# The Partner Modelling Questionnaire: A validated self-report measure of perceptions toward machines as dialogue partners

arXiv: https://arxiv.org/abs/2308.07164 · HTML: https://mlsanity.com/paper/2308.07164/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theory](https://mlsanity.com/paper/2509.22505/) (2025)
- [ChatGPT and U(X): A Rapid Review on Measuring the User Experience](https://mlsanity.com/paper/2503.15808/) (2025)

## Abstract (arXiv)

Recent work has looked to understand user perceptions of speech agent capabilities as dialogue partners (termed partner models), and how this affects user interaction. Yet, currently partner model effects are inferred from language production as no metrics are available to quantify these subjective perceptions more directly. Through three studies, we develop and validate the Partner Modelling Questionnaire (PMQ): an 18-item self-report semantic differential scale designed to reliably measure people's partner models of non-embodied speech interfaces. Through principal component analysis and confirmatory factor analysis, we show that the PMQ scale consists of three factors: communicative competence and dependability, human-likeness in communication, and communicative flexibility. Our studies show that the measure consistently demonstrates good internal reliability, strong test-retest reliability over 12 and 4-week intervals, and predictable convergent/divergent validity. Based on our findings we discuss the multidimensional nature of partner models, whilst identifying key future research avenues that the development of the PMQ facilitates. Notably, this includes the need to identify the activation, sensitivity, and dynamism of partner models in speech interface interaction.