Construction of some classes of balanced cross-over designs from terraces

5 and 6 have recently introduced power-sequence terraces. In this paper we have used these terraces for the construction of some new families of balanced cross-over designs of first and second order which are variance-balanced. We have also used them for the construction of some new families of balanced ternary cross-over designs.     

Data Gap Analysis,Indicator Selection and Index Development: A Case for Developing Economies

Sustainable Development Goals (SDG) and its measurement is a key policy focus across the globe. Despite existence of several indices and reporting agencies, it is a challenging task to identify progress, specifically in the case of developing economies. These economies are predominantly in Asia and Africa and have a great bearing on global performance. These countries have diverse geographical and cultural elements with an active sub-national institutional framework. Data gap coupled with poor data quality and limited financial support does not help their case. Without appropriate indicator selection and index measurement it is not possible to judge the actual current situation or compare the SDG situation around the world. This paper attempts a practical and simpler approach to overcome the constraints, provide direction and facilitate measurement of SDG position in such countries using the case of India. This is done by identifying gaps, suggesting a tailored approach to indicator selection and measurement and further, developing a suitable index for SDG measurement at the sub-national level. The results and findings should be of great interest for policy making and implementing agencies.

     

Bihari Muharram processions in Dhaka city: religious performance as a means of territorialising urban public space by a disenfranchised ethnic minority

The Biharis in Bangladesh are a heavily persecuted ethnic minority confined to slum like camps for nearly five decades. Using data from ethnographic research carried out in a Bihari camp and its surrounding areas in Dhaka city, this visual essay examines the ways in which the Biharis use religious processions to claim visibility, assert community identity and territorialise urban public space in a city that systematically denies them access to citizen rights.     

On totally balanced block designs for competition effects

Competition between neighbouring units in field experiments is a serious source of bias. The study of a competing situation needs construction of an environment in which it can happen and the competing units have to appear in a predetermined pattern. This paper describes methods of constructing incomplete block designs balanced for neighbouring competition effects. The designs obtained are totally balanced in the sense that all the effects, direct and neighbours, are estimated with the same variance. The efficiency of these designs has been computed as compared to a complete block design balanced for neighbours and a catalogue has also been prepared.     

Minimal balanced repeated measurements designs

Experimental designs in which treatments are applied to the experimental units, one at a time, in sequences over a number of periods, have been used in several scientific investigations and are known as repeated measurements designs. Besides direct effects, these designs allow estimation of residual effects of treatments along with adjustment for them. Assuming the existence of first-order residual effects of treatments, Hedayat & Afsarinejad (1975) gave a method of constructing minimal balanced repeated measurements [RM(v,n,p)] design for v treatments using n=2v experimental units for p [=(v+1)/2] periods when v is a prime or prime power. Here, a general method of construction of these designs for all odd v has been given along with an outline for their analysis. In terms of variances of estimated elementary contrasts between treatment effects (direct and residual), these designs are seen to be partially variance balanced based on the circular association scheme.