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Multivariate analysis involves, in the loose sense of the term, more than two variables and, in its strict sense, at least two dependent and two independent variables. Multivariate analysis procedures can be classified in different ways, and no classification is exhaustive, especially due to the dynamics of the field.
With increasing numbers of variables, statistical modeling becomes necessary and more complex. At the same time, these models are more appropriate for social sciences, since in social reality many variables are intertwined and there is rarely one central determination.
Once data are collected and read into a database processable by statistical software, the typical steps in a multivariate data analysis are the following.
- Framing the research question in such a way that it can be modeled mathematically.
- Selecting the right statistical model: every multivariate model searches for certain patterns in data. It might miss other patterns. Using different multivariate methods therefore may lead to different results. Among the theoretical questions multivariate analysis can address are: (a) identifying latent classes; (b) causal analysis; (c) identifying patterns in time; (d) network analysis; and (e) multilevel analysis. Most multivariate procedures can be viewed as a special case of general linear models (GLM).
- Verifying that assumptions and prerequisites for the chosen statistical procedure are met.
- Preparing data for the specific analysis.
- Computing the model using a special statistical computer package such as SAS, SPSS, or Stata.
- The results of data analysis always have to be interpreted.
- Scott, J., & Xie, Y. (eds.) (2005) Quantitative Social Science. Sage, London.