1. 17a.Accessing Data: Manipulating Variables in SPSS ®

2. 17a. Accessing Data: Manipulating Variables in SSPS ® 1 Prerequisites Recommended modules to complete before viewing this module  1. Introduction to the NLTS2 Training Modules  2. NLTS2 Study Overview  3. NLTS2 Study Design and Sampling  NLTS2 Data Sources, either 4. Parent and Youth Surveys or 5. School Surveys, Student Assessments, and Transcripts  NLTS2 Documentation 10. Overview 11. Data Dictionaries 12. Quick References

3. 17a. Accessing Data: Manipulating Variables in SSPS ® 2 Prerequisites Recommended modules to complete before viewing this module (cont’d)  13. Analysis Example: Descriptive/Comparative Using Longitudinal Data  Accessing Data 14a. Files in SPSS 15a. Frequencies in SPSS

4. 17a. Accessing Data: Manipulating Variables in SSPS ® 3 Overview  Purpose  Modifying existing variables  Creating new variables  Summary  Closing  Important information

5. 17a. Accessing Data: Manipulating Variables in SSPS ® 4 NLTS2 restricted-use data NLTS2 data are restricted. Data used in these presentations are from a randomly selected subset of the restricted-use NLTS2 data. Results in these presentations cannot be replicated with the NLTS2 data licensed by NCES.

6. 17a. Accessing Data: Manipulating Variables in SSPS ® 5 Purpose Learn to  Modify an existing variable  Create a new variable  Join/combine data from different sources

7. 17a. Accessing Data: Manipulating Variables in SSPS ® 6 Modifying existing variables How to modify a variable. It is necessary to create a new variable in SPSS to  Collapse categories  Break a continuous variable into categories  Recode a variable. Note about created variables in the NLTS2 database  Our analyses were done in SAS, and this recoding step is usually not necessary in SAS because of the external formats feature.  Collapsed or recategorized variables do not necessarily exist in SAS or SPSS files even if these items appear in published tables.  There are many created variables in the NLTS2 database, but most of them are not simply collapsed versions of an existing variable. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

8. 17a. Accessing Data: Manipulating Variables in SSPS ® 7 Modifying existing variables Syntax to recode into collapsed categories RECODE np1B2a (MISSING=SYSMIS) (Lowest thru 1=1) (2 thru 5=2) (6 thru 10=3) (11 thru Highest=4) INTO np1B2a_Cat. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

9. 17a. Accessing Data: Manipulating Variables in SSPS ® 8 Modifying existing variables Syntax to assign a variable label to the new variable *assign variable label to new categorical variable. VARIABLE LABELS np1B2a_Cat '(np1B2a_cat) Age of youth when diagnosed categorized'. EXECUTE. Syntax to assign value labels * assign value labels to new categorical variable. VALUE LABELS np1B2a_Cat 1 "(1) 1 or younger" 2 "(2) 2 to 5 years of age" 3 "(3) 6 to 10 years of age" 4 "(4) 11 or older". These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

10. 17a. Accessing Data: Manipulating Variables in SSPS ® 9 Modifying existing variables Menu  Transform: Recode into Different Variables  Select the variable to be recoded from the list and click the right-facing arrow.  Give the new variable a name in the box under “Output Variable.”  Assign a label to the new variable in the “Label” box under “Output Variable.”  Click “Change.”  Click on the box marked “Old and New Values,” and a new box pops up.  In the new box, under “Old Values” click the radio button “System or User-missing,” click “System Missing” under “New Values,” and click “Add” next to “Old -- >New.” These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

11. 17a. Accessing Data: Manipulating Variables in SSPS ® 10 Modifying existing variables Menu (cont’d)  For each old to new value(s) Under “Old Values,” click a radio button by an actual value or range of values box. Designate what the old values are, either actual or range of values, in the appropriate box. Assign a new code under “New Values” and click “Add.”  When finished with values, click “Continue” to return to the first box.  In the original box, click “OK” or “Paste” to generate code. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

12. 17a. Accessing Data: Manipulating Variables in SSPS ® 11 Modifying existing variables Look at results. New variable should appear at bottom of “Variable View.” Specify formats so values are meaningful.  In variable view, click on the cell in the “Values” column to bring up a new box.  Enter a value in the “Value” box, a label for that value in the “Label” box, and click “Add.”  Do this for every value. Look at frequency distribution.  Useful to look at a crosstab of the original by the new variable. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

13. 17a. Accessing Data: Manipulating Variables in SSPS ® 12 Modifying existing variables These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

14. 17a. Accessing Data: Manipulating Variables in SSPS ® 13 Modifying existing variables These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

15. 17a. Accessing Data: Manipulating Variables in SSPS ® 14 Modifying existing variables: Example Modifying a variable  Open Wave 3 parent/youth interview file.  Collapse np3NbrProbs into new variable. 0-1 2 3 4-6  Remember to Label variable Add value formats Account for missing values Paste your code. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

16. 17a. Accessing Data: Manipulating Variables in SSPS ® 15 Modifying existing variables: Example These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

17. 17a. Accessing Data: Manipulating Variables in SSPS ® 16 Modifying existing variables: Example These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

