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Examining Same-Sex Couples Using Dyadic Data Methods

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Analytical Family Demography

Part of the book series: The Springer Series on Demographic Methods and Population Analysis ((PSDE,volume 47))

Abstract

The increasing availability of population-level data on same-sex couples has given family demographers greater insight into the characteristics of same-sex couples in the United States as well as the implications of gender and sexuality for individual well-being across multiple outcomes. Whereas most nationally representative research on same-sex couples focuses on individuals, surveys such as the National Health Interview Survey (NHIS) can be used to construct couple-level data files, enabling researchers to consider how couple-level indicators and variables for both partners affect individual well-being. In this chapter, we detail the benefits of dyadic data methods for research on couples and demonstrate a dyadic data analysis of 1262 same-sex couples and 113,642 different-sex couples derived from the 2012 to 2016 NHIS person-level files. Specifically, we examine the effects of respondent gender × partner gender on: reported health status; partner education and reported health; and union status and reported health among those in same-sex versus different-sex couples. Our results suggest that analysis of same-sex couples using dyadic data methods yields more nuanced results than analyses of individuals. For instance, there is a significant positive association between partner college degree and reported health status that is greater in magnitude for women than men—but only if women are in different-sex relationships. In addition, cohabitation and marriage are similarly associated with health for men in same-sex relationships, but cohabitation is associated with poorer health for men and women in different-sex relationships. We conclude with suggestions for future research using dyadic data methods for research on couples.

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Notes

  1. 1.

    This also implies that standard panel-data regression approaches may be adapted for this purpose.

  2. 2.

    The results for the ordered probit models are available upon request.

  3. 3.

    Stata and R code for carrying out this analysis is available in the Appendix.

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Author information

Authors and Affiliations

  1. Department of Sociology, Louisiana State University, Baton Rouge, LA, USA

    Rhiannon A. Kroeger

  2. Department of Sociology and Population Research Center, The University of Texas at Austin, Austin, TX, USA

    Daniel A. Powers

Authors
  1. Rhiannon A. Kroeger
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  2. Daniel A. Powers
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Correspondence to Rhiannon A. Kroeger .

Editor information

Editors and Affiliations

  1. Population Research Institute, Penn State University, University Park, PA, USA

    Robert Schoen

Appendix 1: Syntax for Regression Analyses

Appendix 1: Syntax for Regression Analyses

1.1 Stata Syntax

*Empty Model for ICC mixed health || cid:, cov(exc) reml estat icc ****************************************************************** *Table 3 sort cid pid *M0 empty mixed health || cid:, nocon res(exc) reml

*M1 No Controls- No Interaction mixed health i.fem i.pfem || cid:, nocon res(exc) reml outreg2 using "table3", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) replace

*M2 No Controls- Add Interaction mixed health i.fem##i.pfem || cid:, nocon res(exc) reml outreg2 using "table3", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append

*WM lincom _cons+1.fem *MW lincom _cons+1.pfem *WW lincom _cons+1.fem+1.pfem+1.fem#1.pfem

*M3 Add dyad level control vars mixed health i.fem##i.pfem i.cohab i.numkids || cid:, nocon res(exc) reml outreg2 using "table3", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append

*M4 Add Respondent Controls mixed health i.fem##i.pfem i.cohab i.numkids i.coldeg i.race c.cage_p || cid:, nocon res(exc) reml outreg2 using "table3", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append

