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Key words: spss, windows, version, spss windows, version, spss
SPSS Windows Version Base Data Management & Preparation
Prepare your data for analysis
Once you've accessed your data, you'll need to prepare them for analysis. SPSS Windows Version Base includes a variety of techniques to ensure you'll get to analysis faster and with less hassle. With
SPSS Base, you can use the following:
Data Editor: Provides you with a spreadsheet-like system for defining, entering, editing and displaying data.
Data preparation tools: Easily manage and prepare your data for analysis using the following:
- Easily set up data dictionary information (such as value labels, variable labels and variable types) using the Define Variable Properties tool. A data pass made first enables SPSS
to present a list of values and counts of those values so you can add the information in a more intelligent manner. Once dictionary information is set up, you can apply that information using the Copy Data Properties tool. The data dictionary information acts as a "template" so you can apply it to other data files and to other variables within the same file.
Easily set up your data dictionary (such as value and variable labels and variable types) to
prepare your data for analysis using the Define Variable Properties tool. A data pass made first enables SPSS to present a list of values and counts of those values, so you can intelligently add labels.
- Prepare continuous-level data for analysis. The Visual Bander allows you to easily create bands (e.g., breaking income into income "bands"
of $10,000 or breaking ages into groups). A data pass provides you with a histogram that enables you to specify cutpoints in an intelligent manner. You can automatically create value labels from the specified
cutpoints (e.g., "21-30"). Then save time by automatically creating value labels based on your cutpoints.
This screenshot from a study on occupational prestige shows the Visual Bander in action. The
user specified Age_banded" as the new variable and set cutpoints by age groups.
- You can easily clean your data when you identify duplicate records through the user interface with the Identify Duplicate Copies tool. Set
parameters and flag duplicates and keep track of multiple duplicates per record.
"The Identifying Duplicate Cases command makes unduplicating files incredibly easy. I like the way it allows you to unduplicate on multiple
variables according to multiple sort criteria. I commonly use multiple sorting to unduplicate records to either access the original or most recently revised record. Overall, the speed with which Identifying
Duplicate Cases functioned was great."
- Sheri Sterner,
Supervisor of Research,
Orange Coast College
- Create your own custom programs with the Output Management System. Turn output from SPSS procedures into data (SPSS data files, XML or HTML) to create your own programs for Bootstrapping,
Jackknifing and Leaving One Out methods, and Monte Carlo simulations.
- Take a data file that has multiple records per subject and restructure it so data for each subject are in a single record with the Data Restructure Wizard. No need to set up vectors or loops. This is
particularly helpful if you work with transactional data. You can also do the reverse action, that is, take data from a single record and spread it across multiple cases.
- More accurately describe your data using longer variable names - up to 64 bytes. This enables you to work more easily with data from databases or spreadsheets that have longer variable or more complex
naming conventions. For example, you can maintain variables names on data that you pull from and write back to a Microsoft Excel file saving you time and frustration.
- Prevent the accidental destruction of data by making the dataset read-only. Simply set a global option.
- Make sense and keep track of your data files by adding notes to them using the Data File Comments command in the user interface. This enables you to save a block of text with your SPSS
data file for easy reference (e.g., indicate that you have cleaned your file).
Data transformations: Work with combined data more reliably by "flipping" responses so all your data are in the same direction. This is necessary when
you want to create multiple-item indices which require questions to go in the same direction. You may want to create multiple-item indices when working
with surveys that ask respondents to give both positively worded and negatively worded responses.
More transformation techniques: SPSS has a variety of other transformation techniques that help get data ready for analysis. These capabilities include:
- Compute new variables using arithmetic, cross-case, data and time, logical, missing-value, random-number, statistical or string functions
- Recode string or numeric value
- Recode values into consecutive integers
- Create conditional transformations using DO IF, ELSE IF, ELSE and END IF statements
- Use programming structures such as do repeat-end repeat, loop-end loop and vectors
- Count occurrences of values across variables
- Make transformations permanent or temporary
- Execute transformations immediately, batched or on demand
- Cumulative distribution, inverse cumulative distributions and random number generator functions
- Cumulative distribution and random number generator for discrete distribution functions
- Cumulative distribution for non-central distribution
- Density/probability functions for continuous and discrete distributions
- Non-central density/probability functions
- Tail probabilities
- Auxiliary function
Back to SPSS Base page.
Back to SPSS software main page.
Key words: spss, windows, version, spss windows, version, spss
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