Frequently Asked Questions

General

How does the Public-Use data set compare to the Restricted-Use data set?

Public-use data contains all the data from the In-home Interviews, just a smaller sampling. The smaller sample limits deductive disclosure risk. It is 1/3 of the total Add Health Sample Members (AHSM) who fall into the Core and High Education Black samples. (Note: because some AHSMs are in both samples, one cannot simply add the core sample total to the high education black sample total.) Add Health public-use data can be downloaded from the three sources listed on the Add Health Public-Use Data page.

The sample drawing for public-use data is wave specific. New samples are drawn based on the final N at each wave.

3rd of W1	3rd of W2	3rd if W3	3rd of W4
6915	4913	5066	5234
3rd of Core & Hi Ed Bl Samples W1	3rd of Core & Hi Ed Bl Samples W2	3rd of Core & Hi Ed Bl Samples W3	3rd of Core & Hi Ed Bl Samples W4
6504	4834	4882	5114

Public-use data does not contain ID numbers of friends, siblings or romantic partners, so the data cannot be linked. The public-use data also does not contain files on the following. These require a restricted-use contract

1. Obesity and Neighborhood Environment
2. Genetics, disposition,
3. Political context
4. Alcohol density

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How do I determine which data would best fit my research needs?

Add Health has created the following Data Decision chart to help you decide if the public-use data or restricted-use data best fit your research needs, or if your project would be considered an ancillary study.

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How much space do I need to accommodate all Add Health data sets that are available?

The Add Health data require less than 4 GB of storage space, but you will also need to have space available for software and temp files created by the software, depending on your computing configuration. Additionally, 4 to 8 GB RAM are needed for processing.

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Are geocodes included in the restricted-use data?

Geocodes are not available with the restricted-use data because of deductive disclosure concerns. Add Health, however, has established a set of requirements for investigators seeking to add supplemental contextual data to Add Health. 

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What type of geographic data are available?

Region is the only ‘real’ geographical representation in the Add Health restricted-use data files. Nothing below region is available because of deductive disclosure risks. We have pseudo codes for state, county, census tract, and block group.

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Can the Add Health data be linked to Census information (neighborhood)?

Add Health does not provide geocodes with the restricted-use data which would allow you to add your own Census data. Many Census variables have been linked to the Add Health data. Descriptions of Add Health contextual data and the codebooks are available from Contextual Data Files on the Codebooks page.

Add Health has established a set of requirements for investigators seeking to add supplemental contextual data to Add Health. A brief introduction to the Ancillary Study proposal process and costs is available in A Guide to Ancillary Studies in Add Health. For more information check Ancillary Studies page. 

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Can supplemental contextual or biological data be added to Add Health?

Investigators seeking to add supplemental, contextual, or biological data to Add Health may do so under the auspices of an Add Health ancillary study. An ancillary study is any study that derives support from independent funds outside the Add Health Program Project, and does one or more of the following:

• Collects new, original questionnaire data on Add Health respondents.
• Merges secondary data sources onto Add Health respondent or school records and requires personal identifiers (e.g., geocodes) to perform these linkages.
• Collects new biospecimens from Add Health respondents.
• Uses archived biospecimens collected by the Add Health study.

For information on ancillary studies and costs please review A Guide to Ancillary Studies in Add Health. Before considering an ancillary study, review the existing Add Health data sets. For more information check Ancillary Studies page.

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How do I read a SAS export file?

The following SAS commands will allow you to read a SAS export file:

libname in xport ‘/directory path where file is located/SAS export file name’;
data wave1;
set in.SAS dataset name;
run;

For example, the Add Health data set name on the CD is ALLWAVE1.EXP, the internal SAS data set name is ALLWAVE1, and your CD drive is D:

libname in xport ‘d:allwave1.xpt’;
data wave1;
set in.allwave1;
run;

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How do I read a SAS export file with STATA?

If you are using a version of STATA before version 13, use the following STATA command to read a SAS export file.

fdause datasetname.xpt

If you are using STATA 13 and later, use the following STATA command to read a SAS export file.

import sasxport datasetname.xpt

If the SAS file is named data setname.exp, rename the file to data setname.xpt before running the STATA command.

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How do I read a SAS export file with SPSS?

The following SPSS command allows you to read a SAS export file.

GET SAS DATA=”\folder\data setname.xpt”.

If the SAS file is named data setname.exp, rename the file to data setname.xpt before running the SPSS command.

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What numbers should be used for the NIH Inclusion Enrollment Report?

The Wave I inclusion enrollment numbers are available in this example Inclusion Enrollment Report. In addition, see Cumulative Inclusion Enrollment Report.

What are the codes for anti-hypertensive medications?

