A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics

Edelsbrunner, Peter Adriaan; Flaig, Maja; Schneider, Michael

doi:10.1080/19345747.2022.2118197

Cited by 9 publications

(6 citation statements)

References 43 publications

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“…Well-known information criteria are the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC; Vrieze, 2012). There are further criteria, such as the sample-size adjusted BIC, the consistent AIC, and the AIC3 (Edelsbrunner, Flaig, & Schneider, 2023). All criteria balance an indicator of the absolute fit of a model (the likelihood) with model complexity indicated by the number of parameters of the model (Vandekerckhove et al, 2015).…”

Section: Information Criteriamentioning

confidence: 99%

“…There are at least three ways to decide which information criterion to choose for a specific model comparison. First, there are simulation studies that help judging the adequacy of different information criteria for different kinds of models (e.g., latent variable models, Vrieze, 2012, or mixture models, Edelsbrunner et al, 2023). Second, the different information criteria draw on different kinds of statistical theory that can help in deciding which one to use.…”

Section: Information Criteriamentioning

confidence: 99%

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Improving the Utility of Non-Significant Results for Educational Research: A Review and Recommendations

Edelsbrunner,

Thurn

2023

Preprint

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When used appropriately, non-significant p-values have the potential to further our understanding of what does not work in education, and why. When misinterpreted, they can trigger misguided conclusions, for example about the absence of an effect of an educational intervention, or about a difference in the efficacy of different interventions. We examined the frequency of non-significant p-values in recent volumes of peer-reviewed educational research journals. We also examined how frequently researchers misinterpret non-significance to imply the absence of an effect, or a difference to another significant effect. Within a random sample of 50 peer-reviewed articles, we found that of 528 statistically tested hypotheses, 253 (48%) were non-significant. Of these, 142 (56%) were erroneously interpreted to indicate the absence of an effect, and 59 (23%) to indicate a difference to another significant effect. For 97 (38%) of non-significant results, such misinterpretations were linked to potentially misguided implications for educational theory, practice, or policy. We outline valid ways for dealing with non-significant p-values to improve their utility for education, discussing potential reasons for these misinterpretations and implications for research.

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Section: Information Criteriamentioning

confidence: 99%

Section: Information Criteriamentioning

confidence: 99%

Improving the Utility of Non-Significant Results for Educational Research: A Review and Recommendations

Edelsbrunner,

Thurn

2023

Preprint

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“…As is common practice in latent profile analysis, the model with the actual number of profiles interpreted and used for further analyses was then selected based on fit indices and theoretical considerations (Ferguson et al, 2020;Harring & Hodis, 2016;. To this end, we relied in particular on the fit indices BIC, aBIC, and the VLMR-likelihood ratio test (Edelsbrunner & Flaig, 2021;Ferguson et al, 2020;Lo et al, 2001). For the BIC and aBIC, lower estimates indicate a better relative model fit (for explanations of these fit indices, see Edelsbrunner & Flaig, 2021), and for the VLMR, the model with the highest number of profiles reaching significance should be selected (Ferguson et al, 2020;Harring & Hodis, 2016;.…”

Section: Analytic Approachmentioning

confidence: 99%

“…To this end, we relied in particular on the fit indices BIC, aBIC, and the VLMR-likelihood ratio test (Edelsbrunner & Flaig, 2021;Ferguson et al, 2020;Lo et al, 2001). For the BIC and aBIC, lower estimates indicate a better relative model fit (for explanations of these fit indices, see Edelsbrunner & Flaig, 2021), and for the VLMR, the model with the highest number of profiles reaching significance should be selected (Ferguson et al, 2020;Harring & Hodis, 2016;.…”

Section: Analytic Approachmentioning

confidence: 99%

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Individualized instruction - conceptual and empirical examinations of necessary conditions for its effectiveness

