Use of cognitive measurement tools in prediction of psychological wellbeing

2.50
Hdl Handle:
http://hdl.handle.net/10547/622694
Title:
Use of cognitive measurement tools in prediction of psychological wellbeing
Authors:
Hashempour, Faramarz
Abstract:
Prediction of psychological wellbeing was investigated utilising a specific set of cognitive measures. This study considered a mixed method approach, progressing in three main phases. First study (the pilot study) involved (n=147) participants where data analysis was conducted using ANOVA, multiple regression and Structural Equation Modelling (SEM). The Pilot study considered six measures of thinking Style or Dysfunctional Attitude Scale (DAS-24), Attributional Style Questionnaire (ASQ-6 negative), Meta-cognitive Awareness Questionnaire (MAQ), Mastery/control, Cybernetic Coping Scale (CCS-15) and Beck Depression Inventory BDI-II. The correlation analysis showed positive association between variables with predictive approximation of 30% for depressive symptoms. The pilot study’s confirmatory factor and path analysis results produced supporting evidence of predictive quality with a good fit with model. The second phase comprised of a two-wave panel survey which included most of the measures from study one but added a 12-item version of Eysenck’s Personality Inventory, while the Patient Health Questionnaire (PHQ-9) and General Anxiety Disorder (GAD-7) measures replaced the BDI-II. Regression analysis indicated that approximately 50% of the variance in PHQ scores could be predicted with DAS-24, mastery, ASQ and Neuroticism being the strongest predictors. A second regression analysis predicted 65% of the variance in GAD7 scores with DAS success and perfectionism sub factor being the strongest predictor. A series of confirmatory factor analysis was conducted as well as regression and covariance analysis of the identified variables. Longitudinal path analyses were performed indicating that approximately 74% of the variance in PHQ9 scores and 71% of the variance in GAD7 scores at time two could be predicted, with the time one well-being measures the strongest predictors. The most striking findings related to the role of Neuroticism in prediction of psychological wellbeing. Third phase of this mixed method study considered qualitative approach, using framework analysis. Participants were twelve clinicians who currently working with clients with depressive or anxiety based difficulties. The main findings indicated that all previously identified independent variables of thinking style, perception, control and though awareness contributing towards psychological wellbeing. Other notable observation included participant’s clinical training modality that influenced the choice of responses. Overall tested hypotheses in both modalities of studies provided additional knowledge and understanding by offering a unique theoretical perspective, where the correlation between psychological wellbeing and cognitive processes could be predicted when utilising specific sets of measures.
Citation:
Hashempour, F. (2016) 'Use of cognitive measurement tools in prediction of psychological wellbeing'. PhD thesis. University of Bedfordshire.
Publisher:
University of Bedfordshire
Issue Date:
Feb-2016
URI:
http://hdl.handle.net/10547/622694
Type:
Thesis or dissertation
Language:
en
Appears in Collections:
PhD e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorHashempour, Faramarzen
dc.date.accessioned2018-04-30T10:59:19Z-
dc.date.available2018-04-30T10:59:19Z-
dc.date.issued2016-02-
dc.identifier.citationHashempour, F. (2016) 'Use of cognitive measurement tools in prediction of psychological wellbeing'. PhD thesis. University of Bedfordshire.en
dc.identifier.urihttp://hdl.handle.net/10547/622694-
dc.description.abstractPrediction of psychological wellbeing was investigated utilising a specific set of cognitive measures. This study considered a mixed method approach, progressing in three main phases. First study (the pilot study) involved (n=147) participants where data analysis was conducted using ANOVA, multiple regression and Structural Equation Modelling (SEM). The Pilot study considered six measures of thinking Style or Dysfunctional Attitude Scale (DAS-24), Attributional Style Questionnaire (ASQ-6 negative), Meta-cognitive Awareness Questionnaire (MAQ), Mastery/control, Cybernetic Coping Scale (CCS-15) and Beck Depression Inventory BDI-II. The correlation analysis showed positive association between variables with predictive approximation of 30% for depressive symptoms. The pilot study’s confirmatory factor and path analysis results produced supporting evidence of predictive quality with a good fit with model. The second phase comprised of a two-wave panel survey which included most of the measures from study one but added a 12-item version of Eysenck’s Personality Inventory, while the Patient Health Questionnaire (PHQ-9) and General Anxiety Disorder (GAD-7) measures replaced the BDI-II. Regression analysis indicated that approximately 50% of the variance in PHQ scores could be predicted with DAS-24, mastery, ASQ and Neuroticism being the strongest predictors. A second regression analysis predicted 65% of the variance in GAD7 scores with DAS success and perfectionism sub factor being the strongest predictor. A series of confirmatory factor analysis was conducted as well as regression and covariance analysis of the identified variables. Longitudinal path analyses were performed indicating that approximately 74% of the variance in PHQ9 scores and 71% of the variance in GAD7 scores at time two could be predicted, with the time one well-being measures the strongest predictors. The most striking findings related to the role of Neuroticism in prediction of psychological wellbeing. Third phase of this mixed method study considered qualitative approach, using framework analysis. Participants were twelve clinicians who currently working with clients with depressive or anxiety based difficulties. The main findings indicated that all previously identified independent variables of thinking style, perception, control and though awareness contributing towards psychological wellbeing. Other notable observation included participant’s clinical training modality that influenced the choice of responses. Overall tested hypotheses in both modalities of studies provided additional knowledge and understanding by offering a unique theoretical perspective, where the correlation between psychological wellbeing and cognitive processes could be predicted when utilising specific sets of measures.en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectdysfunctional attitudesen
dc.subjectcopingen
dc.subjectlongitudinal studyen
dc.subjectpsychological wellbeingen
dc.subjectneuroticismen
dc.subjectcognitive measurementen
dc.subjectC800 Psychologyen
dc.titleUse of cognitive measurement tools in prediction of psychological wellbeingen
dc.typeThesis or dissertationen
dc.type.qualificationnamePhDen_GB
dc.type.qualificationlevelPhDen
dc.publisher.institutionUniversity of Bedfordshireen
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