A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model

Cetin-Berber, Dee D.; Leite, Walter L.

doi:10.1080/10705511.2018.1428806

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…However, our findings did show that having a committed goal (in comparison to not having one) made a difference in achievement when coupled with grit. Second, the study used the complete sample ( N = 549) across two grades instead of the original and lager sample in each grade ( N = 1171 and N = 765, respectively for 8th and 9th grade) due to concerns about missing data procedures for mixture modeling (Cetin-Berber and Leite 2018 ; Sterba 2016 ). The high attrition rate may have reduced statistical power when using the “smaller” complete sample.…”

Section: Discussionmentioning

confidence: 99%

Putting the Goal Back into Grit: Academic Goal Commitment, Grit, and Academic Achievement

Tang

Wang

Parada

et al. 2020

J Youth Adolescence

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Grit has recently been challenged for its weak predictive power and the incompleteness of its measurement. This study addressed these issues by taking a developmental, person-oriented approach to study academic-related goal commitment and grit and their effects on academic achievement. Using longitudinal data among Finnish eighth and ninth graders (n = 549, 59.4% female, age = 14–16), the longitudinal changes in grit and academic goal commitment profiles were investigated through latent profile and latent transition analyses. Four profiles were identified across two grades: High committed-persistent and moderate consistency (~17%), Moderate (~60%), Low committed-persistent and moderate-low consistency (~8%) and Extremely low committed-persistent and moderate-low consistency (~12%). The students in the High committed-persistent and moderate consistency profile had the highest academic achievement of all the profiles when controlled for gender, socioeconomic status, conscientiousness, and academic persistence. The results revealed that students’ profiles changed between the eighth and ninth grades, with more than one-third of the High committed-persistent and moderate consistency adolescents dropping from this group. Further analysis showed that the profiles varied by educational aspiration, gender, and socioeconomic status. These findings imply that the combination of grit and academic goal commitment influences academic achievement; however, this combination is less common, unstable, and affected by internal and external factors. The study provided important implications on the weak grit effect and the ways to improve it.

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Section: Discussionmentioning

confidence: 99%

Putting the Goal Back into Grit: Academic Goal Commitment, Grit, and Academic Achievement

Tang

Wang

Parada

et al. 2020

J Youth Adolescence

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“…By doing so, Collins et al treated a discrete latent outcome as an observed outcome. This problem has been recognized in the methodology literature, and remedies have been proposed to use a latent discrete variable in the regression model in its original form to avoid biased estimation of regression coefficients (3)(4)(5). We also were surprised to see that contralateral knee OA status was not considered as a potential predictor.…”

Section: To the Editormentioning

confidence: 98%

Trajectories of structural disease progression in knee osteoarthritis: comment on the article by Collins et al

Riddle

Dumenci

2021

Arthritis Care & Research

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“…A detailed overview of adaptations to model these mechanisms is provided by Enders (2011) and Muthén et al (2011). More recent approaches to missing data include a shared-parameter model (Gottfredson et al, 2014;Cetin-Berber and Leite, 2018).…”

Section: Growth Mixture Modelingmentioning

confidence: 99%

Clustering of longitudinal data: A tutorial on a variety of approaches

Teuling¹,

Pauws²,

Heuvel³

2021

Preprint

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During the past two decades, methods for identifying groups with different trends in longitudinal data have become of increasing interest across many areas of research. To support researchers, we summarize the guidance from the literature regarding longitudinal clustering. Moreover, we present a selection of methods for longitudinal clustering, including group-based trajectory modeling (GBTM), growth mixture modeling (GMM), and longitudinal k-means (KML). The methods are introduced at a basic level, and strengths, limitations, and model extensions are listed. Following the recent developments in data collection, attention is given to the applicability of these methods to intensive longitudinal data (ILD). We demonstrate the application of the methods on a synthetic dataset using packages available in R.

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A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model

Cited by 6 publications

References 33 publications

Putting the Goal Back into Grit: Academic Goal Commitment, Grit, and Academic Achievement

Putting the Goal Back into Grit: Academic Goal Commitment, Grit, and Academic Achievement

Trajectories of structural disease progression in knee osteoarthritis: comment on the article by Collins et al

Clustering of longitudinal data: A tutorial on a variety of approaches

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