ABSTRACT

Over the last years, analyzing the dynamic reciprocal relationship between communication processes (i.e. mediated and interpersonal communication) and associated attitudes, beliefs, or behaviors has become more salient in communication research. However, methodological studies that focus on such communication dynamics are rare. In this paper, we present a model that tackles two statistical challenges of interrelated communication dynamics: (1) measuring the reciprocal relationship between communication processes and associated outcomes in distinct sequences and (2) the disaggregation of intraindividual and interindividual effects. To do so, we rely on latent growth models using structural equation modeling and extend them with a piecewise/spline regression approach. We refer to the combined model as the Sequential Latent Growth Model with Structured Residuals (SLGM-SR). Focusing on political communication as well as media effects and selection research, we present an empirical illustration and a modeling strategy by applying the model to short-term campaign panel data collected during the German federal elections in 2013. Further, we discuss (dis)-advantages of the sequential approach in the context of communication dynamics.

抽象的

在过去的几年中，分析沟通过程（即，中介和人际沟通）与相关态度，信念或行为之间的动态相互关系在沟通研究中变得更加重要。但是，关注这种交流动态的方法学研究很少。在本文中，我们提出了一个模型，该模型解决了相互关联的沟通动力学的两个统计挑战：（1）测量沟通过程与相关结果在不同序列中的相互关系，以及（2）个体间和个体间效应的分解。为此，我们依靠使用结构方程模型的潜在增长模型，并使用分段/样条回归方法对其进行扩展。我们将组合模型称为具有结构残差的顺序潜在增长模型（SLGM-SR）。着重于政治传播以及媒体效果和选择研究，我们通过将模型应用于2013年德国联邦大选期间收集的短期竞选小组数据来提供实证说明和建模策略。此外，我们讨论（dis）-顺序方法在通信动力学中的优势。