Buy Now
Demo
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 Binner enables you to easily create bins or bands (for example, break income into "bins" 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
To learn more:
Buy Now
Demo