The codes for the anti-hypertensive medications are:

‘040-047-xxx’ ‘BETA-BLOCKERS’

‘040-049-156’ ‘THIAZIDE DIURETICS’

‘040-042-xxx’ ‘ACE-INHIBITORS’

‘040-043-xxx’ ‘ANTI-ADRENERGICS (peripherally acting)’

‘040-044-xxx’ ‘ANTI-ADRENERGICS (centrally acting)’

‘040-048-xxx’ ‘CALCIUM CHANNEL BLOCKERS’

‘040-053-xxx’ ‘VASODILATORS’

‘040-056-xxx’ ‘AT2 RECEPTOR BLOCKERS’

‘040-055-xxx’ ‘COMBO ANTI-HYPERTENSIVES’

Anti-hypertensive medication codes used in the following article: Nguyen QC, Tabor JW, Entzel PP, Lau Y, Suchindran C, Hussey JM, Halpern CT, Harris KM, Whitsel EA. Discordance in national estimates of hypertension among young adults. Epidemiology 2011;22(4):532-541.

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Sampling and Design Effects

How do I correct for the design effects of the Add Health sampling process?

“Guidelines for Analyzing Add Health Data” discusses how to correct for design effects and the unequal probability of selection to ensure that your analysis results are nationally representative with unbiased estimates.

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What variables from the public-use data should be used to correct for design effects?

“Guidelines for Analyzing Add Health Data” refers to variables from the Add Health restricted-use data.

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How were adolescents identified as eligible for special oversamples for the in-home interview?

An adolescent’s answer to a specific question or questions on the In-School Questionnaire determined his or her eligibility for inclusion in an oversample. For example, an adolescent who marked “Chinese” as his or her Asian or Pacific Islander background was eligible for the Chinese oversample. The genetic oversamples were identified in two ways. All adolescents who indicated they were twins were sampled with certainty. When an adolescent indicated at least one other household member in grades 7 through 12 with whom he or she did not share a biological mother and/or biological father, they were added to the pool of potential half-siblings and other, non-related adolescents. Full siblings were not oversampled.

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Wave I

How many Wave I in-home respondents have in-school questionnaire data?

15,356 of the Wave I in-home respondents also have in-school data.

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What was the response rate for the Wave I school administrator questionnaire?

A total of 132 schools were included in the Add Health Wave I sample. An administrator from each school was asked to complete a questionnaire. The response rate among administrators was 98.5%.

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What was the response rate for the Wave I parent questionnaire?

The parent questionnaire response rate was 85.4% for the child-specific data.

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What is the best way to compute race in the Add Health Wave I in-home data?

Wave I in-home interview variables* used to construct RACE.

H1GI4 Are you of Hispanic or Latino origin?
H1GI6A What is your race? white
H1GI6B What is your race? black or African American
H1GI6C What is your race? American Indian or Native American
H1GI6D What is your race? Asian or Pacific Islander
H1GI6EWhat is your race? other

A single race variable (RACE) was constructed from the six variables listed above. If the respondent answered “yes” to section 1, question 4 (Are you of Hispanic or Latino origin?), that respondent was given a race designation of “Hispanic” and eliminated from any race category that was marked in section 1, question 6 (What is your race?).

In question 6, respondents were able to mark more than one answer, however they were placed in only one race category in the RACE variable. If the respondent marked “black or African American” and any other race, they were designated as black or African American, and eliminated from the other marked categories. The process was repeated for the remaining race categories in the following order: Asian, Native American, other, and white.

* The racial groups for the in-school questionnaire variables are listed in a different order.

Example code:
/* Hispanic or Latino, All Races */
if h1gi4=1 then race=1;
/* Black or African American, Non-Hispanic */
else if h1gi6b=1 then race=2;
/* Asian or Pacific Islander, Non-Hispanic */
else if h1gi6d=1 then race=3;
/* American Indian or Native American, Non-Hispanic */
else if h1gi6c=1 then race=4;
/* Other, Non-Hispanic */
else if h1gi6e=1 then race=5;
/* White, Non-Hispanic */
else if h1gi6a=1 then race=6;

The information provided in the program code repository by the Add Health team is a service to the Add Health research community. It is provided “as is” with no guarantees as to suitability for a particular purpose.

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What is the best way to compute age in the Add Health Wave I in-home data?

To compute a Wave I age variable with the Wave I data, use the following variables and formula:

IMONTH – Month interview completed
IDAY – Day interview completed
IYEAR – Year interview completed
H1GI1M – What is your birth date? month [and year]
H1GI1Y – What is your birth date? [month and] year

The respondent’s age is constructed using the interview completion date and date of birth variables. Because only the month and year of birth are available, 15 is used as the day of birth when calculating age. Consult the Introduction to the Adolescent In-Home Codebook to be sure to take into account the respondents whose birth date and/or interview date is incorrect. Additionally, a few birth dates were corrected during the four waves of data collection so the Wave I date of birth should be compared to the last wave of data for the respondent. The last wave of participation is considered the most correct.

SAS programming code that can be used to construct a Wave I AGE variable using Wave I variables is provided below.

idate=mdy(imonth,iday,iyear); bdate=mdy(h1gi1m,15,h1gi1y); age=int((idate-bdate) / 365.25);

The code to construct Wave I age in Stata is below.

recode h1gilm (96=.), gen (w1bmonth) recode h1gi1y (96=.), gen (w1byear) gen w1bdate = mdy(w1bmonth, 15,1900+w1byear) format w1bdate %d gen w1idate=mdy(imonth, iday,1900+iyear) format w1idate %d gen w1age=int((w1idate-w1bdate)/365.25)

This information is provided by the Add Health team as a service to the Add Health research community. It is provided “as is” with no guarantees as to suitability for a particular purpose.

