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

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.

2020 ◽  
Vol 42 (12) ◽  
pp. 1148-1154
Author(s):  
Lakeshia Cousin ◽  
Laura Redwine ◽  
Christina Bricker ◽  
Kevin Kip ◽  
Harleah Buck

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.


Author(s):  
Mark Shevlin

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.


2020 ◽  
pp. 001316442096316
Author(s):  
Tenko Raykov ◽  
Lisa Calvocoressi

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.


2017 ◽  
Vol 28 (4) ◽  
pp. 986-1002 ◽  
Author(s):  
Deng Pan ◽  
Kai Kang ◽  
Chunjie Wang ◽  
Xinyuan Song

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.


2020 ◽  
pp. 140349482097455
Author(s):  
Johanna Haraldsson ◽  
Ronnie Pingel ◽  
Lena Nordgren ◽  
Ylva Tindberg ◽  
Per Kristiansson

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.


1997 ◽  
Vol 24 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Michael W. Browne ◽  
Krishna Tateneni

2018 ◽  
Vol 66 ◽  
pp. S11-S12 ◽  
Author(s):  
A. Coni ◽  
S. Mellone ◽  
M. Colpo ◽  
S. Bandinelli ◽  
L. Chiari

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