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SPSSAU Version Updates
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The SPSSAU project (). SPSSAU. (Version .0) [Online Application Software]. Retrieved from https://www.spssau.net.
Added Power Analysis.
Power Analysis module was added, providing sample size calculation and power analysis for methods such as mean difference, rate difference, variance, correlation, and linear regression. New methods include Delphi Expert Method, Fuzzy Analytic Hierarchy Process (FAHP), and Best Worst Method (BWM) for weight calculation. The Machine Learning module added new ensemble learning algorithms, including GBDT, AdaBoost, CatBoost, Extremely Randomized Trees, and LightGBM. The EXCEL export supports three-line table.
Added Spatial Econometrics.
Spatial Econometrics module was added, providing Spatial OLS, Spatial Lag Model (SLM), Spatial Error Model (SEM), Spatial Autoregressive Combined Model (SAC), Spatial Durbin Model (SDM), and Spatial Durbin Error Model (SDEM). It also supports Spatial Weight Construction, Spatial Lag of X Model (SLX), Spatial Panel Model, and Seemingly Unrelated Regression (SUR). Optimized ROC Curve joint diagnostics and user interaction experience.
Added multiple research algorithms.
Added MANOVA (Multivariate Analysis of Variance), NRI/IDI analysis in the Medical Research module, calibration curves, and DCA Curve. The Gini coefficient was also added, and optimizations were made to the Text Analysis module, including sentiment direction judgment standards and LDA Topic Modeling.
Added Text Analysis.
Text Analysis module was added, supporting word segmentation, word cloud, and custom word cloud analysis for text data, as well as text clustering (by row and by word). It supports sentiment analysis, social network analysis, and LDA topic modeling. New features include new word discovery, custom stopword libraries, sentiment word libraries, and new word libraries. Released as SPSSAU 24.0 version. Also includes various optimization of detailed functionalities.
Optimized algorithms and functionalities.
Added Collinearity Analysis and optimized the computation speed for Conditional Logistic Regression. Conditional Logistic Regression and Cox regression include collinearity diagnostics. Factor Analysis adds the Promax rotation method. Optimized Correlation Analysis outputs, improved the logic for handling quality outliers, and resolved issues with algorithms encountering special symbols.
Added Meta Analysis.
Meta Analysis module was added, offering comprehensive Meta Analysis and output indicators. It supports Meta Analysis for continuous variables, binary classification, single rates, mean values, correlation coefficients, OR or HR values, general inverse variance, and P-value merging. Forest plots are provided for effect size analysis, and funnel plots for publication bias tests, including Egger's and Begg's tests. Supports Subgroup Meta-regression and offers sensitivity tests, Cumulative Meta Analysis, and Meta-regression.
Optimized multiple methods and functionalities.
Machine Learning includes regression tasks, with automatic task category detection and manual selection options. Optimized the X and Y axis coordinate display, as well as the scale lines and dashed line effects for various visualizations. Improved interactivity in Subtotal results. Descriptive Analysis includes outlier detection for missing data. Enhanced interactivity in Generate Variable. Mediation Analysis (including parallel mediation, chain mediation, and moderated mediation) supports percentile bootstrap methods and outputs total indirect effect values.
Optimized multiple methods and functionalities.
Stepwise Regression (including Binary Logistic Regression and Stepwise Linear Regression) outputs intermediate iteration values. Structural Equation Model adds the PCFI fitting index. Factor Analysis or Principal Component includes the MSA index and correlation matrix. Correlation Analysis outputs a new right triangle format, and Binary Logistic Regression includes collinearity diagnostics. ROC Curve includes joint diagnostics. OLS Regression adds direct dummy handling for categorical variable X. Propensity Score Matching (PSM) supports 1:N matching. Generate Variable includes automatic sample grouping and inverse Box-Cox functionality. Optimized Machine Learning models.
Optimized functionalities.
Optimized the following: ridge regression with VIF value, linear regression with tolerance level, cluster analysis with new indicators, kernel density plots with more kernel function options, and mediation analysis with more standardized table outputs. Added output of actual sample size and missing sample size for multiple analysis methods. Other data-related optimizations include the "My Data" page, data preview, and data download functionality.
Added new algorithms and optimizations.
New algorithms added to the comprehensive evaluation category, including Fisher's Chi Square calculation for m × n, Theil Index, Dagum Gini Coefficient, Moran's Index, Malmquist Index, Variance Decomposition, Dynamic Panel Model, SBM and super-efficiency SBM, Conjoint Analysis, and Zero Inflated Model. Released as SPSSAU 23.0 version.
Added multiple new methods.
Machine Learning module added, offering algorithms such as Decision Tree, Random Forest, KNN, Naive Bayes, and Support Vector Machine. Comprehensive Evaluation module adds Markov Forecasting. Econometrics Research module introduces Vector AutoRegression (VAR) and Time Series Plot. Within-group Interrater Reliability (RWG) is added, and Binary Logistic Regression includes marginal effects. The Moderated Mediation Effect adds the Index of MM.
Added multiple new methods.
