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A Demonstration of the Generalizability of Twin-based Research on Antisocial Behavior

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Abstract

Researchers typically analyze samples of twin pairs in order to decompose trait variance into genetic and environmental components. This methodological technique, referred to as twin-based research, rests on several assumptions that must be satisfied in order to produce unbiased results. While research has analyzed the tenability of certain assumptions such as equal environments, less attention has been given to whether results gleaned from samples of twins generalize to the broader population of non-twins. The current study analyzed data drawn from the National Longitudinal Study of Adolescent Health and findings suggested twins do not systematically differ from the general population of non-twins on many measures of behavior and development. Furthermore, the effects of specific covariates on measures of antisocial behavior did not appear to differ across twin status. In sum, evidence concerning the etiology of antisocial behavior (e.g., heritability estimates) gleaned from twin-based research is likely to generalize to the non-twin population.

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Notes

  1. There are at least three ways to identify twins in the Add Health data: (1) use the “twin sample flag”; (2) use information gleaned from question 23 in the school interview (“Are you a twin?”); or (3) use the “pairs” data. We elected to utilize the pairs data to identify twins because these respondents are confirmed to have been part of a twin pair by the Add Health researchers. As a sensitivity analysis, however, we combined information from the twin sample flag with information gleaned from the pairs data. Specifically, we used the twin sample flag variable as the primary twin identifier but anyone who was uniquely identified as a twin in the pairs data was also coded as a twin in the analysis. This coding scheme identified a larger number of twins than any one measure alone and a cross-tab between the twin sample flag and question 23 from the school survey resulted in only 14 cases appearing in the off-diagonals (suggesting a reporting error on part of the respondent during the in-school surveys). When this alternative twin identification scheme was utilized in the analysis, the overall pattern of findings was substantively similar to those presented in the text and tables. Any difference in findings that emerged when using this alternative twin identification scheme is noted in the paper.

  2. Due to the oversampling of twins, the weighted results are reported here. Differences that emerged when analyzing unweighted cases (i.e., including cases with missing weights/cluster variables) are noted in the paper.

  3. Ideally, standard errors would also be adjusted to account for twins being clustered within families. Based on Add Health recommendations (Chantala and Tabor 1999), however, the sample selection was assumed to be conducted “with replacement.” Chantala and Tabor (1999: 6) note, “The information needed to make finite population corrections for analyzing the dataset as a ‘without replacement design’ is not available.” Because the sample was assumed to be conducted with replacement, the “survey design” package in Stata 12.0 (i.e., svyset) does not take into consideration any secondary sampling unit information (i.e., a request to adjust standard errors based on a family ID variable). More on this point in the Discussion section.

  4. When the alternative twin identification strategy was employed (see footnote 1) two additional differences emerged between twins and non-twins (only those which passed the Bonferroni correction [0.05/27 = 0.0019] are reported): twins displayed slightly lower verbal IQ scores (p = 0.0005) and twins were more likely to be Black (p < 0.001) than non-twins. The following variables/scales did not differ across twins and non-twins when the alternative twin identification scheme was used: victimization (p = 0.77), number of sexual partners (p = 0.005), and maternal age at childbirth (p = 0.006).

  5. When the sampling weight and cluster variables were omitted, the following variables were significantly different across twin status after a Bonferroni correction (0.05/27 = 0.0019): victimization (twins reported less victimization), network popularity (twins were more popular), number of sexual partners (twins reported fewer sexual partners), biological maturity (twins reported less biological maturity), birth weight (twins had lower birth weight), maternal age at childbirth (twins were born to older mothers), parental permissiveness (twins reported less permissive parents), and maternal attachment (twins reported more attachment).

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Acknowledgments

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

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  1. School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA

    J. C. Barnes

  2. College of Criminal Justice, Sam Houston State University, P.O. Box 2296, Huntsville, TX, 77341, USA

    Brian B. Boutwell

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  1. J. C. Barnes
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  2. Brian B. Boutwell
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Correspondence to J. C. Barnes.

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Edited by Sarah Medland.

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Barnes, J.C., Boutwell, B.B. A Demonstration of the Generalizability of Twin-based Research on Antisocial Behavior. Behav Genet 43, 120–131 (2013). https://doi.org/10.1007/s10519-012-9580-8

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