The Experts below are selected from a list of 27237 Experts worldwide ranked by ideXlab platform
Tihomir Asparouhov - One of the best experts on this subject based on the ideXlab platform.
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bayesian structural equation modeling a more flexible representation of Substantive Theory
Psychological Methods, 2012Co-Authors: Bengt Muthen, Tihomir AsparouhovAbstract:This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects Substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximumlikelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford’s (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.
Bengt Muthen - One of the best experts on this subject based on the ideXlab platform.
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bayesian structural equation modeling a more flexible representation of Substantive Theory
Psychological Methods, 2012Co-Authors: Bengt Muthen, Tihomir AsparouhovAbstract:This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects Substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximumlikelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford’s (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.
Eija Paavilainen - One of the best experts on this subject based on the ideXlab platform.
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using grounded Theory to create a Substantive Theory of promoting schoolchildren s mental health
Nurse Researcher, 2013Co-Authors: Kristiina Puolakka, Kirsimaria Haapasalopesu, Irma Kiikkala, Paivi Astedtkurki, Eija PaavilainenAbstract:AIM To discuss the creation of a Substantive Theory using grounded Theory. This article provides an example of generating Theory from a study of mental health promotion at a high school in Finland. BACKGROUND Grounded Theory is a method for creating explanatory Theory. It is a valuable tool for health professionals when studying phenomena that affect patients' health, offering a deeper understanding of nursing methods and knowledge. DATA SOURCES Interviews with school employees, students and parents, and verbal responses to the 'school wellbeing profile survey', as well as working group memos related to the development activities. Participating children were aged between 12 and 15. REVIEW METHODS The analysis was conducted by applying the grounded Theory method and involved open coding of the material, constant comparison, axial coding and selective coding after identifying the core category. DISCUSSION AND CONCLUSIONS The analysis produced concepts about mental health promotion in school and assumptions about relationships. Grounded Theory proved to be an effective means of eliciting people's viewpoints on mental health promotion. The personal views of different parties make it easier to identify an action applicable to practice.
Paivi Astedtkurki - One of the best experts on this subject based on the ideXlab platform.
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using grounded Theory to create a Substantive Theory of promoting schoolchildren s mental health
Nurse Researcher, 2013Co-Authors: Kristiina Puolakka, Kirsimaria Haapasalopesu, Irma Kiikkala, Paivi Astedtkurki, Eija PaavilainenAbstract:AIM To discuss the creation of a Substantive Theory using grounded Theory. This article provides an example of generating Theory from a study of mental health promotion at a high school in Finland. BACKGROUND Grounded Theory is a method for creating explanatory Theory. It is a valuable tool for health professionals when studying phenomena that affect patients' health, offering a deeper understanding of nursing methods and knowledge. DATA SOURCES Interviews with school employees, students and parents, and verbal responses to the 'school wellbeing profile survey', as well as working group memos related to the development activities. Participating children were aged between 12 and 15. REVIEW METHODS The analysis was conducted by applying the grounded Theory method and involved open coding of the material, constant comparison, axial coding and selective coding after identifying the core category. DISCUSSION AND CONCLUSIONS The analysis produced concepts about mental health promotion in school and assumptions about relationships. Grounded Theory proved to be an effective means of eliciting people's viewpoints on mental health promotion. The personal views of different parties make it easier to identify an action applicable to practice.
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from Substantive Theory towards a family nursing scale
Nurse Researcher, 2009Co-Authors: Hanna Maijala, Paivi AstedtkurkiAbstract:Introduction This paper describes the process of developing and testing a family nursing scale called Family Care during Child’s Illness (FCCI). The scale is based on Maijala’s (2004) Substantive interaction Theory. The empirical focus is on the provision of interactive nursing care for families of children aged one to three who require acute hospital care primarily for somatic reasons. The scale is intended for the purpose of measuring caregivers’ assessments of care provision in families where the child is potentially in a very serious and precarious situation and where there is much uncertainty (Meiers and Tomlinson 2003, Gedaly-Duff et al 2005). Typically, the need for acute hospital care is due to an accident, the onset of diabetes, cancer or an infectious disease.
Kenneth A Bollen - One of the best experts on this subject based on the ideXlab platform.
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the latent variable autoregressive latent trajectory model a general framework for longitudinal data analysis
Structural Equation Modeling, 2018Co-Authors: Silvia Bianconcini, Kenneth A BollenAbstract:In recent years, longitudinal data have become increasingly relevant in many applications, heightening interest in selecting the best longitudinal model to analyze them. Too often, traditional practice rather than Substantive Theory guides the specific model selected. This opens the possibility that alternative models might better correspond to the data. In this paper, we present a general longitudinal model that we call the Latent Variable-Autoregressive Latent Trajectory (LV-ALT) model that includes most other longitudinal models with continuous outcomes as special cases. It is capable of specializing to most models dictated by Theory or prior research while having the capacity to compare them to alternative ones. If there is little guidance on the best model, the LV-ALT provides a way to determine the appropriate empirical match to the data. We present the model, discuss its identification and estimation, and illustrate how the LV-ALT reveals new things about a widely used empirical example.
