共查询到20条相似文献，搜索用时 31 毫秒
1.
Mixed-dyadic data, collected from distinguishable (nonexchangeable) or indistinguishable (exchangeable) dyads, require statistical analysis techniques that model the variation within dyads and between dyads appropriately. The purpose of this article is to provide a tutorial for performing structural equation modeling analyses of cross-sectional and longitudinal models for mixed independent variable dyadic data, and to clarify questions regarding various dyadic data analysis specifications that have not been addressed elsewhere. Artificially generated data similar to the Newlywed Project and the Swedish Adoption Twin Study on Aging were used to illustrate analysis models for distinguishable and indistinguishable dyads, respectively. Due to their widespread use among applied researchers, the AMOS and Mplus statistical analysis software packages were used to analyze the dyadic data structural equation models illustrated here. These analysis models are presented in sufficient detail to allow researchers to perform these analyses using their preferred statistical analysis software package. 相似文献
2.
Daniel L. Murphy S. Natasha Beretvas Keenan A. Pituch 《Structural equation modeling》2013,20(3):430-448
This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a second model that included first-order autoregressive and moving average autocorrelation parameters. The results indicated that the estimates of the overall trend in the data were accurate regardless of model specification across most conditions. Variance components estimates were biased across many conditions but improved as sample size and series length increased. In general, the two models that incorporated autocorrelation parameters performed well when sample size and series length were large. The COFM had the best overall performance. 相似文献
3.
The assessment of mediation in dyadic data is an important issue if researchers are to test process models. Using an extended version of the actor–partner interdependence model the estimation and testing of mediation is complex, especially when dyad members are distinguishable (e.g., heterosexual couples). We show how the complexity of the model can be reduced by assuming specific dyadic patterns. Using structural equation modeling, we demonstrate how specific mediating effects and contrasts among effects can be tested by phantom models that permit point and bootstrap interval estimates. We illustrate the assessment of mediation and the strategies to simplify the model using data from heterosexual couples. 相似文献
4.
In dyadic research, the actor–partner interdependence model (APIM) is widely used to model the effect of a predictor measured across dyad members on one’s own and one’s partner outcome. When such dyadic data are measured repeatedly over time, both the non-independence within couples and the non-independence over time need to be accounted for. In this paper, we present a longitudinal extension of the APIM, the L-APIM, that allows for both stable and time-varying sources of non-independence. Its implementation is readily available in multilevel software, such as proc mixed in SAS, but is lacking in the structural equation modeling (SEM) framework. We tackle the computational challenges associated with its SEM-implementation and propose a user-friendly free application for the L-APIM, which can be found at http://fgisteli.shinyapps.io/Shiny_LDD. As an illustration, we explore the actor and partner effects of positive relationship feelings on next day’s intimacy using 3-week diary data of 66 heterosexual couples. 相似文献
5.
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. 相似文献
6.
Elizabeth M. Planalp Han Du Julie M. Braungart-Rieker 《Structural equation modeling》2017,24(1):129-147
Although methodology articles have increasingly emphasized the need to analyze data from two members of a dyad simultaneously, the most popular method in substantive applications is to examine dyad members separately. This might be due to the underappreciation of the extra information simultaneous modeling strategies can provide. Therefore, the goal of this study was to compare multiple growth curve modeling approaches for longitudinal dyadic data (LDD) in both structural equation modeling and multilevel modeling frameworks. Models separately assessing change over time for distinguishable dyad members are compared to simultaneous models fitted to LDD from both dyad members. Furthermore, we compared the simultaneous default versus dependent approaches (whether dyad pairs’ Level 1 [or unique] residuals are allowed to covary and differ in variance). Results indicated that estimates of variance and covariance components led to conflicting results. We recommend the simultaneous dependent approach for inferring differences in change over time within a dyad. 相似文献
7.
A Second-Order Conditionally Linear Mixed Effects Model With Observed and Latent Variable Covariates
Jeffrey R. Harring Nidhi Kohli Rebecca D. Silverman Deborah L. Speece 《Structural equation modeling》2013,20(1):118-136
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a nonlinear manner are common to all subjects. In this article we describe how a variant of the Michaelis–Menten (M–M) function can be fit within this modeling framework using Mplus 6.0. We demonstrate how observed and latent covariates can be incorporated to help explain individual differences in growth characteristics. Features of the model including an explication of key analytic decision points are illustrated using longitudinal reading data. To aid in making this class of models accessible, annotated Mplus code is provided. 相似文献
8.
