Modeling of psychological data sets through network models: An application in metacognition, depression and anxiety.

• Grant number: 18/24479-7
• Support type: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): June 01, 2019
• Status: Discontinued
• Field of knowledge: Humanities - Psychology - Fundamentals and Assessments in Psychology
• Principal researcher: Patrícia Waltz Schelini
• Grantee: Alex Bacadini França
• Home Institution: Centro de Educação e Ciências Humanas (CECH). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

Background: Metacognition can be defined as knowledge and cognition about the cognitive phenomenon, referring to thoughts and knowledge that individuals have about their own thoughts and cognitive processes. As such, metacognition plays an important role in the daily life of the elderly, influencing social functions, quality of life and self-efficacy. Recents studies suggests that targeting metacognitive processes can help alleviate mood and anxiety disorders. Considering the vulnerability of the elderly in the face of anxiety and depression disorders the aim of the study is to investigate the relationships between depressive, anxiety and metacognition symptoms in the elderly through network analysis in a set of cross- sectional data. Method: The network will be based on the set of items from the following instruments: the Activity Questionnaire (FAQ), Patient Health Questionnaire (PHQ-9), Hospital Anxiety Scale and Depression (HAD) and the Senior Metacognition Scale (EMETA-S) administered in 185 individuals aged 60 years and over, men and women with indicative of depression and anxiety without cognitive impairment and and in control group ((N e 185). The network will be estimated through the graphical LASSO technique in combination with a walktrap algorithm to obtain the clusters within the network and the connections between the nodes in the data set. The resulting network architecture will be analyzed by appropriate network analysis techniques including the centrality of the symptoms and the network clusters.