scholarly journals An Analysis of Socio-Economic Strains and Population Gains: Urban and Rural Communities of Canada 1981-2001

2007 ◽  
Vol 34 (2) ◽  
pp. 191
Author(s):  
Fernando Mata ◽  
Ray D. Bollman
Keyword(s):  
Rural Communities ◽  
Structural Equation ◽  
Predictive Power ◽  
Structural Equation Models ◽  
Population Changes ◽  
Data Set ◽  
Rural And Urban ◽  
Statistics Canada ◽  
Demographic Shifts ◽  
Over Time

Important demographic shifts have occurred in Canada in the last decades. As a consequence of these shifts, many geographical communities have won or lost substantial number of residents between 1981 and 2001. Using the CCS (consolidated census subdivision) data set of the Agriculture Division of Statistics Canada, the paper explores the linkages between socio-economic strains and population changes affecting communities in a variety of regional and provincial contexts. A total of 2,607 rural and urban consolidated census subdivisions were examined across five census periods. Quasi simplex structural equation models using unemployment, earnings and poverty as indicators were tested on a variety of communities located in various OECD regions and provinces. Although the predictive power of strains on population gains was found to be limited in the models, a higher level of strain was persistently found to be negatively associated with population gains regardless of regional and provincial groupings of communities. Socio-economic strains were also observed to be relatively stable over time across a variety of geographies.

HRM practices and the multifaceted nature of organization performance

2018 ◽  
Vol 13 (3) ◽  
pp. 315-334 ◽  
Author(s):  
Abdallah Taamneh ◽  
Abdallah Khalaf Alsaad ◽  
Hamzah Elrehail
Keyword(s):  
Organizational Performance ◽  
Organizational Citizenship Behavior ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Performance Measurement System ◽  
Organization Performance ◽  
Data Set ◽  
Content Type ◽  
Hrm Practices ◽  
The Impact

Purpose The purpose of this paper is to investigate the impact of human resource management (HRM) practices on the performance of Jordanian banks as determined by using the balanced scorecard (BSC) performance measurement system and by testing the effect of organizational citizenship behavior (OCB) as a possible mediator variable. Design/methodology/approach A questionnaire was used to collect data from the study sample, which consisted of 230 managers working in various banks across Jordan. The study hypotheses were then tested using SPSS and AMOS software by applying structural equation models (SEMs). Findings The data set revealed that the use of HRM practices had a significant impact on both employee OCB and bank performance in all the four dimensions of the BSC (financial, customer satisfaction, internal processes, learning and growth). In addition, OCB was found to have a positive significant impact on organizational performance. Moreover, results indicated that OCB partially mediates the relationship between HRM practices and organizational performance. Originality/value The authors examine the impact HRM practices on the organization performance through the mediation role of OCB. The results obtained from this study extend the existing literature by providing evidences from non-western country such as Jordan. Based on the findings, the theoretical and practical implications of the study as well as limitations and suggestions for future studies are also discussed.

Relationship of economic development, family income and health status in China: The moderating role of environmental pollution perception

2020 ◽  
Vol 25 (13-14) ◽  
pp. 2499-2510
Author(s):  
Lijuan Chen ◽  
Youqing Fan ◽  
Wei Guo
Keyword(s):  
Economic Development ◽  
Health Status ◽  
Environmental Pollution ◽  
Structural Equation ◽  
Family Income ◽  
Structural Equation Models ◽  
General Social Survey ◽  
Mediating Effect ◽  
Hierarchical Regression ◽  
Data Set

Based on the matched data set of the 2013 Chinese General Social Survey and the gross domestic product per capita data extracted from China Statistical Yearbook 2013, this study used hierarchical regression analysis and structural equation models to examine whether environmental pollution perception would moderate the association between economic development and health status. Results revealed that economic development had a mediating effect via family income on health status. A moderated actor effect showed higher level of pollution perception weakens the relationship between family income and health status. Our findings suggested that appropriate environmental regulations should be implemented to sustain healthy economic growth in China.

scholarly journals Understanding Between-Person Interventions With Time-Intensive Longitudinal Outcome Data: Longitudinal Mediation Analyses

