss kappa, proportions, ratio statistics and meta-analysis. Workspace Statistics mode: ROC curves with AUC, standard error and coordinates, DeLong comparison of two AUCs and precision–recall curves, weighted and Fleiss multirater kappa, PROPORTIONS with every test and interval method in the manual, ratio statistics, and meta-analysis of binary and continuous outcomes with seven τ² estimators, Knapp–Hartung, Egger's test, forest plots and meta-regression.
In SPSS: ROC curves, weighted and Fleiss kappa, proportions, ratio statistics and meta-analysis.
Open: ROC / WEIGHTED KAPPA / Meta Analysis

Models and segmentation19

REGRESSION
Linear regression with coefficient tests and diagnostics. Workspace Statistics mode: Enter, Stepwise, Forward, Backward and Remove blocks; model summary with R² change and Durbin–Watson, ANOVA, coefficients with confidence intervals, part and partial correlations, tolerance and VIF, collinearity, casewise and residual diagnostics.
In SPSS: Linear regression with coefficient tests and diagnostics.
Open: REGRESSION
LOGISTIC REGRESSION
Binary logistic regression with fit statistics and a classification table. Workspace Statistics mode: binary logistic regression with Enter, Forward:LR and Backward:LR, omnibus tests, −2LL with Cox & Snell and Nagelkerke R², Hosmer–Lemeshow, classification table, indicator-coded categorical covariates and odds ratios with confidence intervals.
In SPSS: Binary logistic regression with fit statistics and a classification table.
Open: LOGISTIC REGRESSION
PROCESS macro models (mediation and moderation)
Hayes' PROCESS add-on macro (not part of SPSS Statistics): regression-based mediation, moderation and conditional process models. Workspace Statistics mode, independent implementation not affiliated with PROCESS: models 4 (parallel mediation), 1 (moderation), 6 (serial, two mediators), 7 and 14 (moderated mediation) with OLS or HC3 paths, seeded percentile bootstrap intervals for indirect effects, completely standardized effects, contrasts, Sobel, conditional effects, Johnson–Neyman, simple slopes and the index of moderated mediation. Continuous outcomes only.
In SPSS: Hayes' PROCESS add-on macro (not part of SPSS Statistics): regression-based mediation, moderation and conditional process models.
Open: PROCESS macro models (mediation and moderation)
GLM / UNIANOVA
Factorial ANOVA and general linear models. Workspace Statistics mode: GLM Univariate (factorial ANOVA and ANCOVA, Type I–IV sums of squares, estimated marginal means, Levene, ten post hoc tests and Waller–Duncan homogeneous subsets), GLM Multivariate (MANOVA with Box M), GLM Repeated Measures (Mauchly, Greenhouse–Geisser and Huynh–Feldt) and VARCOMP.
In SPSS: Factorial ANOVA and general linear models.
Open: GLM / UNIANOVA
QUICK CLUSTER
K-means cluster analysis. SPSS-exact QUICK CLUSTER in workspace Statistics mode (initial centres, iteration history, ANOVA, memberships), plus deterministic k-means segmentation with profiles in the Segment Builder.
In SPSS: K-means cluster analysis.
Open: QUICK CLUSTER
CATPCA / CATREG / PRINCALS / OVERALS
Optimal scaling: categorical principal components and regression, and the Gifi procedures PRINCALS (nonlinear PCA) and OVERALS (nonlinear canonical correlation). Workspace Statistics mode: nominal, ordinal and numeric scaling levels by alternating least squares; eigenvalues, loadings and quantifications for CATPCA; R², standardized coefficients and Pratt importance for CATREG; PRINCALS and OVERALS with multiple nominal, single nominal, ordinal and numeric levels, loss and fit per set and canonical correlations. Spline levels are not included.
In SPSS: Optimal scaling: categorical principal components and regression, and the Gifi procedures PRINCALS (nonlinear PCA) and OVERALS (nonlinear canonical correlation).
Open: CATPCA / CATREG / PRINCALS / OVERALS
ORTHOPLAN
Orthogonal main-effects plans for conjoint studies. Workspace Statistics mode: orthogonal plans with holdout cards, an orthogonality check and SPSS-style CSV cards.
In SPSS: Orthogonal main-effects plans for conjoint studies.
Open: ORTHOPLAN
CONJOINT
Conjoint analysis of ratings or rankings. Workspace Statistics mode: part-worth utilities with standard errors, importance values, Pearson R and Kendall tau (including holdouts), reversals and max-utility, BTL and logit simulations, per subject and pooled.
In SPSS: Conjoint analysis of ratings or rankings.
Open: CONJOINT
TREE (CHAID, CRT)
Decision trees for segmentation and prediction. Workspace Statistics mode: CHAID and Exhaustive CHAID with Bonferroni-adjusted merging and splitting, and CRT with Gini or twoing, surrogates and cost-complexity pruning; interactive tree, gains, risk and classification tables, and cross-validation. Also QUEST (ANOVA F, chi-square and Levene split-variable selection with quadratic-discriminant split points).
In SPSS: Decision trees for segmentation and prediction.
