Monte Carlo PCA for Parallel Analysis Computes Parallel Analysis criteria (eigenvalues) by performing a Monte Carlo simulation
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Monte Carlo PCA for Parallel Analysis Description

Monte Carlo PCA for Parallel Analysis is a compact application that can easily calculate the results of a Monte Carlo analysis. As the name clearly states, the program is designed to speed up the calculations required for generating the values for a parallel analysis. The Monte Carlo method is widely used in statistics and other exact sciences in order to determine the probability of a certain event. It is based on performing multiple experiments and calculating the probability distribution based on the results. If you do not want or you are unable to perform the experiments by yourself, this tool allows you to easily simulate the results by specifying the input parameters. Basically, you just need to specify the number of variables, subjects and replications in order to generate the results. During the correlation matrix calculation, a status bar is displayed in order to check when the operation is finished. When working with low level variable numbers, the program provides results almost instantly but the time depends of the number of variables that you need to use. After the simulation is finished, the results are displayed in the main window and can be printed or with just one click. You also have the option to copy the report and format the text in another program if you need to publish the information. Although it does not include a documentation, a description of all the operations required to generate the report is displayed by clicking on the question mark from the right side of the main window. Unfortunately, Monte Carlo PCA for Parallel Analysis can only provide you with the final result of the experiment and does not include the random values in order to estimate the calculation accuracy. You need to test the application for yourself in order to check its efficiency. Reviewed by Sorin Cirneala, last updated on December 5th, 2013

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