2020 ◽  
Author(s):  
Corina Berli ◽  
Jennifer Inauen ◽  
Gertraud Stadler ◽  
Urte Scholz ◽  
Patrick E Shrout
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Temporal Dynamics ◽  
Health Behavior Change ◽  
Outcome Data ◽  
Longitudinal Mediation ◽  
Behavior Change Interventions ◽  
Longitudinal Outcome ◽  
The Impact ◽  
Over Time

Abstract Background Mediation analysis is an important tool for understanding the processes through which interventions affect health outcomes over time. Typically the temporal intervals between X, M, and Y are fixed by design, and little focus is given to the temporal dynamics of the processes. Purpose In this article, we aim to highlight the importance of considering the timing of the causal effects of a between-person intervention X, on M and Y, resulting in a deeper understanding of mediation. Methods We provide a framework for examining the impact of a between-person intervention X on M and Y over time when M and Y are measured repeatedly. Five conceptual and analytic steps involve visualizing the effects of the intervention on Y, M, the relationship of M and Y, and the mediating process over time and selecting an appropriate analytic model. Results We demonstrate how these steps can be applied to two empirical examples of health behavior change interventions. We show that the patterns of longitudinal mediation can be fit with versions of longitudinal multilevel structural equation models that represent how the magnitude of direct and indirect effects vary over time. Conclusions We urge researchers and methodologists to pay more attention to temporal dynamics in the causal analysis of interventions.

scholarly journals Healthy Places for Children: The Critical Role of Engagement, Common Vision, and Collaboration

2020 ◽  
Vol 17 (24) ◽  
pp. 9277 ◽  
Author(s):  
Mildred E. Warner ◽  
Xue Zhang
Keyword(s):  
Rural Communities ◽  
Rural Areas ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Ecological Model ◽  
Critical Role ◽  
Child Poverty ◽  
Health Providers ◽  
Child Friendly ◽  
Common Vision

Planning plays a critical role in promoting healthy communities for children. We conducted a national survey of United States (US) cities and counties in 2019 and found only half of the 1312 responding communities report they give attention to the needs of children in their community plans. Those that do, provide more services and have more child-friendly zoning codes. We use a human ecological framework to build structural equation models of child-friendly zoning and services. We find communities with more engagement of families with children and youth and a common vision across generational, race, and ethnic lines report higher levels of child-friendly zoning and services. Collaboration between health providers and schools builds trust and leads to more services. However, child-friendly zoning is lower in communities with higher child poverty, and in suburbs and rural areas. Our results support a dynamic human ecological model where the processes of collaboration, inclusion, and engagement are key to creating healthy places for children. These processes may be especially important in addressing the unique challenges of suburban and rural communities.

scholarly journals Longitudinal predictors of the development of a calling: new evidence for the a posteriori hypothesis

2018 ◽  
Author(s):  
Anna Dalla Rosa ◽  
Michelangelo Vianello ◽  
Pasquale Anselmi
Keyword(s):  
Professional Identity ◽  
Structural Equation ◽  
Structural Equation Models ◽  
A Priori ◽  
A Posteriori ◽  
Supportive Environment ◽  
Positive Experiences ◽  
Clear Idea ◽  
New Evidence ◽  
Over Time

The literature is far from providing a clear answer about the development of callings over time. It has been hypothesized that calling is a consequence of positive experiences in a domain (a posteriori hypothesis), or that it is the antecedent of career choices and development (a priori hypothesis), or both (reciprocal hypothesis). To investigate which hypothesis better describes the development of a calling, a three-wave longitudinal study was conducted in which we tested the temporal precedence between calling and (1) clarity of professional identity, (2) engagement in learning activities, and (3) presence of a supportive social environment. Four competing structural equation models were estimated and compared. The results suggest that clarity of professional identity, engagement in learning, and social support positively predict calling rather than the opposite, and they provide support for the a posteriori hypothesis of calling development. Students who are actively engaged in their studies and have a clear idea of their occupational future are more likely to develop a calling over time. In addition, the results suggest that the presence of a supportive environment helps students to develop their calling. Implications for theory and research on calling are discussed.

scholarly journals Impact of Social Innovation on Innovation and Business Growth in Mexico

2021 ◽  
Vol 14 (6) ◽  
pp. 59
Author(s):  
Angie-Lorena Riaño-Castillo ◽  
Gonzalo Maldonado Guzman ◽  
Ruben Michael Rodriguez González ◽  
Sandra Yesenia Pinzón Castro
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Social Innovation ◽  
Data Set ◽  
Service Companies ◽  
Innovation Activities ◽  
Confirmatory Factor ◽  
The Relationship ◽  
Industry Professionals ◽  
Self Administered Questionnaire