Econometrics Research module adds Granger Causality Test, EG Cointegration, Johansen Cointegration Test, and the Error Correction Model (ECM). It supports simultaneous ADF (Augmented Dickey-Fuller) tests. The Partial Autocorrelation Function plot offers more indicators, and ARIMA is optimized. New features include uploading TXT files for word cloud analysis, texture styles for visual plots, and Word document export for results. Additionally, the help manual's search experience has been optimized.
Added multiple new methods.
The Econometrics Research module adds RDD (Regression Discontinuity Design). The Comprehensive Evaluation module introduces the Comprehensive Index Method and the Obstacle Degree Model. The Visualization module adds Kernel Density Plot and Violin Plot. More user-friendly data feedback and prompts are provided.
Added new methods and functionalities.
Introduced Heckman two-step models, bin processing in scatterplots, alphabetical labeling in Post-hoc comparisons, and multiple stepwise regression methods. Improved existing methods like conditional logistic regression, panel models, grouped regression, HLM, DEA, repeated measures ANOVA, and mediation analysis. Launched the SPSSAU mini-program, supporting student authentication, queries, and access to learning resources.
Added new methods and functionalities.
Introduced Deming regression, combined charts, and bubble charts. Enhanced the bootstrap sampling parameter for mediation analysis and prediction period parameters for prediction algorithms. Added Delong's test to ROC curves and made various functional optimizations.
SPSSAU 22.0 released with new methods and functionalities.
Included McDonald's Omega and Theta reliability coefficients in reliability analysis, Duncan's new multiple range test and SNK Q test in Post-hoc comparisons, small-probability p-values in grey forecasting, generalized association in grey correlation, date-related data handling, and Johnson transformations.
Added new algorithms.
Added new algorithms to the comprehensive evaluation category, including DEA (Data Envelopment Analysis), DEMATEL, and VIKOR. New models like Lasso Regression, Nonlinear Regression, and Hierarchical Linear Model (HLM) were introduced. Functional optimizations include adding Trend Chi Square Test and Chow Test for grouped regressions. Enhanced the interactive data input for AHP (Analytic Hierarchy Process).
Upgraded algorithms.
Upgraded three algorithms: Structural Equation Model (SEM), Confirmatory Factor Analysis (CFA), and Path Analysis. The enhancements include direct output of MI index values and establishing covariance relationships between items for model adjustments. Added multiple comparison options for Chi Square TEST. Comprehensive evaluation algorithms offer "composite scores" and data processing parameters, along with RMSE indicators.
Introduced customer experience algorithms.
Added algorithms for customer experience analysis: NPS (Net Promoter Score), Kano Model, and Rfm Model, applicable for analyzing and mining customer value. These algorithms are included in Survey Research and Advanced Methods.
Added econometrics module algorithms.
Introduced algorithms addressing endogeneity issues, including Two Stage Least Squares (TSLS) and Generalized Method of Moments (GMM). Added Propensity Score Matching (PSM) and Difference-in-Differences (DID).
Integrated evaluation module algorithms.
Introduced comprehensive evaluation-related algorithms, such as entropy-TOPSIS, information-weighted methods, coupling coordination degree models, CRITIC weighting, RSR rank-sum ratio, and independent weighting. Added exponential smoothing and the GM11 grey forecasting model for weight calculations and short-term data predictions. Functional upgrades: Enabled cloud storage of analysis results, allowing complete cloud-based storage and sharing of analysis results or data files.
Expanded medical research module.
Added algorithms to the existing medical research module, including conditional logistic regression, repeated measures ANOVA, range analysis, Ridit analysis, negative binomial regression, dose-response analysis, Bland-Altman plots, and Kaplan-Meier curves. The visualization module supports Pareto charts and cluster diagrams.
Enriched sociological research algorithms.
Added algorithms related to moderator or mediator effects, supporting parallel mediation, chain mediation, and moderator effects with diverse data processing. Introduced moderated mediation algorithms (supporting seven types of models) and incorporated grouped regression into the econometrics research module to enhance moderator effect analysis. The medical research module included calculations for odds ratios (OR), generalized estimating equation models, and negative binomial regression models.
SPSSAU 20.0 released with Discriminant Analysis, Correspondence Analysis, t test Summary, z Test, and Quantile Regression.
Implemented Linear Discriminant Analysis for class prediction and Correspondence Analysis for visualizing data relationships. Added Summary t test, Mean and Proportion z Test for streamlined medical research computations. Quantile Regression supports stability checks and deeper exploration of impact relationships.
SPSSAU 19.0 released with Confirmatory Factor Analysis and Econometric Methods.
Introduced Confirmatory Factor Analysis for assessing convergent and discriminant validity, as well as Common Method Variance (CMV) validation. Added econometric algorithms for addressing heteroscedasticity in measurement studies.
SPSSAU 18.0 released with Hierarchical Clustering, K-Prototype Clustering, and Effect Size.
Included Hierarchical Clustering for variable analysis and K-Prototype Clustering for mixed data types in sample clustering. Added Effect Size calculation for ANOVA, t test, and Chi Square Test to evaluate significance.
