CoDa-session in the ESRA 2017
Next year we'll have a special session on CoDa in the ESRA conference (Lisbon, July 17th-21h) organized by some members of COSDA-project (Girona, Spain)
Convenor: Dr Berta Ferrer-rosell (University of Lleida)
Coordinator 1: Dr Marina Vives-mestres (University of Girona)
Coordinator 2: Dr Juan Jose Egozcue (Tecnical University of Catalonia )
Statistical compositions are common in the chemical and biological analysis in the fields of geology and biology, among others. Typically the size is irrelevant and mainly the proportion or the relative importance of each component is of interest. In survey measurement, the so-called ipsative data also consist of positive data arrays with a fixed sum and which convey information on the relative importance of each component. Examples include surveys measuring compositions of household budgets (% spent in each product category), time-use surveys (24-hour total), educational instruments allocating a total number of points into different abilities or orientations (e.g. Kolb’s learning styles), and social network compositions (% of family members, friends, neighbours, etc.). Beyond ipsative measures, Likert items can also be understood as a distribution of response frequencies adding up to 100% and ranking items containing k stimuli as a distribution of response options adding up to k(k+1)/2.
Statistical analysis of compositional data focus on the relative importance components. A popular approach is to transform compositional data by means of logarithms of ratios of components before applying otherwise standard analysis methods.
Standard statistical methods such as ANOVA, linear regression and cluster analysis have a well documented tradition in compositional data analysis although there is room for improving the methods and make them more user friendly to a wider audience. Less has been done regarding typical survey research analysis methods, for instance, multivariate analysis methods and latent-variable methods. The naive analysis of raw proportions is of common practice even if it is plagued with statistical problems (inconsistent inferences, spurious correlations, and unclear interpretation, among others). The session aims to bridge methodological knowledge between the natural and social sciences in order to narrow this gap.