Longitudinal studies of achievement growth using latent variable modeling

Muthén, Bengt; Khoo, Siek-Toon

doi:10.1016/s1041-6080(99)80135-6

Cited by 181 publications

(201 citation statements)

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“…Latent growth curve analysis was used to analyze the data (Muthe´n & Khoo, 1998). This type of analysis is equivalent to multilevel (growth curve) analysis, but it uses the structural equation modelling framework to build the models instead of the multilevel framework (Curran, 2003).…”

Section: Analytical Approachmentioning

confidence: 99%

“…The analyses are carried out by maximum likelihood estimation using the Mplus software package version 3.11 (Muthe´n & Muthe´n, 1998-2004. The adjusted-goodness-of-fit index (AGFI), the comparative fit index (CFI), and the root mean square error of approximation (RMSEA) are used to assess model fit.…”

Section: School Effectiveness and School Improvement 241mentioning

confidence: 99%

“…In a third and final step, we will add two time-invariant predictors at the individual level, more specifically, gender and intelligence as predictors of the growth parameters of the two outcomes, in order to answer the questions whether boys and girls differ in the development of the two outcomes and whether high-ability students show different developmental trajectories than low-ability students. The analyses are carried out by maximum likelihood estimation using the Mplus software package version 3.11 (Muthe´n & Muthe´n, 1998-2004. The adjusted-goodness-of-fit index (AGFI), the comparative fit index (CFI), and the root mean square error of approximation (RMSEA) are used to assess model fit.…”

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confidence: 99%

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School effects on the development of motivation toward learning tasks and the development of academic self-concept in secondary education: a multivariate latent growth curve approach

gaer

Fraine

Pustjens

et al. 2009

School Effectiveness and School Improvement

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Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden.The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.School effects on the development of motivation toward learning tasks and the development of academic self-concept in secondary education: a multivariate latent growth curve approach The main objective of the present study is to gain more insight into the school effects on the development of 2 noncognitive student outcomes, namely, the motivation toward learning tasks and the academic self-concept, and, more specifically, on the consistency of the school effects on these 2 outcomes. Data were drawn from the ''Longitudinaal Onderzoek Secundair Onderwijs'' (LOSO)-project and consisted of 2,687 students in 50 schools that were tested repeatedly at 4 different time points during secondary education (Grades 7-12). A multivariate multilevel latent growth curve model was used to analyze the data. The results showed that the school effects on the development of the noncognitive outcomes were considerable, and, more importantly, the school effects were larger on growth than on student status. Schools that were effective for the development of the motivation toward learning tasks also proved to be effective for the development of academic self-concept. However, the consistency of the school effects resulted largely from intake differences between schools.Keywords: multivariate multilevel latent growth curve model; motivation toward learning tasks; academic self-concept; secondary education IntroductionMeasuring and explaining school effects have been the central concern in school effectiveness research over the past decades. The vast amount of studies in the field has yielded an extensive knowledge base and expertise. However, several problems regarding the measurement of school effects remain underaddressed and problematic (Teddlie & Reynolds, 2000). The present study addresses three of these problems: school effects on students' noncognitive outcomes, school effects on students' progress, and consistency of school effects.First, despite several pleas, the dependent variable (or effectiveness criterion) in most studies is still student achievement. Noncognitive outcomes, such as learning styles, academic self-concept, values, or attitudes, are only seldom studied, probably because they are more dif...

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Section: Analytical Approachmentioning

confidence: 99%

Section: School Effectiveness and School Improvement 241mentioning

confidence: 99%

mentioning

confidence: 99%

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School effects on the development of motivation toward learning tasks and the development of academic self-concept in secondary education: a multivariate latent growth curve approach

gaer

Fraine

Pustjens

et al. 2009

School Effectiveness and School Improvement

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“…A short introduction to LGC analysis is presented below. More detailed introductions to latent growth curve modeling are presented, for instance, by Willett and Sayer (1994), MacCallum et al (1997), Muthén and Khoo (1998) and Duncan, Duncan, Strycker, Li and Alpert (1999).…”

