A study of dermatoglyphic variability in the populations of central Wales and Shropshire

Abstract

The research topic undertaken involved a dermatoglyphic investigation of the mid-Wales borderland of Powys and Salop. The material was presented in the following manner. Section One contained a summary of Welsh history, particularly of Powys (Montgomeryshire) and western Salop. Attention was drawn to the evidence of migration and invasions. Available data on genetic studies in Wales and the borders were considered and a brief review of multivariate analysis on dermatoglyphic material made. Section Two gave the methodology used in reading the prints collected as well as the scheme followed in collecting the data. Definitions of three partitioning parameters were given, these being the child's birthplace, parent's common birthplace and ancestry as defined by surname. Further information on dermographic variables as drawn from the collected questionnaires was reported. Section Three gave univariate statistical results for all the variables available, when the data set was partitioned by single parameters and when the parameters were in combination. Evidence was given as to which parameter was most "efficient" as a divisor and how this related to other parameters. The ability of the various variable sets to give statistical differences between subsets was reported. Geographical variation was investigated by a one way analysis of variance subsequent to definition of how regionalisation was obtained. Both parametric and non-parametric statistics were used and a summary of the univariate statistical results made. Section Four dealt with multivariate statistical results. First the theory of discriminant function analysis was given and a short consideration of distance coefficients made. Non-metric Multidimensional scaling and clustering techniques were considered. Using the same parameters as in Section Three, distance coefficients between various combinations of these divisiors were made. The resulting regionalisation obtained was compared with the univariate statistical results and the D (^2) between regions compared by predictors by bivariate and multiple regression techniques.