AI Article Synopsis
- MultiLevel Simultaneous Component Analysis (MLSCA) is a technique designed for analyzing complex data with two levels, allowing researchers to explore relationships between variables at each level through separate models.
- Despite its successful application in various fields, MLSCA faces challenges such as lengthy computation times for large datasets and the lack of user-friendly software for implementation.
- This paper proposes a solution by introducing a computational shortcut for MLSCA and presents an accessible MLSCA package, available for Windows users without needing MATLAB.
Article Abstract
MultiLevel Simultaneous Component Analysis (MLSCA) is a data-analytical technique for multivariate two-level data. MLSCA sheds light on the associations between the variables at both levels by specifying separate submodels for each level. Each submodel consists of a component model. Although MLSCA has already been successfully applied in diverse areas within and outside the behavioral sciences, its use is hampered by two issues. First, as MLSCA solutions are fitted by means of iterative algorithms, analyzing large data sets (i.e., data sets with many level one units) may take a lot of computation time. Second, easily accessible software for estimating MLSCA models is lacking so far. In this paper, we address both issues. Specifically, we discuss a computational shortcut for MLSCA fitting. Moreover, we present the MLSCA package, which was built in MATLAB, but is also available in a version that can be used on any Windows computer, without having MATLAB installed.
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| http://dx.doi.org/10.3758/s13428-015-0626-8 | DOI Listing |
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