*M5 Add Partner Controls mixed health i.fem##i.pfem i.cohab i.numkids i.coldeg i.race c.cage_p /// i.coldeg_pid i.race_pid c.cage_p_pid || cid:, nocon res(exc) reml outreg2 using "table3", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append ***************************************************************** *Table 4 *M1- no interactions: mixed health i.fem i.pfem i.coldeg_pid i.coldeg i.race i.race_pid /// c.cage_p c.cage_p_pid i.cohab i.numkids || cid:, nocon res(exc)reml outreg2 using "table4", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) replace *M2- fem##coldeg mixed health i.fem i.pfem i.coldeg_pid i.fem##i.coldeg_pid i.coldeg i.race i.race_pid /// c.cage_p c.cage_p_pid i.cohab i.numkids || cid:, nocon res(exc) reml outreg2 using "table4", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append *M3- pfem##coldeg mixed health i.fem i.pfem i.coldeg_pid i.pfem##i.coldeg_pid i.coldeg i.race i.race_pid /// c.cage_p c.cage_p_pid i.cohab i.numkids || cid:, nocon res(exc) reml outreg2 using "table4", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append *M4- fem##pfem##coldeg mixed health i.fem##i.pfem##i.coldeg_pid i.coldeg i.race i.race_pid /// c.cage_p c.cage_p_pid i.cohab i.numkids || cid:, nocon res(exc) reml outreg2 using "table4", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append *effect for MM is 1.coldeg_pid *effect for WM lincom 1.coldeg_pid+1.fem#1.coldeg_pid *effect for MW lincom 1.coldeg_pid+1.pfem#1.coldeg_pid *effect for WW lincom 1.coldeg_pid+1.fem#1.coldeg_pid+1.pfem#1. coldeg_pid+1.fem#1.pfem#1.coldeg_pid *WW vs MM lincom (1.coldeg_pid)-(1.coldeg_pid+1.fem#1.coldeg_pid+1.pfem#1. coldeg_pid+1.fem#1.pfem#1.coldeg_pid) *WW vs MW lincom (1.coldeg_pid+1.pfem#1.coldeg_pid)-(1.coldeg_pid+1.fem#1. coldeg_pid+1.pfem#1.coldeg_pid+1.fem#1.pfem#1.coldeg_pid) *WW vs WM lincom (1.coldeg_pid+1.fem#1.coldeg_pid+1.pfem#1.coldeg_pid+1. fem#1.pfem#1.coldeg_pid)-(1.coldeg_pid+1.fem#1.coldeg_pid) *MW vs WM lincom (1.coldeg_pid+1.pfem#1.coldeg_pid)-(1.coldeg_pid+1. fem#1.coldeg_pid) ***************************************************************** *Table 5 *M1- no interactions: mixed health i.fem i.pfem i.cohab /// i.coldeg i.coldeg_pid i.race i.race_pid c.cage_p c.cage_p_pid i. numkids /// || cid:, nocon res(exc) reml outreg2 using "table5", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) replace *M2- i.fem##i.cohab mixed health i.fem i.pfem i.cohab i.fem#i.cohab /// i.coldeg i.coldeg_pid i.race i.race_pid c.cage_p c.cage_p_pid i. numkids /// || cid:, nocon res(exc) reml outreg2 using "table5", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append *M3- i.pfem##i.cohab mixed health i.fem i.pfem i.cohab i.pfem#i.cohab /// i.coldeg i.coldeg_pid i.race i.race_pid c.cage_p c.cage_p_pid i. numkids /// || cid:, nocon res(exc) reml outreg2 using "table5", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append *M4- i.fem##i.pfem##i.cohab mixed health i.fem##i.pfem##i.cohab /// i.coldeg i.coldeg_pid i.race i.race_pid c.cage_p c.cage_p_pid i.numkids /// || cid:, nocon res(exc) reml outreg2 using "table5", excel symbol(***, **, *, +) alpha(0.001, 0.01, 0.05, 0.10) dec(2) append *effect for MM is 1.cohab *effect for WM lincom 1.cohab+1.fem#1.cohab *effect for MW lincom 1.cohab+1.pfem#1.cohab *effect for WW lincom 1.cohab+1.fem#1.cohab+1.pfem#1.cohab+1.fem#1.pfem#1.cohab *WW vs MM lincom (1.cohab)-(1.cohab+1.fem#1.cohab+1.pfem#1.cohab+ 1.fem#1.pfem#1.cohab) *WW vs MW lincom (1.cohab+1.pfem#1.cohab)-(1.cohab+1.fem#1.cohab+1.pfem#1. cohab+1.fem#1.pfem#1.cohab) *WW vs WM lincom (1.cohab+1.fem#1.cohab+1.pfem#1.cohab+1.fem#1.pfem#1. cohab)-(1.cohab+1.fem#1.cohab) *MW vs WM lincom (1.cohab+1.pfem#1.cohab)-(1.cohab+1.fem#1.cohab)

