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Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences.

ABSTRACT: This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues.

SUBMITTER: Stamovlasis D

PROVIDER: S-EPMC5911829 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences.

Stamovlasis Dimitrios D Papageorgiou George G Tsitsipis Georgios G Tsikalas Themistoklis T Vaiopoulou Julie J

Frontiers in psychology 20180416

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, t ...[more]

PMID: 29713300

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Project description:Importance:Many children begin interacting with screen media as early as infancy. Although screen time is associated with negative developmental consequences, few longitudinal studies in the United States have examined covariates of screen time among children under 3 years of age. Objectives:To identify trajectories of screen time among children aged 1 to 3 years, to examine their association with screen use at 8 years of age, and to assess potential determinants of screen time. Design, Setting, and Participants:This prospective birth cohort study included 3895 children (3083 singletons and 812 unrelated multiples) in New York State who had screen time data available for at least 1 time point from 1 to 3 years of age; 1156 children had data at 8 years. The study spanned September 4, 2007, through June 12, 2014, in the first phase, and August 29, 2014, through November 15, 2019, in the second phase. Data analysis for the present study was conducted from September 28, 2018, to July 15, 2019. Main Outcomes and Measures:Maternal reports of children's television, movie, and computer game times were summed for total daily screen time at 12, 18, 24, 30, and 36 months of age. Two screen time trajectories (low and increasing use) were classified by cluster analysis, and logistic regression was used to model risk factors for the increasing trajectory. Children exhibiting the highest 10th percentile of screen use at each point were examined, and linear mixed models were used to identify risk factors of this high exposure category. Results:Among the 3895 children included in the analysis (2031 boys [52.1%] and 1864 girls [47.9%]), median daily screen time increased from 30 (interquartile range, 0-60) minutes at 12 months of age to 120 (interquartile range, 75-200) minutes at 36 months of age. Of 1045 children with complete data at all 5 time points, 279 (26.7%) had an increasing screen time trajectory. Female child sex (adjusted odds ratio [aOR],?0.90; 95% CI, 0.81-0.99) and graduate school levels of paternal (aOR, 0.73; 95% CI, 0.56-0.95) and maternal (aOR, 0.60; 95% CI, 0.47-0.77) education, compared with having completed college, were associated with lower risk of increasing trajectory. Maternal nulliparity was associated with higher risk of increasing trajectory (aOR, 1.14; 95% CI, 1.00-1.30). Children with an increasing trajectory from 1 to 3 years of age had an additional 22 (95% CI, 11-33) minutes per day of screen time at 8 years of age. Covariates associated with the highest 10th percentile of screen exposure included paterman graduate school education compared with college (aOR,?0.63; 95% CI, 0.39-0.99), maternal graduate school education compared with college (aOR, 0.55; 95% CI, 0.37-0.82), maternal nulliparity (aOR, 1.98; 95% CI, 1.50-2.61), twins compared with singletons (aOR, 1.41; 95% CI, 1.05-1.91), non-Hispanic black compared with non-Hispanic white race/ethnicity (aOR, 4.77; 95% CI, 2.25-10.10), and type of care (home-based care aOR, 2.17 [95% CI, 1.38-3.41]; parental care aOR, 2.11 [95% CI, 1.41-3.15]) compared with center-based care. Conclusions and Relevance:These findings suggest that a range of parental and child characteristics are associated with screen time. Screen time habits appear to track from as early as infancy, emphasizing the need for earlier interventions.

Project description:Background: Latent class analysis (LCA) can be used to identify subgroups of children with similar patterns of mental health symptoms and/or strengths. The method is becoming more commonly used in child mental health research, but there are reservations about the replicability, reliability, and validity of findings. Objective: A systematic literature review was conducted to investigate the extent to which LCA has been used to study population mental health in children, and whether replicable, reliable and valid findings have been demonstrated. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A search of literature, published between January 1998 and December 2017, was carried out using MEDLINE, EMBASE, PsycInfo, Scopus, ERIC, ASSIA, and Google Scholar. A total of 2,748 studies were initially identified, of which 23 were eligible for review. The review examined the methods which studies had used to choose the number of mental health classes, the classes that they found, and whether there was evidence for the validity and reliability of the classes. Results: Reviewed studies used LCA to investigate both disparate mental health symptoms, and those associated with specific disorders. The corpus of studies using similar indicators was small. Differences in the criteria used to select the final LCA model were found between studies. All studies found meaningful or useful subgroups, but there were differences in the extent to which the validity and reliability of classes were explicitly demonstrated. Conclusions : LCA is a useful tool for studying and classifying child mental health at the population level. Recommendations are made to improve the application and reporting of LCA and to increase confidence in findings in the future, including use of a range of indices and criteria when enumerating classes, clear reporting of methods for replicability, and making efforts to establish the validity and reliability of identified classes.

Dataset Information

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences.

Publications

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences.

Similar Datasets

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