The Hopelessness Scale: A Factor Analysis

2007 ◽  
Vol 100 (2) ◽  
pp. 375-378 ◽  
Author(s):  
Maurizio Pompili ◽  
Roberto Tatarelli ◽  
James R. Rogers ◽  
David Lester
Keyword(s):  
Factor Analysis ◽  
Principal Axis ◽  
Factor Model ◽  
Psychiatric Patients ◽  
Confirmatory Factor ◽  
Beck Hopelessness Scale ◽  
Principal Axis Factor Analysis ◽  
Correlated Factors ◽  
Principal Axis Factor

A confirmatory factor analysis of the Beck Hopelessness Scale in a sample of 340 Italian students did not support the 3-factor model reported for previous samples of psychiatric patients. A follow-up principal axis factor analysis yielded two interpretable correlated factors, suggesting that the structure of the scale may differ across clinical and nonclinical groups and as a function of nationality.

scholarly journals Latent structure of cognition in schizophrenia: a confirmatory factor analysis of the MATRICS Consensus Cognitive Battery (MCCB)

2015 ◽  
Vol 45 (12) ◽  
pp. 2657-2666 ◽  
Author(s):  
A. McCleery ◽  
M. F. Green ◽  
G. S. Hellemann ◽  
L. E. Baade ◽  
J. M. Gold ◽  
...  
Keyword(s):  
Working Memory ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Model ◽  
Visual Learning ◽  
Verbal Learning ◽  
Speed Of Processing ◽  
Confirmatory Factor ◽  
Correlated Factors ◽  
Cognitive Dimensions

BackgroundThe number of separable cognitive dimensions in schizophrenia has been debated. Guided by the extant factor analytic literature, the NIMH Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative selected seven cognitive domains relevant to treatment studies in schizophrenia: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition. These domains are assessed in the MATRICS Consensus Cognitive Battery (MCCB). The aim of this study was to conduct a confirmatory factor analysis (CFA) of the beta battery of the MCCB to compare the fit of the MATRICS consensus seven-domain model to other models in the current literature on cognition in schizophrenia.MethodUsing data from 281 schizophrenia outpatients, we compared the seven correlated factors model with alternative models. Specifically, we compared the 7-factor model to (a) a single-factor model, (b) a three correlated factors model including speed of processing, working memory, and general cognition, and (c) a hierarchical model in which seven first-order factors loaded onto a second-order general cognitive factor.ResultsMultiple fit indices indicated the seven correlated factors model was the best fit for the data and provided significant improvement in model fit beyond the comparison models.ConclusionsThese results support the assessment of these seven cognitive dimensions in clinical trials of interventions to improve cognition in schizophrenia. Because these cognitive factors are separable to some degree, it is plausible that specific interventions may have differential effects on the domains.

The LIVES Daily Hassles Scale and Its Relation to Life Satisfaction

Assessment ◽  
2021 ◽  
pp. 107319112110478
Author(s):  
Shagini Udayar ◽  
Ieva Urbanaviciute ◽  
Davide Morselli ◽  
Grégoire Bollmann ◽  
Jérome Rossier ◽  
...  
Keyword(s):  
Life Satisfaction ◽  
Factor Analysis ◽  
Principal Axis ◽  
Daily Hassles ◽  
Hierarchical Regression ◽  
Panel Surveys ◽  
Five Factors ◽  
Confirmatory Factor ◽  
Principal Axis Factor Analysis ◽  
German Speaking

Although daily hassles have been of interest since the 1980s, only a few tools have been developed to assess them. Most of them are checklists or open-ended questions that are demanding for participants in panel surveys. Therefore, to facilitate daily hassles integration into large surveys, the aim of this study was to present a new tool assessing daily hassles, the LIVES–Daily Hassles Scale (LIVES-DHS), and to examine its relation to life satisfaction, in a sample of 1,170 French- and German-speaking adults living in Switzerland. In a first random subsample, we conducted a principal axis factor analysis, and the results suggested a five-factor solution. Furthermore, we conducted a confirmatory factor analysis on a second random subsample, and it supported the hierarchical factor structure of the scale. The LIVES-DHS consists of 18 items represented by five factors that describe five sources of daily hassles: financial, physical, relational, environmental, and professional. The bivariate correlations showed that the LIVES-DHS could differentiate the concept of daily hassles from associated concepts. Finally, the hierarchical regression showed that daily hassles negatively predicted life satisfaction and added a significant incremental variance beyond that accounted for by age, gender, household income, education level, and personality traits.

