Archives of Sexual Behavior

, Volume 43, Issue 1, pp 21–33 | Cite as

Methods for the Design and Analysis of Relationship and Partner Effects on Sexual Health

  • Brian MustanskiEmail author
  • Tyrel Starks
  • Michael E. Newcomb
Special Section: Sexual Health in Gay and Bisexual Male Couples


Sexual intercourse involves two people and many aspects of sexual health are influenced by, if not dependent on, interpersonal processes. Yet, the majority of sexual health research involves the study of individuals. The collection and analysis of dyadic data present additional complexities compared to the study of individuals. The aim of this article was to describe methods for the study of dyadic processes related to sexual health. One-sided designs, including the PLM, involve a single individual reporting on the characteristics of multiple romantic or sexual relationships and the associations of these factors with sexual health outcomes are then estimated. This approach has been used to study how relationship factors, such as if the relationship is serious or casual, are associated with engagement in HIV risk behaviors. Such data can be collected cross-sectionally, longitudinally or through the use of diaries. Two-sided designs, including the actor–partner interdependence model, are used when data are obtained from both members of the dyad. The goal of such approaches is to disentangle intra- and inter-personal effects on outcomes (e.g., the ages of an individual and his partner may influence sexual frequency). In distinguishable datasets, there is some variable that allows the analyst to differentiate between partners within dyads, such as HIV status in a serodiscordant couple. When analyzing data from these dyads, effects can be assigned to specific types of partners. In exchangeable dyadic datasets, no variable is present that distinguishes between couple members across all dyads. Extensions of these approaches are described.


Dyadic relationships MSM Gay HIV Romantic relationships Sexual health Sexual orientation 



During the preparation of this manuscript, Brian Mustanski was supported as a Principal Investigator on a grant for research on relationships and the health of YMSM from the National Institute of Mental Health (R21MH095413). He was also supported for research on LGBT Health from the William T. Grant Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health, the National Institutes of Health, or the William T. Grant Foundation. This article was an output of a meeting on male couples and sexual health co-organized by Drs. Jeffrey Parsons and Brian Mustanski. We thank anonymous reviewers for their helpful comments on drafts of the article.


