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1.
Natacha Carragher Jonathan Templin Phillip Jones Boaz Shulruf Gary Velan 《Educational Measurement》2019,38(1):89-91
In this ITEMS module, we provide a didactic overview of the specification, estimation, evaluation, and interpretation steps for diagnostic measurement/classification models (DCMs), which are a promising psychometric modeling approach. These models can provide detailed skill‐ or attribute‐specific feedback to respondents along multiple latent dimensions and hold theoretical and practical appeal for a variety of fields. We use a current unified modeling framework—the log‐linear cognitive diagnosis model (LCDM)—as well as a series of quality‐control checklists for data analysts and scientific users to review the foundational concepts, practical steps, and interpretational principles for these models. We demonstrate how the models and checklists can be applied in real‐life data‐analysis contexts. A library of macros and supporting files for Excel, SAS, and Mplus are provided along with video tutorials for key practices. 相似文献
2.
Models of change typically assume longitudinal measurement invariance. Key constructs are often measured by ordered-categorical indicators (e.g., Likert scale items). If tests based on such indicators do not support longitudinal measurement invariance, it would be useful to gauge the practical significance of the detected non-invariance. The authors focus on the commonly used second-order latent growth curve model, proposing a sensitivity analysis that compares the growth parameter estimates from a model assuming the highest achieved level of measurement invariance to those from a model assuming a higher, incorrect level of measurement invariance as a measure of practical significance. A simulation study investigated the practical significance of non-invariance in different locations (loadings, thresholds, uniquenesses) in second-order latent linear growth models. The mean linear slope was affected by non-invariance in the loadings and thresholds, the intercept variance was affected by non-invariance in the uniquenesses, and the linear slope variance and intercept–slope covariance were affected by non-invariance in all three locations. 相似文献
3.
Popular longitudinal models allow for prediction of growth trajectories in alternative ways. In latent class growth models (LCGMs), person-level covariates predict membership in discrete latent classes that each holistically define an entire trajectory of change (e.g., a high-stable class vs. late-onset class vs. moderate-desisting class). In random coefficient growth models (RCGMs, also known as latent curve models), however, person-level covariates separately predict continuously distributed latent growth factors (e.g., an intercept vs. slope factor). This article first explains how complex and nonlinear interactions between predictors and time are recovered in different ways via LCGM versus RCGM specifications. Then a simulation comparison illustrates that, aside from some modest efficiency differences, such predictor relationships can be recovered approximately equally well by either model—regardless of which model generated the data. Our results also provide an empirical rationale for integrating findings about prediction of individual change across LCGMs and RCGMs in practice. 相似文献
4.
Daniel McNeish 《Journal of Experimental Education》2013,81(4):690-701
Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML tends to perform poorly with small-sample growth models. This report demonstrates that the fault lies not with how FIML accommodates missingness but rather with maximum likelihood estimation itself. We discuss how the less popular restricted likelihood form of FIML, along with small-sample-appropriate methods, yields trustworthy estimates for growth models with small samples and missing data. That is, previously reported small sample issues with FIML are attributable to finite sample bias of maximum likelihood estimation not direct likelihood. Estimation issues pertinent to joint multiple imputation and predictive mean matching are also included and discussed. 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.
Min-Jeong Jeon Guemin Lee Jeong-Won Hwang Sang-Jin Kang 《Asia Pacific Education Review》2009,10(2):149-158
The purpose of this study was to investigate the methods of estimating the reliability of school-level scores using generalizability
theory and multilevel models. Two approaches, ‘student within schools’ and ‘students within schools and subject areas,’ were
conceptualized and implemented in this study. Four methods resulting from the combination of these two approaches with generalizability
theory and multilevel models were compared for both balanced and unbalanced data. The generalizability theory and multilevel
models for the ‘students within schools’ approach produced the same variance components and reliability estimates for the
balanced data, while failing to do so for the unbalanced data. The different results from the two models can be explained
by the fact that they administer different procedures in estimating the variance components used, in turn, to estimate reliability.
Among the estimation methods investigated in this study, the generalizability theory model with the ‘students nested within
schools crossed with subject areas’ design produced the lowest reliability estimates. Fully nested designs such as (students:schools)
or (subject areas:students:schools) would not have any significant impact on reliability estimates of school-level scores.
Both methods provide very similar reliability estimates of school-level scores. 相似文献
7.
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
8.
This article presents several longitudinal mediation models in the framework of latent growth curve modeling and provides a detailed account of how such models can be constructed. Logical and statistical challenges that might arise when such analyses are conducted are also discussed. Specifically, we discuss how the initial status (intercept) and change (slope) of the putative mediator variable can be appropriately included in the causal chain between the independent and dependent variables in longitudinal mediation models. We further address whether the slope of the dependent variable should be controlled for the dependent variable's intercept to improve the conceptual relevance of the mediation models. The models proposed are illustrated by analyzing a longitudinal data set. We conclude that for certain research questions in developmental science, a multiple mediation model where the dependent variable's slope is controlled for its intercept can be considered an adequate analytical model. However, such models also show several limitations. 相似文献
9.
