Related Experiment Video
Updated: May 23, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
TETRA-COM: a comprehensive SPSS program for estimating the tetrachoric correlation
Urbano Lorenzo-Seva1, Pere J Ferrando
1Universitat Rovira i Virgili, Tarragona, Spain. urbano.lorenzo@urv.cat
Abstract:
We provide an SPSS program that implements descriptive and inferential procedures for estimating tetrachoric correlations. These procedures have two main purposes: (1) bivariate estimation in contingency tables and (2) constructing a correlation matrix to be used as input for factor analysis (in particular, the SPSS FACTOR procedure). In both cases, the program computes accurate point estimates, as well as standard errors and confidence intervals that are correct for any population value. For purpose (1), the program computes the contingency table together with five other measures of association. For purpose (2), the program checks the positive definiteness of the matrix, and if it is found not to be Gramian, performs a nonlinear smoothing procedure at the user's request. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.
Related Concept Videos
Statistical Package for the Social Sciences (SPSS)
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
Microsoft Excel: Pearson's Correlation
Spearman's Rank Correlation Test
Spearman's test calculates correlation by...
Kendall's Tau Test
A τ value of +1 indicates that...
Calculating and Interpreting the Linear Correlation Coefficient
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...

