Correlation between two items with different scales

I want to examine a correlation between empathy of bystander and acting to help the victim. I have a question How do you feel and think when you witness violence: 1) he deserves it, 2) I don't feel

RDE Ordinal Interactions

popower: Power and Sample Size for Ordinal Response in Hmisc: Harrell Miscellaneous

item.alpha function - RDocumentation

<p>This function computes point estimate and confidence interval for the coefficient alpha (aka Cronbach's alpha), hierarchical alpha, and ordinal alpha (aka categorical alpha) along with standardized factor loadings and alpha if item deleted. By default, the function computes coefficient alpha based on unweighted least squares (ULS) parameter estimates using pairwise deletion in the presence of missing data that provides equivalent results compared to the formula-based coefficient alpha computed by using e.g. the <code>alpha</code> function in the <span class="pkg">psych</span> package by William Revelle (2025).</p>

A Measurement Is a Choice and Stevens’ Scales of Measurement Do Not Help Make It: A Response to Chalmers - Bruno D. Zumbo, Edward Kroc, 2019

Chalmers recently published a critique of the use of ordinal α proposed in Zumbo et al. as a measure of test reliability in certain research settings. In this r...

On Misconceptions and the Limited Usefulness of Ordinal Alpha - R. Philip Chalmers, 2018

This article discusses the theoretical and practical contributions of Zumbo, Gadermann, and Zeisser’s family of ordinal reliability statistics. Implications, in...

Ordered Regression Models: a Tutorial - Prevention Science

Ordinal outcomes are common in the social, behavioral, and health sciences, but there is no commonly accepted approach to analyzing them. Researchers make a number of different seemingly arbitrary recoding decisions implying different levels of measurement and theoretical assumptions. As a result, a wide array of models are used to analyze ordinal outcomes, including the linear regression model, binary response model, ordered models, and count models. In this tutorial, we present a diverse set of ordered models (most of which are under-utilized in applied research) and argue that researchers should approach the analysis of ordinal outcomes in a more systematic fashion by taking into consideration both theoretical and empirical concerns, and prioritizing ordered models given the flexibility they provide. Additionally, we consider the challenges that ordinal independent variables pose for analysts that often go unnoticed in the literature and offer simple ways to decide how to include ordinal independent variables in ordered regression models in ways that are easier to justify on conceptual and empirical grounds. We illustrate several ordered regression models with an empirical example, general self-rated health, and conclude with recommendations for building a sounder approach to ordinal data analysis.

Using latent2likert

latent2likert

Plotting ordinal logistic predicted effects on latent scale of ordinal outcome? · Issue #507 · strengejacke/ggeffects

When estimating an ordinal logistic the output conventionally appears as something like: However, a more interpretable output could be to have the ordinal logistic predictions generated on the late...