title: A method for detecting errors in data of growth studies.
abstract: The paper describes a new method used for data cleaning in growth and development studies. The method is essentially based on the assumption that an extreme value of a certain variable might be suspicious if other highly correlated variables show low compatibility with it and that, on the other hand, an extreme but possible value tends not to be erroneous if it is reinforced by another extreme but possible value of a highly correlated variable. Each variable was therefore included in at least two ratios with the highest correlated variables. A program was designed to detect extreme values of individual variables and extreme ratio values. This procedure with ratios was used to help to detect possible errors and discriminate them from true extreme values. Seen in the light of corrected data files against existing data files, the number of corrections was approximately 4.4%. If the real number of corrected errors is compared to the total number of subjects, this percentage reached 5.5%. If, when correcting, the real value was not detected with certainty, the erroneous value was then simply deleted. From our results, there is reason to believe that with this method few detectable errors will escape from the cleaning procedure. Conclusions for future correction procedures and for future growth studies in general, are also given in the paper.
mesh_terms: D000886:Anthropometry; D003201:Computers; D003625:Data Collection / Q000379:methods*; D006128:Growth; D006801:Humans; D013223:Statistics as Topic; D013597:Systems Analysis