Scholarlink

<p>This study investigates how different conditioning model specifications affect the stability of marginal and subgroup achievement distributions in large-scale assessments. Through both a simulation and an empirical example reflecting typical operational designs, we examine the robustness of these distributions under alternative latent regression models. We compare parameter estimates obtained from generating parameters with aggregated estimates derived from plausible values. Our findings show that marginal and subgroup achievement distributions are consistently stable and well-recovered across several specifications, aligning with established results from linear regression and multiple imputation. These results provide practical guidance for methodological practice and strengthen the interpretation of large-scale assessment outcomes, bridging technical rigor with applied research needs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The basics of conditioning models: stability of marginal and conditional achievement to model specification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月2日

查看全文
  • Leslie Rutkowski,
  • David Rutkowski

摘要

This study investigates how different conditioning model specifications affect the stability of marginal and subgroup achievement distributions in large-scale assessments. Through both a simulation and an empirical example reflecting typical operational designs, we examine the robustness of these distributions under alternative latent regression models. We compare parameter estimates obtained from generating parameters with aggregated estimates derived from plausible values. Our findings show that marginal and subgroup achievement distributions are consistently stable and well-recovered across several specifications, aligning with established results from linear regression and multiple imputation. These results provide practical guidance for methodological practice and strengthen the interpretation of large-scale assessment outcomes, bridging technical rigor with applied research needs.