Spss 16.0.1 Complete Suite
SPSS (originally, Statistical Package for the Social Sciences) was released in its first version in 1968 after being founded by Norman Nie, then a political science postgraduate at Stanford University,[1] and now Research Professor in the Department of Political Science at Stanford and Professor Emeritus of Political Science at the University of Chicago.[2] SPSS is among the most widely used programs for statistical analysis in social science. It is used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations and others. In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation (a metadata dictionary is stored with the data) are features of the base software.
Statistics included in the base software:
* Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore, Descriptive Ratio Statistics
* Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial, distances), Nonparametric tests
* Prediction for numerical outcomes: Linear regression
* Prediction for identifying groups: Factor analysis, cluster analysis (two-step, K-means, hierarchical), Discriminant
The many features of SPSS are accessible via pull-down menus or can be programmed with a proprietary 4GL command syntax language. Command syntax programming has the benefits of reproducibility and handling complex data manipulations and analyses. The pull-down menu interface also generates command syntax, though the default settings have to be changed to make the syntax visible to the user. Programs can be run interactively, or unattended using the supplied Production Job Facility.
Additionally a "macro" language can be used to write command language subroutines and a Python programmability extension can access the information in the data dictionary and data and dynamically build command syntax programs. The Python programmability extension, introduced in SPSS 14, replaced the less functional SAX Basic "scripts" for most purposes, although SaxBasic remains available. In addition, the Python extension allows SPSS to run any of the statistics in the free software package. From version 14 onwards SPSS can be driven externally by a Python or a VB.NET program using supplied "plug-ins".
SPSS places constraints on internal file structure, data types, data processing and matching files, which together considerably simplify programming. SPSS datasets have a 2-dimensional table structure where the rows typically represent cases (such as individuals or households) and the columns represent measurements (such as age, sex or household income). Only 2 data types are defined: numeric and text (or "string"). All data processing occurs sequentially case-by-case through the file. Files can be matched one-to-one and one-to-many, but not many-to-many.The graphical user interface has two views which can be toggled by clicking on one of the two tabs in the bottom left of the SPSS window. The 'Data View' shows a spreadsheet view of the cases (rows) and variables (columns). The 'Variable View' displays the metadata dictionary where each row represents a variable and shows the variable name, variable label, value
label(s), print width, measurement type and a variety of other characteristics. Cells in both views can be manually edited, defining the file structure and allowing data entry without using command syntax. This may be sufficient for small datasets.
Larger datasets such as statistical surveys are more often created in data entry software, or entered during computer-assisted personal interviewing, by scanning and using optical character recognition and optical mark recognition software, or by direct capture from online questionnaires. These datasets are then read into SPSS.
SPSS can read and write data from ASCII text files (including hierarchical files), other statistics packages, spreadsheets
and databases. SPSS can read and write to external relational database tables via ODBC and SQL. Statistical output is to a proprietary file format (*.spo file, supporting pivot tables) for which, in addition to the in-package viewer, a stand-alone reader is provided. The proprietary output can be exported to text or micr*soft Word. Alternatively, output can be captured as data (using the OMS command), as text, tab-delimited text, HTML, XML, SPSS dataset or a variety of graphic image formats (JPEG, PNG, BMP and EMF).
Add-on modules provide additional capabilities. The available modules are:
* SPSS Programmability Extension (added in version 14). Allows Python programming control of SPSS.
* SPSS Data Validation (added in version 14). Allows programming of logical checks and reporting of suspicious values.
* SPSS Regression Models - Logistic regression, ordinal regression, multinomial logistic regression, and mixed models (multilevel models).
* SPSS Advanced Models - Multivariate GLM and repeated measures ANOVA (removed from base system in version 14).
* SPSS Classification Trees. Creates classification and decision trees for identifying groups and predicting behaviour.
* SPSS Tables. Allows user-defined control of output for reports.
* SPSS Exact Tests. Allows statistical testing on small samples.
* SPSS Categories
* SPSS Trends
* SPSS Conjoint
* SPSS Missing Value Analysis. Simple regression-based imputation.
* SPSS Map
* SPSS Complex Samples (added in Version 12). Adjusts for stratification and clustering and other sample selection biases.
SPSS Server is a version of SPSS with a client/server architecture. It has some features not available in the desktop version, such as scoring functions.
Homepage:
Code:
http://www.spss.com/spss/
Code:
http://www.megaupload.com/?d=P4G13XER
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SPSS Amos v.16
Amos is powerful structural equation modeling (SEM) software that enables you to support your research and theories by extending standard multivariate analysis methods, including regression, factor analysis, correlation, and analysis of variance. In Amos, you specify, estimate, assess, and present your model in an intuitive path diagram to show hypothesized relationships among variables.
You can build attitudinal and behavioral models in Amos that more realistically reflect complex relationships, because any numeric variable, whether observed (such as data from a survey) or latent (such as satisfaction and loyalty), can be used to predict any other numeric variable. These models can help you confirm complex relationships.
Amos provides you with powerful and easy-to-use structural equation modeling (SEM) software. Create more realistic models than if you used standard multivariate statistics or multiple regression models alone. Using Amos, you specify, estimate, assess, and present your model in an intuitive path diagram to show hypothesized relationships among variables. This enables you to test and confirm the validity of claims such as "value drives loyalty" in minutes, not hours.
Gain new insights using observed and latent variables
Amos enables you to build models that more realistically reflect complex relationships with the ability to use observed variables such as survey data or latent variables like “satisfaction” to predict any other numeric variable. Structural equation modeling, sometimes called path analysis, helps you gain additional insight into causal models and the strength of variable relationships.
Expanded statistical options based on Bayesian estimation
With Amos, you can perform estimation with ordered-categorical and censored data, enabling you to:
Create a model based on non-numerical data without having to assign numerical scores to the data
Work with censored data without having to make assumptions other than normality
You can also impute numerical values for ordered-categorical data or censored data, so you can create a complete numerical dataset when one is required. Or, impute values for missing values in the new dataset. You also have the option of estimating posterior predictive distributions to determine probable values for missing or partially missing data in a latent variable model.
Homepage:
Code:
http://www.spss.com/amos/
Code:
http://rapid*share.com/files/70865832/s0076.rar
Code:
for@llmyFriends
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