Although a significant nonprobability that is few (qualitative and quantitative) consist of information from both lovers in relationships, a number of these research reports have analyzed individuals as opposed to adopting techniques that can analyze dyadic information (for quantitative exceptions, see Clausell & Roisman, 2009; Parsons, Starks, Gamarel, & Grov, 2012; Totenhagen et al., 2012; for qualitative exceptions, see Moore, 2008; Reczek & Umberson, 2012; Umberson et al, in press). Yet leading household scholars call to get more research that analyzes dyadic-/couple-level data (Carr & Springer, 2010). Dyadic data and techniques offer a promising strategy for learning same- and different-sex couples across gendered relational contexts and for further considering how gender identity and presentation matter across and within these contexts. We have now touch on some unique aspects of dyadic information analysis for quantitative studies of same-sex partners, but we refer readers elsewhere for comprehensive guides to analyzing quantitative dyadic information, in both basic (Kenny, Kashy, & Cook, 2006) and especially for same-sex partners (Smith, Sayer, & Goldberg, 2013), as well as for analyzing qualitative dyadic information (Eisikovits & Koren, 2010).
Many methods to analyzing dyadic information need that users of a dyad be distinguishable from one another (Kenny et al., 2006). Studies that examine gender impacts in different-sex partners can differentiate dyad people on such basis as intercourse of partner, but intercourse of partner may not be utilized to tell apart between people in same-sex dyads. To estimate sex results in multilevel models comparing exact same- and different-sex partners, scientists may use the factorial technique developed by T. V. Western and peers (2008). This process calls when it comes to addition of three gender results in a provided model: (a) gender of respondent, (b) sex of partner, and (c) the relationship between sex of respondent and sex of partner. Goldberg and peers (2010) utilized this technique to illustrate gendered characteristics of observed parenting abilities and relationship quality across 
exact exact same- and different-sex partners before and after use and discovered that both exact same- and different-sex moms and dads encounter a decrease in relationship quality through the very very first several years of parenting but that females experience steeper decreases in love across relationship types.
Dyadic diary information
Dyadic journal methods might provide utility that is particular advancing our comprehension of gendered relational contexts. These procedures include the number of information from both lovers in a dyad, typically via brief day-to-day questionnaires, during a period of times or days (Bolger & Laurenceau, 2013). This method is well suited for examining relationship dynamics that unfold over short periods of the time ( ag e.g., the result of day-to-day anxiety amounts on relationship conflict) and contains been utilized extensively within the scholarly research of different-sex partners, in specific to look at gender variations in relationship experiences and effects. Totenhagen et al. (2012) additionally used journal information to examine gents and ladies in same-sex couples and discovered that daily anxiety had been dramatically and adversely correlated with relationship closeness, relationship satisfaction, and intimate satisfaction in similar methods for males and ladies. Diary information gathered from both lovers in same- and different-sex contexts would make it easy for future studies to conduct longitudinal analyses of daily changes in reciprocal relationship characteristics and results along with to take into account whether and just how these procedures differ by gendered relationship context consequently they are potentially moderated by gender identity and sex presentation.
Quasi-Experimental Designs
Quasi-experimental designs that test the results of social policies on couples and individuals in same-sex relationships provide another research strategy that is promising. These designs offer a way to deal with concerns of causal inference by taking a look at information across spot (in other terms., across state and nationwide contexts) and over time—in particular, pre and post the utilization of exclusionary ( e.g., same-sex wedding bans) or inclusionary ( ag e.g., legalization of same-sex wedding) policies (Hatzenbuehler et al., 2012; Hatzenbuehler, Keyes, & Hasin, 2009; Hatzenbuehler, McLaughlin, Keyes, & Hasin, 2010; see Shadish, Cook, & Campbell, 2002, regarding quasi-experimental techniques). This process turns the methodological challenge of the constantly changing appropriate landscape into an exciting chance to start thinking about exactly just how social policies influence relationships and how this impact can vary across age cohorts. As an example, researchers might test the consequences of policy execution on relationship marriage or quality development across age cohorts.