Supply of engineering techniques and software design patterns in psychoanalysis and psychometrics sciences
Omid Shokrollahi

TL;DR
This paper integrates software engineering, AI, and psychometric models to enhance psychotherapy, particularly CBT, by inferring hidden human variables through advanced data modeling and machine learning techniques.
Contribution
It introduces a novel combination of psychometric modeling, machine learning, and software engineering patterns to improve psychotherapeutic methods and understanding of mental health data.
Findings
Effective modeling of hidden abilities using Item Response Theory.
Identification of differential item functioning (DIF) across groups.
Application of software engineering patterns to psychotherapeutic data analysis.
Abstract
The purpose of this study is to introduce software technologies and models and artificial intelligence algorithms to improve the weaknesses of CBT (Cognitive Behavior Therapy) method in psychotherapy. The presentation method for this purpose is the implementation of psychometric experiments in which the hidden human variables are inferred from the answers of tests. In this report, we describe the various models of Item Response Theory and measure the hidden components of ability and complementary parameters of the reality of the individual's situation. Psychometrics, selecting the appropriate model and estimating its parameters have been introduced and implemented using R language developed libraries. Due to the high flexibility of the Multi variant Rasch mixture Model, machine learning has been applied to this method of data modeling. BIC and CML were used to determine the number of…
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Taxonomy
TopicsMental Health Research Topics · Psychotherapy Techniques and Applications · Software Engineering Research
