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Use a small number of latent variables to explain variability among many observed variables.

Screen Shot and Video:

This app can be used to identify latent variables to explain variability of input data.
It supports two methods: Principal Components and Maximum Likelihood.
Two orthogonal methods and one oblique method can be used to rotate factors so that the loading matrix can be simple.

Download Factor.opx file, and then drag-and-drop onto the Origin workspace. An icon will appear in the Apps Gallery window.
NOTE: This tool requires OriginPro.


  1. Make a worksheet for input data active. Click the Factor Analysis icon in the Apps Gallery window.
  2. In the dialog, select columns from the worksheet as Variables in Input tab.
    If observation label column exists, choose it as Observation Labels.
  3. In the Settings tab, choose a factor analysis method.
    Two methods are available: Principal Components and Maximum Likelihood.
    For the first method, you can choose to analyze Correlation or Covariance matrix.
    For the second, you can set Maximum Number of Iterations.
    In both methods, you can set Number of Factors to Extract, and it must be no more than number of variables in the input data.
  4. In the Rotation tab, choose a method to rotate factor loadings from Method drop-down list.
    Four methods are available: NoneQuartimaxVarimax and Promax.
    For the last three methods, you can choose Maximum Number of Iterations in rotation.
    For Promax method, you can set a value to Power, and it must be greater than 1.
  5. In the Quantities tab, choose which quantities to compute.
    Quantities options include Descriptive StatisticsCorrelation MatrixUnrotated Loadings, Residual MatrixScore Coefficients and Scores.
    If rotation is chosen, Rotated Loadings and Rotation Matrix are also available.
  6. In the Plots tab, following plots are available: 
    Scree PlotVariance PlotLoading PlotScore Plot and Biplot
    Graph Type option can determine whether to create 2D (first two factors) or 3D (first three factors) for last three graphs.
  7. Click OK button. A report sheet, a report data sheet and a plot data sheet will be created.

Sample OPJU File
This app provides a sample OPJU file.  Right click on the Factor Analysis icon in the Apps Gallery window, and choose Show Samples Folder from the short-cut menu. A folder will open. Drag-and-drop the project file FactorSample.opju from the folder onto Origin. The Notes window in the project shows detailed steps.
Note: If you wish to save the OPJU after changing, it is recommended that you save to a different folder location (e.g. User Files Folder).


  1. If a row in input data contains one or more missing values, the entire row will be excluded from the analysis.
  2. Number of rows for input data must be greater than number of variables.


Reviews and Comments:
08/30/2021OriginLabHi ajgor74,

Thanks for your suggestions.

We added them to our bug tracking database.

08/30/2021ajgor74Dear Author
it's a very usefull app since most of people get stuck on PCA and do not use Factro Analysi properly.
I could suggest that it would be usefull to add the opportunity to choose which component to plot in case of more than 2 components is set, and moreover to include some extra tools already available on the "Enanched version of PCA" app (as requested by Zhan Hui Lu time ago). Thanks

08/29/2021OriginLabHi, naser.khwv

Could you send us your project file so that we can further identify what happened.

You can send it from the link below

please help me. I do exactly step by step several times but it doesn't work.

05/15/2020OriginLabHi Zhan Hui Lu,
I replied you in your email.

05/04/2020zhhluu@gmail.comDear author,
Thank you for your app. It is a great tool. I have a suggestion. Would it be possible to make this app a similar to the app - an enhanced version of Principal Component Analysis (ver.1.5)? in which it is possible to assign a "Group" variable, by that way the data points can be marked in a different colour for each group in a biplot.
Thank you again for your excellent job!
Zhan Hui Lu