Individuals and contexts

In traditional survey research, the individual is often seen in isolation from his or her contexts, whereas qualitative research emphasizes contextualization. Multilevel analysis can be seen as a tool for contextualising quantitative statistical analysis. In many fields of research, the processes to be studied involve two or more levels of analysis. Learning takes place in schools and it does not seem right to ignore this fact when studying learning outcomes. Another example is from the field of public health, where the famous Wilkinson hypothesis implies that income inequality in a community can influence the health of the inhabitants. A third example is from the study of wage determination, where the wages of employees can be seen as dependent on the profitability of the firm as well as on the human capital of the employees. In these examples, research questions emerge from a combination of theories at the individual and contextual level.

Hierarchical data structures

In multilevel analysis, the basic data structure is hierarchical. Units at a lower level are nested within units at higher levels. The table below lists four examples. In the first example, employees are nested within firms. In the two next examples, which could apply to the European Social Survey, respondents are nested within countries; first in a two-level model, then within regions and countries in a three-level model. The last example in the table is the structure of the multilevel model of change. The lowest level consists of measurements made on two or more occasions nested within students within schools. This model requires a repeated measurement or panel design, and it will not be covered in this package. It cannot be applied to data from the European Social Survey since the ESS is based on a cross-sectional design. Several rounds of the ESS can, however, be analysed using multilevel models, but the estimation of changes only applies to the country level. Individual change is unobserved due to the cross-sectional design.

Table 2.1. Examples of multilevel data structures
Level 3 Countries Schools
Level 2 Firms Countries Regions Students
Level 1 Employees Respondents Respondents Occasions

Which variables can constitute a level?

Individuals, schools and countries are typical levels, but gender and social class are not candidates for levels. Why is this so? The technical difference is that the former variables identify the units in the data file, whereas gender and class are candidates for explanatory variables at the individual level. For a variable to become a level it is necessary that it identifies units sampled from a population, such as individuals, families, firms, schools, regions and countries. Ideally, both the level 1 and the level 2 units should be random samples from their respective populations. They will thus constitute random classifications, whereas gender and social class with only a few non-exchangeable values constitute fixed classifications. Regions and countries are commonly used as levels although they are seldom randomly sampled. This is a common practice, although it can be seen as a violation of one of the assumptions of multilevel models.

For some variables, there may be a choice. In longitudinal data, occasions (time) can be defined as a level or as an explanatory variable. Occupational groups can be defined as a level or collapsed into categories or scored and used as an explanatory variable. The same applies to industry classifications in studies of firms.

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