Masked analysis for small-scale cluster randomized controlled trials

被引：2

作者：

Ferron, John M. ^{[1
]}

Nguyen, Diep ^{[2
]}

Dedrick, Robert F. ^{[1
]}

Suldo, Shannon M. ^{[1
]}

Shaunessy-Dedrick, Elizabeth ^{[3
]}

机构：

[1] Univ S Florida, Dept Educ & Psychol Studies, 4202 East Fowler Ave,EDU105, Tampa, FL 33620 USA

[2] Univ S Florida, Dept Med Educ, Tampa, FL 33620 USA

[3] Univ S Florida, Dept Language Literacy Ed D Except Educ & Phys Ed, Tampa, FL 33620 USA

来源：

BEHAVIOR RESEARCH METHODS | 2022年 / 54卷 / 04期

关键词：

Randomization; Masked graphs; Randomization test; Pilot study; Cluster RCT; VISUAL ANALYSIS; STUDENTS;

D O I：

10.3758/s13428-021-01708-0

中图分类号：

B841 [心理学研究方法];

学科分类号：

040201 ;

摘要：

Researchers conducting small-scale cluster randomized controlled trials (RCTs) during the pilot testing of an intervention often look for evidence of promise to justify an efficacy trial. We developed a method to test for intervention effects that is adaptive (i.e., responsive to data exploration), requires few assumptions, and is statistically valid (i.e., controls the type I error rate), by adapting masked visual analysis techniques to cluster RCTs. We illustrate the creation of masked graphs and their analysis using data from a pilot study in which 15 high school programs were randomly assigned to either business as usual or an intervention developed to promote psychological and academic well-being in 9th grade students in accelerated coursework. We conclude that in small-scale cluster RCTs there can be benefits of testing for effects without a priori specification of a statistical model or test statistic.

引用

页码：1701 / 1714

页数：14