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PSPPis free software; you can redistribute it and/or modify it under theterms of the GNU General Public License as published by the FreeSoftware Foundation; either version 3 of the License, or (at youroption) any later version.

SPSS Statistics is a statistical software suite developed by IBM for data management, advanced analytics, multivariate analysis, business intelligence, and criminal investigation. Long produced by SPSS Inc., it was acquired by IBM in 2009. Current versions (post 2015) have the brand name: IBM SPSS Statistics.

SPSS is a widely used program for statistical analysis in social science.[6] It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners,[7] and others. The original SPSS manual (Nie, Bent & Hull, 1970)[8] has been described as one of "sociology's most influential books" for allowing ordinary researchers to do their own statistical analysis.[9] In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation (a metadata dictionary is stored in the datafile) are features of the base software.

Additionally a "macro" language can be used to write command language subroutines. 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 R. From version 14 onwards, SPSS can be driven externally by a Python or a VB.NET program using supplied "plug-ins". (From Version 20 onwards, these two scripting facilities, as well as many scripts, are included on the installation media and are normally installed by default.)

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 Statistics window. The 'Data View' shows a spreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the data cells can only contain numbers or text, and formulas cannot be stored in these cells. 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 Statistics version 13.0 for Mac OS X was not compatible with Intel-based Macintosh computers, due to the Rosetta emulation software causing errors in calculations. SPSS Statistics 15.0 for Windows needed a downloadable hotfix to be installed in order to be compatible with Windows Vista.

The copyright statements and licenses applicable to the open source software components distributed in vSphere 7.0 are available at You need to log in to your My VMware account. Then, from the Downloads menu, select vSphere. On the Open Source tab, you can also download the source files for any GPL, LGPL, or other similar licenses that require the source code or modifications to source code to be made available for the most recent available release of vSphere.

It may be important for researchers to calculate a measure of precision for some estimates based on the NEDS sample data. Variance estimates must take into account both the sampling design and the form of the statistic. The sampling design consisted of a stratified, single-stage cluster sample. A stratified random sample of hospital-owned EDs (clusters) was drawn and then all ED visits were included from each selected hospital. To accurately calculate variances from the NEDS, appropriate statistical software and techniques must be used. For detailed instructions, refer to the HCUP Methods Series report #2003-02 Calculating Nationwide Inpatient Sample Variances on the HCUP-US website (www.hcup-us.ahrq.gov/). The HCUP Nationwide Inpatient Sample (NIS) prior to 2012 used stratified sample design similar to the NEDS, so techniques appropriate for the NIS prior to 2012 are also appropriate for the NEDS.

In most cases, computer programs are readily available to perform these calculations. Several statistical programming packages allow weighted analyses.12 For example, nearly all SAS procedures incorporate weights. In addition, several statistical analysis programs have been developed to specifically calculate statistics and their standard errors from survey data. Version 8 or later of SAS contains procedures (PROC SURVEYMEANS and PROC SURVEYREG) for calculating statistics based on specific sampling designs. Stata and SUDAAN are two other common statistical software packages that perform calculations for numerous statistics arising from the stratified, single-stage cluster sampling design. Examples of the use of SAS, SUDAAN, and Stata to calculate NIS variances are presented in the special report Calculating Nationwide Inpatient Sample Variances on the HCUP-US website ( -us.ahrq.gov). Although the examples using the NIS also apply to the NEDS, it should be noted that the NEDS is a much larger data set. Please consult the documentation for the different software packages concerning the use of large databases. For an excellent review of programs to calculate statistics from survey data, visit the following website: -soft/.

SoftwarePASSOverview OverviewProceduresVideosDocumentationBuy Now Sample Size & PowerPASS software provides sample size tools for over 1100 statistical test and confidence interval scenarios - more than double the capability of any other sample size software. Each tool has been carefully validated with published articles and/or texts.

In PASS, you can estimate the sample size for a statistical test or confidence interval in a few short steps. If you need guidance during any of the steps, PASS has excellent documentation, there are free training videos, and you can contact our team of PhD statisticians.

Herein, we introduce Meta-Analyst, a novel, powerful, intuitive, and free meta-analysis program for the meta-analysis of a variety of problems. Meta-Analyst is implemented in C# atop of the Microsoft .NET framework, and features a graphical user interface. The software performs several meta-analysis and meta-regression models for binary and continuous outcomes, as well as analyses for diagnostic and prognostic test studies in the frequentist and Bayesian frameworks. Moreover, Meta-Analyst includes a flexible tool to edit and customize generated meta-analysis graphs (e.g., forest plots) and provides output in many formats (images, Adobe PDF, Microsoft Word-ready RTF). The software architecture employed allows for rapid changes to be made to either the Graphical User Interface (GUI) or to the analytic modules.

We verified the numerical precision of Meta-Analyst by comparing its output with that from standard meta-analysis routines in Stata over a large database of 11,803 meta-analyses of binary outcome data, and 6,881 meta-analyses of continuous outcome data from the Cochrane Library of Systematic Reviews. Results from analyses of diagnostic and prognostic test studies have been verified in a limited number of meta-analyses versus MetaDisc and MetaTest. Bayesian statistical analyses use the OpenBUGS calculation engine (and are thus as accurate as the standalone OpenBUGS software).

For Bayesian analyses, we invoke OpenBUGS [14] on the back-end and then present the output to the user via the Meta-Analyst interface. Using OpenBUGS for Bayesian analyses provides two major benefits: OpenBUGS is a popular piece of software that has been thoroughly tested by the statistical community. Second, it incorporates a programming language that enables us to implement in Meta-Analyst any model that can be fit in OpenBugs. We use IronPython [15], an implementation of the Python [16] programming language that runs on the .NET virtual machine to facilitate rapid data processing and text manipulation. This is particularly useful for file I/O and for our interaction with the OpenBUGS library, which requires us to generate model, data and initial value text files dynamically and write them to disk (see Figure 2).

In order to attain widespread use, meta-analysis software must be easy to use. In particular, requiring that users learn an entire language to run their analyses will prohibit general adaptation of a program. Dedicated meta-analysis programs such as MIX, Comprehensive Meta-analysis, and MetaDiSc are appealing due to their small learning curve. On the other hand, by their very nature, such programs are less flexible than general statistical packages. For example, they have no scripting functionality, which precludes their use for large-scale empirical research or simulation studies. Further, they are not able to perform advanced analyses, such as bivariate diagnostic test meta-analyses, because they cannot maximize difficult likelihood functions, and they cannot be readily extended to include additional analytic options.

The current version of Meta-Analyst is made available free of charge to interested researchers. It runs on any version of Windows that is compatible with the .NET platform (comprising Windows 98, ME, NT 4.0, 2000, XP and Vista). We have already started development of a cross-platform completely open-source version of the software that uses the R statistical language, and will be readily modifiable and extendable by any interested party -analyst-. 2b1af7f3a8