Regression is one of the most common analyses in statistics. Most of us learn it in grad school, and we learned it in a specific software. Maybe SPSS, maybe another software package. The thing is, depending on your training and when you did it, there is SO MUCH to know about doing a regression analysis in SPSS. There are the general procedures everyone needs to know, the options that are important for testing assumptions and plotting results for reporting, and more. And, perhaps most surprisingly, there are two totally different procedures for running linear regression in SPSS: Regression and ..read more

I recently received a great question in a comment about whether the assumptions of normality, constant variance, and independence in linear models are about the errors, εi, or the response variable, Yi. The asker had a situation where Y, the response, was not normally distributed, but the residuals were. Quick Answer:  It’s just the errors. In fact, if you look at any (good) statistics textbook on linear models, you’ll see below the model, stating the assumptions: εi ~ i.i.d. N(0, σ²) That εi is the random error term. The i.i.d. means every error is independent and identically distri ..read more

The objective for quasi-experimental designs is to establish cause and effect relationships between the dependent and independent variables. However, they have one big challenge in achieving this objective: lack of an established control group. There are ways, though, to create a post-hoc control group. One way is to match non-treated subjects with treated subjects. The most common matching method is Propensity Score Matching. Gaining popularity as a matching method is Coarsened Exact Matching. How are these matching methods different? To understand the differences, this Stats Amore Training e ..read more

From our first 2 posts, you should be comfortable navigating the windows and menus of Stata. We can now get into the real meat of programming in Stata: do-files. Why Do-Files? A do-file is a Stata file that provides a list of commands to run. You can run an entire do-file at once, or you can highlight and run particular lines from the file. If you set up your do-file correctly, you can just click “run” after opening it. The do-file will set you to the correct directory, open your dataset, do all analyses, and save any graphs or results you want saved. I’ll start off by saying this: Any analysi ..read more

Do you ever wish your data analysis project were a little more organized? Statistical analysis projects vary in complexity, ranging from a single run t-test to multi-analyst, multi-year projects with large and diverse datasets, time consuming models, frequent data/code updates, and complex reporting. Having organized systems is always a good idea— and for projects on the complex end, preparing process flow, file structure, version control and intermediate computations can help to reduce chaos and increase the likelihood of successful outcomes. In this training, you’ll learn common ways to ma ..read more

One of those tricky, but necessary, concepts in statistics is the difference between crossed and nested factors. As a reminder, a factor is any categorical independent variable. In experiments, or any randomized designs, these factors are often manipulated. Experimental manipulations (like Treatment vs. Control) are factors. Observational categorical predictors, such as gender, time point, poverty status, etc., are also factors. Whether the factor is observational or manipulated won’t affect the analysis, but it will affect the conclusions you draw from the results. When there is only one fact ..read more

If you have run mixed models much at all, you have undoubtedly been haunted by some version of this very obtuse warning: “The Hessian (or G or D) Matrix is not positive definite. Convergence has stopped.” Or “The Model has not Converged. Parameter Estimates from the last iteration are displayed.” What on earth does that mean? Let’s start with some background. If you’ve never taken matrix algebra, these concepts can be overwhelming. So I’m going to simplify them into the basic issues that arise for you, the data analyst. If you’d like a more mathematical and thorough answer, see one of the refe ..read more

There are not a lot of statistical methods designed just to analyze ordinal variables. 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. 1. Analyze ordinal variables as if they’re nominal Ordinal variables are fundamentally categorical. One simple option is to ignore the order in the variable’s categories and treat it as nominal. There are many options for analyzing categorical variables that have no o ..read more

One issue in data analysis that feels like it should be obvious, but often isn’t, is setting up your data. The kinds of issues involved include: What is a variable? What is a unit of observation? Which data should go in each row of the data matrix? Answering these practical questions is one of those skills that comes with experience, especially in complicated data sets. Even so, it’s extremely important. If the data isn’t set up right, the software won’t be able to run any of your analyses. And in many data situations, you will need to set up the data different ways for different parts of th ..read more

In statistical practice, there are many situations where best practices are clear. There are many, though, where they aren’t. The granddaddy of these practices is adjusting p-values when you make multiple comparisons. There are good reasons to do it and good reasons not to. It depends on the situation. At the heart of the issue is a concept called Family-wise Error Rate (FWER). FWER is the probability that you will get at least one Type I error, in a set (or family) of tests (Tukey, 1953). Recall that a Type I error is when you reject your null hypothesis even though it was true. Remember, whe ..read more