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Self-Report Tests of Personality

Self-Report Tests of Personality

Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

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Updated: Jun 3, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

Diagnosis of Pain Deception Using Minnesota Multiphasic Personality Inventory-2 Based on XGBoost Machine Learning

Hyewon Chung¹, Kihwan Nam², Subin Lee¹

¹Department of Anesthesiology and Pain Medicine, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea.

Medicina (Kaunas, Lithuania)

|January 8, 2025

Summary

Machine learning analysis of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) effectively detects pain deception. This approach offers improved diagnostic accuracy compared to traditional methods.

Keywords:

MMPI deception logistic model machine learning malingering pain personality tests psychosocial intervention salivary alpha-amylase

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Last Updated: Jun 3, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

Dynamic Quantitative Sensory Testing to Characterize Central Pain Processing

Dynamic Quantitative Sensory Testing to Characterize Central Pain Processing

Published on: February 16, 2017

Area of Science:

Psychology
Medical Informatics
Machine Learning

Background:

Assessing pain deception is difficult due to its subjective nature.
Pain deception is a form of psychological intervention where individuals feign pain.
Objective diagnostic tools for pain deception are needed.

Purpose of the Study:

To evaluate the diagnostic value of pain deception using machine learning (ML) analysis of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales.
To compare ML diagnostic performance against logistic regression.
To determine accuracy, precision, recall, and f1-score for pain deception diagnosis.

Main Methods:

A single-blinded, randomized controlled trial with 96 participants allocated to deception (D) and non-deception (ND) groups.
Participants in the D group were taught to feign pain.
XGBoost ML algorithm was applied to analyze selected MMPI-2 scales (sMMPI-2).

Main Results:

Logistic regression analysis showed no diagnostic value for pain or MMPI-2.
ML analysis of sMMPI-2 scales achieved an accuracy of 0.724.
ML analysis yielded a precision of 0.692, recall of 0.692, and f1-score of 0.692.

Conclusions:

Machine learning analysis of MMPI-2 data demonstrates diagnostic capability for pain deception.
ML outperforms conventional logistic regression in diagnosing pain deception.
Considering multiple MMPI-2 scales and patterns enhances diagnostic accuracy.