  1. Alferes, V. R., & Kenny, D. A. (2009). SPSS programs for the measurement of nonindependence in standard dyadic designs. Behavioral Research Methods, 41, 47–54.CrossRefGoogle Scholar
  2. Barron, D. (1992). The analysis of count data: Over-dispersion and autocorrelation. Sociological Methodology, 22, 179–220.CrossRefGoogle Scholar
  3. Boone, M. R., Cook, S. H., & Wilson, P. (2013). Substance use and sexual risk behavior in HIV-positive men who have sex with men: An episode-level analysis. AIDS and Behavior, 17, 1883–1887.CrossRefPubMedGoogle Scholar
  4. Carpenter, K. M., Stoner, S. A., Mikko, A. N., Dhanak, L. P., & Parsons, J. T. (2010). Efficacy of a web-based intervention to reduce sexual risk in men who have sex with men. AIDS and Behavior, 14, 549–557.PubMedCentralCrossRefPubMedGoogle Scholar
  5. CDC. (2012). HIV surveillance report, 2010. Atlanta, GA: U.S. Department of Health and Human Services.Google Scholar
  6. Clerkin, E. M., Newcomb, M. E., & Mustanski, B. (2011). Unpacking the racial disparity in HIV rates: The effect of race on risky sexual behavior among Black young men who have sex with men (YMSM). Journal of Behavioral Medicine, 34, 237–243.CrossRefPubMedGoogle Scholar
  7. Cook, W. L., & Snyder, D. K. (2005). Analyzing nonindependent outcomes in couple therapy using the actor–partner interdependence model. Journal of Family Psychology, 19, 133–141.CrossRefPubMedGoogle Scholar
  8. Cooper, M. L. (2002). Alcohol use and risky sexual behavior among college students and youth: Evaluating the evidence. Journal of Studies on Alcohol, 14, 101–117.CrossRefGoogle Scholar
  9. Cooper, M. L. (2010). Toward a person × situation model of sexual risk-taking behaviors: Illuminating the conditional effects of traits across sexual situations and relationship contexts. Journal of Personality and Social Psychology, 98, 319–341.CrossRefPubMedGoogle Scholar
  10. Darbes, L. A., & Lewis, M. A. (2005). HIV-specific social support predicts less sexual risk behavior in gay male couples. Health Psychology, 24, 617–622.CrossRefPubMedGoogle Scholar
  11. Donenberg, G. R., Emerson, E., Bryant, F. B., Wilson, H., & Weber-Shifrin, E. (2001). Understanding AIDS-risk behavior among adolescents in psychiatric care: Links to psychopathology and peer relationships. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 642–653.PubMedCentralCrossRefPubMedGoogle Scholar
  12. Downey, L., Ryan, R., Roffman, R., & Kulich, M. (1995). How could I forgot? Inaccurate memories of sexuall intimate moments. Journal of Sex Research, 32, 177–191.CrossRefGoogle Scholar
  13. Gillmore, M., Morrison, D. M., Leigh, B. C., Hoppe, M. J., Gaylord, J., & Rainey, D. T. (2002). Does “high = high risk”? An event-based analysis of the relationship between substance use and unprotected sex among gay and bisexual men. AIDS and Behavior, 6, 361–370.Google Scholar
  14. Goodreau, S. M., Carnegie, N. B., Vittinghoff, E., Lama, J. R., Sanchez, J., Grinsztejn, B., et al. (2012). What drives the US and Peruvian HIV epidemics in men who have sex with men (MSM)? PLoS ONE, 7, e50522.PubMedCentralCrossRefPubMedGoogle Scholar
  15. Gooty, J., & Yammarino, F. J. (2011). Dyads in organizational research: Conceptual issues and multilevel analyses. Organizational Research Methods, 14, 456–483.CrossRefGoogle Scholar
  16. Griffin, D. W., & Gonzalez, R. (1995). Correlational analysis of dyad-level data in the exchangeable case. Psychological Bulletin, 118, 430–439.CrossRefGoogle Scholar
  17. Grov, C., Golub, S. A., Mustanski, B., & Parsons, J. T. (2010). Sexual compulsivity, state affect, and sexual risk behavior in a daily diary study of gay and bisexual men. Psychology of Addictive Behaviors, 24, 487–497.CrossRefPubMedGoogle Scholar
  18. Heagerty, P. J., & Zeger, S. L. (2000). Marginalized multilevel models and likelihood inference (with comments and a rejoinder by the authors). Statistical Science, 15, 1–26.Google Scholar
  19. Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2012). Modeling between-subject and within-subject variances in ecological momentary assessment data using mixed-effects location scale models. Statistics in Medicine, 31, 3328–3336.CrossRefPubMedGoogle Scholar
  20. Hoppe, M. J., Morrison, D. M., Gillmore, M. R., Beadnell, B., Higa, D. H., & Leigh, B. C. (2008). Agreement of daily diary and retrospective measures of condom use. AIDS and Behavior, 12, 113–117.PubMedCentralCrossRefPubMedGoogle Scholar
  21. Hu, F. B., Goldberg, J., Hedeker, D., Flay, B. R., & Pentz, M. A. (1998). Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes. American Journal of Epidemiology, 147, 694–703.CrossRefPubMedGoogle Scholar
  22. Hubbard, A. E., Ahern, J., Fleischer, N. L., Van der Laan, M., Lippman, S. A., Jewell, N., et al. (2010). To GEE or not to GEE: Comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology, 21, 467–474.CrossRefPubMedGoogle Scholar
  23. Judd, C. M., Kenny, D. A., & McClelland, G. H. (2001). Estimating and testing mediation and moderation in within-subject designs. Psychological Methods, 6, 115–134.CrossRefPubMedGoogle Scholar
  24. Karney, B. R., Hops, H., Redding, C. A., Reis, H. T., Rothman, A. J., & Simpson, J. A. (2010). A framework for incorporating dyads in models of HIV-prevention. AIDS and Behavior, 14, 189–203.PubMedCentralCrossRefPubMedGoogle Scholar
  25. Kenny, D., Bolger, N., & Kashy, D. (2002). Traditional methods for estimating multilevel models. In D. S. Moskowitz & S. L. Hershberger (Eds.), Modeling intraindividual variability with repeated measures data: Methods and applications (pp. 1–21). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  26. Kenny, D. A., & Cook, W. (1999). Partner effects in relationship research: Conceptual issues, analytic difficulties, and illustrations. Personal Relationships, 6, 433–448.CrossRefGoogle Scholar
  27. Kenny, D. A., & Judd, C. M. (1986). Consequences of violating the independence assumption in analysis of variance. Psychological Bulletin, 99, 422–431.CrossRefGoogle Scholar
