scholarly journals Is China Moving toward Healthy Aging? A Tracking Study Based on 5 Phases of CLHLS Data

2020 ◽  
Vol 17 (12) ◽  
pp. 4343
Author(s):  
Yinan Yang ◽  
Yingying Meng
Keyword(s):  
Older People ◽  
Physical Health ◽  
Latent Variable ◽  
Structural Equation ◽  
Healthy Aging ◽  
Factor Model ◽  
Equation Modeling ◽  
Elderly Individuals ◽  
Comparison Results ◽  
Elderly Populations

Health is the key to the aging problem, and “healthy aging” depicts the overall changing trends in the health of all elderly individuals in a society. Based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data from the years 2002, 2005, 2008, 2011 and 2014, this article investigates whether there is a trend of “healthy aging” in China. A second-order factor model including four dimensions of physical health, functional status, mental health and social health was constructed to measure a latent variable, “Health_elders”. The further multigroup comparison results by structural equation modeling showed that, with the exception of 2008, the Health_elders in 2002, 2005, 2011 and 2014 displayed an upward trend, and the mean differences in Health_elders across five periods were significant. These findings indicate that on the whole, compared with older people in the past, older people in more recent periods are healthier, which supports the trend of “healthy aging” in China. In terms of cohorts, the average health levels of male, town-residing elderly populations are higher, while the healthy aging trends among female, rural and urban elderly populations are stronger. Moreover, the physical health levels of the 60–74 years-old cohort are decreasing, and the participation of elderly individuals in social activities is low, which are the weaknesses in the healthy aging process in China.

A New Dimension of Organizational Justice: Procedural Voice

2009 ◽  
Vol 105 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Denise Jepsen ◽  
John Rodwell
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Justice ◽  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Equation Modeling ◽  
Wide Range

Dimensionality of the Colquitt justice measures was investigated across a wide range of service occupations. Structural equation modeling of data from 410 survey respondents found support for the 4-factor model of justice (procedural, distributive, interpersonal, and informational), although significant improvement of model fit was obtained by including a new latent variable, “procedural voice,” which taps employees' desire to express their views and feelings and influence results. The model was confirmed in a second sample ( N = 505) in the same organization six months later.

scholarly journals The Latent-Variable Structure of the Addenbrooke’s Cognitive Examination-Revised

2019 ◽  
Vol 35 (2) ◽  
pp. 205-212 ◽  
Author(s):  
Madeleine L Connolly ◽  
Stephen C Bowden ◽  
Leonie C Simpson ◽  
Malcolm Horne ◽  
Sarah McGregor
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Five Factor Model ◽  
Variable Structure ◽  
Equation Modeling ◽  
Long Term Memory ◽  
Chc Theory ◽  
Three Factor ◽  
Addenbrooke’S Cognitive Examination

Abstract Objectives To establish a theoretically justified factor structure for the Addenbrooke’s Cognitive Examination-Revised (ACE-R). Methods Our sample comprised 288 patients with Parkinson’s disease (179 men and 109 women). The mean age of participants was 66.66 (SD = 8.93). Confirmatory factor analysis (CFA) was used to evaluate the test developers’ five-factor model of the ACE-R, and alternative models as guided by the Cattell–Horn–Carroll (CHC) theory. Exploratory structural equation modeling (ESEM) was also employed to examine alternative factor structures to ensure that a good candidate model was not overlooked. Results A three-factor CHC-guided CFA and a similar three-factor ESEM model both showed acceptable overall fit, and interpretable factor structures. The three-factor CFA model showed two factors of pure CHC constructs: acquired knowledge (Gc), and visuospatial ability (Gv), and one combined factor, namely, long-term memory retrieval, fluency, and working memory (Glr-Gsm). The three-factor ESEM model showed three factors essentially in line with the CFA results. Conclusion The three-factor CHC-guided CFA model was selected as the best model to guide clinical interpretation of cognitive variables underlying ACE-R scores.

