Is Multiple Imputation Possible in the Context of Survival Analysis?

Sure. One of the big advantages of multiple imputation is that you can use it for any analysis.

It’s one of the reasons big data libraries use it–no matter how researchers are using the data, the missing data is handled the same, and handled well.

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What is the difference between MAR and MCAR missing data?

One of the important issues with missing data is the missing data mechanism.

It’s important because it affects how much the missing data bias your results, so you have to take it into account when choosing an approach to deal with the missing data.

The concepts of these mechanisms can be a bit abstract.

And to top it off, two of these mechanisms have confusing names: Missing Completely at Random and Missing at Random.

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Using Case Summaries in SPSS to Debug your Variable Creation

Here’s a little SPSS tip.

When you create new variables, whether it’s through the Recode, Compute, or some other command, you need to check that it worked the way you think it did.

(As an aside, I hope this goes without saying, but never, never, never, never use Recode into Same Variable. Always Recode into New Variable so you

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R Tutorial Series

I actually wish R had been around, and I wish all the great resources for learning it that exist now, existed then.

Here’s one of them. A very lovely-looking R tutorial series by John M. Quick.

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How to do a Chi-square test when you only have proportions and denominators

How do you then do a cross-tabulation in SPSS when you do not have a dataset with the values of the two variables of interest? For example, if you do a critical appraisal of a published study and only have proportions and denominators. In this article it will be demonstrated how SPSS can come up with a cross table and do a Chi-square test in both situations. And you will see that the results are exactly the same.

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Recoding Variables in SPSS Menus and Syntax

If you don’t have many variables to recode, say one or two, it’s not a big deal to use the menus (but at least paste the code, so you have a record of what you did later!).

But if you have more than just one or two, all those mouse-clicks get old, fast.

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Approaches to Repeated Measures Data: Repeated Measures ANOVA, Marginal, and Mixed Models

In a marginal model, we can directly estimate the correlations among each individual’s residuals. (We do assume the residuals across different individuals are independent of each other). We can specify that they are equally correlated, as in the RM ANOVA, but we’re not limited to that assumption. Each correlation can be unique, or measurements closer in time can have higher correlations than those farther away. There are a number of common patterns that the residuals tend to take.

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R code for the Random Intercept and Random Slope Models Webinar

If you’ve ever run Mixed models, you know that correctly specifying in your statistical software the model you think you’re specifying is half the battle.

As I usually do, I demonstrated an example in the webinar using SPSS. And I made the data available in .csv format for anyone who wanted to try it out in the software of their choosing.

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Mixed Models for Logistic Regression in SPSS

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:

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The Difference Between Clustered, Longitudinal, and Repeated Measures Data

In repeated measures data, the dependent variable is measured more than once for each subject. Usually, there is some independent variable (often called a within-subject factor) that changes with each measurement.

And in longitudinal data, the dependent variable is measured at several time points for each subject, often over a relatively long period of time.

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