18. 17a. Accessing Data: Manipulating Variables in SSPS ® 17 Creating new variables How to create a new variable. The values in the new variable can be the results of calculations, assignments, or logic. A new variable can be created from an existing variable or from multiple variables, including variables from other sources and/or waves.  Variables from other sources/waves must be added to the active data file before the new variable is created. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

19. 17a. Accessing Data: Manipulating Variables in SSPS ® 18 Creating new variables Be aware of any coding differences between the variables when combining values. Decide what to do with missing values. Example: Create a variable using parent interview data from Waves 1, 2, and 3.  Has a student been suspended and/or expelled in any wave? These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

20. 17a. Accessing Data: Manipulating Variables in SSPS ® 19 Creating new variables Syntax IF (np1D7h=0 and np2D5d=0 and np3D5d=0 and np4D5d=0) np4D5d_ever=0. IF (np1D7h=1 or np2D5d=1 or np3D5d=1 or np4D5d=1) np4D5d_ever = 1. IF (np1D7h=1 and np2D5d=1 and np3D5d=1 and np4D5d=1) np4D5d_ever = 2. IF (MISSING(np1D7h) or MISSING(np2D5d) or MISSING(np3D5d) or MISSING(np4D5d)) np4D5d_ever = -999. EXECUTE. This code will result in a variable that  Requires a value for every wave  Is 0 if never suspended/expelled  Is 1 if suspended/expelled in any wave  Is 2 if suspend/expelled in all three waves. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

21. 17a. Accessing Data: Manipulating Variables in SSPS ® 20 Creating new variables These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

22. 17a. Accessing Data: Manipulating Variables in SSPS ® 21 Creating new variables Menu  Transform: Compute  Enter a variable name under “Target Variable.”  Click “Type & Label” and assign a label.  If applicable, find and select the source variable(s) and click the right-facing arrow to move the variable name into the “Numeric Expression” box.  Enter functions/operations from the keypad boxes or select from the list of functions.  For logical conditions, click “If…” and build the condition in the pop-up box.  Click “OK” or “Paste.”  For multiple conditions (i.e., if-then-else), repeat all steps. Specify conditions in order of overriding conditions. If true, each subsequent condition will override the previous condition. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

23. 17a. Accessing Data: Manipulating Variables in SSPS ® 22 Creating new variables: Example Creating a new variable  Open the Wave 4 parent/youth interview file.  Bring in np1F7 from Wave 1, np2P8_J4 from Wave 2, and np3P8_J4 from Wave 3 interview files.  Create a new variable np4P8_J4_ever (ever done volunteer or community service).  Initialize value to “0” if any value in np1F7, np2P8_J4, np3P8_J4, or np4P8_J4 is “0.”  Reassign to “1” if any value in np1F7, np2P8_J4, np3P8_J4, or np4P8_J4 is “1.” These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

24. 17a. Accessing Data: Manipulating Variables in SSPS ® 23 Creating new variables: Example Creating a new variable (cont’d)  Assign variable label and value labels.  Run a frequency of np4P8_J4_ever.  Run a crosstabulation of np4P8_J4_ever by np4P8_J4. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

25. 17a. Accessing Data: Manipulating Variables in SSPS ® 24 Creating new variables: Example Code for example IF (np1F7=0 or np2P8_J4 = 0 or np3P8_J4=0 or np4P8_J4=0) np4P8_J4_ever = 0. IF (np1F7=1 or np2P8_J4=1 or np3P8_J4=1 or np4P8_J4=1) np4P8_J4_ever = 1. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

26. 17a. Accessing Data: Manipulating Variables in SSPS ® 25 Creating new variables: Example These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

27. 17a. Accessing Data: Manipulating Variables in SSPS ® 26 Creating new variables: Example These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

28. 17a. Accessing Data: Manipulating Variables in SSPS ® 27 Summary Be aware of differences in coding between similar variables when building composite variables. Missing values must be considered.  Know how missing values are being coded, particularly when using more than one variable to create another.  Joined data are more likely to have missing values. Weights  Generally, the analysis weight should be the weight from the smallest sample when combining data.  When filling in values for a variable in an active file with values from another, it is OK to use the weight in the active file. Strongly recommended: Paste your code when creating variables. These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

29. 17a. Accessing Data: Manipulating Variables in SSPS ® 28 Summary Know the values, mind the missing, and watch your weights! These results cannot be replicated with full dataset; all output in modules generated with a random subset of the full data.

30. 17a. Accessing Data: Manipulating Variables in SSPS ® 29 Closing Topics discussed in this module  Modifying existing variables  Creating new variables  Summary Next module  18a. Complex Samples Procedures in SPSS

31. 17a. Accessing Data: Manipulating Variables in SSPS ® 30 Important information  NLTS2 website contains reports, data tables, and other project-related information http://nlts2.org/http://nlts2.org/  Information about obtaining the NLTS2 database and documentation can be found on the NCES website http://nces.ed.gov/statprog/rudman/http://nces.ed.gov/statprog/rudman/  General information about restricted data licenses can be found on the NCES website http://nces.ed.gov/statprog/instruct.asphttp://nces.ed.gov/statprog/instruct.asp  E-mail address: nlts2@sri.com