The development of social innovation activities is a relatively recent topic in the innovation literature, and is increasingly gaining the attention of researchers, academics, and industry professionals. However, little has been written about the relationship between social innovation, service innovation activities and the level of growth of service companies, so this research aims to fill this gap in the literature to through an extensive review of the literature. Likewise, a self-administered questionnaire was distributed to a sample of 300 service companies in Mexico, analyzing the data set through confirmatory factor analysis and structural equation models.

scholarly journals Causality on longitudinal data: Stable specification search in constrained structural equation modeling

2017 ◽  
Vol 27 (12) ◽  
pp. 3814-3834 ◽  
Author(s):  
Ridho Rahmadi ◽  
Perry Groot ◽  
Marieke HC van Rijn ◽  
Jan AJG van den Brand ◽  
Marianne Heins ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Chronic Fatigue ◽  
Causal Modeling ◽  
Model Complexity ◽  
Equation Modeling ◽  
Data Set ◽  
Finite Data

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

scholarly journals Disaggregating Associations of Between-Person Differences in Change over Time from Associations of Within-Person Fluctuation in Longitudinal Data

2021 ◽  
Author(s):  
Lesa Hoffman
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Time Varying ◽  
Simulation Methods ◽  
Longitudinal Models ◽  
Time Specific ◽  
Over Time

In longitudinal models with time-varying predictors, the need to distinguish their within-person (WP) relations of time-specific residuals from their between-person (BP) relations of individual means is relatively well-known. In contrast, the need to further distinguish their BP relations of individual time slopes has received much less attention. This article addresses the deleterious impact that ignoring effects of individual time slopes in time-varying predictors can have on the recovery of BP intercept and WP residual relations in commonly used variants of longitudinal models. Using simulation methods and analyses of example data, this problem is demonstrated within univariate longitudinal models (i.e., multilevel or mixed-effects models using observed predictors), as well as in multivariate longitudinal models (i.e., structural equation models using latent predictors, including those for cross-lagged relations). Recommendations are provided for how to avoid conflating the BP and WP associations of longitudinal variables in practice.

scholarly journals The Perceived Impact of COVID-19 on Comfort Food Consumption over Time: The Mediational Role of Emotional Distress

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1910
Author(s):  
Camila Salazar-Fernández ◽  
Daniela Palet ◽  
Paola A. Haeger ◽  
Francisca Román Mella
Keyword(s):  
Food Consumption ◽  
Emotional Distress ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Negative Impact ◽  
Convenience Sample ◽  
Comfort Food ◽  
Perceived Impact ◽  
Over Time

The COVID-19 pandemic has had a significant impact on populations at an economic, health, and on an interpersonal level, it is still unclear how it has affected health-risk behaviors, such as comfort food consumption over time. This study longitudinally examines the effect of the perceived impact of COVID-19 on comfort food consumption and whether this effect is mediated by emotional distress. A convenience sample of 1048 students and university staff (academic and non-academic) from two universities completed monthly online surveys during the COVID-19 pandemic across six waves (W; W1 to W6). Participants reported their perceived impact of COVID-19 (economic, interpersonal, and health), comfort food consumption, and emotional distress (DASS-21). Using structural equation models, we found an indirect longitudinal effect of the perceived impact of COVID-19 (W1) on comfort food consumption (W3 to W6) through increased emotional distress (W2). The perceived negative impact of COVID-19 on comfort food consumption was fully mediated by the emotional distress during the first waves (W3 and W4), ending in a partial mediation in the last waves (W5 and W6). These findings contribute to disentangling the mechanisms by which the perceived impact of COVID-19 affects comfort food consumption over time, and highlight the role of emotional distress. Future interventions should address comfort food consumption by focusing on handling emotional distress during a crisis.

scholarly journals Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level

2017 ◽  
Vol 42 (4) ◽  
pp. 405-431 ◽  
Author(s):  
Victoria Savalei ◽  
Mijke Rhemtulla
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Estimation Method ◽  
Analytic Approach ◽  
Parameter Estimates ◽  
Two Stage ◽  
Data Set ◽  
Item Level

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data—that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study.

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