Showing papers on "Path analysis (statistics) published in 2010"

Multivariate data analysis : a global perspective

Abstract: 1 Introduction: Models and Model Building Section I Understanding and Preparing for Multivariate Analysis 2 Cleaning and Transforming Data 3 Factor Analysis Section II Analysis Using Dependece Techniques 4 Simple and Multiple Regression Analysis 5 Canonical correlation 6 Conjoint analysis 7 Multiple Discriminant Analysis and Logistic Regression 8 ANOVA and MANOVA Section III Analysis using Interdependence Techniques 9 Group data and Cluster Analysis 10 MDS and Correspondence Analysis Structural Equation Modeling 11 SEM: An Introduction 12 Application of SEM

Factors Affecting Technology Integration in K-12 Classrooms: A Path Model.

Abstract: The purpose of this study was to examine the direct and indirect effects of teachers’ individual characteristics and perceptions of environmental factors that influence their technology integration in the classroom. A research-based path model was developed to explain causal relationships between these factors and was tested based on data gathered from 1,382 Tennessee public school teachers. The results provided significant evidence that the developed model is useful in explaining factors affecting technology integration and the relationships between the factors.

Revisiting Interpretation of Canonical Correlation Analysis: A Tutorial and Demonstration of Canonical Commonality Analysis.

Abstract: In the face of multicollinearity, researchers face challenges interpreting canonical correlation analysis (CCA) results. Although standardized function and structure coefficients provide insight into the canonical variates produced, they fall short when researchers want to fully report canonical effects. This article revisits the interpretation of CCA results, providing a tutorial and demonstrating canonical commonalty analysis. Commonality analysis fully explains the canonical effects produced by using the variables in a given canonical set to partition the variance of canonical variates produced from the other canonical set. Conducting canonical commonality analysis without the aid of software is laborious and may be untenable, depending on the number of noteworthy canonical functions and variables in either canonical set. Commonality analysis software is identified for the canonical correlation case and we demonstrate its use in facilitating model interpretation. Data from Holzinger and Swineford (1939) are employed to test a hypothetical theory that problem-solving skills are predicted by fundamental math ability.

Goals, Efficacy and Metacognitive Self-Regulation A Path Analysis

Abstract: The purpose of this study was to illustrate the relationship between self-efficacy, task value, goal orientations, metacognitive self-regulation, self-regulation and learning strategies and to investigate the unique contribution of each on the variability in students’ total scores of 12 exams Our study revealed that students’ self-efficacy, task-value, self-regulation, and elaboration are significantly positively correlated with total scores Path analysis demonstrated that self-efficacy was the strongest predictor of total score and positively predicted mastery goals, but negatively predicted avoidant goals The study reveals positive direct effect of mastery goals on metacognitive self-regulation In addition, positive direct effects of metacognitive self-regulation on deep learning strategies and on self-regulatory strategies are found However, some expected direct effects were not represented with significant parameters in the model Performance-approach goals were not a significant predictor of other variables in the model Also, there were no significant direct effects of mastery goals nor metacognitive self-regulation and deep learning strategies on total scores which were discussed here

Approaching multivariate analysis : a practical introduction

Abstract: Preface. 1. Multivariate Techniques in Context. 2. Analysis of Variance (ANOVA). 3. Multivariate Analysis of Variance (MANOVA). 4. Multiple Regression. 5. Analysis of covariance (ANCOVA). 6. Partial Correlation, Mediation and Moderation. 7. Path Analysis. 8. Factor Analysis. 9. Discriminant Analysis and Logistic Regression. 10. Cluster Analysis. 11. Multidimensional Scaling. 12. Loglinear Models. 13. Poisson Regression. 14. Survival Analysis. 15. Longitudinal Data. Appendix: SPSS and SAS Syntax.

Genetic variances, heritability, correlation and path coefficient analysis in yellow maize crosses (Zea mays L.).

