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11. NLTS2 Documentation: Data Dictionaries

11. NLTS2 Documentation: Data Dictionaries. 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

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11. NLTS2 Documentation: Data Dictionaries

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  1. 11. NLTS2 Documentation: Data Dictionaries

  2. 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 • 10. NLTS2 Documentation Overview

  3. Overview • Purpose • Data dictionary contents • File specifications • Variable prefix • Missing values • Variable documentation • Variable documentation details • Parent/youth Part 2 documentation distinctions • Transcript data documentation distinctions • Supplemental documentation • Closing • Important information

  4. Purpose • The data dictionary section of the documentation is the most detailed for individual data items. • The data dictionary includes specific information about each item such as • Which respondents are included in the data element if there is skip logic applied. • Documentation of any modification made to the data element, such as a logical assignment to change a value. • Variable names of corresponding items in other Waves. • Users should refer to the data dictionary before specifying any analysis.

  5. Purpose • Why use the data dictionary rather than the data collection instruments? • Data collection instruments are extremely useful. • Can be a quick reference for finding an item • Show the item in the context of other items • Contain the exact wording of questions that respondents were asked • However, only the data dictionaries describe • Complex skip logic, especially from CATI instruments • Data issues, such as an addition of response categories from one wave to the next • Any programmatic modifications, assignments, or recoding of the data, such as setting a value to yes if a prior response is yes

  6. Data dictionary contents • There is a data dictionary for every data collection source within each wave. • Every dictionary begins with a linked contents. • Links go to • File specifications. • Variable descriptions by section or topic area.

  7. Data dictionary contents example

  8. File specifications • The first section of the data dictionary is “File Specifications,” which lists • The associated file name • The data collection source • The prefix for variable names in the file • Linking variable (always “ID”) • Missing values

  9. File specifications: Example

  10. File specifications • Variable prefix • The prefix for variable names in the file applies to most but not all variables. • With a few exceptions, variables found in this file begin with the variable prefix. • There are specialized variables that have another prefix structure, such as wave-specific demographic variables. • Example: W2_Age2003 is the age of youth during theWave 2 Parent/Youth data collection and W2_Age2004 is the age of youth for the Wave 2 school data collection; the prefix is Wave 2.

  11. File specifications • Missing values • Can be found in this file. • Note about missing values • User-defined missing values specify why a variable is missing. • Missing values are excluded from calculations in procedures unless the user specifies options to include them. • Data were developed in SAS and converted to SPSS. • There are differences in how missing values are defined and stored in SAS and SPSS.

  12. File specifications • Missing values in SAS • System default missing in SAS is a “.” • User-defined missing values in SAS can have a value from “.a” to “.z” • Missing values in a numeric variable have a numeric value in a SAS logical statement. • For example, the logical statement “If npr1B4 < 1” would include all cases for which the value is “0”, a negative number, or a missing value.

  13. File specifications • Missing values in SPSS • SPSS system missing is a “.” in the data and appears as “System” in SPSS analysis output under “Missing.” • SPSS allows for three distinct user-defined missing values, fewer than SAS. • With the range option, users can define a range of missing values to work around the limitation of three distinct missing values. • Missing values are represented as negative numbers in the NLTS2 SPSS data. • -980 through -999 are in the missing values range. • Missing values in SPSS do not have a numeric value in a logical statement, unlike in SAS. • For example, “IF (npr1B4 < 1) B4New = 0.” would result in a missing value in B4New if npr1B4 is missing.

  14. Variable documentation • After “File specifications,” the dictionary lists all variables in tabular format. • The variables in the data dictionary are organized by section, matching the sections in data collection instruments (source data). • Within each section, there are two sets of variables. • Variables that come directly from the data collection instruments. • Variables created from source data within that section. • Variable descriptions include • Name, variable type, variable values, source(s), and information about skip logic, assignments made, and corresponding variable names in other waves.

  15. Variable documentation • Variables that come directly from the data collection instruments (source data) • Variable names usually have the uniform variable prefix. • Source data are drawn from the section, question number, and subitems in the source instrument. • It can be relatively straightforward to find an item in an instrument and locate it in the dictionary. • Example: variable name np4E2c • The “np4” prefix is NLTS2 Parent/Youth Survey Wave 4. • The “E2c” is Section E of the Parent/Youth Instrument, Question 2, subitem C.

