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Disentangling Person-Dependent and Item-Dependent Causal Effects: Applications of Item Response Theory to the Estimation of Treatment Effect Heterogeneity. EdWorkingPaper No. 23-881

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Title: Disentangling Person-Dependent and Item-Dependent Causal Effects: Applications of Item Response Theory to the Estimation of Treatment Effect Heterogeneity. EdWorkingPaper No. 23-881
Language: English
Authors: Joshua B. Gilbert, Luke W. Miratrix, Mridul Joshi, Benjamin W. Domingue, Annenberg Institute for School Reform at Brown University
Source: Annenberg Institute for School Reform at Brown University. 2024.
Availability: Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: annenberg@brown.edu; Web site: https://annenberg.brown.edu/
Peer Reviewed: N
Page Count: 41
Publication Date: 2024
Document Type: Reports - Research
Education Level: Elementary Education
Early Childhood Education
Grade 2
Primary Education
Descriptors: Causal Models, Item Response Theory, Statistical Inference, Psychometrics, Educational Assessment, Randomized Controlled Trials, Reading Instruction, Intervention, Elementary School Students, Grade 2, Reading Tests, Pretesting, Correlation, Effect Size, Test Items, Item Analysis, Difficulty Level, Test Construction
Abstract: 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. This study demonstrates that identical patterns of HTE on test score outcomes can emerge either from variation in treatment effects due to a pre-intervention participant characteristic or from correlations between treatment effects and item easiness parameters. We demonstrate analytically and through simulation that these two scenarios cannot be distinguished if analysis is based on summary scores alone. We then describe a novel approach that identifies the relevant data-generating process by leveraging item-level data. We apply our approach to a randomized trial of a reading intervention in second grade, and show that any apparent HTE by pretest ability is driven by the correlation between treatment effect size and item easiness. Our results highlight the potential of employing measurement principles in causal analysis, beyond their common use in test construction.
Abstractor: As Provided
Entry Date: 2025
Accession Number: ED672224
Database: ERIC
FullText Text:
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  Data: Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: annenberg@brown.edu; Web site: https://annenberg.brown.edu/
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  Data: 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. This study demonstrates that identical patterns of HTE on test score outcomes can emerge either from variation in treatment effects due to a pre-intervention participant characteristic or from correlations between treatment effects and item easiness parameters. We demonstrate analytically and through simulation that these two scenarios cannot be distinguished if analysis is based on summary scores alone. We then describe a novel approach that identifies the relevant data-generating process by leveraging item-level data. We apply our approach to a randomized trial of a reading intervention in second grade, and show that any apparent HTE by pretest ability is driven by the correlation between treatment effect size and item easiness. Our results highlight the potential of employing measurement principles in causal analysis, beyond their common use in test construction.
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  Data: ED672224
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      – Text: English
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        PageCount: 41
    Subjects:
      – SubjectFull: Causal Models
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Statistical Inference
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      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Educational Assessment
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      – SubjectFull: Randomized Controlled Trials
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      – SubjectFull: Reading Instruction
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      – SubjectFull: Elementary School Students
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      – SubjectFull: Grade 2
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      – SubjectFull: Reading Tests
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      – SubjectFull: Pretesting
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      – SubjectFull: Correlation
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      – SubjectFull: Effect Size
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      – SubjectFull: Test Items
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      – SubjectFull: Item Analysis
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      – SubjectFull: Difficulty Level
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      – SubjectFull: Test Construction
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    Titles:
      – TitleFull: Disentangling Person-Dependent and Item-Dependent Causal Effects: Applications of Item Response Theory to the Estimation of Treatment Effect Heterogeneity. EdWorkingPaper No. 23-881
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              Y: 2024
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