We propose a dyadic Item Response Theory (dIRT) model for measuring interactions of pairs of individuals when the responses to items represent the actions (or behaviors, perceptions, etc.) of each individual (actor) made within the context of a dyad formed with another individual (partner). Examples of its use include the assessment of collaborative problem solving or the evaluation of intra-team dynamics. The dIRT model generalizes both Item Response Theory models for measurement and the Social Relations Model for dyadic data. The responses of an actor when paired with a partner are modeled as a function of not only the actor’s inclination to act and the partner’s tendency to elicit that action, but also the unique relationship of the pair, represented by two directional, possibly correlated, interaction latent variables. Generalizations are discussed, such as accommodating triads or larger groups. Estimation is performed using Markov-chain Monte Carlo implemented in Stan, making it straightforward to extend the dIRT model in various ways. Specifically, we show how the basic dIRT model can be extended to accommodate latent regressions, multilevel settings with cluster-level random effects, as well as joint modeling of dyadic data and a distal outcome. A simulation study demonstrates that estimation performs well. We apply our proposed approach to speed-dating data and find new evidence of pairwise interactions between participants, describing a mutual attraction that is inadequately characterized by individual properties alone.

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions.

When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares, and conduct inference using “dyadic-robust” variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because indirect links may exist due to network connections, generating correlated outcomes. Hence, “dyadic-robust” estimators can be biased in such situations. We develop a consistent variance estimator for such contexts by leveraging results in network statistics. Our estimator has good finite-sample properties in simulations, while allowing for decay in spillover effects. We illustrate our message with an application to politicians’ voting behavior when they are seating neighbors in the European Parliament.

Quantitative empirical inquiry in international relations often relies on dyadic data. Standard analytic techniques do not account for the fact that dyads are not generally independent of one another. That is, when dyads share a constituent member (e.g., a common country), they may be statistically dependent, or “clustered.” Recent work has developed dyadic clustering robust standard errors (DCRSEs) that account for this dependence. Using these DCRSEs, we reanalyzed all empirical articles published in International Organization between January 2014 and January 2020 that feature dyadic data. We find that published standard errors for key explanatory variables are, on average, approximately half as large as DCRSEs, suggesting that dyadic clustering is leading researchers to severely underestimate uncertainty. However, most (67% of) statistically significant findings remain statistically significant when using DCRSEs. We conclude that accounting for dyadic clustering is both important and feasible, and offer software in R and Stata to facilitate use of DCRSEs in future research.

Intensity of love was examined in a representative community sample of 2,988 couples, from the Region of Madrid (Spain). Gender differences revealed no significant differences in intensity of love. Twenty-five percent of the participants who were in a relationship of less than 10 years and eleven percent of the participants in a relationship of more than 30 years reported being “very intensely in love” F(3, 5.972) = 191.32, p < .001, ηp 2 = 0.08. The cognitive variables, thinking positively about the partner and thinking about the partner when apart, were significant predictors of the intensity of long-term love for both men and women. Affection was also positively associated with intense love for men (r = .47, p < .001) and women (r = .52, p < .001); sexual intercourse was for women (r = .36, p < .001) but not for men. Wanting to know the whereabouts of partner was associated with the intensity of love for women (r = .15, p < .05), but not for men. Engaging in novel activities with partner was not associated with intense love for male or females. As predicted, love declined significantly in the first decade of marriage, but not much after that. The percentage of men and women who reported being intensely in love was lower than that obtained in the U.S.

This study explored how perceptions of resource control levels are related to perceptions of friendship functioning in late adolescent friendship dyads (92 female dyads, 43 male dyads; mean age = 19.59 years). Resource control, relationship influence, and positive and negative friendship features were determined using self- and friend-reports. Contrary to expectations, a latent actor-partner interdependence model (Olsen & Kenny, 2006) revealed uniformly positive effects of perceived resource control on friendship functioning. Perceived resource control was positively associated with positive friendship features and unrelated to negative friendship features for both friends. Results highlight the importance of studying friendship within the context of power and considering relationships and power as complementary rather than opposing elements of the social system.

Background: Caregivers of individuals with dementia are biased in their rating of mental health measures of the care receiver. This study examines caregiver burden and depression as predictors of this bias for mild cognitive impairment and mild Alzheimer's disease in different domains.

Methods: The sample consisted of 202 persons: 60 with mild cognitive impairment, 41 with mild Alzheimer's disease, and 101 caregivers. Discrepancy scores were calculated by subtracting the mean caregiver score from the respective mean patient score on the following assessment instruments: the Geriatric Depression Scale, Apathy Evaluation Scale, Bayer-Activities of Daily Living Scale, and Quality of Life-AD scale. Caregiver burden and depression were assessed by the Zarit Burden Interview and the Center for Epidemiologic Studies Depression Scale.

Results: Intraclass correlation coefficients were low for apathy (0.38), daily functioning (0.38), and quality of life (0.30) and moderate for depression (0.49). These domains showed negative rating discrepancies, which indicates caregiver rating bias for all four domains. Regression analyses revealed that caregiver burden significantly contributed to explaining these discrepancies in the domains apathy, daily functioning, and quality of life.

Conclusion: Caregiver rating bias can be attributed to caregiver burden. When caregiver burden is present, data based on caregiver ratings should therefore be interpreted with caution.