Please first read the documentation for Links79Pair. That dataset contains the same pairs/rows, but only a subset of the variables/columns.
NOTE: In Nov 2013, the variable naming scheme changed in order to be more consistent across variables. For variables
that are measured separately for both subjects (eg, Gender), the subjects' variable name will have an _S1 or _S2
appended to it. For instance, the variables LastSurvey_S1 and LastSurvey_S2 correspond to the last surveys completed
by the pair's first and second subject, respectively. Similarly, the functions CreatePairLinksDoubleEntered() and
CreatePairLinksSingleEntered() now by default append _S1 and _S2, instead of _1 and _2. However this can be
modified using the 'subject1Qualifier' and 'subject2Qualifier' parameters.
A data frame with 11,075 observations on the following 22 variables. There is one row per unique pair of subjects, irrespective of order.
ExtendedID see the variable of the same name in Links79Pair
SubjectTag_S1 see the variable of the same name in Links79Pair
SubjectTag_S2 see the variable of the same name in Links79Pair
R see the variable of the same name in Links79Pair
RFull This is a superset of R. This includes all the R values we estimated, while R (i.e., the variable above) excludes values like R=0 for Gen1Housemates, and the associated relationships based on this R value (i.e., Gen2Cousins and AuntNieces).
RelationshipPath see the variable of the same name in Links79Pair
EverSharedHouse Indicate if the pair likely live in the same house. This is TRUE for Gen1Housemates, Gen2Siblings, and ParentChild. This is FALSE for AuntNiece and Gen2Cousins
IsMz Indicates if the pair is from the same zygote (ie, they are identical twins/triplets). This variable is a factor, with levels No=0, Yes=1, DoNotKnow=255.
LastSurvey_S1 The year of Subject1's most recently completed survey. This may be different that the survey's administration date.
LastSurvey_S2 The year of Subject2's most recently completed survey. This may be different that the survey's administration date.
RImplicitPass1 The pair's R coefficient, using only implicit information. Interpolation was NOT used.
RImplicit The pair's R coefficient, using only implicit information. Interpolation was used.
RImplicit2004 The pair's R coefficient released in our previous projects (need reference). This variable is provided primarily for previous users wishing to replicate previous analyses.
RExplicitPass1 The pair's R coefficient, using only explicit information. Interpolation was NOT used.
RExplicit The pair's R coefficient, using only explicit information. Interpolation was used.
RExplicitOlderSibVersion The pair's R coefficient, according to the explicit item responses of the older sibling.
RExplicitYoungerSibVersion The pair's R coefficient, according to the explicit item responses of the younger sibling.
RPass1 The pair's estimated R coefficient, using both implicit and explicit information. Interpolation was NOT used. The variable R is identically constructed, but it did use interpolation.
Generation_S1 The generation of the first subject. Values for Gen1 and Gen2 are 1 and 2, respectively.
Generation_S2 The generation of the second subject. Values for Gen1 and Gen2 are 1 and 2, respectively.
SubjectID_S1 The ID value assigned by NLS to the first subject. For Gen1 Subjects, this is their "CaseID" (ie, R00001.00). For Gen2 subjects, this is their "CID" (ie, C00001.00).
SubjectID_S2 The ID value assigned by NLS to the second subject.
MathStandardized_S1 The PIAT-Math score for Subject1. See ExtraOutcomes79 for more information about its source.
MathStandardized_S2 The PIAT-Math score for Subject2.
HeightZGenderAge_S1 The early adult height for Subject1. See ExtraOutcomes79 for more information about its source.
HeightZGenderAge_S2 The early adult height for Subject2.
Will Beasley
Specifies the relatedness coefficient (ie, 'R') between subjects in the same extended family. Each row represents a unique relationship pair. An extended family with \(k\) subjects will have \(k\)(\(k\)-1)/2 rows. Typically, Subject1 is older while Subject2 is younger.
RelationshipPath variable. Code written using this dataset should
NOT assume it contains only Gen2 sibling pairs. See an example of
filtering the relationship category in the in Links79Pair
documentation.
The specific steps to determine the R coefficient will be described in an upcoming publication. The following information may influence the decisions of an applied researcher.
A distinction is made between 'Explicit' and 'Implicit' information. Explicit information comes from survey items that directly address the subject's relationships. For instance in 2006, surveys asked if the sibling pair share the same biological father (eg, Y19940.00 and T00020.00). Implicit information comes from items where the subject typically isn't aware that their responses may be used to determine genetic relatedness. For instance, if two siblings have biological fathers with the same month of death (eg, R37722.00 and R37723.00), it may be reasonable to assume they share the same biological father.
'Interpolation' is our lingo when other siblings are used to leverage insight into the current pair. For example, assume Subject 101, 102, and 103 have the same mother. Further assume 101 and 102 report they share a biological father, and that 101 and 103 share one too. Finally, assume that we don't have information about the relationship between 102 and 103. If we are comfortable with our level of uncertainty of these determinations, then we can interpolate/infer that 102 and 103 are full-siblings as well.
The math and height scores are duplicated from ExtraOutcomes79, but are included here to make some examples more concise and accessible.
Download CSV If you're using the NlsyLinks package in R, the dataset is automatically available. To use it in a different environment, download the csv, which is readable by all statistical software. links-metadata-2017-79.yml documents the dataset version information.
library(NlsyLinks) # Load the package into the current R session.
# olderR <- Links79PairExpanded$RExplicitOlderSibVersion # Declare a concise variable name.
# youngerR <- Links79PairExpanded$RExplicitYoungerSibVersion # Declare a concise variable name.
# plot(jitter(olderR), jitter(youngerR)) # Scatterplot the siblings' responses.
# table( youngerR, olderR) # Table of the relationship between the siblings' responses.
# ftable(youngerR, olderR, dnn=c("Younger's Version", "Older's Version")) # A formatted table.
# write.csv(
# Links79PairExpanded,
# file ='~/NlsyLinksStaging/Links79PairExpanded.csv',
# row.names = FALSE
# )
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