共查询到20条相似文献,搜索用时 15 毫秒
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多阶段增长模型的方法比较 总被引:1,自引:0,他引:1
多阶段增长模型(Piecewise Growth Modeling,PGM)可以解决发展趋势中具有转折点的情形,并且相对其他复杂的曲线增长模型,解释更简单.已有的统计方法主要通过多层线性模型和潜变量增长模型对多阶段模型进行估计.通过模拟研究,用HLM6.0和Mplus6.0对上述两种模型分别进行估计,结果发现在参数估计的精度上,两种估计方法没有差异,只是在犯一类错误的概率上后者略小.进一步通过对错误模型的探讨发现,在样本量小(n=50),斜率变化小(△b=0.2)时,用线性模型拟合数据而非PGM所犯错误概率较小,整体拟合更佳.但随着样本的增加和斜率变化的增加,错误模型的犯错概率明显增大.故在实际应用中,为了能更好拟合数据,研究者应根据数据本身的情况选择恰当的模型. 相似文献
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《Multivariate behavioral research》2013,48(4):313-338
Longitudinal data sets typically suffer from attrition and other forms of missing data. When this common problem occurs, several researchers have demonstrated that correct maximum likelihood estimation with missing data can be obtained under mild assumptions concerning the missing data mechanism. With reasonable substantive theory, a mixture of cross-sectional and longitudinal methods developed within multiple-group structural equation modeling can provide a strong basis for inference about developmental change. Using an approach to the analysis of missing data, the present study investigated developmental trends in adolescent (N = 759) alcohol, marijuana, and cigarette use across a 5-year period using multiple-group latent growth modeling. An associative model revealed that common developmental trends existed for all three substances. Age and gender were included in the model as predictors of initial status and developmental change. Findings discuss the utility of latent variable structural equation modeling techniques and missing data approaches in the study of developmental change. 相似文献
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Considering that group comparisons are common in social science, we examined two latent group mean testing methods when groups of interest were either at the between or within level of multilevel data: multiple-group multilevel confirmatory factor analysis (MG ML CFA) and multilevel multiple-indicators multiple-causes modeling (ML MIMIC). The performance of these methods were investigated through three Monte Carlo studies. In Studies 1 and 2, either factor variances or residual variances were manipulated to be heterogeneous between groups. In Study 3, which focused on within-level multiple-group analysis, six different model specifications were considered depending on how to model the intra-class group correlation (i.e., correlation between random effect factors for groups within cluster). The results of simulations generally supported the adequacy of MG ML CFA and ML MIMIC for multiple-group analysis with multilevel data. The two methods did not show any notable difference in the latent group mean testing across three studies. Finally, a demonstration with real data and guidelines in selecting an appropriate approach to multilevel multiple-group analysis are provided. 相似文献
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Jaehwa Choi Jeffrey R. Harring Gregory R. Hancock 《Multivariate behavioral research》2013,48(5):620-645
Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following logistic (sigmoidal; S-shape) response functions. Such trajectories are assumed in a variety of psychological and educational settings where learning occurs over time, and yet applications using the logistic model in growth modeling methodology have been sparse. The logistic function, in particular, may not be utilized as often because software options remain limited. In this article we show how a specialized version of the logistic function can be modeled using conventional structural equation modeling software. The specialization is a reparameterization of the logistic function whose new parameters correspond to scientifically interesting characteristics of the growth process. In addition to providing an example using simulated data, we show how this nonlinear functional form can be fit using transformed subject-level data obtained through a learning task from an air traffic controller simulation experiment. LISREL syntax for the empirical example is provided. 相似文献
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Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables 总被引:2,自引:0,他引:2
This paper uses log-linear models with latent variables (Hagenaars, in Loglinear Models with Latent Variables, 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly
defined and discussed. In addition, because the log-linear model with latent variables is a general model for cognitive diagnosis,
new alternatives to modeling the functional relationship between attribute mastery and the probability of a correct response
are discussed. 相似文献
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In recent years, there has been a growing interest among researchers in the use of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, in part due to advances in and availability of computer software designed for this purpose (e.g., Mplus and SAS Proc Traj). Latent growth modeling approaches, such as latent class growth analysis (LCGA) and growth mixture modeling (GMM), have been increasingly recognized for their usefulness for identifying homogeneous subpopulations within the larger heterogeneous population and for the identification of meaningful groups or classes of individuals. The purpose of this paper is to provide an overview of LCGA and GMM, compare the different techniques of latent growth modeling, discuss current debates and issues, and provide readers with a practical guide for conducting LCGA and GMM using the Mplus software. 相似文献