Open: TREE (CHAID, CRT)
TWOSTEP CLUSTER
Two-step clustering of mixed continuous and categorical variables. Workspace Statistics mode: CF-tree pre-clustering, log-likelihood or Euclidean distance, automatic cluster count by BIC or AIC, cluster profiles, silhouette and predictor importance.
In SPSS: Two-step clustering of mixed continuous and categorical variables.
Open: TWOSTEP CLUSTER
CLUSTER
Hierarchical cluster analysis. Workspace Statistics mode: seven linkage methods, agglomeration schedule, memberships for a range of solutions, silhouette and a rescaled dendrogram.
In SPSS: Hierarchical cluster analysis.
Open: CLUSTER
DISCRIMINANT
Linear discriminant analysis. Workspace Statistics mode: direct or stepwise (Wilks) entry, Box M, eigenvalues and canonical correlations, standardized, structure and unstandardized coefficients, centroids, Fisher classification functions and original and leave-one-out classification.
In SPSS: Linear discriminant analysis.
Open: DISCRIMINANT
PROXIMITIES
Distance and similarity matrices. Workspace Statistics mode: interval, count and 27 binary measures, seven standardizations and transforms, between cases or between variables.
In SPSS: Distance and similarity matrices.
Open: PROXIMITIES
NOMREG / PLUM / GENLIN / GENLOG
Multinomial and ordinal logistic regression and generalized linear models. Workspace Statistics mode: NOMREG (with forward, backward and stepwise effect selection), PLUM (five links, parallel-lines test), GENLIN with seven distributions and GEE, PROBIT with fiducial limits, CURVEFIT, NLR/CNLR, WLS, 2SLS, quantile regression, HILOGLINEAR and GENLOG. Also LOGLINEAR with custom designs, deviation contrasts and the analysis of dispersion, and GENLINMIXED by REPL-PQL with random intercepts and slopes. Main effects only for the regression-type designs.
In SPSS: Multinomial and ordinal logistic regression and generalized linear models.
Open: NOMREG / PLUM / GENLIN / GENLOG
KM / COXREG / SURVIVAL
Survival analysis: Kaplan–Meier, life tables and Cox regression. Workspace Statistics mode: Kaplan–Meier with log-rank, Breslow and Tarone–Ware tests and survival plots, actuarial life tables with the Gehan test, and Cox regression with Breslow ties, omnibus tests, hazard ratios, forward and backward stepwise selection (conditional, LR or Wald) and a time-dependent covariate in T_.
In SPSS: Survival analysis: Kaplan–Meier, life tables and Cox regression.
Open: KM / COXREG / SURVIVAL
ARIMA / EXSMOOTH / TSMODEL
Time-series forecasting and decomposition. Workspace Statistics mode, with a define-dates step that turns respondent rows into periods: Expert Modeler, exponential smoothing (12 models), seasonal ARIMA by exact maximum likelihood, seasonal decomposition, ACF/PACF, CCF, AREG, univariate SPECTRA, CREATE and FIT. ARIMA can detect all seven TSMODEL outlier types (additive, innovational, level shift, transient, seasonal additive, local trend and additive patch) and estimate them jointly with the model; transfer functions are not included.
In SPSS: Time-series forecasting and decomposition.
Open: ARIMA / EXSMOOTH / TSMODEL
MIXED / KNN / NAIVE BAYES
Linear mixed models, nearest neighbors and naive Bayes. Workspace Statistics mode: linear mixed models (random effects, repeated covariance structures, REML or ML, Type III tests with Satterthwaite df), nearest-neighbor analysis with cross-validated k and feature selection, and naive Bayes with forward selection. Neural networks are not included yet.
In SPSS: Linear mixed models, nearest neighbors and naive Bayes.
Open: MIXED / KNN / NAIVE BAYES
SIMULATION
Monte Carlo simulation of a model with uncertain inputs. Workspace Statistics mode: Monte Carlo simulation of a target expression from inputs with chosen distributions, with summary, percentiles, histogram and sensitivity.
In SPSS: Monte Carlo simulation of a model with uncertain inputs.
Open: SIMULATION
MLP / RBF / PLS / LINEAR / SELECTPRED / AIM
Neural networks, partial least squares, automatic linear modeling, predictor screening and cluster attribute importance. Workspace Statistics mode: multilayer perceptron and radial basis function networks with seeded partitions, classification tables, ROC, gains and predictor importance; PLS regression with VIP; automatic linear modeling with data preparation, stepwise or best-subsets selection; predictor screening and ranking; and attribute importance for clusters. Network weights will not match SPSS exactly because its random start is undocumented. Also DETECTANOMALY (TwoStep peer groups, anomaly index and reasons), bagging and boosting ensembles of linear models, and an Automated Data Preparation (ADP) preview with a download of the transformed fields.
In SPSS: Neural networks, partial least squares, automatic linear modeling, predictor screening and cluster attribute importance.
Open: MLP / RBF / PLS / LINEAR / SELECTPRED / AIM