Abstract: The present study was undertaken to study the gene action, narrow sense heritability, interrelationships among traits and path coefficient analysis for grain yield and its components, plant and ear height, leaf area index (LAI), specific leaf weight (SLW) and physiological maturity (Ph. M). Fifteen hybrids produced using a half diallel fashion in 2008 season were evaluated for grain yield and its components and morphophysiological traits during 2009 season. The obtained results indicated that all estimates of additive (VA) and dominance (VD) variance were significant for all characteristics with exception of additive variance for specific leaf weight Also, dominance variance for leaf area index, plant and ear height, ear length, and number of kernel per row. However the magnitude of VA was consistently larger than that of VD for all characteristics with exception of specific leaf weight, silking date, stay green, 100kernel weight and grain yield where VD values were larger than VA values.High narrow sense heritability estimates were detected for leaf area index (96%), number of kernel per row (86%), plant height (85%), ear height (83%), physiological maturity (82%), number of rows per ear (77%), ear length (73%) and ear diameter (62%) and Emphasizing that the additive genetic variance was the major component of genetic variation in the inheritance of these traits and the effectiveness of selection for improving these traits. However, moderate narrow sense heritability estimates were obtained for leaf angel (48%), specific leaf area (46%) and 100kernel weight (44%). While estimates were low for grain yield (39%), silking date (34%), stay green (27%) and specific leaf weight (26%). These results indicated the importance of choosing the suitable segregating generations for exhibiting the best expression of gene of different studied traits. Correlation coefficients among traits indicated that grain yield was positively and significantly associated with number of kernel per row (0.589), ear length (0.465), and leaf area index (0.497).The path coefficient analysis was calculated to detect the relative importance of characters contributing to grain yield. Data showed that each of leaf area index, ear diameter and physiological maturity had high positive direct effects on grain yield.

Chapter 24 Analyzing Factorial Data Using PLS: Application in an Online Complaining Context

Abstract: Structural equation modeling (SEM) can be employed to emulate more traditional analysis techniques, such as MANOVA, discriminant analysis, and canon- ical correlation analysis. Recently, it has been realized that this emulation is not restricted to covariance-based SEM, but can easily be extended to components- based SEM, or partials least squares (PLS) path analysis (Guinot et al. 2001; Tenenhaus et al. 2005; Wetzels et al. 2005). In this paper, we will apply PLS path analysis to a fixed-effects, between-subjects factorial design in an online complaint- handling context. The results of our empirical study reveal that satisfaction with online recovery is determined by the level of both procedural and distributive jus- tice. Furthermore, customers' satisfaction with the way their complaints are handled has a positive influence on the customers' intentions to repurchase and to spread pos- itive word of mouth. Taking into account the entire chain of effects, we find that the influence of justice perceptions on behavioral intentions is almost fully mediated by satisfaction. From a managerial perspective, the results of our study provide insight into how to design effective complaint-handling strategies in order to maintain a satisfied and loyal customer base.

Analyzing Factorial Data Using PLS: Application in an Online Complaining Context

Abstract: Structural equation modeling (SEM) can be employed to emulate more traditional analysis techniques, such as MANOVA, discriminant analysis, and canonical correlation analysis. Recently, it has been realized that this emulation is not restricted to covariance-based SEM, but can easily be extended to components-based SEM, or partials least squares (PLS) path analysis (Guinot et al. 2001; Tenenhaus et al. 2005; Wetzels et al. 2005). In this paper, we will apply PLS path analysis to a fixed-effects, between-subjects factorial design in an online complaint-handling context. The results of our empirical study reveal that satisfaction with online recovery is determined by the level of both procedural and distributive justice. Furthermore, customers’ satisfaction with the way their complaints are handled has a positive influence on the customers’ intentions to repurchase and to spread positive word of mouth. Taking into account the entire chain of effects, we find that the influence of justice perceptions on behavioral intentions is almost fully mediated by satisfaction. From a managerial perspective, the results of our study provide insight into how to design effective complaint-handling strategies in order to maintain a satisfied and loyal customer base.

Path analysis for discrete variables: The role of education in social mobility

Abstract: An important open question in sociology with obvious policy implications is how to assess the magnitude of the effect of educational attainment on intergenerational social mobility. To examine this, we propose a general method of path analysis, which can be used to estimate direct and indirect effects even in systems where some of the variables are categorical. It provides an additive decomposition of total effects which is exact when the effects are expressed as mean differences, and approximate but typically quite accurate for other measures of association such as log-odds-ratios. Estimates of the effects and their standard errors can be calculated by using standard output for fitted models. The method is illustrated by an analysis of British survey data on social mobility.

