Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
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 Correlation01:18

Microsoft Excel: Pearson's Correlation

Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying that as one...
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Kendall's Tau Test01:16

Kendall's Tau Test

Kendall's tau test, also known as the Kendall rank coefficient test, is a nonparametric method for assessing association between two variables. This test is particularly useful for identifying significant correlations when the distributions of the sample and population are unknown. Developed in 1938 by the British statistician Sir Maurice George Kendall, the tau coefficient (denoted as τ) serves as a rank correlation coefficient, with values ranging from -1 to +1.
A τ value of +1 indicates that...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Assessing the Unconditional and Conditional External Validity of Noncognitive Test Scores: A Unifying Model-Based Proposal.

Educational and psychological measurement·2026
Same author

The Japanese version of the barcelona music reward questionnaire (J-BMRQ) confirms the cross-cultural generalizability of the "five-factor" model.

PloS one·2026
Same author

Measurement properties of the Spanish version of assessment of survivor concerns in cancer patients.

Scientific reports·2025
Same author

Likert Scales: A Practical Guide to Design, Construction and Use.

Psicothema·2025
Same author

A Multidimensional Continuous Response Model for Measuring Unipolar Traits.

Applied psychological measurement·2025
Same author

Assessing the Properties and Functioning of Model-Based Sum Scores in Multidimensional Measures With Local Item Dependencies: A Comprehensive Proposal.

Educational and psychological measurement·2025
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: May 23, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Behavior Research Methods
|April 6, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an SPSS program for estimating tetrachoric correlations, crucial for bivariate analyses and factor analysis. The program provides accurate estimates, standard errors, and confidence intervals for robust statistical insights.

More Related Videos

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

Related Experiment Videos

Last Updated: May 23, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

Area of Science:

  • Psychometrics
  • Statistical Software

Background:

  • Tetrachoric correlation estimation is vital for analyzing dichotomous variables.
  • Existing methods may lack comprehensive procedures for bivariate analysis and factor analysis input.

Purpose of the Study:

  • To present an SPSS program for estimating tetrachoric correlations.
  • To facilitate bivariate estimation in contingency tables and correlation matrix construction for factor analysis.

Main Methods:

  • Development of an SPSS program with descriptive and inferential procedures.
  • Implementation of bivariate estimation, including contingency tables and measures of association.
  • Procedures for constructing correlation matrices for factor analysis, including positive definiteness checks and nonlinear smoothing.

Main Results:

  • The program computes accurate point estimates, standard errors, and confidence intervals for tetrachoric correlations.
  • It provides five additional measures of association for bivariate analyses.
  • The program ensures matrix positive definiteness for factor analysis input, applying smoothing if necessary.

Conclusions:

  • The developed SPSS program offers a comprehensive tool for tetrachoric correlation estimation.
  • It enhances the accuracy and utility of correlation estimates for both bivariate and factor analysis applications.