SAS

Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood

Two methods for dealing with missing data, vast improvements over traditional approaches, have become available in mainstream statistical software in the last few years.

Both of the methods discussed here require that the data are missing at random–not related to the missing values. If this assumption holds, resulting estimates (i.e., regression coefficients and standard errors) will be unbiased with no loss of power.

The first method is Multiple Imputation (MI). Just like the old-fashioned imputation [Read more…] about Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood

An Easy Way to Reverse Code Scale items

by Karen Grace-Martin 35 Comments

Before you run a Cronbach’s alpha or factor analysis on scale items, it’s generally a good idea to reverse code items that are negatively worded so that a high value indicates the same type of response on every item.

So for example let’s say you have 20 items each on a 1 to 7 scale. For most items, a 7 may indicate a positive attitude toward some issue, but for a few items, a 1 indicates a positive attitude.

I want to show you a very quick and easy way to reverse code them using a single command line. This works in any software. [Read more…] about An Easy Way to Reverse Code Scale items

Dummy Code Software Defaults Mess With All of Us

by Karen Grace-Martin Leave a Comment

In my last blog post, I wrote about a mistake I once made when I didn’t realize the defaults for dummy coding were different in two SPSS procedures (Binary Logistic and GEE).

Ironically, about the same time I wrote it, I was having a conversation with Ann Maria de Mars on Twitter. She was trying to figure out why her logistic regression model fit results were identical in SAS Proc Logistic and SPSS Binary Logistic, but the coefficients in SAS were half those of SPSS.

It was ironic because I, of course, didn’t recognize it as the same issue and wasn’t much help.

But Ann Maria investigated and discovered that it came down to differences in the defaults for coding categorical predictors in SAS and SPSS that did it. Her detailed and humorous explanation is here.

Some takeaways for you, the researcher and data analyst:

1. Give yourself a break if you hit a snag. Even very experienced data analysts, statisticians who understand what they’re doing, get stumped sometimes. Don’t ever think that performing data analysis is an IQ test. You’re bringing together many skills and complex tools.

2. Learn thy software. In my last post, I phrased it “Know thy software”, but this is where you get to know it. Snags are good opportunities to investigate the details of your software, just like Ann Maria did. If you can think of it as a challenge to figure out–a puzzle–it can actually be fun.

Make friends with your syntax manuals.

3. Get help when you need it. Statistical software packages *are* complex tools. You don’t have to know everything to use them

Ask colleagues. Call customer support. Call a stat consultant. That’s what they’re there for.

4. A great way to check your work is to run your test two different ways. It’s another reason to be able to use at least two stat software packages. I’m not suggesting you have to run every analysis twice. But when a result looks strange, or you want to double-check a specific important model, this can be a good strategy for testing things out.

It may be that your results aren’t telling you what you think they are.

[Logistic_Regression_Workshop]

When Dummy Codes are Backwards, Your Stat Software may be Messing With You

by Karen Grace-Martin 2 Comments

One of the tricky parts about dummy coded (0/1) variables is keeping track of what’s a 0 and what’s a 1.

This is made particularly tricky because sometimes your software switches them on you.

Here’s one example in a question I received recently. The context was a Linear Mixed Model, but this can happen in other procedures as well.

I dummy code my categorical variables “0” or “1” but for some reason in the [Read more…] about When Dummy Codes are Backwards, Your Stat Software may be Messing With You

Mixed Models for Logistic Regression in SPSS

by Karen Grace-Martin 47 Comments

Can I use SPSS MIXED models for (a) ordinal logistic regression, and (b) multi-nomial logistic regression?

Every once in a while I get emailed a question that I think others will find helpful. This is definitely one of them.

My answer:

No.

(And by the way, this is all true in SAS as well. I’ll include the SAS versions in parentheses). [Read more…] about Mixed Models for Logistic Regression in SPSS

When the Hessian Matrix Goes Wacky

by Karen Grace-Martin 10 Comments

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, [Read more…] about When the Hessian Matrix Goes Wacky