#statstab #410 Ordered Regression Models: a Tutorial

Thoughts: A very comprehensive paper on analysing ordinal data.

#orderedregression #regression #ordinal #tutorial #likert #probit #logit

https://link.springer.com/article/10.1007/s11121-021-01302-y

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.

SpringerLink

#statstab #405 Best Practices for Estimating, Interpreting, and
Presenting Nonlinear Interaction Effects

Thoughts: Guidance on nonlinear interactions, reporting (probabilities) and visualisations.

#probit #logit #logisticregression #nonlinear #guide

https://sociologicalscience.com/download/vol-6/february/SocSci_v6_81to117.pdf

#statstab #339 Better discrete choice modeling through the rank ordered logit

Thoughts: MaxDiff, rank ordered logit, and how to model discrete choice data.

#choicedata #MaxDiff #rank #logit #ordered #r #choice #data #Gumbel

https://andytimm.github.io/posts/doing_maxdiff_better/better_maxdiff.html

Better discrete choice modeling through the rank ordered logit – Andy Timm

Or- a mathematically correct model of a psychologically coherent concept

Andy Timm
Das ist schon eine ganz anständige Zahl, aber der Fehler der Briefwahlprognose ist leider größer, so dass da auch Werte unter 0 ⁠% und über 100 ⁠% rauskommen: Grüne 136 ⁠%, SPD 83 ⁠%, FDP 28 ⁠%, AfD 1 ⁠%, CDU −49 ⁠%, Linke −53 ⁠%, Rest −25 ⁠%, Ungültige −20 ⁠% (alles bezogen auf Wähler). Das ist also so direkt nicht realistisch, aber es ist zumindest ein Anhaltspunkt für eine Tendenz. Gerechnet hab ich mit #Logit-Differenzen und Normierung. [3/5]
Danach logarithmischer Abfall der Dichte vom #Erwartungswert (kann negativ sein, da das Maximum im Allgemeinen etwas abseits liegt). Dahinter diverse #Potenzmittel (∞, 2, 1.5, 1) der Abweichungen nach #Logit und in #Prozentpunkte⁠n. #RMSE (Root Mean Square Error) ist generell der beste Standard. Logit bewertet Abweichungen bei kleineren Parteien ziemlich stark; bei Prozentpunkten sind sie dagegen im Verhältnis zu zufälligen Stichproben unterbewertet. [2/2]