The third consideration highlighted is interesting from a hierarchy of evidence point of view, as some authors might want to rank studies using multilevel models at the top of the hierarchy of evidence. This is because they can capture between and within country differences in attitudes not captured by other analysis methods. However, most scholars that do not use this method demonstrate awareness of the methodological issues involved when using different level (country and individual) variables. The latter is deemed as acceptable quality-wise, in particular as it is argued in this thesis that the choice of method should be guided by the research questions and needs. It is also important to note that multilevel modelling itself has weaknesses, in particular as the number of countries most commonly included are lower than what multilevel models require. The exact minimum number of higher-level units i.e. countries, is debated but Blekesaune and Quadagno (2003) suggest that ‘Standard methodologies assume that we have a large number of nations, 30 or more’ (2003, p. 419). However, they later use a sampling method that enables them to justify a lower number, 24 countries. Others argue that a minimum of 20 is justifiable. This is a significant problem for studies of attitudes to welfare as there might be little comparable data for 20 or more countries. As a result, multilevel modelling should not always be used, and this thesis maintains the argument that no method can be rated as at the top of a hierarchy. This view is embedded in the argument that each type of statistical and methodological techniques has their weaknesses and this goes for multilevel analysis as well as OLS regression. A separate issue in regards to multi-level modelling is how authors should report results from multilevel models. Some display coefficients, fixed and random variance and model fit through -2Loglikelihood (Van Oorschot and Uunk 2007) or ICC, whilst others do not report the model fit (Andersen and Singer 2008). This might be due to the fact that multilevel modelling is a relatively new method and no convention has come into place, but the choice on which parameters to include might be something authors should discuss in articles, at least until
The third consideration highlighted is interesting from a hierarchy of evidence point of view, as some authors might want to rank studies using multilevel models at the top of the hierarchy of evidence. This is because they can capture between and within country differences in attitudes not captured by other analysis methods. However, most scholars that do not use this method demonstrate awareness of the methodological issues involved when using different level (country and individual) variables. The latter is deemed as acceptable quality-wise, in particular as it is argued in this thesis that the choice of method should be guided by the research questions and needs. It is also important to note that multilevel modelling itself has weaknesses, in particular as the number of countries most commonly included are lower than what multilevel models require. The exact minimum number of higher-level units i.e. countries, is debated but Blekesaune and Quadagno (2003) suggest that ‘Standard methodologies assume that we have a large number of nations, 30 or more’ (2003, p. 419). However, they later use a sampling method that enables them to justify a lower number, 24 countries. Others argue that a minimum of 20 is justifiable. This is a significant problem for studies of attitudes to welfare as there might be little comparable data for 20 or more countries. As a result, multilevel modelling should not always be used, and this thesis maintains the argument that no method can be rated as at the top of a hierarchy. This view is embedded in the argument that each type of statistical and methodological techniques has their weaknesses and this goes for multilevel analysis as well as OLS regression. A separate issue in regards to multi-level modelling is how authors should report results from multilevel models. Some display coefficients, fixed and random variance and model fit through -2Loglikelihood (Van Oorschot and Uunk 2007) or ICC, whilst others do not report the model fit (Andersen and Singer 2008). This might be due to the fact that multilevel modelling is a relatively new method and no convention has come into place, but the choice on which parameters to include might be something authors should discuss in articles, at least until
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