  16. Variable documentation • Variables created from source data • Variables that are created using data from the associated section are listed at the end of the section. • Created variables typically have names that describe the variable rather than relate to a data collection source, but with the same prefix as the source variables. • Variable np3_JobCompNow is[np3] Parent/Youth interview Wave 3 [JobCompNow] currently competitively employed • Collapsed variables, i.e., variables combined from two or more items, sometimes list all contributing variables in the name • Variable np4U8a_J15a is[np4] Parent/Youth Wave 4 [U8a] question U8a [_] combined with [J15a] question J15a

  17. Variable documentation • In addition to variables related to particular items from data collection instruments, there are some other key variables. • Demographic variables that are used for many NLTS2 analyses and published Web tables • Weights, including replicate weights • Linking variable “ID” • Preload, CATI, and/or sample variables • The following slide provides a quick glance at the data dictionary with details in following slides.

  18. Variable documentation: Quick look

  19. Variable documentation: Formatting key • Bold text in the dictionary indicates a modification to questionnaire categories as a result of coding and categorizing verbatim responses. • Grey text indicates that there are no data for this item in this wave. • For example, Question R1b was asked in Waves 2 to 4 but not in Wave 5; in Wave 5, R1b is shaded.

  20. Variable documentation details Figure 1-A. Note: See Figure 1, section C in Module 11 Supporting Materials. • Variable name • Name of the variable as it appears in the data file. • In this example, there is a series of variables for item np4F11b, np4F11b_a through np4F11b_h. • Each variable in the series is listed separately.

  21. Variable documentation details Figure 1-B. Note: See Figure 1, section C in Module 11 Supporting Materials. • Source • Item from data collection source. • If multiple instrument sources, items from each data source listed. • This example comes from the question F11b, subitems a-g.

  22. Variable documentation details Figure 1-C. Note: See Figure 1, section C in Module 11 Supporting Materials. • Variable description • Describes the variable. • Often the text of the question from the source instrument • Variable description corresponds with the variable label in the file contents.

  23. Variable documentation details Figure 1-D. Note: See Figure 1, section C in Module 11 Supporting Materials. • Variable description (cont’d) • In this example, the itemis described as types oflife skills training, the subitems are the individual types of life skills traininglisted in this question. • Subitems “a-g” come from the source and “h” is created.

  24. Variable documentation details Figure 1-E. Note: See Figure 1, section C in Module 11 Supporting Materials. • Variable type and values • Shows how the variable is coded and what the codes mean. • Variable type is numeric, date, or character. • The variable values match the variable’s associated format referred to in the SAS contents. • This example is a numeric variable with yes/no values.

  25. Variable documentation details • Notes: Assignments, modifications, or validations Figure 1-F. Note: See Figure 1, section C in Module 11 Supporting Materials. • Describe any changes made to a variable • List logic for making an assignment or modification to an existing variable. • Specify the logic for how new variables were created. • An assignment might increase or decrease the base. • In this example, assignments were made to subitems np4F14_[a-g] to set values to “no” if np4F11a is “no.” • A new subitem np4F11b_h is created using values from np4F11a and np4F14a_f.

  26. Variable documentation details Figure 1-G. Note: See Figure 1, section C in Module 11 Supporting Materials. • Base: Which respondents asked • Logic is expressed as who isincluded, not who is skipped. • Explains varying n’s due toskip logic. • If “All respondents” is noted, itmeans no one is skipped. • In this example, the respondents asked this item were limited to those who had not been in secondary school in the past year and had specified this service since leaving high school. • However, in the notes column in the previous slide there was an assignment made. • Although they were not asked this question, those who were “no” to np4F11a were assigned a “no” to np4F14_[a-g].

  27. Variable documentation details Figure 1-H. Note: See Figure 1, section C in Module 11 Supporting Materials. • Variable name by wave • Along with the variablename for the currentwave, correspondingvariable names are listedby all other waves. • There may be minor differences in the variables between waves, or an item may not have been asked in another wave. • In this example, there is no corresponding set of variables for this item in Wave 1, and the item is slightly different in Wave 5.

