Multivariate techniques are statistical tools.

The basic definition of multivariate analysis is a statistical method that measures relationships between two or more response variables. Multivariate techniques attempt to model reality where each situation, product or decision involves more than a single factor. For example, the decision to purchase a car may take into consideration price, safety features, color and functionality. Modern society has collected masses of data in every field, but the ability to use that data to obtain a clear picture of what is going on and make intelligent decisions is still a challenge.

### Identify Purpose of Research

Multivariate techniques are used to study data sets in consumer and market research, quality control and quality assurance, process optimization and process control, and research and development. These techniques are particularly important in social science research because social researchers are generally unable to use randomized laboratory experiments, like those used in medicine and natural sciences. Here multivariate techniques can statistically estimate relationships between different variables, and correlate how important each one is to the final outcome and where dependencies exist between them.

### Determine Appropriate Statistical Method

Because most data analysis tries to answer complex questions involving more than two variables, these questions are best addressed by multivariate techniques. There are several different multivariate techniques to choose from, based on assumptions about the nature of the data and the type of association under analysis. Each technique tests the theoretical models of a research question about associations against the observed data. The theoretical models are based on facts plus new hypotheses about plausible associations between variables.