Andrew J. Martin Rebecca J. Collie Tracy L. Durksen Emma C. Burns Keiko C.P. Bostwick Ana L. Tarbetsky 《International Journal of Research & Method in Education》2019,42(2):204-219
This review explores predictors and consequences of students’ growth goals and growth mindset in school with particular emphasis on how correlational statistical methods can be applied to illuminate key issues and implications. Study 1 used cross-sectional data and employed structural equation modelling (SEM) to investigate the role of growth goals in mediating the link between interpersonal relationships and academic engagement. Study 2 conducted multi-group path analysis to investigate the role of growth goals in the academic outcomes of two groups of students (ADHD and non-ADHD). Study 3 used longitudinal data and SEM to test a cross-lagged panel design to investigate reciprocal links between growth goals and growth mindset. Study 4 conducted multi-level SEM where the effects of a growth orientation on engagement and achievement were investigated at the student-level (level 1) and the classroom-level (level 2). Taking these four studies together, we aim to show how correlational data and multivariate correlational analyses have been effective in answering research questions in a way that have practical and theoretical implications for students’ academic growth. We also position this review as a substantive-methodological synergy – an approach recently recommended in response to concerns about the increasing polarization of substantive and methodological research and researchers. 相似文献
10.
Diagnostic classification models (aka cognitive or skills diagnosis models) have shown great promise for evaluating mastery on a multidimensional profile of skills as assessed through examinee responses, but continued development and application of these models has been hindered by a lack of readily available software. In this article we demonstrate how diagnostic classification models may be estimated as confirmatory latent class models using Mplus, thus bridging the gap between the technical presentation of these models and their practical use for assessment in research and applied settings. Using a sample English test of three grammatical skills, we describe how diagnostic classification models can be phrased as latent class models within Mplus and how to obtain the syntax and output needed for estimation and interpretation of the model parameters. We also have written a freely available SAS program that can be used to automatically generate the Mplus syntax. We hope this work will ultimately result in greater access to diagnostic classification models throughout the testing community, from researchers to practitioners. 相似文献
11.
Promising methods of reading instruction for elementary school students incorporate peer-assisted learning routines and reading strategies. In addition, models of reading comprehension point to the importance of various determinants of reading competence such as reading fluency and vocabulary knowledge. Multicomponent reading intervention programs need to be evaluated to determine IF and HOW they unfold their effects on the reading competence of elementary school students on the basis of such theoretical and empirical models. Accordingly, the present study was designed as a quasi-experimental study of a 20-lesson peer-assisted and strategy-based multicomponent intervention for whole-class instruction in elementary school. Linear mixed models and latent growth models were used to analyze the longitudinal data (pre-, post- and follow-up test) on the reading competencies (reading fluency, vocabulary knowledge, reading strategy competence, reading comprehension) and intrinsic reading motivation of students in the intervention (N = 187) and control group (N = 177). The results showed an interaction between the groups and the change in reading comprehension, indicating a significantly increased score in the intervention group at the posttest (d = 0.15) but not at the follow-up test (d = 0.12). The results of the latent growth model point to the importance of designing interventions that explicitly integrate reading strategies, reading fluency and vocabulary knowledge and also foster intrinsic reading motivation. In addition, reading fluency was revealed to be the strongest predictor of reading comprehension and the change in fluency over time was closely linked to reading comprehension development. 相似文献
12.
Lorena Ortega Lars-Erik Malmberg Pam Sammons 《School Effectiveness & School Improvement》2018,29(2):308-337
This study investigates school effects on primary school students’ language and mathematics achievement trajectories in Chile, a context of particular interest given its large between-school variability in educational outcomes. The sample features an accelerated longitudinal design (3 time points, 4 cohorts) together spanning Grades 3 to 8 (n = 19,704 students in 156 schools). The magnitudes of school effects on students’ growth trajectories were found to be sizeable (generally larger than school effects in Western industrialised countries) and moderately consistent across school subjects. School composition effects on student achievement status were found for both school subjects. However, there was no evidence of composition effects on student achievement growth. The study provides new evidence on the size and nature of school effects in a developing country context based on state-of-the-art methods (i.e., accelerated longitudinal and growth curve models). 相似文献
13.
Su-Young Kim 《Structural equation modeling》2013,20(2):263-279
Stage-sequential (or multiphase) growth mixture models are useful for delineating potentially different growth processes across multiple phases over time and for determining whether latent subgroups exist within a population. These models are increasingly important as social behavioral scientists are interested in better understanding change processes across distinctively different phases, such as before and after an intervention. One of the less understood issues related to the use of growth mixture models is how to decide on the optimal number of latent classes. The performance of several traditionally used information criteria for determining the number of classes is examined through a Monte Carlo simulation study in single- and multiphase growth mixture models. For thorough examination, the simulation was carried out in 2 perspectives: the models and the factors. The simulation in terms of the models was carried out to see the overall performance of the information criteria within and across the models, whereas the simulation in terms of the factors was carried out to see the effect of each simulation factor on the performance of the information criteria holding the other factors constant. The findings not only support that sample size adjusted Bayesian Information Criterion would be a good choice under more realistic conditions, such as low class separation, smaller sample size, or missing data, but also increase understanding of the performance of information criteria in single- and multiphase growth mixture models. 相似文献
14.
