Using Visual Analysis to Evaluate and Refine Multilevel Models of Single-Case Studies

被引：15

作者：

Baek, Eun Kyeng ^{[1
]}

Petit-Bois, Merlande ^{[1
]}

Van den Noortgate, Wim ^{[2
]}

Beretvas, S. Natasha ^{[3
]}

Ferron, John M. ^{[1
]}

机构：

[1] Univ S Florida, Tampa, FL USA

[2] Katholieke Univ Leuven, Louvain, Belgium

[3] Univ Texas Austin, Austin, TX 78712 USA

来源：

JOURNAL OF SPECIAL EDUCATION | 2016年 / 50卷 / 01期

关键词：

single-case research; multilevel modeling; visual analysis; misspecification; treatment effect; BASE-LINE DATA; MONTE-CARLO; STUDENTS;

D O I：

10.1177/0022466914565367

中图分类号：

G76 [特殊教育];

学科分类号：

040109 ;

摘要：

In special education, multilevel models of single-case research have been used as a method of estimating treatment effects over time and across individuals. Although multilevel models can accurately summarize the effect, it is known that if the model is misspecified, inferences about the effects can be biased. Concern with the potential for model misspecification motivates our method for evaluating multilevel models of single-case data. This method is based on the visual analysis of graphs that have the model-implied individual trajectories superimposed on plots of the raw data. Through the reanalysis of a published study, we show how this visual analysis approach can identify model misspecifications and motivate the consideration of alternative model specifications that lead to better fit.

引用

页码：18 / 26

页数：9

共 21 条

[1]

[Anonymous], 2002, HIERARCHICAL LINEAR

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