Abstract
. Recent technological advances have made it possible to study human behavior by linking novel types of data to more traditional types of psychological data, for example, linking psychological questionnaire data with genetic risk scores. Revealing the variables that are linked throughout these traditional and novel types of data gives crucial insight into the complex interplay between the multiple factors that determine human behavior, for example, the concerted action of genes and environment in the emergence of depression. Little or no theory is available on the link between such traditional and novel types of data, the latter usually consisting of a huge number of variables. The challenge is to select – in an automated way – those variables that are linked throughout the different blocks, and this eludes currently available methods for data analysis. To fill the methodological gap, we here present a novel data integration method.
Highlights
Recent technological advances have made it possible to study human behavior by linking novel types of data to more traditional types of psychological data, for example, linking psychological questionnaire data with genetic risk scores
There is the novel kind of data that are very different from the traditional data we are used to work with: Instead of consisting of a limited number of targeted measurements, they consist of a huge amount of variables that have been collected without a specific focus
In this era of big data, researchers in psychology often have novel types of data available to supplement the more traditional types of data they are accustomed to. This opens the avenue to a more informed understanding of the human behavior system; the different types of data usually probe different components of the behavioral system and by integrating them a more complete view is obtained. To get this deeper understanding that goes beyond a fragmented view, it is crucial that the following questions can be answered: How do the components of the human behavior system interact and what do they contribute independently from the other components? As we argued in this paper, this means disentangling joint sources of variation from specific sources of variation present in the data
Summary
First, the notation and data will be introduced; the model, its estimation, model selection, and some related methods will be discussed. SCaDS runs with fixed values for the number of components, their status (whether they are common or distinctive), and the value of the lasso and ridge tuning parameters Often these are unknown and model selection procedures are needed to guide users of the method in the selection of proper values. Determining the number of components, this method came out as the best choice in terms of accuracy and low computational cost; see Bro, Kjeldahl, Smilde, and Kiers (2008) This method leaves out one or several samples and predicts the scores for each variable in turn based on a model that was obtained from the retained samples: For one up to a large number of components, the mean predicted residual sum of squares (MPRESS) is calculated and the model with the lowest MPRESS is retained.
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