Value-added models and growth-based accountability aim to evaluate school??s performance based on student growth in learning. The current focus is on linking the results from value-added models to the ones from growth-based accountability systems including Adequate Yearly Progress decisions mandated by No Child Left Behind. We present a new statistical approach that extends the current value-added modeling possibilities and focuses on using latent longitudinal growth curves to estimate the probabilities of students reaching proficiency. The aim is to utilize time-series measures of student achievement scores to estimate latent growth curves and use them as predictors of a dichotomous outcome, such as proficiency or passing a high-stakes exam, within a single multilevel longitudinal model. We illustrated this method through analyzing a three-year data set of longitudinal achievement scores and California High School Exit Exam scores from a large urban school district. This latent variable growth logistic model is useful for (1) early identification of students at risk of failing or of those who are most in need; (2) a validation or/and adequacy of student growth over years with relation to distal outcome criteria; (3) evaluation of a longitudinal intervention study. 相似文献
15.
Tacksoo Shin 《Asia Pacific Education Review》2012,13(1):65-76
This study introduced various nonlinear growth models, including the quadratic conventional polynomial model, the fractional
polynomial model, the Sigmoid model, the growth model with negative exponential functions, the multidimensional scaling technique,
and the unstructured growth curve model. It investigated which growth models effectively describe student growth in math and
reading using four-wave longitudinal achievement data. The objective of the study is to provide valuable information to researchers
especially when they consider applying one of the nonlinear models to longitudinal studies. The results showed that the quadratic
conventional polynomial model fit the data best. However, this model seemed to overfit the data and made statistical inference
problematic concerning parameter estimates. Alternative nonlinear models with fewer parameters adequately fit the data and
yielded consistent significance testing results under extreme multicollinearity. It indicates that the alternative models
denoting somewhat simpler models would be selected over the conventional polynomial model with more fixed parameters. Other
practical issues pertaining to these growth models are also discussed. 相似文献
16.
Mediation is one concept that has shaped numerous theories. The list of problems associated with mediation models, however, has been growing. Mediation models based on cross-sectional data can produce unexpected estimates, so much so that making longitudinal or causal inferences is inadvisable. Even longitudinal mediation models have faults, as parameter estimates produced by these models are specific to the lag between observations, leading to much debate over appropriate lag selection. Using continuous time models (CTMs) rather than commonly employed discrete time models, one can estimate lag-independent parameters. We demonstrate methodology that allows for continuous time mediation analyses, with attention to concepts such as indirect and direct effects, partial mediation, the effect of lag, and the lags at which relations become maximal. A simulation compares common longitudinal mediation methods with CTMs. Reanalysis of a published covariance matrix demonstrates that CTMs can be fit to data used in longitudinal mediation studies. 相似文献
17.
Chueh-An Hsieh Alexander von Eye Kimberly Maier Hsin-Jung Hsieh Shi-Hsiung Chen 《Structural equation modeling》2013,20(4):592-615
Applying item response theory models to repeated observations has demonstrated great promise in developmental research. By allowing the researcher to take account of the characteristics of both item response and measurement error in longitudinal trajectory analysis, it improves the reliability and validity of latent growth curve analysis. This has enabled the study, to differentially weigh individual items and examine developmental stability and change over time, to propose a comprehensive modeling framework, combining a measurement model with a structural model. Despite a large number of components requiring attention, this study focuses on model formulation, evaluates the performance of the estimators of model parameters, incorporates prior knowledge from Bayesian analysis, and applies the model using an illustrative example. It is hoped that this fundamental study can demonstrate the breadth of this unified latent growth curve model. 相似文献
18.
19.
In this ITEMS module, we provide a two‐part introduction to the topic of reliability from the perspective of classical test theory (CTT). In the first part, which is directed primarily at beginning learners, we review and build on the content presented in the original didactic ITEMS article by Traub and Rowley (1991). Specifically, we discuss the notion of reliability as an intuitive everyday concept to lay the foundation for its formalization as a reliability coefficient via the basic CTT model. We then walk through the step‐by‐step computation of key reliability indices and discuss the data collection conditions under which each is most suitable. In the second part, which is directed primarily at intermediary learners, we present a distribution‐centered perspective on the same content. We discuss the associated assumptions of various CTT models ranging from parallel to congeneric, and review how these affect the choice of reliability statistics. Throughout the module, we use a customized Excel workbook with sample data and basic data manipulation functionalities to illustrate the computation of individual statistics and to allow for structured independent exploration. In addition, we provide quiz questions with diagnostic feedback as well as short videos that walk through sample exercises within the workbook. 相似文献
20.
《Structural equation modeling》2013,20(3):452-483
This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results, even in the case of models of latent growth fitted to incomplete data. The general purpose of this article is to provide a demonstration that integrates programming features from different software. The most immediate goal is to help researchers implement these LGC models as a useful way to test hypotheses of growth. 相似文献