scholarly journals Understanding adolescent males’ poor mental health and health-compromising behaviours: A factor analysis model on Swedish school-based data

2020 ◽  
pp. 140349482097455
Author(s):  
Johanna Haraldsson ◽  
Ronnie Pingel ◽  
Lena Nordgren ◽  
Ylva Tindberg ◽  
Per Kristiansson
Keyword(s):  
Mental Health ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Response Rate ◽  
Factor Model ◽  
Poor Mental Health ◽  
Adolescent Males ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
School Based

Aim: The aim was to develop a factor model of the clustering of poor mental-health symptoms and health-compromising behaviours (HCBs) in adolescent males. Methods: The study was based on two cross-sectional school-based Swedish surveys in 2011 (response rate 80%, N=2823) and 2014 (response rate 85%, N=2358), both of which comprised questionnaires from males aged 15–16 and 17–18 years. A factor model was developed by exploratory factor analysis on the 2011 survey and validated by confirmatory factor analysis on the 2014 survey. Results: Four aspects of poor mental health and HCBs emerged in the exploratory factor analysis: (a) deviancy as a tendency to substance use and delinquency, (b) unsafety as an inclination towards feelings of unsafety in different environments, (c) gloominess as a tendency towards pessimism and feeling unwell and (d) pain as an inclination to experience physical pain. The model was validated with good model fit. Age did not affect the model structure, but older adolescent males were more influenced by deviancy and gloominess and less by unsafety compared to their younger peers. Conclusions: Separating symptoms of poor mental health and HCBs into four areas – deviancy, unsafety, gloominess and pain – brings new perspectives to the understanding of adolescent males’ health. To the best of our knowledge, our factor model is the first to include unsafety and pain in this context. Whenever a comprehensive approach to the health of adolescent males is needed in the clinic or in the field of public health, this factor model may provide guidance.

Psychometrics of the Gratitude Questionnaire-6 in African-Americans at Risk for Cardiovascular Disease

2020 ◽  
Vol 42 (12) ◽  
pp. 1148-1154
Author(s):  
Lakeshia Cousin ◽  
Laura Redwine ◽  
Christina Bricker ◽  
Kevin Kip ◽  
Harleah Buck
Keyword(s):  
At Risk ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Well Being ◽  
Divergent Validity ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Confirmatory Factor

Psychometrics of the Gratitude Questionnaire-6, which measures dispositional gratitude, was originally estimated in healthy college students. The purpose of this study was to examine the scales’ factor structure, convergent/divergent validity, and reliability among 298 AA adults at risk for CVD in the community. Analyses were performed using bivariate correlations, exploratory factor analysis, and confirmatory factor analysis. The scale demonstrated acceptable estimates for internal consistency (Cronbach’s α = 0.729). Our exploratory factor analysis results yielded a one-factor structure consistent with the original instrument, and the confirmatory factor analysis model was a good fit. Convergent/divergent validity was supported by the association with positive affect (coefficient = 0.482, 95% CI = [0.379, 0.573], spiritual well-being (coefficient = 0.608, 95% CI = [0.519, 0.685], and depressive symptoms (coefficient = −0.378, 95% CI = [−0.475, −0.277]. Findings supported the scale’s reliability and convergent/divergent validity among AAs at risk for CVD.

scholarly journals An exploratory factor analysis model for slum severity index in Mexico City

Urban Studies ◽  
2019 ◽  
Vol 57 (4) ◽  
pp. 789-805 ◽  
Author(s):  
Debraj Roy ◽  
David Bernal ◽  
Michael Lees
Keyword(s):  
Factor Analysis ◽  
International Development ◽  
Mexico City ◽  
Exploratory Factor Analysis ◽  
Urban Areas ◽  
Informal Settlements ◽  
Severity Index ◽  
Development Planning ◽  
Analysis Model ◽  
Factor Analysis Model