Tetzlaff¹

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Individualization can be defined as the adaptation of instructional parameters to relevant characteristics of a specific learner. This definition raises several questions, however: Which characteristics are actually relevant? Which parameters of instruction need to be adjusted, and in which way, to positively interact with those characteristics? In a classroom context, additional questions arise: how can information about the relevant learner characteristics be delivered to the teacher? How can individualized instruction be delivered to each learner in a context that has originally been designed for whole-class instruction? By focusing on the measurement and modelling of learner characteristics and instructional adaptations, this dissertation aims to provide an insight into each of these issues. This dissertation is divided into two parts. The first part is concerned with the theoretical (Paper 1) and statistical (Paper 2) modeling of learner characteristics in the context of individualized instruction. The second part is concerned with the measurement (Paper 3) and implementation (Paper 4) of individualized instruction in the classroom context. Paper 1 summarizes existing research on individualization from different research traditions. From this summary I derive the need for a dynamic conceptualization of learner characteristics (acknowledging that learners change during and in interaction with the learning process) and synthesize a dynamic framework that details the opportunities for individualization on three different timescales. Paper 2 reports results from an exploratory study that investigated the potential benefits of utilizing person-centered analysis for the assessment of multivariate learner prerequisites and their interaction with instruction. We found that latent profiles over several reading related abilities could explain differential effectiveness of self-reported teaching foci in German third grade reading lessons. These findings indicate not just a need for stronger individualization of teaching but also an advantage of multivariate conceptualizations of learner characteristics. Additionally, they show the utility of person-centered approaches for the investigation of such multivariate learner characteristics and their interaction with instruction. In the second part, I investigate possible approaches to the implementation and measurement of individualization in a classroom context. Paper 3 investigates whether teacher-, student- and observer perspectives converge when rating the amount of individualization present in regular classroom instruction. We found considerable agreement between the perspectives, indicating a common understanding of the construct at the classroom level as well as providing some evidence for the validity of the used measurement instruments. Paper 4 replicates findings concerning the effectiveness of formative assessment procedures for fostering reading education, supplemented by a moderator analysis showing that only children with low performance at the beginning of the school-year profited from its implementation. This indicates that the information provided by formative assessment procedures helps teachers to identify struggling readers but does not seem to be utilized for adapting instruction to specific deficits of average or high performing children. In sum, this dissertation contributes to research on individualized instruction by demonstrating necessary conditions for its effectiveness. It posits the need for a dynamic conceptualization of learner characteristics, demonstrates the advantage of multivariate learner profiles, and points out ways towards the successful implementation of individualized instruction in the classroom.

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Modeling Interactions Between Multivariate Learner Characteristics and Interventions: a Person-Centered Approach

Tetzlaff,

Edelsbrunner,

Schmitterer

et al. 2023

Educ Psychol Rev

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Demonstrating the differential effectiveness of instructional approaches for learners is difficult because learners differ on multiple dimensions. The present study tests a person-centered approach to investigating differential effectiveness, in this case of reading instruction. In N = 517 German third-grade students, latent profile analysis identified four subgroups that differed across multiple characteristics consistent with the simple view of reading: poor decoders, poor comprehenders, poor readers, and good readers. Over a school year, different instructional foci showed differential effectiveness for students in these different profiles. An instructional focus on vocabulary primarily benefited good readers at the expense of poor decoders and poor comprehenders, while a focus on advanced reading abilities benefitted poor comprehenders at the expense of poor decoders and good readers. These findings are in contrast to those obtained by multiple regression, which, focusing on only one learner characteristic at a time, would have suggested different and potentially misleading implications for instruction. This study provides initial evidence for the advantages of a person-centered approach to examining differential effectiveness.

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A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics

Cited by 9 publications

References 43 publications

Improving the Utility of Non-Significant Results for Educational Research: A Review and Recommendations

Improving the Utility of Non-Significant Results for Educational Research: A Review and Recommendations

Individualized instruction - conceptual and empirical examinations of necessary conditions for its effectiveness

Modeling Interactions Between Multivariate Learner Characteristics and Interventions: a Person-Centered Approach

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