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1.
In standard canonical correlation analysis （CCA）, the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis （RCCA）, which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%. 相似文献
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Using a hypothetical data set, the authors provide concrete examples to illustrate that canonical correlation analysis is a general linear model, subsuming other parametric procedures as special cases. Specific statistical techniques included in the analysis are t tests, Pearson correlation, multiple regression, ANOVA, MANOVA, and discriminant analysis. The discussion is aided by an initial explanation of the logic of canonical analysis. Further, similarities between the canonical technique and other univariate and multivariate procedures are highlighted. The treatment is intended to reiterate a framework in which statistical concepts can be presented to students. 相似文献
3.
二维最大散度差鉴别准则和二维Fisher鉴别准则抽取的特征具有很强的相关性.本文在此基础上,通过对传统的基于向量的典型相关分析方法进行分析改进,提出了一种新的直接基于图像二维鉴别特征矩阵融合的二维典型相关分析方法,并将其应用于人脸识别的特征融合过程中.较基于向量的典型相关分析,该方法计算过程中构造的协方差矩阵维数大幅度减小.这在一定程度上避免了人脸识别中存在的"高维小样本问题",另一方面也使算法的速度明显提高. 相似文献
4.
The impacts of rainfall direction on the degree of hydrological response to rainfall properties were investigated using comparative rainfall-runoff experiments on a small-scale slope (4 m×1 m), as well as canonical correlation analysis (CCA). The results of the CCA, based on the observed data showed that, under conditions of both upstream and downstream rainfall movements, the hydrological process can be divided into instantaneous and cumulative responses, for which the driving forces are rainfall intensity and total rainfall, and coupling with splash erosion and wash erosion, respectively. The response of peak runoff (P r) to intensity-dominated rainfall action appeared to be the most significant, and also runoff (R) to rainfall-dominated action, both for upstream- and downstream-moving conditions. Furthermore, the responses of sediment erosion in downstream-moving condition were more significant than those in upstream-moving condition. This study indicated that a CCA between rainfall and hydrological characteristics is effective for further exploring the rainfall-runoff-erosion mechanism under conditions of moving rainfall, especially for the downstream movement condition. 相似文献
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结构方程模型(SEM)的原理及操作 总被引：10，自引：0，他引：10
孙连荣 《宁波大学学报(教育科学版)》2005,27(2):31-34,43
结构方程模型(SEM)是应用线性方程系统表示观测变量与潜在变量之间及潜在变量之间关系的一种统计方法。当前，SEM及相应的LISREL软件已成为心理学等社会学科中广泛应用的一种分析思想和技术。文章简要介绍了SEM的特点、原理及LISREL的操作方法。 相似文献
7.
Assessment policy in some countries continues to promote the use of continuous assessment (CA) within classrooms. Several policy claims have been made about the potential of CA to improve education. Using data from a 2010 evaluation of the Trinidad and Tobago Continuous Assessment Programme (CAP), canonical correlation analysis (CCA) was used to unravel the pattern of relationships between institutional variables, professional learning, teacher beliefs, and CA components. The CA components measured were (1) overall use, (2) multiple assessment formats use and (3) formative feedback use. Each CA component was shown to be associated with different variables, with overall use related to several teacher belief factors such as extra-role behaviour. However, multiple assessment formats use added only a small amount of unique variance to the CCA solution. Moreover, the canonical variate for formative feedback use was not statistically significant. These findings have implications for CA as a policy tool. Successful implementation of CA may require high quality professional learning as well as teacher workforce remodelling. Even when implemented successfully, however, CA may not be a useful vehicle for promoting high quality formative assessment or use of multiple assessment formats. 相似文献
8.
