Indexing
All Issues
Submission
Author Guidelines
Reviewers
Editorial Members
Publication Fee
Vol.1 , No. 4, Publication Date: May 8, 2015, Page: 139-146
 in EU22&description=Global reports have shown that there are major shifts in dietary patterns, even in the consumption behaviour of basic staples towards more diversified diets. Accompanying these changes in food consumption at global and regional level is characterized with health consequences. Population in countries undergoing rapid transition is experiencing nutritional transition. The diverse nature of this transition may be the result of differences in socio-demographic factors and other consumer characteristics. Among other factors including urbanization and food industry marketing, the policies of trade liberalisation over the past two decades have implications for health by virtue of being a factor in facilitating the ‘nutrition transition’ that is associated with rising rates of obesity and chronic diseases such as cardiovascular disease and cancer. In this study, we consider and estimate the most accurate association model of the Categorical Data Analysis (CDAS) for the gross human apparent consumption of main food items, fish and seafood (tons live weight) in 22 EU countries. The data used in this study are obtained from the Eurostat and estimated on actual base year from 2003-2012. The analysis of association (ANOAS) table is given in order to ascertain the percentage of the data covered by each model. We estimate the model with the best fit and in conclusion we find out that the Row-Column Effects Association Model (from the multivariate model M=9) has the best fit among all.&picture=http://www.aascit.org/aascit/decorators/img/journal/928.jpg)
| [1] | Joel C. Nwaubani, Faculty of Social Sciences, Department of Applied Informatics, University of Macedonia Thessaloniki, Thessaloniki, Greece. |
| [2] | Antonia Gkouma, A’ General Clinic, 251 General Airforce Hospital Athens, Athens, Greece. |
| [3] | Evangelia Vussa, Group Cater Agency, a Subsidiary Group of Groupoma Thessaloniki, Thessaloniki, Greece. |
Global reports have shown that there are major shifts in dietary patterns, even in the consumption behaviour of basic staples towards more diversified diets. Accompanying these changes in food consumption at global and regional level is characterized with health consequences. Population in countries undergoing rapid transition is experiencing nutritional transition. The diverse nature of this transition may be the result of differences in socio-demographic factors and other consumer characteristics. Among other factors including urbanization and food industry marketing, the policies of trade liberalisation over the past two decades have implications for health by virtue of being a factor in facilitating the ‘nutrition transition’ that is associated with rising rates of obesity and chronic diseases such as cardiovascular disease and cancer. In this study, we consider and estimate the most accurate association model of the Categorical Data Analysis (CDAS) for the gross human apparent consumption of main food items, fish and seafood (tons live weight) in 22 EU countries. The data used in this study are obtained from the Eurostat and estimated on actual base year from 2003-2012. The analysis of association (ANOAS) table is given in order to ascertain the percentage of the data covered by each model. We estimate the model with the best fit and in conclusion we find out that the Row-Column Effects Association Model (from the multivariate model M=9) has the best fit among all.
Keywords
Association Model, Log-Linear Model, Non-Linear Model, EU, Consumption of Main Food Items, Fish and Seafood
Reference
| [01] | Clogg, C.C. (1990), Analysis of Association (ANOAS) program. |
| [02] | Diewert, W. Erwin. (1995) Axiomatic and Economic Approaches to Elementary Price indexes. |
| [03] | Eliason Ρ. Scott-Clifford Clogg (1990), Categorical Data Analysis (CDAS) NBER working P., 510 |
| [04] | Eurostat /JP (2010): European Data Agency, “Eurostat-Food Statistics at regional level”. |
| [05] | Fairbanks, Michael. (2000). Changing the Mind of a Nation: Elements in a Process for Creating Prosperity, “in Culture Matters”, Huntington, editors, “New York: Basic Books”, pp. 270-281. |
| [06] | Goodman, L.A., (1979a). Multiple Models for the Analysis of Occupational Mobility, Tables and Other Kinds of Cross - Classification Tables. “American Journal of Sociology”, 84:804-819. |
| [07] | Goodman, L.A., (1979b). Multiple Models for the Analysis of Occupational Mobility, Tables and Other Kinds of Cross - Classification Tables. “American Journal of Sociology”, 84:804-819. |
| [08] | Goodman, L.A., (1981a). Association models and the Bivariate Normal for Contingency, Tables with Ordered Categories. Biometrica, Volume 68:p. 347-55. |
| [09] | Goodman, L.A., (1981b).Association Models and Canonical Correlation in the Analysis of Cross-Classifications Having Ordered Categories, Journal of American Statistical Association, p.20-34. |
| [10] | Haritou A, Nwaubani J. C., (2008) “Categorical Data Analysis” (University Press) NBER- working Paper, University of Macedonia Thessaloniki Greece, Department of Applied Informatics. |
| [11] | Haritou A, Nwaubani J. C., (2009) “Categorical Data Analysis” (University Press) N-working P. |
| [12] | Haritou A, Nwaubani J. C., (2010) Categorical Data Analysis (University Press) N-working P. |
| [13] | Haritou A, Nwaubani J. C., (2011) “Categorical Data Analysis” (University Press) N-working P. |
| [14] | Kris-Etherton, Penny M., William S., et al. (2002). "Fish Consumption”, Fish Oil, Omega-3 |
| [15] | Roberts et al. 2001. Effects of marine reserves on adjacent fisheries, Science 294, 1920 |
| [16] | Scorletti, E; Byrne, CD (2013). "Omega-3 fatty acids, hepatic lipid metabolism, and nonalcoholic fatty liver disease." Annual review of nutrition 33: 231–48. PMID 23862644. |
| [17] | Secher, NJ (2007). "Does fish oil prevent preterm birth?” Journal of perinatal medicine. 35 Supply 1: S25–7. doi:10.1515/JPM.2007.033. PMID 17302537 |
| [18] | Shetty P., 2002. Nutrition transition in India and Public Health Nutrition 5, 175–182 |
