Past perspectives and new opportunities for the explanatory item response model

Modeling item-level variance of polysyllabic word reading in developing readers: Exploring semantically related child, word, and child-by-word predictors

Across word reading development, there are important and evolving relationships between oral and written semantic knowledge. Recent research has focused on these relationships, with accumulating evidence supporting the role of word knowledge and related word characteristics as important factors influencing polysyllabic word reading abilities. The purpose of this study was to investigate how semantic-related effects across child-level skills (e.g., general vocabulary knowledge), word-level properties (e.g., age of acquisition), child-by-word-level familiarity (e.g., item-level familiarity), and interactions between key child attributes and word characteristics (e.g., word reading skill by age of acquisition) contribute to polysyllabic word reading. Specifically, we emphasize the semantic contributions of word-level features to word reading development, which have been relatively underexplored in the literature. A sample of elementary school students oversampled for word reading difficulty (N = 92) in Grades 3 to 5 read a set of polysyllabic words (J = 45) and completed a battery of reading and language-related measures. Using cross-classified random-effects models and accounting for various control variables, semantic-related variables representing item-level familiarity; child-level set for variability; and word-level age of acquisition and number of morphemes were significant predictors in the main-effects model. A significant interaction between sight word efficiency and age of acquisition indicated higher probabilities of correctly reading polysyllabic words at lower levels of acquisition for better readers. Results indicate important semantic-related influences on polysyllabic word reading at the child, word, and child-by-word levels, suggesting meaningful relationships between knowledge of the orthographic form of a word and semantic knowledge in developing readers.

Decomposing Variation in Vocabulary and Listening Comprehension Task Performance in Spanish and English Into Person, Ecological, and Assessment Differences for Spanish-English Bilingual Children in the United States

PURPOSE

We investigated the contributions of person, ecological, and assessment characteristics to one's performance on vocabulary and listening comprehension tasks in English and Spanish. Person characteristics included English learner status, ecological characteristics included instructional program enrollment (bilingual vs. English immersion) and poverty status, and assessment characteristics included the language of assessment (Spanish vs. English) and linguistic grain size (vocabulary vs. listening comprehension).

METHOD

Data were from 208 Spanish-English emergent bilingual children in Grade 1 in the United States and were analyzed using explanatory item response models.

RESULTS

Substantial variance in the tasks across both languages was attributable to the random effects of assessment/item (74%) and person (26%). English learner status was a significant student predictor of language performance, and the instructional program was a significant ecological predictor of language performance. Regarding assessment characteristics, both the language of assessment and linguistic grain size explained the variance in performance. There were no differences in performance on English assessments between children in the Spanish-English bilingual program and children in the English immersion program. However, on the Spanish tasks, children in the bilingual program outperformed their peers in the English immersion program.

CONCLUSION

These findings highlight the importance of considering not only person/child characteristics but also ecological and assessment characteristics for Spanish-English bilingual children's performance on oral language tasks.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.26972749.

Modeling item-level heterogeneous treatment effects: A tutorial with the glmer function from the lme4 package in R

Recent advancements in education scholarship have introduced Item Response Theory (IRT) models to address treatment heterogeneity at the assessment item level. These models for item-level heterogeneous treatment effects (IL-HTE) enable detailed analyses of treatments that may have varying impacts on individual items within an assessment. This article offers a comprehensive tutorial for applied researchers interested in implementing IL-HTE analysis in R, utilizing the lme4 package. Using empirical data from a second-grade reading comprehension assessment as a running example, this tutorial emphasizes model-building strategies, interpretation techniques, visualization methods, and extensions. By following this tutorial, researchers will gain practical insights into utilizing IL-HTE analysis for enhanced understanding and interpretation of treatment effects at the item level.

Effects of Affix Type and Base Word Transparency on Students' Performance on Different Morphological Awareness Measures

PURPOSE

We examined whether affix type and base word transparency explained variation in third- through sixth-grade students' performance on a number of morphological awareness tasks.

METHOD

Third- through sixth-grade students (n > 500 at each grade) completed morphological awareness tasks from the Morphological Awareness Test for Reading and Spelling, which represent the ways individuals may use their morphological awareness to support reading and spelling. Explanatory item response models were used to understand the role of affix type and base word transparency on students' performance on six morphological awareness tasks.

RESULTS

For all grades, 73%-83% of variance in students' performance was due to differences across individual items. Furthermore, when task effects, affix type, and base word transparency were included simultaneously in the model, affix type was not a significant predictor; there was a significant effect of base word transparency and task. Specifically, the probability of a correct response was greater on task items in which inflected or derived words were transparent with their base word (e.g., friend > friendly) compared to items in which there was a shift in both the phonological and orthographic aspects of the base word (e.g., attend > attention).

CONCLUSIONS

These findings emphasize the importance of considering base word transparency when assessing students' morphological awareness skills with less emphasis on affix type, at least for third- through sixth-grade students. Our results also point to the importance of administering a variety of morphological awareness tasks to fully capture an individual's morphological awareness skills. Collectively, researchers and practitioners should ensure assessment items on multiple measures of morphological awareness vary in their base word transparency to potentially capture a range of student performances.

Estimating Explanatory Extensions of Dichotomous and Polytomous Rasch Models: The eirm Package in R

Explanatory item response modeling (EIRM) enables researchers and practitioners to incorporate item and person properties into item response theory (IRT) models. Unlike traditional IRT models, explanatory IRT models can explain common variability stemming from the shared variance among item clusters and person groups. In this tutorial, we present the R package eirm, which provides a simple and easy-to-use set of tools for preparing data, estimating explanatory IRT models based on the Rasch family, extracting model output, and visualizing model results. We describe how functions in the eirm package can be used for estimating traditional IRT models (e.g., Rasch model, Partial Credit Model, and Rating Scale Model), item-explanatory models (i.e., Linear Logistic Test Model), and person-explanatory models (i.e., latent regression models) for both dichotomous and polytomous responses. In addition to demonstrating the general functionality of the eirm package, we also provide real-data examples with annotated R codes based on the Rosenberg Self-Esteem Scale.

Letter Features as Predictors of Letter-Name Acquisition in Four Languages with Three Scripts

To expand our understanding of script-general and script-specific principles in the learning of letter names, we examined how three characteristics of alphabet letters-their frequency in printed materials, order in the alphabet, and visual similarity to other letters-relate to children's letter-name knowledge in four languages with three distinct scripts (English [N = 318; M age = 4.90], Portuguese [N = 366; M age = 5.80], Korean [N = 168; M age = 5.48], and Hebrew [N = 645; M age = 5.42]). Explanatory item response modeling analysis showed that the frequency of letters in printed materials was consistently related to letter difficulty across the four languages. There were also moderation effects for letter difficulty in English and Korean, and for discriminatory power of letters in Korean. The results suggest that exposure to letters as measured by letter frequency is a language-general mechanism in the learning of alphabet letters.