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Related Concept Videos

Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders01:27

Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders

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Schizophrenia is a neurodevelopmental disorder whose origins are rooted in complex genetic components. Despite our burgeoning understanding, the pathophysiology of this disorder remains incompletely deciphered.
Researchers have identified genetic factors that increase susceptibility to schizophrenia, underscoring the intricate interplay between genetics and environment in disease development. At the core of schizophrenia's pathophysiology is excessive dopaminergic neurotransmission within...
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Theoretical Approaches to Psychological Disorder01:29

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The development of psychological disorders, which are characterized by deviant, maladaptive, and personally distressing behaviors, has been explored through several theoretical approaches.
Biological approach
The biological approach posits that internal, organic factors are the primary causes of such disorders. This perspective emphasizes brain structure and function, genetic predispositions, and neurotransmitter imbalances. For example, schizophrenia has been associated with both genetic...
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Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

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Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
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Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
Genetic Factors in Schizophrenia
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Case Studies01:22

Case Studies

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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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Related Experiment Video

Updated: Nov 6, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Network analysis in psychiatric research.

Cristian Ramos-Vera, Antonio Serpa

    Actas Espanolas De Psiquiatria
    |May 10, 2021
    PubMed
    Summary

    This study explores clinical test measurement models, advancing beyond exploratory factor analysis (EFA) to structural equation modeling for robust latent variable evaluation.

    Area of Science:

    • Psychometrics
    • Clinical Psychology
    • Statistical Modeling

    Background:

    • Volume 48 features studies on clinical test measurement models.
    • Exploratory Factor Analysis (EFA) has been used to examine test structures and underlying traits.
    • EFA is a psychometric method for evaluating latent variable models.

    Purpose of the Study:

    • To evaluate clinical test measurement models.
    • To explore the transition from Exploratory Factor Analysis (EFA) to more robust statistical models.
    • To highlight the advantages of structural equation modeling over traditional factorial analysis.

    Main Methods:

    • Exploratory Factor Analysis (EFA) was used to determine the number of factors.
    • Structural Equation Modeling (SEM) was introduced as a more robust statistical approach.

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  • SEM integrates path analysis and multiple regression to evaluate complex relationships.

    • Main Results:

    • EFA helps identify underlying factors and correlations between variables.
    • Structural Equation Modeling (SEM) offers a more comprehensive evaluation of multiple clinical hypotheses.
    • SEM provides a robust framework for analyzing latent variables.

    Conclusions:

    • Structural Equation Modeling (SEM) represents a significant advancement over EFA for clinical test evaluation.
    • SEM enables the assessment of complex relationships between variables, offering deeper insights.
    • The journal's recent studies highlight the evolving methodologies in psychometric analysis.