Multilevel autoregressive models for longitudinal dyadic data

In social and behavioral science, dyadic research has become more and more popular. In case of cross-sectional dyadic data, one can apply the actor-partner interdependence model (APIM). When dyads are measured repeatedly over time, applied researchers are often hesitant to analyze such data due to the statistical complexity. In this paper, we introduce a user-friendly Shiny-application, called the LDDinSEM-application. The app automatically fits the lagged dependent actor-partner interdependence model (LD-APIM), a multilevel autoregressive model extension of the APIM within the structural equation modeling (SEM) framework. The application allows the researcher to investigate the effects of an antecedent on an outcome, given the previous outcome. We illustrate the app using an empirical example assessing the actor and partner effects of positive relationship feelings on next day's intimacy in heterosexual couples

'I think you understand me' : studying the associations between actual, assumed, and perceived understanding within couples

The current study examined the associations between actual, assumed, and perceived understanding and partners' levels of dyadic adjustment. One hundred fifty-two couples provided questionnaire data (assumed and perceived understanding), participated in a videotaped conflict interaction, and in a video-review task to assess actual understanding (empathic accuracy). The data were analyzed by means of the Actor-Partner Interdependence Model. The results suggest that (a) some aspects of how well someone assumes that (s)he has understood the partner during a preceding conflict interaction were positively associated with his/her own objective level of understanding (actor effect), (b) that someone's perception of how understood (s)he feels was not associated with the partner's objective level of understanding (partner effect), and (c) perceived understanding, but not actual understanding, was positively associated with dyadic adjustment

Multilevel autoregressive models when the number of time points is small

The multilevel autoregressive model disentangles unobserved heterogeneity from state-dependence. Statistically, the random intercept accounts for the dependence of all measurements at different time points on an observed underlying factor, while the lagged dependent predictor allows the outcome to depend on the outcome at the previous time point. In this paper, we consider different implementations of the simplest multilevel autoregressive model, and explore how each of them deals with the endogeneity assumption and the initial conditions problem. We discuss the performance of the no centering approach, the manifest centering approach, and the latent centering approach in the setting where the number of time points is small. We find that some commonly used approaches show bias for the autoregressive parameter. When the outcome at the first time point is considered predetermined, the no centering approach assuming endogeneity performs best

The actor-partner interdependence model for longitudinal dyadic data : an implementation in the SEM framework

In dyadic research, the actor-partner interdependence model (APIM) is widely used to model the effect of a predictor measured across dyad members on one's own and one's partner outcome. When such dyadic data are measured repeatedly over time, both the non-independence within couples and the non-independence over time need to be accounted for. In this paper, we present a longitudinal extension of the APIM, the L-APIM, that allows for both stable and time-varying sources of non-independence. Its implementation is readily available in multilevel software, such as proc mixed in SAS, but is lacking in the structural equation modeling (SEM) framework. We tackle the computational challenges associated with its SEM-implementation and propose a user-friendly free application for the L-APIM, which can be found at . As an illustration, we explore the actor and partner effects of positive relationship feelings on next day's intimacy using 3-week diary data of 66 heterosexual couples

Indistinguishability Tests in the Actor-Partner Interdependence Model

When considering dyadic data, one of the questions is whether the roles of the two dyad members can be considered equal. This question may be answered empirically using indistinguishability tests in the actor-partner interdependence model. In this paper several issues related to such indistinguishability tests are discussed: the difference between maximum likelihood and restricted maximum likelihood based tests for equality in variance parameters; the choice between the structural equation modelling and multilevel modelling framework; and the use of sequential testing rather than one global test for a set of indistinguishability tests. Based on simulation studies, we provide guidelines for best practice. All different types of tests are illustrated with cross-sectional and longitudinal data, and corroborated with corresponding R code.status: publishe

Personality Pathology and Relationship Satisfaction in Dating and Married Couples

Personality disorders (PDs) are inherently associated with deficits in relating to other people. Previous research has shown consistent negative associations between categorical PD symptoms and relationship satisfaction. The present studies extend on these findings by examining the role of maladaptive traits in a number of ways. Self- and partner-reported maladaptive traits of both partners are included. Moreover, the present studies add a couple-centered approach by investigating the effects of actual similarity, perceptual similarity, and perceptual accuracy of the maladaptive trait profile on relationship satisfaction. PDs are conceptualized using 2 dimensional maladaptive trait models, that is, the Dimensional Assessment of Personality Pathology—Basic Questionnaire in Study 1 and the Personality Inventory for DSM–5 in Study 2. A total of 167 heterosexual couples participated in Study 1 and 52 heterosexual couples in Study 2. The actor–partner interdependence model was used to examine the associations between traits and relationship satisfaction, whereas the coefficient of profile agreement was used for the couplecentered analyses. Overall, results showed that the presence of maladaptive traits within romantic relationships has a detrimental effect on relationship satisfaction. Self-ratings on maladaptive traits, how we perceive our partners, and how we are perceived by our partners on maladaptive traits make significant contributions to our relationship (dis)satisfaction. Among the maladaptive traits, negative affect and detachment were most consistently negatively associated with relationship satisfaction. The couple-centered perspective showed less explanatory value but nontrivial associations between perceptual similarity and relationship satisfaction were found in Study 2.status: publishe