Naoya Todo 《Structural equation modeling》2016,23(5):695-712
In longitudinal design, investigating interindividual differences of intraindividual changes enables researchers to better understand the potential variety of development and growth. Although latent growth curve mixture models have been widely used, unstructured finite mixture models (uFMMs) are also useful as a preliminary tool and are expected to be more robust in identifying classes under the influence of possible model misspecifications, which are very common in actual practice. In this study, large-scale simulations were performed in which various normal uFMMs and nonnormal uFMMs were fit to evaluate their utility and the performance of each model selection procedure for estimating the number of classes in longitudinal designs. Results show that normal uFMMs assuming invariance of variance–covariance structures among classes perform better on average. Among model selection procedures, the Calinski–Harabasz statistic, which has a nonparametric nature, performed better on average than information criteria, including the Bayesian information criterion. 相似文献
9.
郭东道 《西安文理学院学报》2005,8(3):54-58
简介并矢展开的基本原理，用并矢展开技术设计了一个双液面控制系统：给出双液面系统结构，用解析法建立了对象数学模型，将模型传递函数矩阵进行并矢展开，设计控制器，判断闭环系统性能，进行闭环仿真。 相似文献
10.
《Parenting, science and practice》2013,13(4):297-320
Objective. The purposes of this study were to identify mother, child, and dyadic determinants of effective mother–child collaboration and to determine the impact of this collaboration on children's cognitive development. Design. Ninety-two mother–child dyads from the Massachusetts site of the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development participated in a site-specific, home-based instructional task through which they were assessed for scaffolding effectiveness. Cognitive characteristics of both mothers and children, as well as dyadic characteristics from infancy, were examined as predictors of effective dyadic scaffolding when the children were in 1st grade. In addition, concurrent cognitive capabilities of the children were regressed on scaffolding while controlling for earlier cognitive test scores. Results. Mothers' verbal intelligence and children's mental development, as well as shared sensitivity, predicted the effectiveness of scaffolding collaborations, which in turn uniquely predicted cognitive capabilities of the children. Conclusions. Effective mother–child scaffolding is a function of individual mother and child characteristics, as well as the nature of the mother–child relationship; scaffolding predicts children's cognitive outcomes. 相似文献
11.
François Findji Marie-Germaine Pêcheux Josette Ruel 《European Journal of Psychology of Education - EJPE》1993,8(1):23-33
Infants’ abilities to focus attention on objects are known to be related to mothers’ mobilizing behaviors. As delayed effects of maternal behaviors at 5 months may be observed in 8-month-olds, mothers may be considered as scaffolding their infant’s attention. However, all dyadic activities are probably not equally propitious to attention mobilizing. In a sample of 30 dyads, studied at 5 and 8 months of age, whole observations were split in four broad categories: care, dyadic play with objects, dyadic play without objects and infant alone. The duration of maternal mobilizing and infant attention focussing were studied within categories. Inter-dyads variability is high, while dyads are stable across ages. Even within the dyadic play with objects, mothers differ widely in the duration and way they mobilize attention. Five-month-olds still need their mother’s support, as they explore less when they are alone, while 8-month-olds are more autonomous. The impact and importance of the various types of dyadic activities on cognitive development are discussed. 相似文献
12.
The latent change score framework allows for estimating a variety of univariate trajectory models, such as the no change, linear change, exponential forms of change, as well as multivariate trajectory models that allow for coupling between two or more constructs. A particularly attractive feature of these models is that it is easy to decompose and interpret aspects of change. One particularly flexible model, the dual change score model, has two components of change: a proportional change component that depends on scores at the previous time point, and a constant change component that is additive. We demonstrate through simulation and an empirical example that in a correctly specified model, the correlation between the proportional change parameter and the mean of the constant change component can approach either ?1 or 1, thus complicating interpretation. We provide recommendations and code to aid researchers’ ability to diagnose this issue in their own data. 相似文献
13.
