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Joshua B. Gilbert – Annenberg Institute for School Reform at Brown University, 2024
When analyzing treatment effects on test scores, researchers face many choices and competing guidance for scoring tests and modeling results. This study examines the impact of scoring choices through simulation and an empirical application. Results show that estimates from multiple methods applied to the same data will vary because two-step models…
Descriptors: Scores, Statistical Bias, Statistical Inference, Scoring
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Nadide Yilmaz; Sümeyye Aktas – Acta Didactica Napocensia, 2023
It was aimed to reveal the informal statistical inferences of primary school students on the concept of variability. A phenomenographic research approach was adopted. A task was adapted and used as a data collection tool. Clinical interviews were conducted with 20 primary school students from each grade level. Data were analyzed within the context…
Descriptors: Elementary School Students, Statistical Inference, Mathematical Concepts, Differences
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Stavroula Saplamidou; Charalampos Sakonidis – Statistics Education Research Journal, 2025
This paper reports on a study concerning the social nature of young students' informal inferential reasoning. Employing inferentialism as a background theory, we examine cognitive and sociocultural aspects of reasoning that arose during group discussions as well as trace relations between those aspects. Following a design experiment approach, we…
Descriptors: Thinking Skills, Grade 2, Cognitive Processes, Sociocultural Patterns
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Joshua B. Gilbert; Luke W. Miratrix; Mridul Joshi; Benjamin W. Domingue – Journal of Educational and Behavioral Statistics, 2025
Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…
Descriptors: Causal Models, Item Response Theory, Statistical Inference, Psychometrics
Joshua B. Gilbert; Luke W. Miratrix; Mridul Joshi; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2024
Analyzing heterogeneous treatment effects (HTE) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and pre-intervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…
Descriptors: Causal Models, Item Response Theory, Statistical Inference, Psychometrics
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Van Norman, Ethan R. – School Psychology Quarterly, 2016
Curriculum-based measurement of oral reading (CBM-R) progress monitoring data is used to measure student response to instruction. Federal legislation permits educators to use CBM-R progress monitoring data as a basis for determining the presence of specific learning disabilities. However, decision making frameworks originally developed for CBM-R…
Descriptors: Oral Reading, Curriculum Based Assessment, Investigations, Progress Monitoring
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Davies, Randall S.; Qudisat, Rasha M. – Educational Research and Evaluation, 2015
This paper summarizes results from a math intervention implemented in a high-poverty urban community. Over 7,300 students from kindergarten to 4th grade in 1 low-socioeconomic-status school district participated in the study. Students from 13 different schools (36 different classroom) participated in the treatment. Comparisons were made to…
Descriptors: Mathematics Instruction, Intervention, Poverty, Urban Areas