scholarly journals Rank-based procedures in factorial designs: hypotheses about non-parametric treatment effects

2016 ◽  
Vol 79 (5) ◽  
pp. 1463-1485 ◽  
Author(s):  
Edgar Brunner ◽  
Frank Konietschke ◽  
Markus Pauly ◽  
Madan L. Puri
Keyword(s):  
Treatment Effects ◽  
Factorial Designs ◽  
Non Parametric

scholarly journals Simultaneous inference on treatment effects in survival studies with factorial designs

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1078-1085 ◽  
Author(s):  
Dan‐Yu Lin ◽  
Jianjian Gong ◽  
Paul Gallo ◽  
Paul H. Bunn ◽  
David Couper
Keyword(s):  
Treatment Effects ◽  
Simultaneous Inference ◽  
Factorial Designs ◽  
Survival Studies

Regression discontinuity design: a guide for strengthening causal inference in HRD

2016 ◽  
Vol 40 (8/9) ◽  
pp. 615-637
Author(s):  
Silvana Chambers
Keyword(s):  
Treatment Effects ◽  
Regression Discontinuity ◽  
Simulated Data ◽  
Training Intervention ◽  
Causal Relationships ◽  
Data Set ◽  
Content Type ◽  
Training Interventions ◽  
Quasi Experimental ◽  
Non Parametric

Purpose Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about training interventions. Design/methodology/approach To demonstrate the key features of RD designs, a simulated data set was generated from actual pre-test and post-test diversity training scores of 276 participants from three organizations in the USA. Parametric and non-parametric analyses were conducted, and graphical presentations were produced. Findings This study found that RD design can be used for evaluating training interventions. The results of the simulated data set yielded statistically significant results for the treatment effects, showing a positive causal effect of the training intervention. The analyses found support for the use of RD models with retrospective training intervention data, eliminating ethical concerns from random group assignment. The results of the non-parametric model provided evidence of the plausibility of finding the right balance between precision of estimates and generalizable results, making it an alternative to experimental designs. Practical implications This study contributes to the HRD field by explicating the implementation of a sophisticated, statistical tool to strengthen causal claims, contributing to an evidence-based HRD approach to practice and providing the R syntax for replicating the analyses contained herein. Originality/value Despite the growing number of scholarly articles being published in HRD journals, very few have used experimental or quasi-experimental design approaches. Therefore, a very limited amount of research has been devoted to uncovering causal relationships.

The Rank Test of Various Treatment Effects

2015 ◽  
Vol 713-715 ◽  
pp. 1974-1977
Author(s):  
Ji Ting Huang
Keyword(s):  
Treatment Effects ◽  
Test Method ◽  
Rank Test ◽  
Sufficient Condition ◽  
Parametric Test ◽  
Non Parametric ◽  
Priori Knowledge

This article focuses on the non-parametric test of a variety treatment effects by using the rank test to construct statistics. Discussing when the priori knowledge sufficient condition, the rank test method is more effective than test method.

scholarly journals Who lacks and who benefits from diet diversity: evidence from (impact) profiling for children in Zimbabwe

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Remco Oostendorp ◽  
Lia van Wesenbeeck ◽  
Ben Sonneveld ◽  
Precious Zikhali
Keyword(s):  
Rural Areas ◽  
Child Nutrition ◽  
Treatment Effects ◽  
Dietary Diversity ◽  
Parametric Method ◽  
Policy Makers ◽  
Diet Diversity ◽  
The Impact ◽  
Matching Techniques ◽  
Non Parametric

Abstract Background The impact of diet diversity—defined as the number of different foods or food groups consumed over a given reference period—on child nutrition outcomes strongly interacts with agro-ecological, institutional, and socio-economic drivers of child food and nutrition security. Yet, the literature on the impact of diet diversity typically estimates average treatment effects, largely ignoring impact heterogeneity among different groups. Methods In this paper, we introduce a new method of profiling to identify groups of treatment units that stand to gain the most from a given intervention. We start from the ‘polling approach’ which provides a fully flexible (non-parametric) method to profile vulnerability patterns (patterns in ‘needs’) across highly heterogeneous environments [35]. Here we combine this polling methodology with matching techniques to identify ‘impact profiles’ showing how impact varies across non-parametric profiles. We use this method to explore the potential for improving child nutrition outcomes, in particular stunting, through targeted improvements in dietary diversity in a physically and socio-economically diverse country, namely Zimbabwe. Complex interaction effects with agro-ecological, institutional and socio-economic conditions are accounted for. Finally, we analyze whether targeting interventions at the neediest (as identified by the polling approach) will also create the largest benefits. Results The dominant profile for stunted children is that they are young (6–12 months), live in poorer/poorest households, in rural areas characterized by significant sloping of the terrain and with one-sided emphasis on maize cultivation and medium dry conditions. When moving from “need” to “maximal impact”, we calculate both the coverage in “need” as well as the impact coverage, and find that targeting on need does not always provide the largest impact. Conclusions Policy-makers need to remain alert that targeting on need is not always the same as targeting on impact. Estimation of heterogeneous treatment effects allows for more efficient targeting. It also enhances the external validity of the estimated impact findings, as the impact of child diet diversity on stunting depends on various agro-ecological variables, and policy-makers can relate these findings to areas outside our study area with similar agro-ecological conditions.

Probabilistic index: an intuitive non-parametric approach to measuring the size of treatment effects

2006 ◽  
Vol 25 (4) ◽  
pp. 591-602 ◽  
Author(s):  
Laura Acion ◽  
John J. Peterson ◽  
Scott Temple ◽  
Stephan Arndt
Keyword(s):  
Treatment Effects ◽  
Parametric Approach ◽  
Non Parametric

Rank-based permutation approaches for non-parametric factorial designs

2017 ◽  
Vol 70 (3) ◽  
pp. 368-390 ◽  
Author(s):  
Maria Umlauft ◽  
Frank Konietschke ◽  
Markus Pauly
Keyword(s):  
Factorial Designs ◽  
Non Parametric
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