Examining the Category Functioning of the ECERS-R Across Eight Data Sets

Fujimoto, Ken; Gordon, Rachel A.; Peng, Fang; Hofer, Kerry G.

doi:10.1177/2332858418758299

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…Rather low ECERS-R total scores were also obtained in many countries (for example, in Netherlands (De Kruif et al, 2009), Bahrein (Hadeed, 2013), Brazil (Mariano et al, 2018), Columbia (Betancur et al, 2021) etc. ), that lead researchers to propose changes in the stop-coding system of ECERS-R indicators assessment that we support (Fujimoto et al, 2018).…”

Section: Discussionmentioning

confidence: 75%

Similarities and differences between CLASS and ECERS-R estimates of educational environment quality

Bukhalenkova,

Almazova,

Aslanova

2023

Front. Psychol.

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The conducted research was devoted to comparison of kindergartens’ educational environment quality evaluation via ECERS-R and CLASS methods. Both methods were applied in the same kindergarten groups. Therefore, in this study we attempted to find out if the educational environment quality assessments acquired via the two methods mentioned above would coincide. We analyzed the results from the cultural-historical psychology perspective. The educational environment quality assessment has been conducted in 83 Moscow kindergarten groups where study 5 to7 years old preschoolers. The correlation analysis results show that the ECERS-R method subscales are not related to the “Emotional support” CLASS domain, however, a significant correlation with the total ECERS-R score has been revealed. The “Classroom Organization” CLASS domain has the highest number of correlations to the ECERS-R subscales (4) as well as to the total ECERS-R score. The “Instructional Support” domain is connected only to the Parents and Staff subscale within the ECERS-R method. As a result of comparing groups with relatively low and high quality of the educational environment, that were identified based on the evaluation via the ECERS-R and CLASS methods, a good agreement between the results has been revealed. However, a fairly large number of groups with high CLASS scores have made it to the pool of average-low ECERS-R scores, which demonstrates a non-linear connection between the educational environment quality evaluations according to these two methods. Research allows to conclude that the ECERS-R and CLASS approaches complement each other well.

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Section: Discussionmentioning

confidence: 75%

Similarities and differences between CLASS and ECERS-R estimates of educational environment quality

Bukhalenkova,

Almazova,

Aslanova

2023

Front. Psychol.

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“…It was also suggested that there is redundancy between the 43 items and that the seven subgroups provided similar measures (Fujimoto, Gordon, & Hofer, 2018;Perlman et al, 2004).…”

Section: Discussionmentioning

confidence: 99%

Child neurodevelopment and the quality of ecological environments for children in the Amazon

Costa

Cavalcante

Lima³

et al. 2023

CLIUM

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The purpose was to analyse the relationship between the quality of the ecological environment and the neurodevelopment of children in an Early Childhood Education Unit in the Amazon. Seventy children from two to five years participated, assessed by the Denver-II neurodevelopment screening test, in three times in the year. The Home Environment Resources Scale, the Family Poverty Index, the Neighbourhood Quality Instrument, and the ECERS-R Scale were applied in two times. The results agreed with the hypothesis that there would be a longitudinal increase in the percentage of children with normal score according to Denver-II and in the environmental quality scores. The findings showed that children belonging to families with a higher poverty rate and poorer environmental quality obtained a higher percentage of delay in neurodevelopment. The explanatory-model showed that the most influential variables in suspect a delay were poverty index, few joint activities with parents, and the low interaction among the neighbours and, at school. However, family poverty is a multidimensional risk factor that cuts across ecological contexts.

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“…This is also the case with other IRT models, such as the graded response model and the partial credit model. The literature on reversals is sparse, and while some researchers (eg, the work of Adams et al) argue that reversals do no necessarily reflect model misfit, others (eg, Fujimoto et al) have argued that reversals may be problematic to the validity of a scale Update sufficient statistics:

𝛍_{j k}^{false(t false)} = 𝛍_{j k}^{false(t - 1 false)} + γ_{t} ({}, {\overset{x}{true}}_{j k} - 𝛍_{j k}^{false(t - 1 false)})

{boldΣ}^{false(t false)} = {boldΣ}^{false(t - 1 false)} + γ_{t} ({} S_{2} - Σ^{(t - 1)}),

where γ t is the current value of the Robbins‐Monro gain coefficient.…”

Section: Saem Algorithm For Exploratory Irt Factor Analysismentioning

confidence: 99%

Stochastic approximation EM for large‐scale exploratory IRT factor analysis

Camilli

Geis²

2019

Statistics in Medicine

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A stochastic approximation EM algorithm (SAEM) is described for exploratory factor analysis of dichotomous or ordinal variables. The factor structure is obtained from sufficient statistics that are updated during iterations with the Robbins-Monro procedure. Two large-scale simulations are reported that compare accuracy and CPU time of the proposed SAEM algorithm to the Metropolis-Hasting Robbins-Monro procedure and to a generalized least squares analysis of the polychoric correlation matrix. A smaller-scale application to real data is also reported, including a method for obtaining standard errors of rotated factor loadings. A simulation study based on the real data analysis is conducted to study bias and error estimates. The SAEM factor algorithm requires minimal lines of code, no derivatives, and no large-matrix inversion. It is programmed entirely in R.

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Examining the Category Functioning of the ECERS-R Across Eight Data Sets

Cited by 12 publications

References 22 publications

Similarities and differences between CLASS and ECERS-R estimates of educational environment quality

Similarities and differences between CLASS and ECERS-R estimates of educational environment quality

Child neurodevelopment and the quality of ecological environments for children in the Amazon

Stochastic approximation EM for large‐scale exploratory IRT factor analysis

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