After you are done with the odyssey of exploratory factor analysis (aka a reliable and valid instrument)…you may find yourself at the beginning of a journey rather than the ending. The process of performing exploratory factor analysis usually seeks to answer whether a given set of items form a coherent factor (or often several factors). […]

Anytime we want to measure something in science we have to take into account that our measurements contains various kinds of error. That error can be random and/or systematic. So what we want to do in our statistical approach to the data is to isolate the true score in a variable and remove the error. This is really what we're trying to do using latent variables for measurement.

We mentioned before that we use Confirmatory Factor Analysis to evaluate whether the relationships among the variables are adequately represented by the hypothesized factor structure. The factor structure (relationships between factors and variables) can be based on theoretical justification or previous findings. Once we estimate the relationship indicators of those factors, the next task is […]

Today, I would like to briefly describe four misconceptions that I feel are commonly perceived by novice researchers in Exploratory Factor Analysis: Misconception 1: The choice between component and common factor extraction procedures is not so important. In Principal Component Analysis, a set of variables is transformed into a smaller set of linear composites known […]

Sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. Or, maybe that's just me. Regardless of how they make you feel, they are super important to do for your study before you collect the data.

We get many questions from clients who use the terms mediator and moderator interchangeably. They are easy to confuse, yet mediation and moderation are two distinct terms that require distinct statistical approaches. The key difference between the concepts can be compared to a case where a moderator lets you know when an association will occur […]

So we’ve looked at the interaction effect between two categorical variables. But let’s make things a little more interesting, shall we? What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? We’ll keep working with our trusty 2014 General Social Survey data set. But this time let’s examine the impact of job prestige level (a continuous variable) and gender (a categorical, dummy coded variable) as our two predictors.

A research study rarely involves just one single statistical test. And multiple testing can result in statistically significant findings just by chance. After all, with the typical Type I error rate of 5% used in most tests, we are allowing ourselves to “get lucky” 1 in 20 times for each test.  When you figure out the probability of Type I error across all the tests, that probability skyrockets. There are a number of ways to control for this chance significance. And as with most things statistical, determining a viable adjustment to control for the chance significance depends on what you are doing. Some approaches are good. Some are not so good. And, sometimes an adjustment isn’t even necessary. In this webinar, Elaine Eisenbeisz will provide an overview of multiple comparisons and why they can be a problem.

One important yet difficult skill in statistics is choosing a type model for different data situations. One key consideration is the dependent variable. For linear models, the dependent variable doesn’t have to be normally distributed, but it does have to be continuous, unbounded, and measured on an interval or ratio scale. Percentages don’t fit these criteria. Yes, they’re continuous and ratio scale. The issue is the boundaries at 0 and 100. Likewise, counts have a boundary at 0 and are discrete, not continuous. The general advice is to analyze these with some variety of a Poisson model. Yet there is a very specific type of variable that can be considered either a count or a percentage, but has its own specific distribution.

When I was in graduate school, stat professors would say “ANOVA is just a special case of linear regression.”  But they never explained why. And I couldn’t figure it out. The model notation is different. The output looks different. The vocabulary is different. The focus of what we’re testing is completely different. How can they […]