# Data Analysis Practice

### Rescaling Sets of Variables to Be on the Same Scale

December 11th, 2018 by

by Christos Giannoulis, PhD

Attributes are often measured using multiple variables with different upper and lower limits. For example, we may have five measures of political orientation, each with a different range of values.

Each variable is measured in a different way. The measures have a different number of categories and the low and high scores on each measure are different.

### Should I Specify a Model Predictor as Categorical or Continuous?

October 22nd, 2018 by

Predictor variables in statistical models can be treated as either continuous or categorical.

Usually, this is a very straightforward decision.

Categorical predictors, like treatment group, marital status, or highest educational degree should be specified as categorical.

Likewise, continuous predictors, like age, systolic blood pressure, or percentage of ground cover should be specified as continuous.

But there are numerical predictors that aren’t continuous. And these can sometimes make sense to treat as continuous and sometimes make sense as categorical.

### Differences in Model Building Between Explanatory and Predictive Models

October 8th, 2018 by

Suppose you are asked to create a model that will predict who will drop out of a program your organization offers. You decide to use a binary logistic regression because your outcome has two values: “0” for not dropping out and “1” for dropping out.

Most of us were trained in building models for the purpose of understanding and explaining the relationships between an outcome and a set of predictors. But model building works differently for purely predictive models. Where do we go from here? (more…)

### The Four Stages of Statistical Skill

September 21st, 2018 by

At The Analysis Factor, we are on a mission to help researchers improve their statistical skills so they can do amazing research.

We all tend to think of “Statistical Analysis” as one big skill, but it’s not.

Over the years of training, coaching, and mentoring data analysts at all stages, I’ve realized there are four fundamental stages of statistical skill:

Stage 1: The Fundamentals

Stage 2: Linear Models

Stage 3: Extensions of Linear Models

Stage 4: Advanced Models

There is also a stage beyond these where the mathematical statisticians dwell. But that stage is required for such a tiny fraction of data analysis projects, we’re going to ignore that one for now.

If you try to master the skill of “statistical analysis” as a whole, it’s going to be overwhelming.

And honestly, you’ll never finish. It’s too big of a field.

But if you can work through these stages, you’ll find you can learn and do just about any statistical analysis you need to. (more…)

### Segmented Regression for Non-Constant Relationships

January 8th, 2018 by

When you put a continuous predictor into a linear regression model, you assume it has a constant relationship with the dependent variable along the predictor’s range. But how can you be certain? What is the best way to measure this?

And most important, what should you do if it clearly isn’t the case?

Let’s explore a few options for capturing a non-linear relationship between X and Y within a linear regression (yes, really). (more…)

### Tricks for Using Word to Make Statistical Syntax Easier

March 13th, 2017 by

We’ve talked a lot around here about the reasons to use syntax — not only menus — in your statistical analyses.

Regardless of which software you use, the syntax file is pretty much always a text file. This is true for R, SPSS, SAS, Stata — just about all of them.

This is important because it means you can use an unlikely tool to help you code: Microsoft Word.

I know what you’re thinking. Word? Really?

Yep, it’s true. Essentially it’s because Word has much better Search-and-Replace options than your stat software’s editor.

Here are a couple features of Word’s search-and-replace that I use to help me code faster: