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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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In the social sciences, we often have data with a certain structure such that individual students are grouped in a class and classes are grouped in a school and so forth. This kind of structure can be extended to the upper level as much as possible. When we have such data, we often say that they have a nested or hierarchical data structure. With such a nested or hierarchical data structure, traditional linear model analysis often creates problems as some of the key assumptions for the analysis such as homoscedasticity and independence of data are violated. Especially, the issue of independence is problematic as the students in the same class tend to share more values on many variables. When some of them are not observed, they vanish into the error term of the linear model and it causes autocorrelation of error terms in the model.
Traditional linear model have two ways of doing research with hierarchically structured data: The one is aggregation and the other is disaggregation. However, each of which has some problems as follows. Aggregation issue includes the problems of 'transformation of meanings', 'ecological fallacy', 'ignorance of original data structure', and 'ignorance of cross-level interaction effect'. Disaggregation issue includes 'multiplication of the number of units', 'design effects', 'intraclass correlation' and finally 'selection effects'. All of these issues are inter-related with each other in a way or another.
For these reasons, it is highly encouraged to use multilevel analysis rather than unilevel analysis for hierarchically structured data. By utilizing the multilevel analysis, we can dramatically reduce the fallacies and biases that might have been incurred by traditional linear model analysis. Also, we can infer more accurate statistics for coefficients. The basic logic of multilevel analysis is that we treat the hierarchically structured data not only as aggregated data but also as disaggregated data. Thus, the structure of data tend to be well treated in the hierarchical linear model analysis.
HLM deals with the data in such a way that the predictors at the level 2 are not assumed to have a direct effect on the individual outcome, but rather on the mean of the group in which each individual is nested. In addition, HLM allows each coefficients to be the outcome variable for level-2 predictors so that we can infer the cross-level interaction effects for the level-1 outcome variable. By exploring the new exciting technique of multilevel analysis, I hope we can fully utilize the advantages of multi-level analysis in the future. I believe that this technique will gain its significance in the near future not only in criminology but also in other scientific field.
Traditional linear model have two ways of doing research with hierarchically structured data: The one is aggregation and the other is disaggregation. However, each of which has some problems as follows. Aggregation issue includes the problems of 'transformation of meanings', 'ecological fallacy', 'ignorance of original data structure', and 'ignorance of cross-level interaction effect'. Disaggregation issue includes 'multiplication of the number of units', 'design effects', 'intraclass correlation' and finally 'selection effects'. All of these issues are inter-related with each other in a way or another.
For these reasons, it is highly encouraged to use multilevel analysis rather than unilevel analysis for hierarchically structured data. By utilizing the multilevel analysis, we can dramatically reduce the fallacies and biases that might have been incurred by traditional linear model analysis. Also, we can infer more accurate statistics for coefficients. The basic logic of multilevel analysis is that we treat the hierarchically structured data not only as aggregated data but also as disaggregated data. Thus, the structure of data tend to be well treated in the hierarchical linear model analysis.
HLM deals with the data in such a way that the predictors at the level 2 are not assumed to have a direct effect on the individual outcome, but rather on the mean of the group in which each individual is nested. In addition, HLM allows each coefficients to be the outcome variable for level-2 predictors so that we can infer the cross-level interaction effects for the level-1 outcome variable. By exploring the new exciting technique of multilevel analysis, I hope we can fully utilize the advantages of multi-level analysis in the future. I believe that this technique will gain its significance in the near future not only in criminology but also in other scientific field.
목차
Ⅰ. 들어가는 말
Ⅱ. 다수준연구란 무엇인가
Ⅲ. 다수준연구의 필요성 및 유의점
Ⅳ. 다수준분석의 논리
Ⅴ. 나가는 말
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UCI(KEPA) : I410-ECN-0101-2012-364-003958641