A Guide for the Estimation of Gender and Sexual Orientation Effects in Dyadic Data: An Actor-Partner Interdependence Model Approach

West, Tessa V.; Popp, Danielle; Kenny, David A.

doi:10.1177/0146167207311199

Cited by 95 publications

(91 citation statements)

References 41 publications

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“…We used the Actor-Partner Interdependence Model (APIM) as the statistical framework for data analysis (West, Popp, & Kenny, 2008). Data were organized in a pairwise manner; as such, individual records were created, but the data for the respondent and his partner were included together in the same case.…”

Section: Methodsmentioning

confidence: 99%

HIV Transmission Risk among HIV Seroconcordant and Serodiscordant Couples: Dyadic Processes of Partner Selection

et al. 2008

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Selecting sex partners of the same HIV status or serosorting is a sexual risk reduction strategy used by many men who have sex with men. However, the effectiveness of serosorting for protection against HIV is potentially limited. We sought to examine how men perceive the protective benefits of factors related to serosorting including beliefs about engaging in serosorting, sexual communication, and perceptions of risk for HIV. Participants were 94 HIV negative seroconcordant (same HIV status) couples, 20 HIV serodiscordant (discrepant HIV status) couples, and 13 HIV positive seroconcordant (same HIV status) couples recruited from a large gay pride festival in the southeastern US. To account for nonindependence found in the couple-level data, we used multilevel modeling which includes dyad in the analysis. Findings demonstrated that participants in seroconcordant relationships were more likely to believe that serosorting reduces concerns for condom use. HIV negative participants in seroconcordant relationships viewed themselves at relatively low risk for HIV transmission even though monogamy within relationships and HIV testing were infrequent. Dyadic analyses demonstrated that partners have a substantial effect on an individual's beliefs and number of unprotected sex partners. We conclude that relationship partners are an important source of influence and, thus, intervening with partners is necessary to reduce HIV transmission risks.

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Section: Methodsmentioning

confidence: 99%

HIV Transmission Risk among HIV Seroconcordant and Serodiscordant Couples: Dyadic Processes of Partner Selection

et al. 2008

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“…For example, Ledermann, Macho, and Kenny (2011) have expanded the APIM presented by Kenny and Ledermann (2010) to enable the assessment of mediating manifest variables and guidelines have been developed by West, Popp, and Kenny (2008) for investigating different types of moderation effects in dyadic data, while others have investigated moderation with SEM (Kivlighan, Marmarosh, & Hilsenroth, 2014;Maleck & Papp, 2015;Papp, Kouros, & Cummings, 2010, e.g.m,). Wickham and Knee (2012) have also suggested the calculation of an interaction term between X 1 and X 2 (i.e., moderation) and have added two other ratios (h = a/x1x2; c = p/x1x2) in order to improve the methodological-substantive synergy between the APIM and interdependence theory.…”

Section: Recent and Future Applications Of The Apimmentioning

confidence: 99%

How to Use the Actor-Partner Interdependence Model (APIM) To Estimate Different Dyadic Patterns in MPLUS: A Step-by-Step Tutorial

Fitzpatrick¹,

Gareau

Lafontaine³

et al. 2016

TQMP

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Dyadic data analysis with distinguishable dyads assesses the variance, not only between dyads, but also within the dyad when members are distinguishable on a known variable. In past research, the Actor-Partner Interdependence Model (APIM) has been the statistical model of choice in order to take into account this interdependence. Although this method has received considerable interest in the past decade, to our knowledge, no specific guide or tutorial exists to describe how to test an APIM model. In order to close this gap, this article will provide researchers with a step-by-step tutorial for assessing the most recent advancements of the APIM with the use of structural equation modeling (SEM). The present tutorial will also utilize the statistical program MPLUS.

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“…This was done using Amos 21.0 to estimate the APIM effects with mediation (see Ledermann, Macho, & Kenny, 2011;and West, Popp, & Kenny, 2008, for more information on mediation using the APIM). To examine the actor effect, I estimated the direct effect of each participant's CU on their own scores on the mediator (i.e., partner= relationship RU) and outcome (i.e., PRQC scores) variables.…”

Section: Mediation Analysesmentioning

confidence: 99%

Doubt Begets Doubt: Causal Uncertainty as a Predictor of Relational Uncertainty in Romantic Relationships

Boucher

2014

Communication Reports

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The current study explored whether causal uncertainty (CU), or doubts about one's ability to understand causes of social events, predicts relational uncertainty (RU) and, in turn, negative relational outcomes in romantic relationships. Seventy-nine couples completed measures of RU, perceived relationship quality, CU, depression, and attachment style. As predicted, higher levels of CU were associated with more partner and relationship uncertainty, and lower relationship quality, even when controlling for depression and attachment. Mediational analyses further demonstrated that causally uncertain participants' negative perceptions of relationship quality stemmed primarily from doubts about their partner's involvement in the relationship.Knobloch and Solomon (1999, 2002a) proposed that people can experience doubts about the level of involvement in their romantic relationship. Such relational uncertainty (RU) can stem from three separate, but related, sources: (1) the self, which generates doubts about your own involvement in the relationship; (2) the partner, which generates doubts about your partner's level of involvement; and (3) the relationship, which generates doubts about the nature of the relationship itself. Although RU is not inherently detrimental in romantic relationships (see Knobloch, 2007), research suggests that RU tends to have negative, rather than positive

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A Guide for the Estimation of Gender and Sexual Orientation Effects in Dyadic Data: An Actor-Partner Interdependence Model Approach

Cited by 95 publications

References 41 publications

HIV Transmission Risk among HIV Seroconcordant and Serodiscordant Couples: Dyadic Processes of Partner Selection

HIV Transmission Risk among HIV Seroconcordant and Serodiscordant Couples: Dyadic Processes of Partner Selection

How to Use the Actor-Partner Interdependence Model (APIM) To Estimate Different Dyadic Patterns in MPLUS: A Step-by-Step Tutorial

Doubt Begets Doubt: Causal Uncertainty as a Predictor of Relational Uncertainty in Romantic Relationships

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