Complex Samples Ordinal Regression

The Complex Samples Ordinal Regression procedure performs regression analysis on a binary or ordinal dependent variable for samples drawn by complex sampling methods. Optionally, you can request analyses for a subpopulation.

Example. Representatives considering a bill before the legislature are interested in whether there is public support for the bill and how support for the bill is related to voter demographics. Pollsters design and conduct interviews according to a complex sampling design. Using Complex Samples Ordinal Regression, you can fit a model for the level of support for the bill based upon voter demographics.

Complex Samples Ordinal Regression Data Considerations

Data. The dependent variable is ordinal. Factors are categorical. Covariates are quantitative variables that are related to the dependent variable. Subpopulation variables can be string or numeric but should be categorical.

Assumptions. The cases in the data file represent a sample from a complex design that should be analyzed according to the specifications in the file selected in the Complex Samples Plan dialog box.

Obtaining Complex Samples Ordinal Regression

This feature requires the Complex Samples option.

  1. From the menus choose:

    Analyze > Complex Samples > Ordinal Regression...

  2. Select a plan file. Optionally, select a custom joint probabilities file.
  3. Click Continue.
  4. In the Complex Samples Ordinal Regression dialog box, select a dependent variable.

Optionally, you can:

  • Select variables for factors and covariates, as appropriate for your data.
  • Specify a variable to define a subpopulation. The analysis is performed only for the selected category of the subpopulation variable, although variances are still properly estimated based on the entire dataset.
  • Select a link function.

Link function. The link function is a transformation of the cumulative probabilities that allows estimation of the model. The following five link functions are available.

  • Logit. f(x)=log(x/(1−x) ). Typically used for evenly distributed categories.
  • Complementary log-log. f(x)=log(−log(1−x)). Typically used when higher categories are more probable.
  • Negative log-log. f(x)=−log(−log(x)). Typically used when lower categories are more probable.
  • Probit. f(x)=Φ−1(x). Typically used when the latent variable is normally distributed.
  • Cauchit (inverse Cauchy). f(x)=tan(π(x−0.5)). Typically used when the latent variable has many extreme values.

This procedure pastes CSORDINAL command syntax.