Stability over Age and Sex of Children's Responses to Embedded Figures Test

1987 ◽  
Vol 64 (2) ◽  
pp. 399-406 ◽  
Author(s):  
Robert C. Hardy ◽  
John Eliot ◽  
Kenneth Burlingame
Keyword(s):  
Factor Analysis ◽  
Principal Axis ◽  
Relevant Factor ◽  
Embedded Figures Test ◽  
Stable Factor ◽  
Embedded Figures ◽  
Principal Axis Factor Analysis ◽  
Similar Factor ◽  
Age And Sex ◽  
Principal Axis Factor

240 children in Grades K to 4 were administered the entire Children's Embedded Figures Test to determine whether similar factor structures are obtained from samples of different sex and age as well as when the population is randomly split. Data were analyzed by principal axis factor analysis and relevant factor structures were compared using Veldman's 1967 program “Relate”. There was one stable factor which was consistent across sex and accounted for about 19% of the variance. Results were inconsistent across grades and indicated a possible lack of agreement of factor structures.

Hopelessness and terminal illness: The construct of hopelessness in patients with advanced AIDS

2004 ◽  
Vol 2 (1) ◽  
pp. 43-53 ◽  
Author(s):  
BARRY ROSENFELD ◽  
CHRISTOPHER GIBSON ◽  
MICHAEL KRAMER ◽  
WILLIAM BREITBART
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Terminal Illness ◽  
Factor Model ◽  
Clinical Samples ◽  
Data Sets ◽  
Two Samples ◽  
Confirmatory Factor ◽  
Beck Hopelessness Scale ◽  
Life Threatening

Objective: Understanding the construct of hopelessness in the context of a life-threatening or terminal illness is a complex and challenging undertaking. The objective of this study was to examine the construct of hopelessness in patients with advanced AIDS by examining the structure of the Beck Hopelessness Scale in this specific population.Methods: For the past three decades, the primary measure used to study hopelessness in a variety of populations has been the Beck Hopelessness Scale. Several factor analytic studies have been published using this scale, with studies of nonclinical samples typically describing a two-factor model (optimism and pessimism), whereas clinical samples have consistently generated a third factor (lack of motivation to make changes). We used confirmatory factor analysis to analyze two data sets in patients with AIDS.Results: Confirmatory factor analysis of the Beck Hopelessness Scale in two samples of patients with far advanced AIDS revealed a clear superiority for a three-factor model.Significance of results: The Beck Hopelessness Scale has unique characteristics when applied to a terminally ill population. The implications of these results for studies of terminal illness are discussed.

A Dimensional Analysis of the Yale-Brown Obsessive Compulsive Scale

1992 ◽  
Vol 70 (1) ◽  
pp. 239-240 ◽  
Author(s):  
William Fals-Stewart
Keyword(s):  
Factor Analysis ◽  
Dimensional Analysis ◽  
Principal Axis ◽  
Obsessive Compulsive ◽  
Principal Axis Factor Analysis ◽  
Promax Rotation ◽  
Principal Axis Factor

A principal axis factor analysis with a Promax rotation was performed on the Yale-Brown Obsessive Compulsive Scale. Although the scale has separate obsession and compulsion indices, only one factor was extracted. The psychometric implications of this finding are discussed.

