Measurement quality issues in dyadic models of relationships

A working paper in the INSEAD Working Paper Series is intended as a means whereby a faculty researcher's thoughts and fmdings may be communicated to interested readers. The paper should be considered preliminary in nature and may require revision. and our Editor and anonymous reviewers for their very helpful comments on this research. Measurement Quality Issues in Dyadic Models of Relationships Interpersonal relationships are an important and integral part of numerous psychological research agendas. Analytical tools have been created in the last ten years that model dyadic interactions, but measurement issues have not been considered in conjunction with these models, and little is known about their empirical performance under sub-optimal measurement quality conditions. We offer two essential approaches to ascertaining the level of measurement error in the observed indicators of social ties and relationships. The first approach combines latent class and social network models in one integrated framework and allows for the simultaneous study of measurement and dyadic structural issues. The second approach develops a two-staged procedure that should be accessible to all users. In the first stage, a probability model based on latent class analysis is estimated which provides an indication of the measurement quality in the data. In the second stage, traditional social network models are estimated. To investigate the implications of different levels of measurement error for interpreting the nature of the network ties and the dyadic parametric performance, we design a monte carlo experiment. Measurement error is simulated as the likelihood of a binary relational choice being misaccurately classified, where misdiagnoses can result from poor inter-item agreement (i.e., unreliability) or poor inter-rater agreement. The simulation can be used by researchers in combination with the two-stage approach. The results of the simulation provide guidelines for situations when social network models can withstand a reasonable degree of sub-optimal measurement quality and highlight adverse conditions which can significantly affect the performance of the modeling approach. Further, the simulation shows that sample size assists in reducing the chances of making Type II errors, but it does not compensate for biases in parameter estimates in the presence of increasing error. Finally, the measurement and dyadic analytical methods are applied to a real data set describing inter-firm relational activity using multiple raters. Recommendations are offered to guide the researcher in making decisions about research design when using dyadic models.