A regression analysis with one dependent variable and 8 independent variables is NOT a multivariate regression. It's a multiple regression. Multivariate analysis ALWAYS refers to the dependent variable.
Before you can write a data analysis plan, you have to choose the best statistical test or model. You have to integrate a lot of information about your research question, your design, your variables, and the data itself.
Every time you analyze data, you start with a research question and end with communicating an answer. In fact, defining that research question is vital to getting to the right answer. But in between those start and end points are twelve other steps. I call this the Data Analysis Pathway. It’s a framework I put […]
A key part of the output in any linear model is the ANOVA table. It has many names in different software procedures, but every regression or ANOVA model has a table with Sums of Squares, degrees of freedom, mean squares, and F tests. Many of us were trained to skip over this table, but
A Gentle Introduction to Random Slopes in Multilevel Modeling …aka, how to look at cool interaction effects for nested data. Do the words “random slopes model” or “random coefficients model” send shivers down your spine? These words don’t have to be so ominous. Journal editors are increasingly asking researchers to analyze their data using this […]
Have you ever compared the list of assumptions for linear regression across two sources? Whether they’re textbooks, lecture notes, or web pages, chances are the assumptions don’t quite line up. Why? Sometimes the authors use different terminology. So it just looks different. And sometimes they’re including not only model assumptions, but inference assumptions and data […]
Few data sets are completely balanced, with equal sample sizes in every condition. But are they really the scary problem your stats professor made them out to be? Only sometimes.
Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.
When our outcome variable is the frequency of occurrence of an event, we will typically use a count model to analyze the results. There are numerous count models. Regardless of the model we use, there is a very important prerequisite that they all share. We must identify the period of time or area of space in which the counts were generated. We must model exposure.
In generalized linear models, there is a link function, which is the link between the mean of Y on the left and the fixed component on the right. In order to make the model fit in a linear form for these other distributions, we often need to take some function of the mean.