Study on genetic variation and relationship between plant characteristics and grain yield in long spike bread wheat (Triticum aestivum L.) genotypes-using multivariate analysis.

Abstract: Zakizadeh. M., M. Esmaeilzadeh and D. Kahrizi. 2010. Study on genetic variation and relationship between plant characteristics and grain yield in long spike bread wheat (Triticum aestivum L.) genotypes-using multivariate analysis. Iranian Journal of Crop Sciences. 12 (2):18-30 (in Persian). To study genetic variation and to determine the relations between different plant characteristics with grain yield, seventy long spike bread wheat genotypes together and two commercial cultivars (Chamran and Bahar) were evaluated using Alpha Lattice design with two replications, in Karaj Field Station, Seed and Plant Improvement Institute (SPII),, Karaj, Iran. Thirty traits including grain yield and its components, plant and grain morphological and quality related traits were measured and recorded for all genotypes. Factor analysis determined three factors that accounted 96% of total variations among genotypes. These factors were yield components, quality related traits and grain yield and its components. Stepwise regression analysis showed that biological yield, grain weight.spike and number. of spike m were of higher importance among other grain yield components. Path analysis coefficients showed that the highest direct effects for grain yield were biological yield, grain weightsSpike and number. of spikes m. Cluster analysis with Ward's method based on all measured traits and quality related traits resulted in three and four groups, respectively.

Analyzing the Relationship between Dependent and Independent Variables in Marketing: A Comparison of Multiple Regression with Path Analysis

Abstract: Multiple regression models continue to be widely used in marketing Within the regression framework, researchers have to grapple with and resolve several contentious issues For example, multicollinearity, non simultaneous estimation of parameters, inherent measurement error in independent variables, absence of overall goodness of fit indices, and lack of compelling guidelines for adding and deleting model variables are some common estimation problems associated with this method In the absence of universally acceptable guidelines, researchers often use judgment calls to deal with these issues Employing ad-hoc approaches, in turn, compromises the potential usefulness of multiple regression models In this paper, we position path analysis as a competing technique that can address in a relatively unambiguous way, many of the above mentioned limitations of multiple regression We illustrate the superiority of path analysis by reanalyzing data from selected marketing studies that have used multiple regression models To enable researchers use path analysis more frequently, we provide a technical appendix depicting use of the EQS software for re-estimating multiple regression models We discuss several implications of our results and outline avenues for future research

A framework for structural equation models in general pedigrees.

Abstract: Background/Aims: Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the meas

Analysis and Interpretation of Multivariate Data

Abstract: Multivariate analysis is concerned with the interrelationships among several variables The data may be metrical, categorical, or a mixture of the two Multivariate data may be, first, summarized by looking at the pair-wise associations Beyond that, the different methods available are designed to explore and elucidate different features of the data The article briefly summarizes the scope and purpose of the following methods: cluster analysis, multidimensional scaling, principal components analysis, latent class analysis, latent profile analysis, latent trait analysis, factor analysis, regression analysis, discriminant analysis, path analysis, correspondence analysis, multilevel analysis, and structural equation analysis

A Study on the Effective Cultural Factors Involving in Protection of the Environment in Tehran

Abstract: In the world of today, the issue of environmental protection is one of the main concerns. This research is created with the purpose of scientifically identification of cultural effects on environmental protection and behaviors. The views analysis is based on Durkheim, Weber, Parsons, Giddens, Tomeh approaches and theories. The research and questionnaire are used for 400 citizens of Tehran selected from two different areas, 5 and 18. Data analysis based on inferential statistics and tables distribution, and in the deductive statistics, Pearson correlation coefficient, multivariable regression and path analysis are used for data analysis as well. Accessing the research in detail and examination of so many variables impact on environmental protection, results showed that the variables such as people's responsibility, sociological behaviors, education and traditional religious values influence on environmental protection and behaviors. But on the other hand, results showed that modeling variable has no effect on environmental protection. It can be concluded that peoples behaviors especially environmental behaviors are originated from their culture and plays an important role in this regard.