SPSSAU 17.0 released with Post-hoc Multiple Comparison Methods.
Added pairwise comparison algorithms for ANOVA, offering five methods: LSD, Scheffe, Tukey, Bonferroni correction, and Tamhane's T2 for unequal variances. For non-parametric test, introduced Nemenyi's multiple pairwise comparisons.
SPSSAU 16.0 released with Weight Calculation Methods and Advanced Visualization Tools.
Introduced weight calculation algorithms including AHP (Analytic Hierarchy Process), Fuzzy Comprehensive Evaluation, Grey Relational Analysis, and TOPSIS. Incorporated ROC curves and quadrant charts (IPA analysis) for enhanced data visualization.
SPSSAU 15.0 released with Orthogonal Design, Chi Square Goodness-of-Fit Test, and Partial Correlation.
Introduced orthogonal experimental design tables for efficient decision-making. Added Chi Square Goodness-of-Fit Test, applicable to single selections, multiple responses, and cross-analysis of multiple responses. Integrated Partial Correlation to control for confounding variables in correlation analysis. Enhanced intelligent text analysis across all algorithms for improved user experience.
SPSSAU 14.0 released with Consistency Testing Methods and Categorical Data Analysis.
Incorporated consistency testing methods, including Kappa Test and Kendall's Coefficient of Concordance. Added ICC (Intraclass Correlation Coefficient) models with options for two-way mixed/random consistency, two-way mixed/random absolute agreement, and one-way random absolute agreement. Introduced paired Chi Square for categorical paired data and Cochran's Q Test for binary data analysis.
SPSSAU 13.0 released with Rank-Sum Test and Runs Test.
Included rank-sum test algorithms for non-normally distributed data, such as one-sample Wilcoxon, paired-sample Wilcoxon, and multi-sample Friedman test. Added Runs Test for randomness testing.
SPSSAU 12.0 released with Curve Regression and Cox Regression.
Included seven types of curve regression: quadratic, cubic, logarithmic, exponential, compound, growth, and S-curve regressions. Added Cox Regression for survival analysis.
SPSSAU 11.0 released with Two Way and Three Way ANOVA and Covariance Analysis.
Incorporated variance analysis models, including Two Way ANOVA with interaction term settings, and Three Way ANOVA with outputs for second- and third-order interaction terms.
SPSSAU 10.0 released with Canonical Correlation Analysis, Error Bar Plot, and PP & QQ Plot.
Introduced Canonical Correlation Analysis and visual statistical plots such as Error Bar Plot, and PP & QQ Plot for normality test.
SPSSAU 9.0 released, introducing Ridge Regression, Robust Regression, and Chi Square Test (for Medical Research), including analysis methods and intelligent text analysis.
Included Ridge Regression for collinearity studies, Robust Regression for outlier handling, and Chi Square Test for Medical Research, along with weighted data format support.
SPSSAU 8.0 released, featuring Scatter Plot, Box Plot, Normality Plot, and Word Cloud.
Incorporated new visual statistical charts, including Scatter Plot, Box Plot, Normality Plot, and Word Cloud. Word Cloud supports weight settings for analysis.
SPSSAU 7.0 released, introducing Entropy Method, Item Analysis, and outlier and invalid sample handling.
Algorithms for questionnaire research include Entropy Method for weight calculation, Item Analysis for scale testing, and outlier handling options such as imputation using null, median, mean, mode, or random values.
SPSSAU 6.0 released with new functionalities and generalized regression models.
Introduced Binary Logistic Regression, Multinomial Logistic Regression, Binary Probit Regression, and Poisson Regression, each with automated text analysis.
SPSSAU 5.0 released with survey research-specific methods, including multiple-choice analysis and validity analysis.
Included algorithms for survey research, such as multiple response analysis for multiple-choice questions, cross-analysis of multiple-choice and single-choice questions, and validity analysis.
SPSSAU 4.0 released, introducing automated text analysis and analysis recommendations.
Added automated text analysis and suggestions for each research algorithm to aid decision-making.
SPSSAU 3.0 released with improved algorithms and visual template functionality.
Introduced visual templates for methods such as Frequency Analysis, Descriptive Analysis, and Cross Tabulation (Chi Square).
SPSSAU 2.0 released as the official version, featuring 22 algorithms with product design optimization.
Addressed bugs from version 1.0 and added Hierarchical Regression, Subtotal, Cross Tabulation (Chi Square), and Reliability Analysis. Enhanced algorithm, development, and testing processes.
SPSSAU 1.0 released as a test version, featuring 18 basic statistical algorithms or processing functions.
Algorithms included: Frequency Analysis, Descriptive Analysis, Correlation Analysis, Linear Regression Analysis, ANOVA, t Test, One Sample t Test, Paired Samples t Test, Normality Test, Non Parametric Test, Title Processing, Data Label, Data Recoding, Generate Variable, Kmeans Cluster, Factor Analysis, Principal Component Analysis, and Stepwise Regression.