EQS 6结构方程建模分析软件-上海卡贝信息技术有限公司

EQS for Windows

Already known for combining technical sophistication with ease of use, EQS for Windows makes structural equations modeling even easier with Diagrammer, EQS' new model drawing tool.

Make just a few clicks on the mouse and you can draw a diagram. Once your model is drawn, choose Build EQS from the scroll-down menu and EQS runs your model based on the diagram. Use of command language is no longer necessary!
When you are ready to present results, simply print the diagram directly or cut and paste into your favorite word processor for final production.

Click On A Feature Below To Learn More:

Spreadsheet Type Data Editor and Flexible Data Import Options
Missing Data Handling and Missing Data Imputation
Comprehensive Data Manager
Data Exploration Tools With Point-and-Click Data Plots
Basic Statistics Including Multiple Regression and Factor Analysis
Diagrammer - SEM Diagram Drawing Tool to Create and Report A Model
Modeling Features
Hardware and Software Requirements

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SPREADSHEET TYPE DATA EDITOR AND FLEXIBLE DATA IMPORT OPTIONS

Spread-sheet type data editor with data entry function
Cut and paste to and from other data sheet
Support DDE (Dynamic Data Exchange)
Read EQS system ESS files
Read free field data using a space, tab, or comma as delimiter
Import variable names if they are embedded in the first case
Read fixed field data using convenient column specifications
Read fixed field data using visual tool to define variable boundary
Read BMDP PC or 386/Dynamic Save files
Read Lotus 1-2-3 WK1 files
Read dBASE III plus DBF files
Write data file with space or tab delimiter with variable labels

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MISSING DATA HANDLING AND MISSING DATA IMPUTATION

Print out missing data diagnosis information
Print out dichotomized correlation matrix for missing data
Support global missing and variable missing value
Provide interactive and visual missing data map
Provide Z-score map
Allow exclusion of cases based on the percentile of missing variables
Impute missing variable using its mean
Impute missing variable using group mean
Impute missing variable using regression estimator

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COMPREHENSIVE DATA MANAGER

Simple method to define variable name and category labels
Case selection function to split cases and conditional selection of cases
Save a subset of data file bases on selected cases
Save a subset of data by selecting variables
Transform data
Join two or more files side by side
Merge to or more files end to end
Recode or collapse data using a visual tool
Point and click to reverse coding of a variable
Sort records
Compute moving average and differences of time lag

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DATA EXPLORATION TOOLS WITH POINT AND CLICK DATA PLOTS

Line Plot
Area Plot
Histogram with a grouping variable
Pie Chart
Bar Chart
Quantile Plot
Quantile-Normal Plot (Normal Probability Plot)
Quantile-Quantile Plot
Scatter Plot and Matrix Plot with brushing function
Box Plot
Error Bar Chart
Multiple Plot (Combine several plots in on window)

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BASIC STATISTICS INCLUDING MULTIPLE REGRESSION AND FACTOR ANALYSIS

Descriptive Statistics
Frequency Tables
t-Test
One-Way and Two-Way ANOVA
CrossTab
Multiple Regression and Stepwise Regression
Covariance and Correlation with list-wise or pair-wise deletion
Factor Analysis with capable of exporting the factor loading matrix to EQS

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DIAGRAMMER - EQS' DRAWING TOOL TO CREATE AND REPORT A MODEL

Very flexible drawing tool
Useful editing tool such as flip, rotation, alignment, group, and break group options
Point and drag to resize a diagram
View starting value, parameter estimates, and standardized solutions in a path
View a subset of diagram and print out only what is visible on the screen
Point and click to build a complex factor structure
Build EQS model automatically without having a syntax error message

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MODELING FEATURES

Data Handling:

Raw data files (both ASCII and ESS formats)
Covariance matrix (both ASCII and ESS formats)
Automatic listwise deletion of cases
Moment matrix (covariance matrix and mean)
Outlier detection
Test of multivariate normality

Model Specification:

Symbols of model notation (V, F, E, D)
Intuitive equation type model specification
Simple equality constraints
General equality constraints
Inequality constraints
Automatic starting values

Methods of Estimation and Statistics:

Least squares
Generalized least squares
Maximum likelihood
Multiple sample analysis
Analysis of means and covariances
Asymptotic distribution free
Elliptical corrections
Satorra-Bentler scaling correction
Robust standard errors
Akaike Information Criterion
Bentler-Bonnet fit indices
Comparative fit index
Other fit indices include:
GFI, AFGI, RMR, standardized RMR, IFI, MFI, and RMSEA
Yaun-Bentler distributed-free statistics
Wright-type standardized solution
Categorical dependent variables

Simulation:

Easy-to-use Monte Carlo simulation
Bootstrap, jackknife
Generate non-normal and categorical data
Generate normal & contaminated normal data

Model Modifications:

Wald test
Wald test against any constants
Rank correlation between two model estimates
Univariate modification indices
Multivariate Lagrange Multiplier (LM) test
LM tests on constraints
LM tests of cross-group equality constraints
LM test on individual fixed parameters
Control sets of fixed parameters in LM test
Exclusion of meaningless paths in LM

Output Control:

Correlations of parameter estimates
Effect decomposition
Standardized effect decomposition
Model covariance/correlation matrices
New model setup with optimal starting values
New model setup with consideration of Wald and LM results
Summary statistics on an output file for further analysis

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HARDWARE AND SOFTWARE REQUIREMENTS FOR WINDOWS

IBM PC 386, 486, Pentium, or compatibles
A math co-processor
At least 8 MB of RAM
At least 5 MB of free disk space
MS Windows 3.1(enhanced mode), Windows 95, 98, 2000 or Win NT