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Aug 13, 2017 5:28 PM
(4087 views)

Hello again,

I need your advice about a rather basic question of statistics: what is the best method to explore the relationship (e.g. Spearman's correlation, Kendall's Tau, other) between semi-quantitative data (graded 0,1,2,3) and continuous variables (see table attached and screenshot)?

Thanks

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I would start with *ordinal logistic regression*. Assuming that you have the ordinal modeling type for the **DATA SEMI-QUANT** response and the continuous modeling type for the **DATA CONTINUOUS** predictor/factor, you can use either **Analyze** > **Fit Y by X** or **Analyze** > **Fit Model**. The **Logistic** platform (from **Fit Y by X** launch) provides initial results like this:

You can also get the ROC Curve and Lift Curve with this platform. Alternatively, you get these initial results from **Ordinal Logistic Fit** (from **Fit Model** launch):

This platform allows more effects in the model such as other predictors and covariates and their transforms (interactions, powers). The Prediction Profiler and the same diagnostic plots are also available.

These regression techniques provide more information than simple measures of association or agreement.

Learn it once, use it forever!

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I would start with *ordinal logistic regression*. Assuming that you have the ordinal modeling type for the **DATA SEMI-QUANT** response and the continuous modeling type for the **DATA CONTINUOUS** predictor/factor, you can use either **Analyze** > **Fit Y by X** or **Analyze** > **Fit Model**. The **Logistic** platform (from **Fit Y by X** launch) provides initial results like this:

You can also get the ROC Curve and Lift Curve with this platform. Alternatively, you get these initial results from **Ordinal Logistic Fit** (from **Fit Model** launch):

This platform allows more effects in the model such as other predictors and covariates and their transforms (interactions, powers). The Prediction Profiler and the same diagnostic plots are also available.

These regression techniques provide more information than simple measures of association or agreement.

Learn it once, use it forever!

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