Today, over half of the world’s population lives in urban areas and it is projected that, by 2050, two out of three people will live in a city. This increased rural–urban migration, coupled with housing poverty, has led to the growth and formation of informal settlements, commonly known as slums. In Mexico, 25% of the urban population now live in informal settlements with varying degrees of deprivation. Although some informal neighbourhoods have contributed to the upward mobility of the inhabitants, the majority still lack basic services. Mexico City and the conurbation around it form a mega city of 21million people that has been growing in a manner qualified as ‘highly unproductive, (that) deepens inequality, raises pollution levels’ (available at:   https://www.smartcitiesdive.com/ex/sustainablecitiescollective/making-way-urban-reform-mexico/176466/ ) and contains the largest slum in the world: Neza-Chalco-Izta. Urban reforms are now aiming to improve the conditions in these slums and therefore it is very important to have reliable tools to measure the changes that are underway. In this paper, we use exploratory factor analysis to define an index of shelter deprivation in Mexico City, namely the Slum Severity Index (SSI), based on the UN-HABITAT’s definition of slum. We apply this novel approach to the Census survey of Mexico and measure the shelter deprivation levels of households from 1990 to 2010. The analysis highlights high variability in housing conditions within Mexico City. We find that the SSI decreased significantly between 1990 and 2000 as a result of several policy reforms but increased between 2000 and 2010. We also show correlations of the SSI with other social factors such as education, health and fertility. We present a validation of the SSI using Grey Level Co-occurrence Matrix (GLCM) features extracted from Very-High Resolution (VHR) remote-sensed satellite images. Finally, we show that the SSI can present a cardinally meaningful assessment of the extent of deprivation compared with a similar index defined by Connolly (Connolly P (2009) Observing the evolution of irregular settlements: Mexico city’s colonias populares, 1990 to 2005. International Development Planning Review 31: 1–35) that studies shelter deprivation in Mexico.

scholarly journals Psychometric Properties of the Serbian Version of the Maslach Burnout Inventory-Human Services Survey: A Validation Study among Anesthesiologists from Belgrade Teaching Hospitals

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bojana Matejić ◽  
Miodrag Milenović ◽  
Darija Kisić Tepavčević ◽  
Dušica Simić ◽  
Tatjana Pekmezović ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Validation Study ◽  
Human Services ◽  
Factor Model ◽  
Five Factor Model ◽  
Maslach Burnout Inventory ◽  
Teaching Hospitals ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Serbian Version

We report findings from a validation study of the translated and culturally adapted Serbian version of Maslach Burnout Inventory-Human Services Survey (MBI-HSS), for a sample of anesthesiologists working in the tertiary healthcare. The results showed the sufficient overall reliability (Cronbach’sα= 0.72) of the scores (items 1–22). The results of Bartlett’s test of sphericity (χ2= 1983.75, df = 231,p<0.001) and Kaiser-Meyer-Olkin measure of sampling adequacy (0.866) provided solid justification for factor analysis. In order to increase sensitivity of this questionnaire, we performed unfitted factor analysis model (eigenvalue greater than 1) which enabled us to extract the most suitable factor structure for our study instrument. The exploratory factor analysis model revealed five factors with eigenvalues greater than 1.0, explaining 62.0% of cumulative variance. Velicer’s MAP test has supported five-factor model with the smallest average squared correlation of 0,184. This study indicated that Serbian version of the MBI-HSS is a reliable and valid instrument to measure burnout among a population of anesthesiologists. Results confirmed strong psychometric characteristics of the study instrument, with recommendations for interpretation of two new factors that may be unique to the Serbian version of the MBI-HSS.

Exploratory and confirmatory factor analysis in clinical and health psychology

2015 ◽  
Author(s):  
Mark Shevlin
Keyword(s):  
Health Psychology ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Clinical Psychologists ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Factors Analysis ◽  
Confirmatory Factor ◽  
Confirmatory Factors

This chapter focuses on exploratory and confirmatory factors analysis (CFA) in clinical and health psychology. It discusses the factor analysis model, how health and clinical psychologists use factor analysis, exploratory factor analysis (EFA), and CFA.

Model Selection and Average Proportion Explained Variance in Exploratory Factor Analysis

2020 ◽  
pp. 001316442096316
Author(s):  
Tenko Raykov ◽  
Lisa Calvocoressi
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Model Choice ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Number Of Factors ◽  
Average Proportion ◽  
Explained Variance

A procedure for evaluating the average R-squared index for a given set of observed variables in an exploratory factor analysis model is discussed. The method can be used as an effective aid in the process of model choice with respect to the number of factors underlying the interrelationships among studied measures. The approach is developed within the framework of exploratory structural equation modeling and is readily applicable with popular statistical software. The outlined procedure is illustrated using a numerical example.

