Psychological phenomena usually consist of many facets. Emotions, for example, refer not only to the conscious representation of the feeling itself ("I am happy") but also to many other changes in the individual (Davidson, Goldsmith, & Scherer, 2003; Kahneman, Diener, & Schwarz, 1999; Larsen & Prizmic-Larsen, this volume, chap. 23). An individual feeling happiness might jump up, feel an inner ease, and even embrace and kiss passersby. Moreover, muscle changes in the face might accompany this feeling, the brain might produce endorphins, and the individual may likely entertain positive thoughts. This simple example shows that a psychological phenomenon has many facets. To understand these emotional reactions it is necessary to have various, appropriate research methods to analyze the diverse facets. A method that measures muscle movements in the face is not appropriate to assess endorphin levels in the brain. Moreover, a method for determining the endorphin level is probably not useful for assessing subjective feelings. A thorough understanding of an emotional reaction requires a set of appropriate multimethod tools to measure the different facets of the phenomena.
Multilevel analyses are a related example of multimethod research programs. Berntson and Cacioppo (this volume, chap. 12) define multilevel analyses as a subset of multimethod approaches where the measures, constructs, and theories extend across levels of organization—from the psychological to the physiological to the cellular and ultimately to the gene and beyond.1 To assess the different levels, different methods are needed. Hence, the first aim of using multimethod approaches is the precise description of the multi-component and multilevel phenomena that are the focus of the behavioral sciences.
A second aim of multimethod research is providing information for detecting general associations between different components and levels of a phenomenon. For example, to analyze the reasons why happy individuals might be healthier, research must show a link between the feeling component and relevant physiological measures that explain individual differences in health. Insight into these processes can be obtained by multimethod research programs. However, general relations between the different components form only one side of the coin. Beyond general associations individual differences must be considered because not all individuals behave in the same way. If an emotional reaction were patterned in a uniform way, measuring one component would suffice when predicting other components. However, strong individual differences often exist when exploring different components.
For example, while two individuals may feel pride after receiving a compliment, one might jump for joy, while the other quietly sits down. Analyzing individual differences in the associations between the components might reveal that the first person grew up in a culture in which pride is a highly appreciated emotion (e.g., the United States), whereas the other was raised in a culture in which pride is undesirable and should not be expressed, for example in East Asian cultures (e.g., Eid & Diener, 2001). Hence, combining multimethod approaches for analyzing individual differences in the covariation of different components of a multi-component phenomenon may help us understand individual and social regulation processes.
These simple examples show that a multimethod research program is necessary for a thorough description of multicomponent phenomena, as well as for analyzing the different components of phenomena to detect general and individual rules of behavior. A classic example of multimethod research strategies is Murray's (1938) famous Explorations in Personality, where he used such diverse methods as aptitude tests, projective tests, questionnaires, interviews, and so forth to learn more about the different components of the personality of the participants of his study.
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