Section: Latent Growth Curve Modeling Of Motivation School Investmenmentioning

confidence: 99%

Issues in longitudinal research on motivation

Stoel

Roeleveld

Peetsma

et al. 2006

Learning and Individual Differences

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This paper discusses two methodological issues regarding the analysis of longitudinal data using structural equation modeling that emerged during the reconsideration of the analysis of a recent study on the relationship between academic motivation and language achievement in elementary education [Stoel R.D., Peetsma, T.T.D. and Roeleveld, J. (2003). Relations between the development of school investment, self-confidence and language achievement in elementary education: a multivariate latent growth curve approach. Learning and individual differences, 13, (pp. 241-262). Amsterdam: Kluwer Academic Press. It is illustrated that standard modeling practices may sometimes lead to incorrect conclusions regarding the concepts under investigation, and that ideally alternative modeling possibilities should be considered in order to check the adequacy of the standard practice. D

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“…Latent growth model . Structural equation modeling The latent growth model (LGM) plays an important role in repeated measure analysis over a limited number of occasions in large samples (e.g., Meredith & Tisak, 1990;Muthén & Khoo, 1998; Preacher, Wichman, MacCallum, & Briggs, 2008, p. 12). The model can not only characterize intraindividual (within subjects) change over time, but also examine interindividual (between subjects) difference by means of random growth coefficients, and is a typical application of hierarchical linear modeling (HLM).…”

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confidence: 99%

Using SAS PROC CALIS to fit Level-1 error covariance structures of latent growth models

Ding

Jane

2012

Behav Res

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In the present article, we demonstrates the use of SAS PROC CALIS to fit various types of Level-1 error covariance structures of latent growth models (LGM). Advantages of the SEM approach, on which PROC CALIS is based, include the capabilities of modeling the change over time for latent constructs, measured by multiple indicators; embedding LGM into a larger latent variable model; incorporating measurement models for latent predictors; and better assessing model fit and the flexibility in specifying error covariance structures. The strength of PROC CALIS is always accompanied with technical coding work, which needs to be specifically addressed. We provide a tutorial on the SAS syntax for modeling the growth of a manifest variable and the growth of a latent construct, focusing the documentation on the specification of Level-1 error covariance structures. Illustrations are conducted with the data generated from two given latent growth models. The coding provided is helpful when the growth model has been well determined and the Level-1 error covariance structure is to be identified.Keywords Error covariance structure . Latent growth model . Structural equation modeling The latent growth model (LGM) plays an important role in repeated measure analysis over a limited number of occasions in large samples (e.g., Meredith & Tisak, 1990;Muthén & Khoo, 1998; Preacher, Wichman, MacCallum, & Briggs, 2008, p. 12). The model can not only characterize intraindividual (within subjects) change over time, but also examine interindividual (between subjects) difference by means of random growth coefficients, and is a typical application of hierarchical linear modeling (HLM). The within-subjects errors over time and the between-subjects errors are conventionally referred to as "Level-1" and "Level-2" errors, respectively. LGM can also be handled by using structural equation modeling (SEM) (e.g., Bauer, 2003;Bollen & Curran, 2006;Chan, 1998;Curran, 2003;Duncan, Duncan, & Hops, 1996; Mehta & Neal 2005;Meredith & Tisak, 1990; Willet & Sayer, 1994). SEM and HLM stem from different statistical theory, and each has developed its own terminology and standard ways of framing research questions. However, there exists much overlap between the two methodologies under some circumstances. Typically, when a two-level data structure arises from the repeated observations of a variable over time for a set of individuals (so that time is hierarchically nested within each individual), SEM is analytically equivalent

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Longitudinal studies of achievement growth using latent variable modeling

Cited by 181 publications

References 6 publications

School effects on the development of motivation toward learning tasks and the development of academic self-concept in secondary education: a multivariate latent growth curve approach

School effects on the development of motivation toward learning tasks and the development of academic self-concept in secondary education: a multivariate latent growth curve approach

Issues in longitudinal research on motivation

Using SAS PROC CALIS to fit Level-1 error covariance structures of latent growth models

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