Perspectives on using video recordings in conversation analytical studies on learning in interaction
Fredrik Rusk Michaela Pörn Fritjof Sahlström Anna Slotte-Lüttge 《International Journal of Research & Method in Education》2015,38(1):39-55
Video is currently used in many studies to document the interaction in conversation analytical (CA) studies on learning. The discussion on the method used in these studies has primarily focused on the analysis or the data construction, whereas the relation between data construction and analysis is rarely brought to attention. The aim of this article is to discuss different approaches to data construction that CA studies in and on learning utilize, and how these approaches facilitate different analysis and understandings of learning and cognition from emic, participants', points of view. Three, partly overlapping, thematic approaches can be discerned: (1) setting-centred, (2) participant-centred and (3) content-centred. The underlying interest of the study seems to influence the data construction, which in turn affects what kind of analysis that can be done. There is a considerable variation in which aspects data sets focus on, where an emphasis in data construction on setting, participant or content also seems to project the subsequent analytic emphasis. This relation between data construction and analysis is important to be aware of and to address. 相似文献
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Shelley A. Blozis Xiaojia Ge Shu Xu Misaki N. Natsuaki Daniel S. Shaw Jenae M. Neiderhiser 《Structural equation modeling》2013,20(2):283-298
Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software. 相似文献
10.
Cara A. Finnegan 《Quarterly Journal of Speech》2013,99(4):377-402
This essay considers the ways that iconoclasm, or the will to control images and vision, appears in canonical and contemporary public sphere theory. John Dewey and Jürgen Habermas enact a paradoxical relation to visuality by repudiating a mass culture of images while preferring “good” images and vision. Yet even when advocating for good vision, both theorists activate a subtle iconoclasm that operates as a perennial tension in their work. The essay concludes by considering the ways in which iconoclasm manifests itself in more recent scholarship in rhetorical studies and suggests circulation as an analytic concept with some promise for helping public sphere theorists develop a more iconophilic relationship to images and vision. 相似文献
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Keke Lai 《Structural equation modeling》2013,20(5):757-777
The comparative fit index (CFI) is one of the most widely-used fit indices in structural equation modeling (SEM). When applying the CFI to model evaluation, although it is universally recognized that the focus should be the population fit, in practice one often considers only the CFI value within a sample and neglects the uncertainty in point estimation. Confidence interval (CI) methods for CFI appeared only recently, but these methods assume multivariate normality, which often fails to hold in practice. In addition, the current methods are applications of the bootstrap and are thus computationally intensive. To better handle nonnormal data and simplify CI construction, in this paper we propose an analytic CI method for CFI without assuming normality. We then carry out simulation studies to compare the new and current methods at various levels of model misfit and nonnormality. Simulation results verify the effectiveness and advantages of the new method. 相似文献
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Mike W.-L. Cheung 《Structural equation modeling》2013,20(1):157-167
Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects modeling (LMM) such as cross-sectional multilevel modeling and latent growth modeling. It is well known that LMM can be formulated as structural equation models. However, one main difference between the implementations in SEM and LMM is that maximum likelihood (ML) estimation is usually used in SEM, whereas restricted (or residual) maximum likelihood (REML) estimation is the default method in most LMM packages. This article shows how REML estimation can be implemented in SEM. Two empirical examples on latent growth model and meta-analysis are used to illustrate the procedures implemented in OpenMx. Issues related to implementing REML in SEM are discussed. 相似文献
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When conducting longitudinal research, the investigation of between-individual differences in patterns of within-individual change can provide important insights. In this article, we use simulation methods to investigate the performance of a model-based exploratory data mining technique—structural equation model trees (SEM trees; Brandmaier, Oertzen, McArdle, & Lindenberger, 2013)—as a tool for detecting population heterogeneity. We use a latent-change score model as a data generation model and manipulate the precision of the information provided by a covariate about the true latent profile as well as other factors, including sample size, under the possible influences of model misspecifications. Simulation results show that, compared with latent growth curve mixture models, SEM trees might be very sensitive to model misspecification in estimating the number of classes. This can be attributed to the lower statistical power in identifying classes, resulting from smaller differences of parameters prescribed by the template model between classes. 相似文献
