Showing papers on "Path analysis (statistics) published in 2005"
TL;DR: It is concluded that, whenever possible, it is better to use a latent variable model in which parcels are used as indicators than a path analysis model using total scale scores.
Abstract: The biasing effects of measurement error in path analysis models can be overcome by the use of latent variable models. In cases where path analysis is used in practice, it is often possible to use parcels as indicators of a latent variable. The purpose of the current study was to compare latent variable models in which parcels were used as indicators of the latent variables, path analysis models of the aggregated variables, and models in which reliability estimates were used to correct for measurement error in path analysis models. Results showed that point estimates of path coefficients were smallest for the path analysis models and largest for the latent variable models. It is concluded that, whenever possible, it is better to use a latent variable model in which parcels are used as indicators than a path analysis model using total scale scores.
611 citations
TL;DR: Path analysis cannot be used to establish causality or even to determine whether a specific model is correct; it can only determine whether the data are consistent with the model, but it is extremely powerful for examining complex models and for comparing different models to determine which one best fits the data.
Abstract: Path analysis is an extension of multiple regression. It goes beyond regression in that it allows for the analysis of more complicated models. In particular, it can examine situations in which there are several final dependent variables and those in which there are "chains" of influence, in that variable A influences variable B, which in turn affects variable C. Despite its previous name of "causal modelling," path analysis cannot be used to establish causality or even to determine whether a specific model is correct; it can only determine whether the data are consistent with the model. However, it is extremely powerful for examining complex models and for comparing different models to determine which one best fits the data. As with many techniques, path analysis has its own unique nomenclature, assumptions, and conventions, which are discussed in this paper.
334 citations
01 Jan 2005
TL;DR: Path analysis as discussed by the authors is an extension of multiple regression, and it can examine situations in which there are several final dependent variables and those where there are "chains" of influence, in that variable A influences variable B, which in turn affects variable C. Despite its previous name of causality, path analysis cannot be used to establish causality or even to determine whether a specific model is correct; it can only determine whether the data are consistent with the model.
Abstract: Path analysis is an extension of multiple regression. It goes beyond regression in that it allows for the analysis of more complicated models. In particular, it can examine situations in which there are several final dependent variables and those in which there are “chains” of influence, in that variable A influences variable B, which in turn affects variable C. Despite its previous name of “causal modelling,” path analysis cannot be used to establish causality or even to determine whether a specific model is correct; it can only determine whether the data are consistent with the model. However, it is extremely powerful for examining complex models and for comparing different models to determine which one best fits the data. As with many techniques, path analysis has its own unique nomenclature, assumptions, and conventions, which are discussed in this paper.
265 citations
Book•
23 Dec 2005
TL;DR: Basic Features of Statistical Analysis and the General Linear Model Multivariate Analysis of Variance Multiple Regression Log-Linear Analysis Logistic Regression Factor Analysis Path Analysis Structural Equation Modelling Time Series Analysis Facet Theory and Smallest Space Analysis Survival or Failure Analysis Repertory Grids
Abstract: Basic Features of Statistical Analysis and the General Linear Model Multivariate Analysis of Variance Multiple Regression Log-Linear Analysis Logistic Regression Factor Analysis Path Analysis Structural Equation Modelling Time Series Analysis Facet Theory and Smallest Space Analysis Survival or Failure Analysis Repertory Grids
221 citations
TL;DR: A new presentation of discriminant analysis consists in setting up patterns associated to the various groups and deriving latent variables in such a way that scores in each group are as highly clustered about their pattern as possible.
89 citations
Book•
08 Dec 2005
TL;DR: In this paper, the authors present a mathematical representation of multivariate data and perform principal components analysis with linear regression, correlation analysis, and canonical variates analysis for multivariate analysis of variance.
