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The conventional way to measure program impacts is to compute the average treatment effect; that is, the difference between a treatment group that received some intervention and a control group that did not. Recently, scholars have recognized that looking only at the average treatment effect may obscure impacts that accrue to subgroups. In an effort to inform subgroup analysis research, this article explains the challenge of treatment group heterogeneity. It then proposes using cluster analysis to identify otherwise difficult-to-identify subgroups within evaluation data. The approach maintains the integrity of the experimental evaluation design, thereby producing unbiased estimates of program impacts by subgroup. This method is applied to data from the evaluation of New York State's Child Assistance Program, a reform that intended to increase work and earnings among welfare recipients. The article interprets the substantive findings and then addresses the advantages and disadvantages of the proposed method.

This study examines the similarities between the current crisis and other systemic crises from the past. The purpose of our research is to discover whether previously used crisis management policies can constitute a referential in choosing the most effective policies for the management of the current crisis. This study highlights important similarities between the current crisis and those that occurred in the past in Norway (1991) and Japan (1997), using a cluster analysis in order to obtain homogeneous groups of crises. Also, through a qualitative analysis of crisis management policies, the study stresses the need to learn from past lessons. ; peer-reviewed

In the course of my 5-year study of indigenous stone structures of the eastern seaboard of the US and Canada, I have recovered locational information on over 5,500 sites containing structures of various types: rock piles, cairns, stone rows, U-shaped structures, standing stones, split-filled boulders, balanced rocks, marked stones, petroglyphs, stone circles, effigies, mounds, platforms, enclosures, and niches. More than half of these sites are found in well-defined site clusters, some of which overlap modern political boundaries. This suggests that their creation precedes the creation of those boundaries, further suggesting their indigenous origin. Two types of cluster analysis – variance mean ratio and nearest-neighbor – were used to determine whether the clusters are statistically real, or if they are simply random placements. The conclusion is that there is close to 0% probability that they are randomly distributed. The implications of this will be explored.

Bayesian model checking; Bayesian hierarchical model; Ecological inference; Election data; Spatial data ; To help settle the debate triggered the day after any election around the origin and destination of the vote of winners and losers, a Bayesian analysis of the results in a pair of consecutive elections is proposed. It is based on a model that simultaneously carries out a cluster analysis of the areas in which the results are broken into and links the results in the two elections of areas in a given cluster through a vote switch matrix. The number of clusters is chosen both through predictive checks as well as by testing whether the residuals are spatially correlated or not. The analysis is tried on the results in Barcelona of a pair of consecutive elections held just four months apart, in 2003 for the Catalan parliament and in 2004 for the Spanish parliament. The proposed approach, which reconstructs individual behavior from aggregated data, can be exported to be a solution for any ecological inference problem where one cannot assume that all the areas are exchangeable the way typically assumed by other ecological inference methods. ; Peer Reviewed ; Postprint (published version)

Bayesian model checking; Bayesian hierarchical model; Ecological inference; Election data; Spatial data ; To help settle the debate triggered the day after any election around the origin and destination of the vote of winners and losers, a Bayesian analysis of the results in a pair of consecutive elections is proposed. It is based on a model that simultaneously carries out a cluster analysis of the areas in which the results are broken into and links the results in the two elections of areas in a given cluster through a vote switch matrix. The number of clusters is chosen both through predictive checks as well as by testing whether the residuals are spatially correlated or not. The analysis is tried on the results in Barcelona of a pair of consecutive elections held just four months apart, in 2003 for the Catalan parliament and in 2004 for the Spanish parliament. The proposed approach, which reconstructs individual behavior from aggregated data, can be exported to be a solution for any ecological inference problem where one cannot assume that all the areas are exchangeable the way typically assumed by other ecological inference methods. ; Peer Reviewed ; Postprint (published version)

Some problems cannot be solved optimally and compromises become necessary. In some cases obtaining an optimal solution may require combining algorithms and iterations. This often occurs when the problem is complex and a single procedure does not reach optimality. This paper shows a conglomerate of algorithms iterated in tasks to form an optimal consortium using cluster analysis. Hierarchical methods and distance measures lead the process. Few companies are desirable in optimal consortium formation. However, this study shows that optimization cannot be predetermined based on a specific fixed number of companies. The experiential exercise forms an optimal consortium of four companies from six shortlisted competitors