By Terry Reedy
This technical file reports using information in paintings conservation examine. the purpose is to envision how statistical analyses were dealt with in released conservation examine experiences and to signify substitute methods. All parts of knowledge analysis—including experimental layout, facts association, and statistical techniques—are evaluated.
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Extra resources for Statistical Analysis in Art Conservation Research
This improvement in the research design would allow one to more reliably examine changes in a palette over time or between artists, as it would allow one to compare with more certainty the consistency within a specific painting versus across paintings. Orna and Mathews (1981) They mineralogically analyzed pigments from the Glajor Gospel book at UCLA to compare the materials used by artists of two separate but nearly contemporaneous workshops and to compare those workshops to others in Byzantium and western Europe.
Regarding each film as the experimental unit, we performed a 3 x 3 repeated measures analysis of variance (RANOVA) with densitometer and filter type as the repeated measures factors giving nine measurements on each film. 3 show that the densitometers are in fact significantly different and that this effect is consistent across all three color filters. Densitometer 1 gave a higher density reading for all 15 film-filter combinations than densitometer 3. Regarding each densitometer as the experimental unit, we also did a 5 x 3 RANOVA, which shows that the different films also give 38 Deterioration significantly different readings.
The authors appear to assume that the observed fading differences can be generalized to other samples, but no evidence is presented here for that, as no replicate analyses are included. The implicit assumption is that if the study were repeated with the same colorants, the curves would look the same, and therefore the observed curve differences are the result of real dye differences and these differences will persist if we repeat the experiment. But that assumption is not backed up with data. It is possible that these differences could also be due to a high variability in testing procedures and results.