Bayesian proportional hazards model with latent variables

2017 ◽  
Vol 28 (4) ◽  
pp. 986-1002 ◽  
Author(s):  
Deng Pan ◽  
Kai Kang ◽  
Chunjie Wang ◽  
Xinyuan Song
Keyword(s):  
Risk Factors ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Latent Variables ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Hazards Model

We consider a joint modeling approach that incorporates latent variables into a proportional hazards model to examine the observed and latent risk factors of the failure time of interest. An exploratory factor analysis model is used to characterize the latent risk factors through multiple observed variables. In commonly used confirmatory factor analysis, the number of latent variables and their observed indicators are specified prior to analysis. By contrast, the exploratory factor analysis model allows such information to be fully determined by the data. A Bayesian approach coupled with efficient sampling methods is developed to conduct statistical inference, and the performance of the proposed methodology is confirmed through simulations. The model is applied to a study on the risk factors of chronic kidney disease for patients with type 2 diabetes.

scholarly journals Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health

2021 ◽  
Vol 18 (3) ◽  
pp. 1202
Author(s):  
Katherine L. Forthman ◽  
Janna M. Colaizzi ◽  
Hung-wen Yeh ◽  
Rayus Kuplicki ◽  
Martin P. Paulus
Keyword(s):  
Mental Health ◽  
Factor Analysis ◽  
African Americans ◽  
Latent Variables ◽  
Factor Model ◽  
Five Factor Model ◽  
Demographic Data ◽  
Poor Mental Health ◽  
Future Research ◽  
Neighborhood Characteristics

Neighborhood characteristics can have profound impacts on resident mental health, but the wide variability in methodologies used across studies makes it difficult to reach a consensus as to the implications of these impacts. The aim of this study was to simplify the assessment of neighborhood influence on mental health. We used a factor analysis approach to reduce the multi-dimensional assessment of a neighborhood using census tracts and demographic data available from the American Community Survey (ACS). Multivariate quantitative characterization of the neighborhood was derived by performing a factor analysis on the 2011–2015 ACS data. The utility of the latent variables was examined by determining the association of these factors with poor mental health measures from the 500 Cities Project 2014–2015 data (2017 release). A five-factor model provided the best fit for the data. Each factor represents a complex multi-dimensional construct. However, based on heuristics and for simplicity we refer to them as (1) Affluence, (2) Singletons in Tract, (3) African Americans in Tract, (4) Seniors in Tract, and (5) Hispanics or Latinos in Tract. African Americans in Tract (with loadings showing larger numbers of people who are black, single moms, and unemployed along with fewer people who are white) and Affluence (with loadings showing higher income, education, and home value) were strongly associated with poor mental health (R2=0.67, R2=0.83). These findings demonstrate the utility of this factor model for future research focused on the relationship between neighborhood characteristics and resident mental health.

An investigation into the factor structure of the Cognitive Therapy Scale – Revised (CTS-R) in a CBT training sample

2021 ◽  
pp. 1-11
Author(s):  
Sarah Beale ◽  
Silia Vitoratou ◽  
Sheena Liness
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Therapy ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Factor Model ◽  
Single Factor ◽  
Behaviour Therapy ◽  
Cognitive Behaviour ◽  
Confirmatory Factor

Abstract Background: Effective monitoring of cognitive behaviour therapy (CBT) competence depends on psychometrically robust assessment methods. While the UK Cognitive Therapy Scale – Revised (CTS-R; Blackburn et al., 2001) has become a widely used competence measure in CBT training, practice and research, its underlying factor structure has never been investigated. Aims: This study aimed to present the first investigation into the factor structure of the CTS-R based on a large sample of postgraduate CBT trainee recordings. Method: Trainees (n = 382) provided 746 mid-treatment audio recordings for depression (n = 373) and anxiety (n = 373) cases scored on the CTS-R by expert markers. Tapes were split into two equal samples counterbalanced by diagnosis and with one tape per trainee. Exploratory factor analysis was conducted. The suggested factor structure and a widely used theoretical two-factor model were tested with confirmatory factor analysis. Measurement invariance was assessed by diagnostic group (depression versus anxiety). Results: Exploratory factor analysis suggested a single-factor solution (98.68% explained variance), which was supported by confirmatory factor analysis. All 12 CTS-R items were found to contribute to this single factor. The univariate model demonstrated full metric invariance and partial scalar invariance by diagnosis, with one item (item 10 – Conceptual Integration) demonstrating scalar non-invariance. Conclusions: Findings indicate that the CTS-R is a robust homogenous measure and do not support division into the widely used theoretical generic versus CBT-specific competency subscales. Investigation into the CTS-R factor structure in other populations is warranted.

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