1.2 R Syntax

# TABLE 3 # M1.a <- lmer(health ~ 1 + (1 | cid), REML=TRUE, dat) summary(M1.a) # takes a while on this M1.b <- gls(health ~ 1, correlation = corCompSymm(form = ~1 | cid), dat) summary(M1.b) # faster M1.c <- geeglm(health ~ 1, id=cid, corstr="exchangeable", family='gaussian', data=dat ) summary(M1.c) M1 <- gls(health ~ fem + pfem, correlation = corCompSymm(form = ~1 | cid), dat) summary(M1) M2 <- gls(health ~ fem*pfem, correlation = corCompSymm(form = ~1 | cid), dat) summary(M2) M3 <- gls(health ~ fem*pfem + cohab + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M3) M4 <- gls(health ~ fem*pfem + cohab + numkids + race + cage_p, correlation = corCompSymm(form = ~1 | cid), dat) summary(M4) M5 <- gls(health ~ fem*pfem + cohab + numkids + race + cage_p + coldeg_pid + race_pid + cage_p_pid, correlation = corCompSymm(form = ~1 | cid), dat) summary(M5) # # TABLE 4 # #M3.1- no interactions: M3.1 <- gls(health ~ fem + pfem + coldeg + coldeg_pid + race + race_pid + cage_p + cage_p_pid + cohab + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M3.1) #M3.2- femXcoldeg M3.2<- gls(health ~ fem + pfem + coldeg + coldeg_pid + fem*coldeg_pid + ace + race_pid + cage_p + rcage_p_pid + cohab + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M3.2) #M3- pfemXcoldeg M3.3<- gls(health ~ fem + pfem + coldeg + coldeg_pid + pfem*coldeg_pid + race + race_pid + cage_p + age_p_pid + cohab + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M3.3) #M4- femXpfemXcoldeg M3.4<- gls(health ~ pfem + coldeg + coldeg_pid + fem*pfem*coldeg_pid + race + race_pid + cage_p + cage_p_pid + cohab + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M3.4) # # TABLE 5 # #M1- All Controls- no interactions: M4.1 <- gls(health ~ fem + pfem + cohab + coldeg + coldeg_pid + race + race_pid + cage_p + cage_p_pid + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M4.1) #M2- femXcohab M4.2<- gls(health ~ fem + pfem + cohab + fem*cohab + coldeg + coldeg_pid + race + race_pid + cage_p + cage_p_pid + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M4.2) #M3- pfemXi.cohab M4.3<- gls(health ~ fem + pfem + cohab + pfem*cohab + coldeg + coldeg_pid + race + race_pid + cage_p + cage_p_pid + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M4.3) #M4- femXpfemXcohab M4.4<- gls(health ~ fem*pfem*cohab + coldeg + coldeg_pid + race + race_pid + cage_p + cage_p_pid + numkids, correlation = corCompSymm(form = ~1 | cid), dat) summary(M4.4) # equiv M4.4a <- geeglm(health ~ fem*pfem*cohab + coldeg + coldeg_pid + race + race_pid + cage_p + cage_p_pid + numkids, id=cid, corstr="exchangeable", family='gaussian', data=dat ) summary(M4.5a)

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Kroeger, R.A., Powers, D.A. (2019). Examining Same-Sex Couples Using Dyadic Data Methods. In: Schoen, R. (eds) Analytical Family Demography. The Springer Series on Demographic Methods and Population Analysis, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-319-93227-9_7

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