Code for this page was tested in Stata As the name implies, multivariate regression is a technique that estimates a single regression model with more than one outcome variable. When there is more than one predictor variable in a multivariate regression model, the model is a multivariate multiple regression.

If you have nested data, you will want to describe the variables at each level of nesting. If you have weighted data, then medians, correlations and histograms may not be part of the description of your variables.

In the analysis part of the results section, you will want to describe your specific hypothesis, the statistical technique that you will be using, and the model e.

This is especially important when your hypothesis involves an interaction. Clearly stating the writing about multivariate analysis software between your hypothesis and the statistical technique and model is important for two reasons.

First, it helps guide your audience through this part of the results section. Second, this connection will make the substantive interpretation of the results easier.

For commonly used techniques, such as ordinary least squares regression, your description may be as short as a single sentence. For more complicated techniques or when using a technique that is likely unfamiliar to your audience, more description and explanation may be required.

Describing the model building process is also important.

If there are categorical variables in your model, clearly state how they were handled e. Most models make assumptions, and you usually want to mention that the assumptions were assessed, but the result of each diagnostic test is usually not included.

If one or more assumptions are grossly violated, further discussion may be warranted. It is not uncommon to mention which statistical package and which version of the package was used to conduct the analysis. Usually, the analyses are ordered from most to least important, except when this will disrupt the flow of your story.

If there are more than a few analyses, indicate whether an alpha control procedure was used, and if so, which one. Almost all studies have at least some missing data. You will want to indicate how the missing data were handled e.

Many journals also require or encourage researchers to include measures of effect sizes. You need to be very specific about which measure you have used, because there are dozens of them.

If you conducted an a priori power analysis, you will want to describe it. Ideally, there will be at least a few days between the time that you finish writing and the time the article or poster is due.

Rereading your article after setting it aside for a while is a great way to catch errors and to check for consistency. It may also be helpful to have a colleague read it over.

Examples After I gave this seminar last time, I found that what most people in the audience wanted was specifics, especially what to say and what not to say in the results section. In fact, many people said they wanted to be shown an output, say of a regression analysis, and then an example of how to write it up.

Unfortunately, this is nearly impossible to do, and I will show you why in just a moment. The best way to write a clear, concise results section is to thoroughly understand the statistical techniques that you used to analyze your data. Another good strategy is to look at articles in your field that report similar analyses for ideas about the exact terminology to use.Writing about multivariate analysis is a surprisingly common task.

Researchers use these advanced statistical techniques to examine relationships among multiple variables, such as exercise, diet, and heart disease, or to forecast information such as future interest rates or feelthefish.coms: 3.

Thus despite the emphasis on intuition, Exploratory Multivariate Analysis does not sacrifice detail; it explores all aspects of the subject. R calculator: To teach any advanced data analysis, the instructor must also teach use of a software package. The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis.

This new edition features even more topics and real-world examples, making it the must Price: $ writing about multivariate analysis, followed by in-class demonstration using such as the “poor/better/best” technique (shown below) to show students examples of how to translate abstract writing principles into concrete sentences or paragraphs; see Miller () or Miller, England, Treiman.

There are many statistical programs produced by software companies, enough to one should decide which software program is more fit to present and analyze the data.

If we have data on ages of trees. Even within one general type of multivariate analysis, such as multiple regression or factor analysis, there may be such a variety of “ways to go” that two analyzers may easily reach quite different conclusions when independently analyzing the same data.

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Eleven Multivariate Analysis Techniques: Key Tools In Your Marketing Research Survival Kit