Application of Latent Growth Curve Analysis With Categorical Responses in Social Behavioral Research
Tae Kyoung Lee Kandauda Wickrama Catherine W. O’Neal 《Structural equation modeling》2018,25(2):294-306
Latent growth modeling allows social behavioral researchers to investigate within-person change and between-person differences in within-person change. Typically, conventional latent growth curve models are applied to continuous variables, where the residuals are assumed to be normally distributed, whereas categorical variables (i.e., binary and ordinal variables), which do not hold to normal distribution assumptions, have rarely been used. This article describes the latent growth curve model with categorical variables, and illustrates applications using Mplus software that are applicable to social behavioral research. The illustrations use marital instability data from the Iowa Youth and Family Project. We close with recommendations for the specification and parameterization of growth models that use both logit and probit link functions. 相似文献
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15.
The current study investigated the extent to which executive functions (EF) affect how prior knowledge predicts hypermedia learning outcomes in primary school children. Learning outcomes were: individual knowledge and transfer, and dyadic assignment quality. Eighty-seven same-sex dyads participated in a hypermedia WebQuest assignment about the heart and living a healthy lifestyle. EF measures were action control and attention control. Dyadic analyses were performed using actor-partner interdependence models with dyads distinguished by EF. Analyses showed that one's own pre-test predicted one's own and partner's post-test for both higher and lower EF dyad members. Furthermore, for dyad members with relative higher EF only, their own and partner's pre-test predicted transfer. Finally, the lower action control dyad member's pre-test and the higher attention control dyad member's pre-test predicted assignment quality. These results show the importance of EF and prior knowledge for deeper conceptual understanding in a collaborative learning setting. 相似文献
16.
This article examines 4 approaches for explaining shared method variance, each applied to a longitudinal trait–state–occasion (TSO) model. Many approaches have been developed to account for shared method variance in multitrait-multimethod (MTMM) data. Some of these MTMM approaches (correlated method, orthogonal method, correlated method minus one, correlated uniqueness) were therefore borrowed in these analyses such that their effectiveness could be evaluated in conjunction with a TSO model. To this end, datasets were generated according to 4 different covariance matrices (each created according to specifications of a model built with 1 of the 4 approaches) and each model was crossed with each type of data. Whereas the correlated method and correlated method minus one approaches encountered many difficulties in convergence, fit, or parameter estimates, the correlated uniqueness and orthogonal method approaches proved to be quite versatile. 相似文献
17.
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
18.
This article shows that the mean and covariance structure of the predetermined autoregressive latent trajectory (ALT) model are very flexible. As a result, the shape of the modeled growth curve can be quite different from what one might expect at first glance. This is illustrated with several numerical examples that show that, for example, a linear trajectory might be present among the model predicted scores even though no latent change parameter was included in the model. In addition, 2 examples are given that show that the predetermined ALT model can fit to data generated by models with model structures that are rather different from that of the ALT model itself. The practical relevance of these findings is demonstrated using an empirical example. We end by providing recommendations for researchers considering the use of the predetermined ALT model. 相似文献
19.
When missingness is suspected to be not at random (MNAR) in longitudinal studies, researchers sometimes compare the fit of a target model that assumes missingness at random (here termed a MAR model) and a model that accommodates a hypothesized MNAR missingness mechanism (here termed a MNAR model). It is well known that such comparisons are only interpretable conditional on the validity of the chosen MNAR model’s assumptions about the missingness mechanism. For that reason, researchers often perform a sensitivity analysis comparing the MAR model to not one, but several, plausible alternative MNAR models. In the social sciences, it is not widely known that such model comparisons can be particularly sensitive to case influence, such that conclusions drawn could depend on a single case. This article describes two convenient diagnostics suited for detecting case influence on MAR–MNAR model comparisons. Both diagnostics require much less computational burden than global influence diagnostics that have been used in other disciplines for MNAR sensitivity analyses. We illustrate the interpretation and implementation of these diagnostics with simulated and empirical latent growth modeling examples. It is hoped that this article increases awareness of the potential for case influence on MAR–MNAR model comparisons and how it could be detected in longitudinal social science applications. 相似文献
20.
The latent growth model (LGM) in structural equation modeling (SEM) may be extended to allow for the modeling of associations among multiple latent growth trajectories, resulting in a multiple domain latent growth model (MDLGM). While the MDLGM is conceived as a more powerful multivariate analysis technique, the examination of its methodological performance is very limited. Hence, the present study compared the power of the MDLGM with that of a set of univariate LGMs for detecting group differences in growth rates over time using a Monte Carlo study with a two-group and two-domain design. The results indicated that there were different scenarios where the power rates for the MDLGM were greater than that of the set of LGMs (and vice versa) due to a joint function of the two domains’ intercorrelation size and the group difference effect size. 相似文献