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Many approaches have been proposed to estimate interactions among latent variables. These methods often assume a specific functional form for the interaction, such as a bilinear interaction. Theory is seldom specific enough to provide a functional form for an interaction, however, so a more exploratory, diagnostic approach may often be required. Bauer (2005) proposed a semiparametric approach that allows for the estimation of interaction effects of unknown functional form among latent variables. A structural equation mixture model (SEMM) is first fit to the data. Then an approximation of the interaction is obtained by aggregating over the mixing components. A simulation study is used to examine the performance of this semiparametric approach to two parametric approaches: the latent moderated structures approach (Klein & Moosbrugger, 2000) and the unconstrained product-indicator approach (Marsh, Wen, & Hau, 2004). Data were generated from four functional forms: main effects only, quadratic trend, bilinear interaction, and exponential interaction. Estimates of bias and root mean squared error of approximation were calculated by comparing the surface used to generate the data and the model-implied surface constructed from each approach. As expected, the parametric approaches were more efficient than the SEMM. For the main effects model, bias was similar for both the SEMM and parametric approaches. For the bilinear interaction, the parametric approaches provided nearly identical results, although the SEMM approach was slightly more biased. When the parametric approaches assumed a bilinear interaction and the data were generated from a quadratic trend or an exponential interaction, the parametric approaches generated biased estimates of the true surface. The SEMM approach approximated the true data generation surface with a similarly low level of bias for all the nonlinear surfaces. For example, Figure 1 shows the true surface for the bilinear interaction along with the SEMM estimated average surface. The results suggest that the SEMM approach can provide a relatively unbiased approximation to variety of nonlinear relationships among latent variables. 相似文献
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This study clarifies the relationships among self leader perceptions, ideal leader prototypes, and leader judgments among college students using latent profile analysis (LPA) and log-linear modeling. LPA was used to identify subgroups of individuals with unique patterns of attributes for self and ideal leaders, providing a holistic view of leadership and how multiple attributes work together within individuals. LPA was also used to identify subgroups of individuals with unique patterns of judgments of leader effectiveness. After identification of subgroups (i.e., profiles), log-linear modeling was used to test 1 baseline model and 5 sets of hypothesized associations among self leader profiles, ideal leader profiles, and leader judgment profiles. Results suggest that self leader profiles are related to ideal leader profiles, which in turn are related to leader judgment profiles, but that there is no direct relation between self leader profiles and leader judgment profiles. 相似文献
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Kjell Härnqvist 《Scandinavian journal of psychology》1997,38(1):55-62
Scores in ability tests administered to students in grades 4–9 were simultaneously factor-analyzed within twelve gender by grade groups. Gender and grade differences in means and variances were studied for five latent ability factors according to a hierarchical model and compared with means and variances in the observed scores.
Girls had higher means than boys in a general ability factor (G), in a residual general speed factor (Gs') and in a residual factor of numerical facility (N'). Boys were higher in a residual vocabulary factor (V') and most of all in a residual spatial visualization factor (Vz'). Boys had larger variances than girls in N' and Gs'. In general the differences in means and variances were in the same direction for the closest corresponding observed scores, but some striking discrepancies between latent and observed scores were found. As a rule, the discrepancies were due to the complexity of the tests where one factor could compensate for another.
In the discussion it was pointed out that some of the differences found were likely to have changed between the testing in the late 1950s and the present. Nevertheless the demonstration of the divergence between analyses of latent vs. observed scores remains valid. 相似文献
Girls had higher means than boys in a general ability factor (G), in a residual general speed factor (Gs') and in a residual factor of numerical facility (N'). Boys were higher in a residual vocabulary factor (V') and most of all in a residual spatial visualization factor (Vz'). Boys had larger variances than girls in N' and Gs'. In general the differences in means and variances were in the same direction for the closest corresponding observed scores, but some striking discrepancies between latent and observed scores were found. As a rule, the discrepancies were due to the complexity of the tests where one factor could compensate for another.
