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Prepare Your Data for Analysis
Once you've accessed your data, you'll need to prepare them for analysis.
Numerous techniques and features built into SPSS Base make it easy to prepare
and manage data prior to analysis. Additionally, you'll find many features
that facilitate file management.
The Data Editor provides you with a spreadsheet-like
system for defining, entering, editing, and displaying data. Use
splitter controls to more quickly and easily understand wide and long datasets.
Enhancements to the Data Editor enable you to find and replace
information, spell check value and variable labels, and sort by variable
name, type, format, and other details.
Data preparation features and tools help you to easily manage
and prepare your data for analysis. You can:
- Open multiple datasets within a single SPSS session. This
enables you to save time and condense steps when merging data files. This
also helps you maintain consistency when copying data dictionary information
between multiple files.
- Easily set up data dictionary information (such as value
and variable labels and variable types) using the Define Variable
Properties tool. A data pass allows SPSS to present a list of values
and counts of those values so you can add information in an intelligent manner.
Once dictionary information is set up, you can apply your 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 allows SPSS to present a list of values and counts of those values, so you can intelligently add labels. Click image to enlarge.
- Prepare continuous-level data for analysis. The Visual Bander enables you to easily create bands (for example, break income into "bands" of 10,000 or break ages into groups). A data pass provides you with a histogram that allows you to specify cutpoints in an intelligent manner. You can automatically create value labels from the specified cutpoints (for example, "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 Binner in action. The user specified "Age_binned" as the new variable and set cutpoints by age groups. Click image to enlarge.
- 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.
- Write back to databases from SPSS by using the Export
to Database Wizard in the interface. For example, create a new
table and export it to your database to keep data in both SPSS and your
database consistent.
- Export SPSS data into other applications, including
spreadsheets and databases that use the CSV (comma-separated value)
file format.
- Create your own dictionary information for variables with Custom
Attributes. For example, create custom attributes describing
transformations for a derived variable with information explaining how
you transformed the variable.
- Work more easily with very wide data files by customizing
Variable Sets. Instantly reduce the variable view to a subset
of selected variables, while keeping the entire file loaded and available
for analysis. This eliminates the need to scroll through hundreds of
variables to visually check a few.
- Create your own custom programs with the Output
Management System (OMS). 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. If you have little or no experience using syntax
in SPSS, you can create custom programs through the OMS interface functionality.
- Easily work with dates and times in SPSS using the Date
and Time Wizard. Use it to calculate with dates and times, create
date/time variables from strings containing variables (such as "03/29/06"),
and bring date/time data from a variety of sources to SPSS. You can also
parse individual date/time variables to apply filters. For example, parse
start dates to examine employees who started with your organization in
2005.
- 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. There's 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 Excel file.
- Ensure data containing long text strings (up to 32,767
bytes) is not truncated or lost when working with open-ended question responses,
data from other software that allows long data strings, and other types
of long text strings.
- Describe categorical data using value labels up to 120
bytes
- Clone or duplicate datasets. This enables you to perform
transformations or do analyses on a duplicate dataset while protecting
the original data.
- Prevent the accidental destruction of data by making the dataset
read-only
- 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 (for example, indicate that you have
cleaned a file).
- Suppress the number of active datasets in the user interface.
This option enables users to choose whether or not to work with multiple
datasets simultaneously.
Data transformations enable you to 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.
SPSS Base has additional transformation techniques that help
get data ready for analysis. These techniques enable you to:
- Compute new variables using arithmetic, cross-case, date and time, logical,
missing-value, random-number, statistical, or string functions
- Count occurrences of values across variables
- Recode string or numeric value
- Change the string length or the data type of an existing variable, using
syntax
- 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
- Make transformations permanent or temporary
- Execute transformations immediately, batched, or on demand
- Find and replace text strings in your data using the find/replace function
- Use cumulative distribution, inverse cumulative distributions, and random
number generator functions
- Work with cumulative distribution and random number generator for discrete
distribution functions
- Use cumulative distribution for non-central distribution
- Use density/probability functions for continuous and discrete distributions
- Use non-central density/probability functions
- Select two-tail probabilities
- Use an auxiliary function
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