Abstract: Introduction. Matrix algebra. Basic multivariate statistics. Graphical representation of multivariate data. Principal components analysis. Biplots. Correspondence analysis. Cluster analysis. Multidimensional scaling. Linear regression analysis. Multivariate analysis of variance. Canonical correlation analysis. Discriminant analysis and canonical variates analysis. Loglinear modelling. Factor analysis. Other latent variable models. Graphical modelling. Data mining.
51 citations
Dissertation•
01 Jan 2005
38 citations
TL;DR: In this article, the authors derived sufficient criteria for identification of path analysis models in which marginalisation is carried out over the hidden variable, based on the structure of the directed acyclic graph associated with the path analysis model.
Abstract: SUMMARY We study criteria for identifiability of path analysis models with one hidden variable. We first derive sufficient criteria for identification of models in which marginalisation is carried out over the hidden variable. The sufficient criteria are based on the structure of the directed acyclic graph associated with the path analysis model and can be derived from the graph. We treat further the identification of models when the hidden variable is conditioned on and establish connections with the extended skew-normal distribution. Finally it is shown that the derived conditions extend the existing graphical criteria for identification.
30 citations
TL;DR: The authors applied Partial Least Square (PLS) analysis to the South African TIMSS-R data to explore the effect of contextual factors at school level and classroom level within South African schools on the aggregated pupils' performance in mathematics.
Abstract: South African pupils performed well below the TIMSS international average in 1995 and 1999 and significantly below all other countries (including the other African countries) in the 1999 study. Path analysis, namely Partial Least Square (PLS) analysis, was applied to the South African TIMSS-R data to explore the effect of contextual factors at school level and classroom level within South African schools on the aggregated pupils' performance in mathematics. The results from the combined school- and classroom-level model revealed a relationship between the location of the schools, teachers' attitudes and beliefs, teaching load, lesson planning, and class size; all of which had direct effects on the South African pupils' aggregated performance in mathematics and in total explained 27% of the variance in the mathematics scores.
27 citations
Journal Article•
17 citations
TL;DR: In this paper, a study of 67 women who have been divorced for 2 or more years, the authors explored the factors enhancing or limiting the social functioning of these individuals and identified such factors as duration of marriage, number of children, involvement of ex-spouse, and family support as determinants of postdivorce adjustment.
Abstract: Explored in this study of 67 women who have been divorced for 2 or more years are the factors enhancing or limiting the social functioning of these individuals. The path analysis identifies such factors as duration of marriage, number of children, involvement of ex-spouse, and family support as determinants of postdivorce adjustment.
Journal Article•
01 Jan 2005
TL;DR: In this paper, a teacher made knowledge test was constructed to measure the knowledge of the respondents about composite fish culture practices and the analysis clearly revealed that, the education and socio-economlc status were favorable psychological and communication variables played and important roles.
Abstract: For the purpose of present investigation a teacher made knowledge test was constructed to measure the knowledge of the respondents about composite fish culture practices. The analysis clearly revealed that, the education and socio-economlc status were favorable psychological and
communication variables played and important roles. However, amongst these psychological, communication and socio-economic variables, innovative proneness, scientific orientation and size of holdings were obtained as the determining factors in the multiple regression analysis. Further, path analysis revealed that scientific orientation through innovative proneness was giving direct effect on the knowledge level of farmers.
15 Oct 2005
TL;DR: In this paper, the early twentieth century origins of graphical models were reviewed and the subsequent major developments from path analysis and conditional independence to graphical models in representing relations among random variables, and, more recently, chains of such multivariate relations.
Abstract: This article reviews the early twentieth century origins of graphical models and outlines the subsequent major developments from path analysis and conditional independence to the use of graphical models in representing relations among random variables, and, more recently, chains of such multivariate relations.
Keywords:
conditional independence;
chain graphs;
multivariate statistics;
path analysis;
structural equation models
15 Oct 2005
TL;DR: Partial correlation is a method for identifying the correlation between two variables, with the effects of a third variable held constant as discussed by the authors, which is mathematically equivalent to the correlation of the residual scores of the two variables regressed upon the control variable.