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What does the Wave I variable COMMID represent?

The COMMID variable groups together the respondents who attend the high school and feeder school that make up the 7 – 12 grade span for the strata.

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Why are there 1,821 respondents without a Grand Sample Weight at Wave I?

The following Wave I cases could not be weighted:

• Cases added in the field.
• Cases selected as a pair (twins, half-sibs) where both were not interviewed.
• Cases without a sample flag.
• Respondents from schools outside of the 80 strata.

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Wave II

What was the response rate for the Wave II school administrator questionnaire?

A total of 132 schools were included in the Add Health Wave II sample. An administrator from each school was asked to complete a questionnaire. The response rate among administrators was 87.0%.

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How do I code gender changes between Wave I and Wave II?

When there is a discrepancy between the Wave I and Wave II gender of a respondent, use the Wave II gender. The restricted-use data include 23 cases in which the Wave I variable BIO_SEX and the Wave II variable BIO_SEX2 do not match. The Wave II data have been confirmed as correct. Wave II includes 7 cases in which the variable SEXFLG2 equals 1. This indicates that the incorrect gender was used to control the questionnaire skips during the interview. The variable BIO_SEX2 was corrected, but answers to questions based on gender will be incorrect.

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Wave III

Where can I find the monograph about biomarkers collected in Wave III?

The monograph “Biomarkers in Wave III of the Add Health Study” outlines relevant procedures, design, and sampling schemes used in the collection of biomarker data, and serves as a user guide for analysis and interpretation.

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How can I obtain a copy of the first release of the Education Data?

The restricted-use Education Data, collected by the Adolescent Health and Academic Achievement Study, is available to Add Health contract holders. For users who already have a contract, contact Add Health to request an order form for the Education Data. A copy of the public-use version of the file can be downloaded from the ICPSR website.

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When I calculate a Wave III respondent age using the birth date (month, 15, year) and date of interview, I do not get the same age for some respondents as the one found in variable CALCAGE3. Why does this happen?

Respondent age calculated during the interviews uses the actual day of birth but is not released with Add Health data. During the Wave III interview, respondent age was calculated by the computer interviewing program and then verified by the respondent. Age discrepancies occur when a respondent is interviewed during his or her birth month.

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How many of the interviewed Wave III public-use sample were originally selected for the core and high education black samples?

Wave III public-use sample = 4,882

Core sample only = 4,490

High education black sample only = 325

Both samples = 67

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Am I allowed to include quotes from the open-ended question on how a mentor helped the young person, asked in Wave III. Am I allowed to put quotes, in my dissertation?

You may not include any open-ended responses in your dissertation as that is a frequency of 1 which is not allowed.

            “In no table should all cases in any row or column be found in a single cell.”

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Wave IV

How many of the interviewed Wave IV public-use sample were originally selected for the core and high education black samples?

Wave IV public-use sample = 5,114

Core sample only = 4,699

High education black sample only = 345

Both samples = 70

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How do I recode the variables for Wave IV, Section 21: Criminal Offending and Victimization?

If your Wave IV in-home interview file is dated before March 2012 it will need to be updated with the following:

The Wave IV, Section 21: Criminal Offending and Victimization variables H4DS13 – H4DS20 contain implausible values for some respondents that need to be recoded. To correct these implausible values, 1) you may request an updated Wave IV data file that contains the recoded values, or 2) you may use the SAS program, Recode Wave IV, Section 21_SAS.pdf, or the Stata program, Recode Wave IV, Section 21_Stata.pdf, with your original Wave IV data file to recode these variables. The program will make the following transformations to the data:

• Temporarily recode values of 6, 8, and 9 for variables H4DS1 –H4DS20 to missing so that these values are not included in the sum of the variables constructed in items 2 and 3.
• Create a variable, VAR1, that is the sum of variables H4DS1 – H4DS11.
• Create another variable, VAR2, that is the sum of variables H4DS13 – H4DS20.
• Recode variables H4DS13 – H4DS20 to missing when VAR1 = 0 and VAR2 = 8.
• There will now be 1,424 observations in the restricted-use data file with a value of missing for variables H4DS13 – H4DS20. The public-use file will have 442 observations with a value of missing.

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Wave V Data Re-release

Three ineligible cases were identified in an earlier release of Wave V data sets. We have amended affected Wave V datasets June 2021 by retaining the three AIDs in the data files, but all related responses have been recoded to be missing values. The following datasets are involved:

Wave V Mixed-Mode Survey (includes Section 16b and Survey Medications)
Wave V Constructed Age
Wave V Mover Distance
Wave I, II, III, IV, & V Grouping Data

The three cases should be removed from data analysis moving forward. This will have no effect on your analyses or use of weights (your N will be reduced by 3 i.e. N=12,297) going forward. Please download the updated datasets from your application in the CPC Data Portal.