Multivariate techniques are statistical methods that measure relationships among variables. They attempt to model reality where each situation, product or decision involves more than a single variable. 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.
What Multivariate Analysis Is Used For
Multivariate techniques are used to study datasets 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.
Why Multivariate Techniques Are Used
Because most data analysis tries to answer complex questions involving more than two variables, these questions are best addressed by multivariate statistical 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.
Multivariate techniques allow researchers to look at relationships between variables in an overarching way and to quantify the relationship between variables. They can control association between variables by using cross tabulation, partial correlation and multiple regressions, and introduce other variables to determine the links between the independent and dependent variables or to specify the conditions under which the association takes place. This gives a much richer and realistic picture than looking at a single variable and provides a powerful test of significance compared to univariate techniques.
Multivariate techniques are complex and involve high level mathematics that require a statistical program to analyze the data. These statistical programs are generally expensive. The results of multivariate analysis are not always easy to interpret and tend to be based on assumptions that may be difficult to assess. For multivariate techniques to give meaningful results, they need a large sample of data; otherwise, the results are meaningless due to high standard errors. Standard errors determine how confident you can be in the results, and you can be more confident in the results from a large sample than a small one. Running statistical programs is fairly straightforward but does require a statistician to make sense of the output.
- Photo Credit Jupiterimages/Comstock/Getty Images
- "Multivariate Analysis: Methods and Applications"; W.R. Dillon and M. Goldstein, 1984
Application of Regression Analysis in Business
Linear regression analysis is a method of analyzing data that has two or more variables. By creating the "best fit" line for...
How to Run a Multivariate Regression in Excel
Multivariate regressions are complicated but useful statistical techniques that often require expensive software to run. However, Microsoft Excel 2007 has a built-in...
The Advantages of Regression Analysis & Forecasting
Managers need information to evaluate what is going on in the external and the internal environments of an organization. Regression analysis is...
The Advantages of MANOVA Over ANOVA
Multivariate analysis of variance (MANOVA), and analysis of variance (ANOVA) tests are statistical methods for analyzing the difference in means between variables....
Advantages & Disadvantages of Finding Variance
In statistics, variance is a measure of the spread of a set of data with respect to the average value, or mean....
The Disadvantages of Linear Regression
Linear regression is a statistical method for examining the relationship between a dependent variable and one or more independent variables. The dependent...
The Disadvantages of Logistic Regression
Logistic regression, also called logit regression or logit modeling, is a statistical technique allowing researchers to create predictive models. The technique is...
The Difference Between Bivariate & Multivariate Analyses
Bivariate and multivariate analyses are statistical methods that help you investigate relationships between data samples. Bivariate analysis looks at two paired data...
Techniques of Organization Development
Organizational development aims to make a company and its employees more efficient and more competitive. Also known as organizational effectiveness, it can...