Weighting and complex samples6

WEIGHT BY
Apply a case (frequency) weight. Declared frequency weights in saved projects. A weight is never guessed from a column name.
In SPSS: Apply a case (frequency) weight.
Open: WEIGHT BY
Raking (SPSSINC RAKE)
Rake to population margins (an SPSS extension command). Guided raking to targets you enter, with trimming, convergence and effective-base diagnostics.
In SPSS: Rake to population margins (an SPSS extension command).
Open: Raking (SPSSINC RAKE)
CSTABULATE / CSDESCRIPTIVES
Complex Samples tables and descriptives. Taylor-linearized or replicate-weight designs with design effects and Rao–Scott or adjusted-Wald tests for saved tables; CSDESCRIPTIVES (means, sums, ratios, population sizes by subpopulation) and CSTABULATE with odds ratios and risks in workspace Statistics mode.
In SPSS: Complex Samples tables and descriptives.
Open: CSTABULATE / CSDESCRIPTIVES
CSGLM / CSLOGISTIC / CSORDINAL / CSCOXREG
Complex Samples linear, logistic, ordinal and Cox models. Workspace Statistics mode, from a CSPLAN-style design (strata, clusters for one or two stages, population sizes): CSGLM, binary and multinomial CSLOGISTIC, CSORDINAL with five links and CSCOXREG, with Wald tests on design degrees of freedom and design effects; also design-aware models in Advanced methods. Main effects only.
In SPSS: Complex Samples linear, logistic, ordinal and Cox models.
Open: CSGLM / CSLOGISTIC / CSORDINAL / CSCOXREG
POWER PROPORTIONS
Sample size and power for proportions. Sample size for a margin of error and for comparing two proportions.
In SPSS: Sample size and power for proportions.
Open: POWER PROPORTIONS
CSSELECT
Draw complex probability samples. Workspace Statistics mode: seeded simple random, systematic, PPS with replacement, PPS systematic and Sampford selection, stratified and clustered, with inclusion probabilities and weights, as a download.
In SPSS: Draw complex probability samples.
Open: CSSELECT

Output3

OUTPUT EXPORT
Export output to Excel, Word, PowerPoint or PDF. Exports to CSV, XLSX and PDF, plus editable PowerPoint and Word reports.
In SPSS: Export output to Excel, Word, PowerPoint or PDF.
Open: OUTPUT EXPORT
SPSS syntax (.sps)
Run commands from a syntax file. Reports › Syntax runs a .sps file or pasted syntax on the open data: COMPUTE, RECODE, IF, SELECT IF, FILTER BY, WEIGHT BY, VARIABLE LABELS, VALUE LABELS, MISSING VALUES and VARIABLE LEVEL, then FREQUENCIES, DESCRIPTIVES, CROSSTABS, rows-by-columns CTABLES, MEANS, T-TEST, ONEWAY, CORRELATIONS, REGRESSION, RELIABILITY and FACTOR. Every other command is listed with the reason it did not run.
In SPSS: Run commands from a syntax file.
Open: SPSS syntax (.sps)
ERROR BARS / PPLOT / SPCHART
Error-bar charts, P-P and Q-Q plots and statistical process control charts. Workspace Statistics mode: error bars for CI, SE or SD by group, P-P and Q-Q plots against 12 distributions, and X-bar/R, X-bar/s, I-MR, p, np, c and u control charts with 11 rule checks.
In SPSS: Error-bar charts, P-P and Q-Q plots and statistical process control charts.
Open: ERROR BARS / PPLOT / SPCHART

What's different

Free, local, and no syntax required.

Free to use
No licence, trial or account. Open the page and start analyzing.
What “free” covers
Your data stays on your device
The workspace reads and analyzes files in your browser. Open your browser’s network panel to check: the source file is never uploaded to Crosstabs servers.
Privacy policy
No syntax required
Pick rows, banners, tests and weights in the interface; each setting maps to an SPSS CROSSTABS subcommand.
SPSS CROSSTABS syntax guide

Honest limits

Known limits

No known gaps remain in the SPSS command families and options Crosstabs tracks. Results follow published formulas and are checked against reference software, but output is not claimed to be identical to SPSS for every option. One limit is deliberate:

Sampling weights are never treated as frequency weights for ordinary significance tests; weighted inference needs a supported saved survey design.

Questions about switching from SPSS

Is Crosstabs really free?

Yes. The browser workspace is free to use with no account, trial or licence key, and analysis runs on your own device. See the capacity envelope.

Does my data upload anywhere?

No. Files you open in the workspace are parsed and analyzed in your browser and are not sent to Crosstabs servers, and the local MCP for AI clients runs on your machine. See the privacy policy.

Can it open my .sav file?

Yes. It reads .sav (compressed or not), .zsav and .por files and keeps variable labels, value labels and numeric user-missing codes and ranges. String missing-value definitions are refused with an explanation rather than imported with different bases. More in opening a .sav file without SPSS.

Does it match SPSS results?

For the statistics it implements, Crosstabs uses standard published formulas and checks them against reference implementations — see the validation evidence. It does not claim identical output for every SPSS option; when you compare, use the same cases, missing-value handling and weights.

Open your .sav file

SPSS is a trademark of IBM Corp. Crosstabs is not affiliated with or endorsed by IBM.