  28. Kenny, D. A., Kashy, D., & Bolger, N. (1998). Data analysis in social psychology. In D. Gilbert, S. Fiske, & G. Lindsey (Eds.), Handbook of social psychology (4th ed., pp. 233–265). New York: McGraw-Hill.Google Scholar
  29. Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic data analysis. New York: Guilford Press.Google Scholar
  30. Laurenceau, J. P., & Bolger, N. (2005). Using diary methods to study marital and family processes. Journal of Family Psychology, 19, 86–97.CrossRefPubMedGoogle Scholar
  31. Leigh, B. C. (2002). Alcohol and condom use: A meta-analysis of event-level studies. Sexually Transmitted Diseases, 29, 476–482.CrossRefPubMedGoogle Scholar
  32. Leigh, B. C., Vanslyke, J. G., Hoppe, M. J., Rainey, D. T., Morrison, D. M., & Gillmore, M. R. (2008). Drinking and condom use: Results from an event-based daily diary. AIDS and Behavior, 12, 104–112.PubMedCentralCrossRefPubMedGoogle Scholar
  33. Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using Stata (2nd ed.). College Station, TX: StataCorp LP.Google Scholar
  34. Marshall, A. D., Panuzio, J., Makin-Byrd, K. N., Taft, C. T., & Holtzworth-Munroe, A. (2011). A multilevel examination of interpartner intimate partner violence and psychological aggression reporting concordance. Behavior Therapy, 42, 364–377.PubMedCentralCrossRefPubMedGoogle Scholar
  35. Mitchell, J. W., Harvey, S. M., Champeau, D., & Seal, D. W. (2012). Relationship factors associated with HIV risk among a sample of gay male couples. AIDS and Behavior, 16, 404–411.PubMedCentralCrossRefPubMedGoogle Scholar
  36. Mustanski, B. (2007). The influence of state and trait affect on HIV risk behaviors: A daily diary study of MSM. Health Psychology, 26, 618–626.CrossRefPubMedGoogle Scholar
  37. Mustanski, B., Newcomb, M. E., & Clerkin, E. M. (2011). Relationship characteristics and sexual risk-taking in young men who have sex with men. Health Psychology, 30, 597–605.PubMedCentralCrossRefPubMedGoogle Scholar
  38. Newcomb, M. E., Clerkin, E. M., & Mustanski, B. (2011). Sensation seeking moderates the effects of alcohol and drug use prior to sex on sexual risk in young men who have sex with men. AIDS and Behavior, 15, 565–575.CrossRefPubMedGoogle Scholar
  39. Newcomb, M. E., & Mustanski, B. (2013). Racial differences in same-race partnering and the effects of sexual partnership characteristics on HIV risk in MSM: A prospective sexual diary study. Journal of Acquired Immune Deficiency Syndromes, 62, 329–333.PubMedCentralCrossRefPubMedGoogle Scholar
  40. Parsons, J. T., Starks, T. J., Gamarel, K. E., & Grov, C. (2012). Non-monogamy and sexual relationship quality among same-sex male couples. Journal of Family Psychology, 26, 669–677.CrossRefPubMedGoogle Scholar
  41. Perisse, A. R., Langenberg, P., Hungerford, L., Boulay, M., Charurat, M., Schechter, M., et al. (2010). Egocentric network data provide additional information for characterizing an individual’s HIV risk profile. AIDS, 24, 291–298.PubMedCentralCrossRefPubMedGoogle Scholar
  42. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.Google Scholar
  43. Rodriguez, G., & Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses. Journal of the Royal Statistical Society: Series A (Statistics in Society), 158, 73–89.CrossRefGoogle Scholar
  44. Schroder, K. E., Carey, M. P., & Vanable, P. A. (2003). Methodological challenges in research on sexual risk behavior: II. Accuracy of self-reports. Annals of Behavioral Medicine, 26, 104–123.PubMedCentralCrossRefPubMedGoogle Scholar
  45. Selig, J. P., McNamara, K. A., Card, N. A., & Little, T. D. (2008). Techniques for modeling dependency in interchangeable dyads. In N. A. Card, J. P. Selig, & T. D. Little (Eds.), Modeling dyadic and interdependent data in the developmental and behavioral sciences (pp. 191–212). New York: Taylor & Francis Group.Google Scholar
  46. Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.CrossRefPubMedGoogle Scholar
  47. Starks, T. J., Gamarel, K. E., & Johnson, M. O. (2013). Relationship characteristics and HIV transmission risk in same-sex male couples in HIV serodiscordant relationships. Archives of Sexual Behavior. doi: 10.1007/s10508-013-0216-8.
  48. Sullivan, P. S., Salazar, L., Buchbinder, S., & Sanchez, T. H. (2009). Estimating the proportion of HIV transmissions from main sex partners among men who have sex with men in five US cities. AIDS, 23, 1153–1162.CrossRefPubMedGoogle Scholar
  49. Sunner, L. E., Walls, C., Blood, E. A., Mehta, C. M., & Shrier, L. A. (2013). Feasibility and utility of momentary sampling of sex events in young couples. Journal of Sex Research, 50, 688–696.CrossRefPubMedGoogle Scholar
  50. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Boston: Pearson Education.Google Scholar
  51. Vosburgh, H. W., Mansergh, G., Sullivan, P. S., & Purcell, D. W. (2012). A review of the literature on event-level substance use and sexual risk behavior among men who have sex with men. AIDS and Behavior, 16, 1394–1410.CrossRefPubMedGoogle Scholar
  52. Weinhardt, L. S., & Carey, M. P. (2000). Does alcohol lead to sexual risk behavior? Findings from event-level research. Annual Review of Sex Research, 11, 125–157.PubMedCentralPubMedGoogle Scholar
  53. Zea, M. C., Reisen, C. A., Poppen, P. J., & Bianchi, F. T. (2009). Unprotected anal intercourse among immigrant Latino MSM: The role of characteristics of the person and the sexual encounter. AIDS and Behavior, 13, 700–715.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Brian Mustanski
    • 1
    Email author
  • Tyrel Starks
    • 2
    • 3
  • Michael E. Newcomb
    • 1
  1. 1.Department of Medical Social SciencesNorthwestern UniversityChicagoUSA
  2. 2.Department of PsychologyPace UniversityNew YorkUSA
  3. 3.The Center for HIV/AIDS Educational Studies and Training (CHEST)New YorkUSA

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