![]() The Microsoft Visual C++ 2008 Redistributable must also be installed on the computer XLSTAT is deployed on. Step 2: Installing the Microsoft Visual C++ 2008 Redistributable Knowing this 32-bit or 64-bit detail is required for the steps below. The detail to be retrieved is the one framed in red: Version of Excel Here is how to do so: open Excel then follow the steps described by the picture of the software that looks like your version. Step 1: Finding out your version of Excelīecause XLSTAT has to work along with Excel, you have to find out what is the version of Excel that you have ( 32-bit or 64-bit) before being able to install our software. To make sure your system meets all the requirements XLSTAT needs to work properly, please follow this quick guide here. It should guide you with the manual steps that are may have failed to be done automatically by our installer. The following video addresses Factor Analysis with an illustration using XLSTAT.Should you experience any issue with the automated XLSTAT installer, you may try to install by following the details on this page. The chart below represents the map for F1 and F2. XLSTAT displays the 2D maps on the selected factors. The following table gives the factor scores after the varimax rotation, which are the estimated coordinates of the observations on the factor axes. Other charts mixing other factors can be displayed. The following chart gives the position of the variables on axes F1 and F2. The second factor is loaded on Form of letter, Experience, and Suitability.įrom these results, we can understand that the individuals that have high scores on the first factor are promising salesmen, while for other jobs such as management, individuals with high coordinates on the second and third factors might be more appropriate. These results are used to interpret the meaning of the (rotated) factors.įrom this table we can see that the first factor is highly positively related to Ambition, Self-confidence, Salesmanship and Lucidity. The next results we want to look at, are the factor loadings after the varimax rotation. For a given factor, high loadings become higher, low loadings become lower, and intermediate loadings become either lower or higher. The varimax rotation makes the interpretation easier by maximizing the variance of the squared factors loadings by column. Next, we can see that the varimax rotation has changed the way each factor explains part of the variance. With the principal components analysis we would have obtained the following results: Note: the eigenvalues displayed above are those obtained with the principal factors extraction method. ![]() We can see that with 4 factors we keep 75.5 % of the variability of the initial data. The next table shows the eigenvalues resulting from the factor analysis. The reproduced and residual correlation matrices allow to verify if the factor analysis model is fine or not, and where it fails to reproduce correlations. An alpha of 0.914 means that there is some redundancy among the selected variables. The standardized Cronbach's alpha is computed for the whole input table. We can see that some of the correlations are quite high (0.883 for Grasp and Lucidity). The first results that are displayed are the summary statistics of the selected variables, and the correlation matrix between the variables. ![]() Interpreting the results of a Factor Analysis The computations begin once you have clicked on OK. The following options have been activated for the outputs and the charts. In the Options tab we select the varimax option for the rotation that will be applied to the first two factors. The Observations labels are also selected in the corresponding field. Once you've clicked on the button, the Factor analysis dialog box appears. Setting up a Factor Analysis in XLSTATĪfter opening XLSTAT, select the XLSTAT / Analyzing data / Factor analysis commanD (see below). We will apply it here to generate seven factors, and we will do a varimax rotation to facilitate the interpretation of the results. XLSTAT default method is the Principal factor method applied iteratively. Several methods are available for computing factor analysis. Therefore a factor analysis was conducted to determine the fewer underlying factors. The data are from and correspond to 48 applicants for a position in firm who have been judged on 15 variables:īecause many correlations between the variables are high, it was felt that the judge might be confusing some of the variables, or that some variables might be redundant. This tutorial will help you set up and interpret a Factor Analysis (FA) in Excel using the XLSTAT software.
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