scholarly journals How to Compare Psychometric Factor and Network Models

2020 ◽  
Vol 8 (4) ◽  
pp. 35
Author(s):  
Kees-Jan Kan ◽  
Hannelies de Jonge ◽  
Han L. J. van der Maas ◽  
Stephen Z. Levine ◽  
Sacha Epskamp
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Empirical Finding ◽  
Network Models ◽  
Latent Variable Models ◽  
R Package ◽  
Equation Modeling ◽  
Adult Intelligence Scale ◽  
Intelligence Scale

In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.

scholarly journals Is China Moving Towards Healthy Aging? —A tracking study based on 5 phases of CLHLS data

2020 ◽  
Author(s):  
Yinan Yang ◽  
Yingying Meng
Keyword(s):  
Older People ◽  
Physical Health ◽  
Elderly People ◽  
Healthy Aging ◽  
Elderly Population ◽  
The Elderly ◽  
The World ◽  
Elderly Populations ◽  
The Mean ◽  
Mean Differences

Abstract BackgroundMost of the countries are entering an aging society in the world. China has the largest number of elderly people in the world and “healthy aging” is the key way for China to cope with the challenges of population aging. This paper aims to investigate whether there is a trend of "healthy aging" in China using the longitudinal data.MethodsThe data used in this study were from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data of 2002, 2005, 2008, 2011 and 2014. A second-order factor model including four dimensions of physical health, functional status, mental health and social health was constructed to measure a latent variable, “Health_elders” and the structural mean model was further used to the examine the significance of the mean differences in “Health_elders” across five periods.ResultsThe results showed that, with the exception of 2008, the Health_elders in 2002, 2005, 2011 and 2014 displayed an upward trend, and the mean differences in Health_elders across five periods were significant. These findings indicate that on the whole, compared with older people in the past, older people in more recent periods are healthier, which supports the trend of "healthy aging" in China. In terms of groups, the health levels of male, town-residing elderly populations are higher, but the healthy aging trends among female elderly people and rural and urban elderly populations are stronger. Regarding the physical health of the elderly population, the health levels of the 60-74 years old cohort are decreasing, and the participation of elderly individuals in social activities is low, which is the weakness in the healthy aging process in ChinaConclusionThe health status of the elderly population is generally on the rise, indicating that China's aging is moving towards healthy aging. So the government should take more measures to encourage the medical and health system to adapt to the aging situation and requirements as soon as possible.

Brief Symptom Inventory Factor Structure in Antisocial Adolescents: Implications for Juvenile Justice

2011 ◽  
Vol 22 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Ahmed Whitt ◽  
Matthew O. Howard
Keyword(s):  
Juvenile Justice ◽  
Factor Structure ◽  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Psychiatric Patients ◽  
Brief Symptom Inventory ◽  
Equation Modeling ◽  
Symptom Inventory ◽  
Antisocial Youth

Objectives: The Brief Symptom Inventory (BSI) is widely used in juvenile justice settings; however, little is known regarding its factor structure in antisocial youth. The authors evaluated the BSI factor structure in a state residential treatment population. Methods: 707 adolescents completed the BSI. Exploratory and confirmatory factor analyses were used to assess factor structure. Intergroup variability was examined using multiple-group structural equation modeling. Results: Findings supported a 6-factor, 25-item model explaining 49.5%of sample variance. The derived structure differed from prior findings with adult psychiatric patients by including a suicidal ideation latent variable and excluding several developmentally inappropriate factors. Conclusion: There may be problems associated with indiscriminant application of the original BSI factor model to juvenile justice populations.