  28. Variable documentation details • Some of the columns noted above contain information not found elsewhere. • “Base” and “Notes” columns are key for understanding the nature of a variable. • Provide documentation about who is included in an item and any changes made to the data. • Particularly important when using CATI data with complex skip logic. • “Variable name by wave” is a resource for finding longitudinal items. • Provides wave-by-wave variable names. • Indicates if item not collected in a given wave and notes if item differs in other waves.

  29. Parent/youth Part 2 documentationdistinctions • Waves 2 to 5 Parent/Youth Survey has a Part 2 that is completed by either the youth or the parent/guardian. • Documentation for Part 2 in these waves includes all sources and variable names. • For each item, variables are listed in the following order: youth item, the parent/guardian item, and a collapsed youth/parent item. • For collapsed items in cases where there is a value for both items, priority is given to the youth value. • Usually there is either a parent/guardian value or a youth value.

  30. Parent/youth Part 2 documentation: Quick look

  31. Parent/youth Part 2 documentation Figure 2-A. Note: See Figure 2, section C in Module 11 Supporting Materials. The item is “Youth has done volunteer or community service in the past 12 months”. np5P8 is the youth item, np5J4 the parent/guardian, and np5P8_J4 is the combined youth/parent guardian item. Data come from interviews (youth item P8 and parent item J4) and mail questionnaires (youth A7a and parent Q20b).

  32. Parent/youth Part 2 documentation Figure 2-B. Note: See Figure 2, section C in Module 11 Supporting Materials. • This example is a numeric variable that has a yes or no value. • Notes: As we have seen in the previous slide, data come from multiple sources. • Youth interview and youth mail questionnaire, parent/guardian interview, abbreviated interview, and mail questionnaires. • Coding of combined item is described.

  33. Parent/youth Part 2 documentation Figure 2-C. Note: See Figure 2, section C in Module 11 Supporting Materials. All youth respondents were asked this question and all Parent Part 2 respondents were asked. There was no youth interview in Wave 1, but otherwise there are corresponding variable names for each wave for youth, parent/guardian, and combined.

  34. Transcript data documentation distinctions • Transcript data are in multiple files. • Each file is documented in a separate section in the transcript data dictionary. • Files are either from source data or are summarized data from course-level transcript data. • Files can have a single record or multiple records per student depending on the type of transcript data.

  35. Transcript data documentation • Source data files • Overall: One record per student with any transcript data. • By year: Multiple records per student with one record for every school year recorded in transcripts. • Course level: Multiple records per student with one record for every course within a grading period. • Summary data files • Overall summary: One record per student with complete transcript data summarizing course taking across all grades attended. • By grade summary: Multiple records per student; one record for every grade attended summarizing course taking within a grade.

  36. Transcript data documentation: Quick look

  37. Supplemental documentation • Transcript dictionary • List of course codes and course categories • Key to composite variable names in summarized data • Parent/youth survey dictionaries • Types of medications • Job codes • Assessment dictionaries • Direct and alternate assessment references • Cross-instrument data dictionary • Decision rules for cross-instrument data

  38. Documentation summary • The data documentation contains a wealth of information organized in a variety of ways. • It is good practice to refer to the data dictionary before proceeding with analysis. • Finding a question in a data collection instrument does not provide enough information about that item. • The data dictionary describes each item, including information about skip logic and modifications made to variable values.

  39. Closing • Topics discussed in this module • Purpose • Data dictionary contents • File specifications • Variable prefix • Missing values • Variable documentation • Variable documentation details • Parent/youth Part 2 documentation distinctions • Transcript data documentation distinctions • Supplemental documentation • Next module: • 12: NLTS2 Documentation: Quick References

  40. Important information • NLTS2 website contains reports, data tables, and other project-related information http://nlts2.org/ • Information about obtaining the NLTS2 database and documentation can be found on the NCES website http://nces.ed.gov/statprog/rudman/ • General information about restricted data licenses can be found on the NCES websitehttp://nces.ed.gov/statprog/instruct.asp • E-mail address: nlts2@sri.com

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