Is it really ok to treat Likert items as continuous? And can you just decide to combine Likert items to make a scale? Likert-type data is extremely common—and so are questions like these about how to analyze it appropriately. [Read more…] about Member Training: Analyzing Likert Scale Data
Choosing a Statistical Test
When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.
Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.
Have you ever wondered why there are so many different types of experimental designs, and how a researcher would go about choosing among them to best address their research questions? [Read more…] about Member Training: Classic Experimental Designs
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. 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 together years ago, inspired by a client who kept getting stuck in Weed #1. But I’ve honed it over the years of assisting thousands of researchers with their analysis.
But that doesn’t mean that you’re stuck with few options. There are more than you’d think.
Some are better than others, but it depends on the situation and research questions.
Here are five options when your dependent variable is ordinal.
[Read more…] about Five Ways to Analyze Ordinal Variables (Some Better than Others)