A Psychometric Study of the Quality Perception Questionnaire

2012 ◽  
Vol 71 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Raffaele Cioffi† ◽  
Anna Coluccia ◽  
Fabio Ferretti ◽  
Francesca Lorini ◽  
Aristide Saggino ◽  
...  
Keyword(s):  
Structural Equation ◽  
Factor Model ◽  
Equation Modeling ◽  
Fit Indices ◽  
Quality Perception ◽  
Italian Survey ◽  
Confirmatory Factor ◽  
Instrument Measuring ◽  
Hospital Structures

The present paper reexamines the psychometric properties of the Quality Perception Questionnaire (QPQ), an Italian survey instrument measuring patients’ perceptions of the quality of a recent hospital admission experience, in a sample of 4400 patients (Mage = 56.42 years; SD = 19.71 years, 48.8% females). The 14-item survey measures four factors: satisfaction with medical doctors, nursing staff, auxiliary staff, and hospital structures. First, we tested two models using a confirmatory factor analysis (structural equation modeling): a four orthogonal factor and a four oblique factor model. The SEM fit indices and the χ² difference suggested the acceptance of the second model. We then did a simulation using a bootstrap with 1000 replications. Results confirmed the four oblique factor solution. Third, we tested whether there were significant differences with respect to age or sex. The multivariate general linear model showed no significant differences in the factors with respect to sex or age.

Working Memory, Mental Prospection, Time Orientation, and Cognitive Insight

2013 ◽  
Vol 34 (3) ◽  
pp. 159-169 ◽  
Author(s):  
Sevtap Cinan ◽  
Aslı Doğan
Keyword(s):  
Working Memory ◽  
Structural Equation ◽  
Factor Model ◽  
Time Orientation ◽  
Equation Modeling ◽  
Cognitive Insight ◽  
Confirmatory Factor ◽  
Negative Effect ◽  
Positive Effect ◽  
Three Factor

This research is new in its attempt to take future time orientation, morningness orientation, and prospective memory as measures of mental prospection, and to examine a three-factor model that assumes working memory, mental prospection, and cognitive insight are independent but related higher-order cognitive constructs by using confirmatory factor analysis (CFA). The three-factor model produced a good fit to the data. An alternative one-factor model was tested and rejected. The results suggest that working memory and cognitive insight are distinguishable, related constructs, and that both are distinct from, but negatively associated with, mental prospection. In addition, structural equation modeling (SEM) showed that working memory had a strong positive effect on cognitive insight and a moderate negative effect on mental prospection.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

scholarly journals Postpartum depression symptoms in survey-based research: a structural equation analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Che Wan Jasimah Bt Wan Mohamed Radzi ◽  
Hashem Salarzadeh Jenatabadi ◽  
Nadia Samsudin
Keyword(s):  
Postpartum Depression ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Public Health Problem ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Postpartum Women ◽  
Unhealthy Food ◽  
High Level

Abstract Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

scholarly journals Insights Into the Correspondence Between Land Use Pattern and Spatial Distribution of Rail Transit Services

2021 ◽  
Author(s):  
Zhongqi Wang ◽  
Qi Han ◽  
Bauke de Vries ◽  
Li Dai
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Operations Management ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Rail Transit ◽  
Land Use Patterns ◽  
Transit Services ◽  
The Relationship

AbstractThe identification of the relationship between land use and transport lays the foundation for integrated land use and transport planning and management. This work aims to investigate how rail transit is linked to land use. The research on the relationship between land use and rail-based transport is dominated by the impacts of rail projects on land use, without an in-depth understanding of the reverse. However, it is important to note that issues of operation management rather than new constructions deserve greater attention for regions with established rail networks. Given that there is a correspondence between land use patterns and spatial distribution of heavy railway transit (HRT) services at such regions, the study area (i.e., the Netherlands) is partitioned by the Voronoi diagram of HRT stations and the causal relationship between land use and HRT services is examined by structural equation modeling (SEM). The case study of Helmond (a Dutch city) shows the potential of the SEM model for discussing the rail station selection problem in a multiple transit station region (MTSR). Furthermore, in this study, the node place model is adapted with the derivatives of the SEM model (i.e., the latent variable scores for rail service levels and land use characteristics), which are assigned as node and place indexes respectively, to analyze and differentiate the integration of land use and HRT services at the regional level. The answer to whether and how land use affects rail transit services from this study strengthens the scientific basis for rail transit operations management. The SEM model and the modified node place model are complementary to be used as analytical and decision-making tools for rail transit-oriented regional development.

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