The Factor Structure of the Hospital Anxiety and Depression Scale (HADS) in a Traumatic Brain Injury (TBI) Population

2011 ◽  
Vol 12 (1) ◽  
pp. 22-32 ◽  
Author(s):  
Clive Skilbeck ◽  
Kieran Holm ◽  
Mark Slatyer ◽  
Matthew Thomas ◽  
Tony Bell
Keyword(s):  
Factor Analysis ◽  
Factor Model ◽  
Depression Scale ◽  
Post Injury ◽  
Gender And Age ◽  
Confirmatory Factor ◽  
Severe Tbi ◽  
Anxiety Depression ◽  
Best Fit

Abstractrimary Objectives:To examine the HADS structure in TBI, using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), and investigate the effects of TBI severity, Gender, and Age on factor scores.Methods and Procedures:HADS data from 186 TBI patients in a population study in Tasmania were subjected to EFA. HADS data from a second sample of 185 TBI participants in the same study underwent CFA. One-month follow-up data were used, allowing inclusion of severe TBI patients while still being early post-injury. Factor loadings were used to investigate the effects of demographic and clinical variables at 2 weeks post-injury.Results:While EFA suggested 2-factor (anxiety, depression) and 3-factor (anxiety, psychomotor, depression) structures provided adequate descriptions, CFA strongly supported the 3-factor model. Using this model, significant effects of TBI severity were noted on Psychomotor and Depression scores at 2 weeks post-injury. Males reported significantly fewer symptoms on all 3 factors, as did younger participants.Conclusions:CFA indicates that a 3-factor model provides the best fit for HADS data in TBI. One factor, Psychomotor, has been relatively neglected in the literature, and the current findings suggest its assessment and rehabilitation should receive more attention.

Happiness and Self-Actualization under Conditions of Strain: A Sample of Homeless Men

2000 ◽  
Vol 90 (1) ◽  
pp. 191-203
Author(s):  
John R. Sumerlin ◽  
Charles M. Bundrick
Keyword(s):  
Factor Analysis ◽  
Principal Axis ◽  
Human Potential ◽  
Existential Psychology ◽  
Homeless Men ◽  
Self Actualization ◽  
Principal Axis Factor Analysis ◽  
Principal Axis Factor

Maslow's contention that happiness includes striving and is more than a hedonistic construct was tested in a sample of 146 homeless men. Since all participants were homeless, environmental experiences can be considered as largely held constant, allowing an examination of personality and happiness under conditions of strain. Principal axis factor analysis with promax and oblique rotations placed scores from the measures Unhappy–Happy and the Brief Index of Self-actualization on the same factor which supported Maslow's idea. Humanistic-existential psychology has interest in the happiness construct particularly regarding the development of human potential.

scholarly journals Perceptions of the dimensions of the fairness of affirmative action: A pilot study

2006 ◽  
Vol 37 (3) ◽  
pp. 53-65 ◽  
Author(s):  
L. P. Vermeulen ◽  
M. Coetzee
Keyword(s):  
South Africa ◽  
Factor Analysis ◽  
Affirmative Action ◽  
Principal Axis ◽  
Financial Institution ◽  
Effective Management ◽  
Varimax Rotation ◽  
Principal Axis Factor Analysis ◽  
The Relationship ◽  
Principal Axis Factor

The purpose of this study was to identify the dimensions of affirmative action (AA) fairness in order to develop a valid and reliable questionnaire to assess employees’ perceptions of the fairness of AA decisions and practices, and to explore the relationship between employees’ biographical characteristics and their perceptions of the dimensions of AA fairness. The research sample consisted of 349 participants connected to a large financial institution in South Africa. Principal axis factor analysis with a varimax rotation was performed on the data in order to uncover the different factors that employees perceived to be important for the fair and just management of affirmative action practices. Four factors define AA fairness: namely interactional, procedural (input), procedural (criteria) and distributive justice. One-way MANOVAs and associated ANOVAs revealed that the importance of the justice factors in AA fairness differed significantly across ethnicity and staff category. This study enables a better understanding of the dimensionality of AA fairness. It should ultimately contribute to more effective management of AA in the workplace.

Sign in / Sign up

Export Citation Format

Share Document