Missing Values 17.0
Build Better Models When You Estimate Missing Data
Missing data can seriously affect your results. If you ignore missing data or assume that excluding missing data is sufficient, you risk reaching invalid and insignificant results. To ensure that you enter the data analysis stage using data that takes missing values into account, make SPSS Missing Values (formerly called SPSS Missing Value Analysis) part of your data management and preparation step.
SPSS Missing Values, is a critical tool for anyone concerned about data validty, including survey researchers, social scientists, data miners, and market researchers.
Uncover missing data patterns
With SPSS Missing Value Analysis, you can easily examine data from several different angles using one of six diagnostic reports to uncover missing data patterns. You can then estimate summary statistics and impute missing values through regression or expectation maximization algorithms (EM algorithms). SPSS Missing Value Analysis helps you to:
- Diagnose if you have a serious missing data imputation problem
- Replace missing values with estimates—for example, impute your missing data with the regression or EM algorithms
Quickly and easily diagnose your missing data
Quickly diagnose a serious missing data problem using the data patterns report, which provides a case-by-case overview of your data. This report helps you determine the extent of missing data; it displays a snapshot of each type of missing value and any extreme values for each case.
Use multiple imputation to replace missing data values
In SPSS Missing Values 17.0, a new multiple imputation procedure will help you understand patterns of “missingness” in your dataset and enable you to replace missing values with plausible estimates. It offers a fully automatic imputation mode that chooses the most suitable imputation method based on characteristics of your data, while also allowing you to customize your imputation model.
Several complete datasets are generated (typically, three to five), each with a different set of replacement values. Next, you can model the individual datasets using the usual techniques, such as linear regression, to produce parameter estimates for each dataset. Then obtain final parameter estimates. This involves pooling the individual sets of parameter estimates obtained in step two and computing inferential statistics that take into account variation within and between imputations.
Analysis of the individual datasets and pooling of the results are supported via select existing SPSS Statistics procedures such as REGRESSION. When operating on datasets with imputed values, existing procedures will automatically produce pooled parameter estimates.
Reach more valid conclusions
Replace missing values with estimates and increase the chance of receiving statistically significant results. Remove hidden bias from your data by replacing missing values with estimates to include all groups in your analysis—even those with poor responsiveness.
SPSS Missing Values is available in English, Japanese, French, German, Italian, Spanish, Chinese, Polish, Korean, and Russian.
System requirements
For SPSS Statistics Base 17.0 for Windows
Operating System
MicrosoftWindows XP (32-bit versions) or Vista(32-bit or 64-bit versions)
Hardware
Intelor AMD x86 processor running at 1GHz or higher
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 450MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
For connecting with SPSS Statistics Base Server, a network adapter running the TCP/IP network protocol
Software
Web browser: Internet Explorer 6 or above
For SPSS Statistics Base 17.0 for Mac OS X
Operating system: Apple Mac OS X 10.4 (Tiger) or Mac OSX 10.5 (Leopard)
Hardware
PowerPC or Intel processor
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 800MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Safari 1.3.1, MozillaFirefox1.5 or higher, or Netscape7.2 or higher
Java Standard Edition 5.0 (J2SE 5.0)
SPSS Statistics Base 17.0 for Linux
Operating system*
Any Linux OS that meets the following requirements:
Kernel 2.6.9.42 or higher
glibc 2.3.4 or higher
XFree86-4.0 or higher
libstdc++5
Hardware
Processor: Intel or AMD x86 processor running at 1 GHz or higher
RAM: 512MB RAM; 1GB recommended
450 MB of available hard-disk space
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Web browser: Konqueror 3.4.1 or higher, or Firefox 1.0.6 or higher, or Netscape 7.2 or higher
*Note: SPSS Statistics 17.0 was tested on and is supported only on Red HatEnterprise Linux 4 Desktop and Debian4.0
SPSS Statistics add-on modules
All SPSS Statistics 17.0 add-on modules require SPSS Statistics Base 17.0.
No other system requirements are necessary.
Amos 17.0
Operating system: Windows XP or Windows Vista
Hardware:
Memory: 256MB RAM minimum
125MB or more available hard-drive space
Web browser: Internet Explorer 6.0
SPSS Statistics Server 17.0
Operating system: Windows Server 2003 or Windows Server 2008 (32-bit or 64-bit) or Windows Server 2008 (32-bit or 64-bit); Sun Solaris (SPARC) 9 and later (64-bit only); IBMAIX5.3 and later; or Red Hat Enterprise Linux ES4 and later (64-bit); HP-UX 11i (64-bit Itanium)
Hardware
Minimum CPU: Two CPUs recommended, running at 1GHz or higher
Memory: 512MB RAM per expected concurrent user
Minimum free drive space: 300MB
Required temporary disk space: Calculate by multiplying 2.5 x number of users x expected size of dataset in megabytes
SPSS Statistics Adapter for SPSS Predictive Enterprise Services
Requires Statistics Base 17.0 and SPSS Predictive Enterprise Services
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