Abstract: Partial correlation is a method for identifying the correlation between two variables, with the effects of a third variable held constant. It is mathematically equivalent to the correlation between the residual scores of the two variables regressed upon the control variable. Partial correlation is conceptually related to, and an integral part of, a number of other useful statistical methods, including path analysis, structural equation modeling, factor analysis, and stepwise multiple regression.
Keywords:
partial correlation;
zero-order correlation;
control variable;
first-order correlation
01 Jan 2005
Journal Article•
15 Oct 2005
TL;DR: Path analysis as discussed by the authors can be used to decompose the covariance between two variables in a structural equation model into additive components, thus helping to understand how the interrelationships between many variables in an equation model predict the expected moment structure implied by that model.
Abstract: Path analysis refers to the calculation of moment structures within and between variables, for example, covariances or correlations, implied by a set of simultaneous linear regression equations – one type of structural equation model. Path diagrams of structural equation models offer a graphical representation with exact one-to-one mapping to the simultaneous linear regression equations. Path analysis allows one to decompose the covariance between two variables in a structural equation model into additive components, thus helping to understand how the interrelationships between many variables in a model predict the covariance between two selected variables. By performing path analysis calculations on all variables and pairs of variables in a model, one may calculate the expected moment structure implied by that model. Path analysis preceded and led to the development of modern structural equation modeling methods of parameter estimation.
Journal Article•
Journal Article•
Journal Article•
TL;DR: In this paper, a path analysis model for the biggest variance of phenotype traits with principal component and completed decision-making analysis and showed a way to judge the main weight-bearing trait, confinement trait and intermedial trait from principal component.
Abstract: By using decision-making analysis of path analysis,this paper built a path analysis model for the biggest variance of phenotype traits with principal component and completed decision-making analysis and showed a way to judge the main weight-bearing trait,confinement trait and intermedial trait from principal component.So breeding operator can choose the traits from individual expression to achieve the expectation of genetic process by selection.
Journal Article•
TL;DR: Choi et al. as mentioned in this paper analyzed path model of the research variables and found that paid-leave, communication, reward, motivator, job satisfaction and organizational identification were direct or indirect predictors of the work performance.
Abstract: Purpose: The aim of this study was to analysis path model of the research variables. Methods: The subjects of this study were 647 nurses who were working in the 8 general hospitals located in Seoul and Incheon area. The data were collected by self-reporting questionnaires. The data were analyzed using descriptive statistics and path analysis. Results: In the modified path model, overall fitness indexes were = 223.27, goodness of fit index=0.90, root mean square residual=0.039, root mean square error of approximation=0.12, non-normed fit index=0.96, and normed fit index=0.90. From the model, among research variables that influence organizational effectiveness motivator, job satisfaction and organizational identification affected directly work performance. In internal marketing factors, paid-leave, communication and reward affected directly motivator. Motivator and hygiene factors affected directly job satisfaction, organizational commitment and organizational identification. Conclusion: With these findings, paid-leave, communication, reward, motivator, job satisfaction and organizational identification were direct or indirect predictors of the work performance. Therefore nursing managers ought to develop internal marketing strategies and motivation enhancing system for nurses based on this path model in order to improve the nursing organizational effectiveness.
Journal Article•
Journal Article•
TL;DR: Familiarity, ease of access, trust, and awareness will all be important for the future.
Abstract: 目的探讨军校医学生心理应激的影响因素及其相互作用方式.方法选用军校医学生日常困扰评定量表、应付方式问卷、特质焦虑问卷、自尊量表和流调中心用抑郁量表对935名军校医学生进行测试,采用SPSS10.0对数据进行统计分析.结果日常困扰主要通过间接作用影响军校医学生的心理应激;自尊和特质焦虑对心理应激具有直接作用和间接作用.应付方式是日常困扰、个性特点影响心理应激的中间环节.结论维护军校医学生的身心健康,可以把优化其个性结构,加强应对训练作为一个切人点.