Common Methods
Survey Research
Data Visualization
Data Processing
Advanced Methods
Experimental/Medical Research
Comprehensive Evaluation
Econometric Research
Machine Learning
Meta Analysis
Text Analysis
Spatial Econometrics
Power Analysis
Batch Processing
0
Data Labels
Batch Processing
0
Batch Processing
0
Compute Variable
Batch Processing
0
Settings
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Judgment Standard
- =
- <
- >
- ± Standard Deviation
-
Outlier Handling
-
Batch Processing
0
Settings
-
Judgment Standard
- > %
- > %
-
Invalid Sample
-
-
Select the number of factors and corresponding levels, then 'start'".
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
Select the order of the judgment matrix white cells, and click "Start".
Suitable for t test with summary data (mean, standard deviation, and sample size).
- Group 1Group 2
- Mean
- Standard Deviation
- Sample Size
- Mean Difference
- Confidence Level
- Hypothesis Test
- Group 1Group 2
- Mean
- Standard Deviation
- Sample Size
- Mean Difference
- Confidence Level
- Hypothesis Test
Suitable for z test with summary data (mean, standard deviation, and sample size) for large sample (N > 30).
- Group 1Group 2
- Mean
- Standard Deviation
- Sample Size
- Mean Difference
- Confidence Level
- Hypothesis Test
- Group 1Group 2
- Mean
- Standard Deviation
- Sample Size
- Mean Difference
- Confidence Level
- Hypothesis Test
Proportion z test is applicable for large sample data (N>30).
- Group 1Group 2
- Event
- Total
- Proportion Difference
- Confidence Level
- Hypothesis Test
- Group 1Group 2
- Events
- Total
- Proportion Difference
- Confidence Level
- Hypothesis Test
- Positive (Bad)Negative (Good)
- Exposed Group
- Control Group
- Confidence Level
- Independent Variable (X) Dependent Variable (Y)
- Example Below
-
- The relationships are 5 pairs as follows:
- - A affects B- B affects D- C affects D
- - A affects D- C affects B
- Dependent Variable (Y)
- Independent Variable (X)
- Independent variable (X2)
- Independent variable (X3)
Set Parameter (Optional)
| Parameter | Initial value |
Lower limit (Usually not set) |
Upper limit (Usually not set) |
|---|
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
Chi-square tests differences between X and Y when both are categorical. Adjust for confounding factors like gender with stratified chi-square. SPSSAU supports 2*2*K data (X and Y are 2-category, K stratums).
- Group(X)
- Outcome(Y)
- Factor (Stratification)
- Weight
Bubble charts display the relationship between X and Y, with bubble size representing Z. To show the 'label' for each point, simply add the corresponding 'Label'.
- X
- Y
- Z (Bubble size, optional)
- Bubble category (Optional)
- Label (Optional)
Please edit the 'Initial probability' and 'State transition matrix', and you can paste (Ctrl+V) or modify the data.
| State | State 1 | State 2 | State 3 |
| Probability |
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
The data format is: 1 row represents the score of 1 expert, 1 column represents 1 indicator.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
Please input or paste the complementarity matrix. The main diagonal should be 0.5, and the sum of each pair of symmetric elements must equal 1.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
Tip: BO and OW should be placed separately. If multiple experts are involved, input multiple rows.
Input the Best-to-other (BO) data. If multiple experts are involved, use multiple rows.
Input the Others-to-worst (OW) . If multiple experts are involved, use multiple rows.
Limited to four values: 1/2/3/4, where higher scores mean higher effectiveness, usually 3 or 4 points.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
-
Calculation Item
- Optional: Year
- Group (Optional)
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
Edit the data, then start.
Select old model prediction, new model prediction, and gold standard.
-
Prediction (Old model)
-
Prediction (New model)
- Gold Standard
-
Jump Threshold
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
-
Theil Index
- Similar to GDP (Y)
- Similar to Population (N)
- Similar to Per Capita GDP
- Year (Optional)
- Group 1 (e.g., Region) (Optional)
- Group 2 (e.g., Province) (Optional)
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
Please enter latitude and longitude
After pasting (Ctrl+V) or modifying the data, click 'Start' to continue.
Upload .gal or .gwl format
- 【1】The SPSSAU project (2024). SPSSAU. (Version 24.0) [Online Application
Software]. Retrieved from https://spssau.net.
【2】PySAL Developers. (2018). PySAL: Python Spatial Analysis Library (23.7). GeoDa Center for Geospatial Analysis and Computation.
Select Purpose
Please select
Compare the
difference between the mean and one test value
Basic Parameter
Input Data
Input Data
Mean of Group 2
Standard Deviation of Group 2
Select Purpose
Please select
Study the mean
differences of one factor across different groups
Basic Parameter
Input Data
Number of Levels for Factor B
Number of Levels for Factor C
Select Purpose
Please select
Compare rate with
population rate
Basic Parameter
Input Data
Input Data
Select Purpose
Please select
Compare the
correlation coefficient with the expected value
Basic Parameter
Input Data
Select Purpose
Please select
Power analysis for
R-squared or R-squared change in hierarchical regression
Basic Parameter
Input Data
Select Purpose
Please select
Power analysis for
differences between coefficients in binary logistic regression
Basic Parameter
Input Data
Title Edit
Delete