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Tenko Raykov Thanos Patelis George A. Marcoulides Chun-Lung Lee 《Structural equation modeling》2016,23(1):111-115
This article outlines an interval estimation procedure that can be used in a 3-level setting to evaluate the proportion of outcome variance attributable to the second level of clustering. The method is useful for examining the necessity of including a possibly omitted intermediate level of nesting in analyses of data from a multilevel study, and represents an informative addendum to current statistical tests of second-level variance. The approach is developed within the framework of latent variable modeling and can be used as an aid in the process of choosing between 2-level and 3-level models in a hierarchical design. The discussed procedure is illustrated with an empirical example. 相似文献
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Stochastic differential equation (SDE) models are a promising method for modeling intraindividual change and variability. Applications of SDEs in the social sciences are relatively limited, as these models present conceptual and programming challenges. This article presents a novel method for conceptualizing SDEs. This method uses structural equation modeling (SEM) conventions to simplify SDE specification, the flexibility of SEM to expand the range of SDEs that can be fit, and SEM diagram conventions to facilitate the teaching of SDE concepts. This method is a variation of latent difference scores (McArdle, 2009; McArdle & Hamagami, 2001) and the oversampling approach (Singer, 2012), and approximates the advantages of analytic methods such as the exact discrete model (Oud & Jansen, 2000) while retaining the modeling fiexibility of methods such as latent differential equation modeling (Boker, Neale, & Rausch, 2004). A simulation and empirical example are presented to illustrate that this method can be implemented on current computing hardware, produces good approximations of analytic solutions, and can flexibly accommodate novel models. 相似文献
16.
陈仁恩 《广播电视大学学报》2003,(3):68-71
统计是一门调查、整理、分析的方法论科学．其研究对象是现象总体的数量特征和数量关系，它的几个基本范畴脉承统计活动的全过程。对“对象”、对这些“范畴”的深刻理解，是教好、学好统计学这门课程的基础。 相似文献
17.
Mike W.-L. Cheung 《Structural equation modeling》2013,20(3):481-509
Meta-analysis is the statistical analysis of a collection of analysis results from individual studies, conducted for the purpose of integrating the findings. Structural equation modeling (SEM), on the other hand, is a multivariate technique for testing hypothetical models with latent and observed variables. This article shows that fixed-effects meta-analyses with the following characteristics can be modeled in the SEM framework: (a) using any type of effect size; (b) including categorical and continuous moderators; and (c) including multivariate effect sizes. Empirical examples in LISREL syntax are used to demonstrate the equivalence between the meta-analytic and SEM approaches. Future directions for and extensions to this approach are discussed. 相似文献
18.
Read aloud is a testing accommodation that has been studied by many researchers, and its use on K‐12 assessments continues to be debated because of its potential to change the measured construct or unfairly increase test scores. This study is a summary of quantitative research on the read aloud accommodation. Previous studies contributed information to compute average effect sizes for students with disabilities, students without disabilities, and the difference between groups for reading and mathematics using a random effects meta‐analytic approach. Results suggest that (1) effect sizes are larger for reading than for math for both student groups, (2) the read aloud accommodation increases reading test scores for both groups, but more so for students with disabilities, and (3) mathematics scores gains due to the read aloud accommodation are small for both students with and without disabilities, on average. There was some evidence to suggest larger effects in elementary school relative to middle and high school and possible mode effects, but more studies are needed within levels of the moderator variables to conduct statistical tests. 相似文献
19.
David Wilkinson 《Performance Improvement Quarterly》1999,12(4):71-81
Cost-consequences analysts Kaufman and Watkins (1996) have proposed cost-consequences analysis (CCA) as a means for leaders and decision-makers to estimate whether the value of results obtained is worth the investment. They argue that CCA offers a “coarse-grain” estimate of return-on-investment when there is not the necessity nor time and/or resources available for a complete determination of all of the variables that actually go into a return-on-investment analysis. They further contend that CCA differs from other evaluation tools in that it emphasizes outcomes, whereas the other tools utilize incomplete measures of both costs and returns. This paper contends, after assessing the theoretical underpinnings of cost-benefit analysis (CBA), together with its use in practice, that there is no substantive difference between CCA and CBA. In other words, CCA does not add anything new to the evaluation tools available to program evaluators. In arguing this, the paper also addresses some issues of what is required for good program evaluation, no matter which tool is used. 相似文献
20.
Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist. 相似文献