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Social actors are often nested within multiple levels or classifications that share one or more members, giving rise to multimodal data. Such data are multilevel and complex if the actor-nesting is not mutually exclusive. Consider, for example, individual actors nested within teams. Due to existing organizational structures or other a priori arrangements, actors are often nested within multiple teams that share one or more members, thus giving rise to affiliation data. This study uses affiliation networks to represent teams and individuals, with links representing team membership; social relations between individuals are represented using one-mode networks. We propose an extension of correspondence analysis to multiple levels, incorporating multiple relations and attributes at individual and team levels, and demonstrate it with two illustrative examples, one involving different cultures. We also show how results serve as an exploratory stepping-stone for generating hypotheses that can then be tested in a more focused manner using confirmatory techniques such as p*/ERGM. In management research, where data is often inherently nested, this technique could be a helpful preliminary analysis tool. [ABSTRACT FROM AUTHOR]