In the discussion it was pointed out that some of the differences found were likely to have changed between the testing in the late 1950s and the present. Nevertheless the demonstration of the divergence between analyses of latent vs. observed scores remains valid. 相似文献
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Terry E. Duncan Susan C. Duncan 《Journal of psychopathology and behavioral assessment》2004,26(4):271-278
This paper presents a latent variable approach for the estimation of treatment effects within a pooled interrupted time series (ITS) design. Although considered quasi-experimental, the ITS design has been noted as representing one of the strongest alternatives to the randomized experiment, making it highly appropriate for use in documenting the presence of effects that might warrant further evaluation in a large-scale randomized study. Results suggest that the latent variable growth modeling (LGM) is capable of detecting simultaneous differences in both level and slope, and provides tests of significance for these two necessary indicators of an ITS intervention effect. As shown in the analyses, the LGM framework provides a comprehensive and flexible approach to research design and data analysis, making available to a wide audience of researchers an analytical framework for a variety of analyses of growth and developmental processes. 相似文献
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Kevin Grimm Zhiyong Zhang Fumiaki Hamagami Michèle Mazzocco 《Multivariate behavioral research》2013,48(1):117-143
A general equadion is presented, covering all arbitrary values for the true population splits, for obtaining the true population phi, given observed cell frequencies for a selected sample, and true population splits. A nongeneral solution is also offered, based on the use of the G Index. Demonstrations with hypothetical data are given. 相似文献
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Common method variance (CMV) is an ongoing topic of debate and concern in the organizational literature. We present four latent variable confirmatory factor analysis model designs for assessing and controlling for CMV—those for unmeasured latent method constructs, Marker Variables, Measured Cause Variables, as well as a new hybrid design wherein these three types of method latent variables are used concurrently. We then describe a comprehensive analysis strategy that can be used with these four designs and provide a demonstration using the new design, the Hybrid Method Variables Model. In our discussion, we comment on different issues related to implementing these designs and analyses, provide supporting practical guidance, and, finally, advocate for the use of the Hybrid Method Variables Model. Through these means, we hope to promote a more comprehensive and consistent approach to the assessment of CMV in the organizational literature and more extensive use of hybrid models that include multiple types of latent method variables to assess CMV. 相似文献
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Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for
inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information
and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a
broad class of semiparametric Bayesian SEMs, which allow mixed categorical and continuous manifest variables while also allowing
the latent variables to have unknown distributions. In order to include typical identifiability restrictions on the latent
variable distributions, we rely on centered Dirichlet process (CDP) and CDP mixture (CDPM) models. The CDP will induce a latent
class model with an unknown number of classes, while the CDPM will induce a latent trait model with unknown densities for
the latent traits. A simple and efficient Markov chain Monte Carlo algorithm is developed for posterior computation, and the
methods are illustrated using simulated examples, and several applications. 相似文献
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Researchers frequently have only categorical data to analyze and cannot, for theoretical or methodological reasons, assume that the observed variables are discrete representations of an underlying continuous variable. We present latent class analysis as an alternative method of measuring latent variables in these circumstances. Latent class analysis does not require the assumptions of factor analyses about the nature of manifest and latent variables, but does allow the use of more precise model selection than techniques such as cluster analysis. We modeled the lifetime substance use of American Indian youth. The latent class model of American Indian teenagers' substance use had four classes: Abstaining, Predominantly Alcohol, Predominantly Alcohol and Marijuana, and Plural Substance. We then demonstrated the usefulness of this latent variable by using it to differentiate levels of several variables in a manner consistent with Social Cognitive Theory. 相似文献
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The purpose of this study was to demonstrate the use of Latent Growth Modeling (LGM) as a method for estimating reliability of Curriculum-Based Measurement (CBM) progress-monitoring data. The LGM approach permits the error associated with each measure to differ at each time point, thus providing an alternative method for examining of the reliability of CBM reading aloud data over repeated measurements. The analysis revealed that the reliability of CBM data was not a fixed property of the measure, but it changed with time. The study demonstrates the need to consider reliability in new ways with respect to the use of CBM data as repeated measures. 相似文献
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Barbara M. Byrne Wendy W. T. Lam Richard Fielding 《Journal of personality assessment》2013,95(6):536-546
Latent growth curve (LGC) modeling within the framework of structural equation modeling (SEM) is now highly regarded as one of the most powerful and informative approaches to the analysis of longitudinal data (see, e.g., Curran &; Hussong, 2003). Whereas LGC modeling enables researchers to test for differences in developmental trajectories across time, conventional repeated measures analyses do not provide this opportunity. Nonetheless, a review of studies reported in most psychology journals reveals scant application of this methodological approach. One possible explanation for this limited use of LGC modeling is a lack of knowledge related to its application. The intent of this article, then, is to address this deficiency by presenting an annotated application of LGC modeling to health psychology data. Based on a sample of 405 Hong Kong Chinese women who recently underwent breast cancer surgery, we walk the readers through SEM modeling procedures that test for differences in both the initial status and rate of change in Psychological Morbidity and Social Adjustment at 1, 4, and 8 months postsurgery. We interpret findings from both a methodological and a substantive perspective. 相似文献