OptinMon

To increase power:

1. Increase alpha
2. Conduct a one-tailed test
3. Increase the effect size
4. Decrease random error
5. Increase sample size

Sound so simple, right?  The reality is that although these 5 ways all work (more…)

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Some approaches to missing data work well in some situations, but perform very poorly in others.  So it’s really important to get a good idea of the type and pattern of missingness in your data.  You may even take different missing data approaches to different variables.

Matt Blackwell of the Harvard Social Science Statistics blog has come up with a nice way to visualize the missingness patterns in a data set.  (I’m a big fan of graphing data to understand it).  He calls it a Missingness Map.

The only drawback seems to be that it will be cumbersome for large data sets.

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Most Multiple Imputation methods assume multivariate normality, so a common question is how to impute missing values from categorical variables.

Paul Allison, one of my favorite authors of statistical information for researchers, did a study that showed that the most common method actually gives worse results that listwise deletion.  (Did I mention I’ve used it myself?) (more…)

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I was recently asked about whether it’s okay to treat a likert scale as continuous as a predictor in a regression model.  Here’s my reply.  In the question, the researcher asked about logistic regression, but the same answer applies to all regression models.

1. There is a difference between a likert scale item (a single 1-7 scale, eg.) and a full likert scale , which is composed of multiple items.  If it is a full likert scale, with a combination of multiple items, go ahead and treat it as numerical. (more…)

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In choosing an approach to missing data, there are a number of things to consider.  But you need to keep in mind what you’re aiming for before you can even consider which approach to take.

There are three criteria we’re aiming for with any missing data technique:

1. Unbiased parameter estimates:  Whether you’re estimating means, regressions, or odds ratios, you want your parameter estimates to be accurate representations of the actual population parameters.  In statistical terms, that means the estimates should be unbiased.  If all the (more…)

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There are two ways to run a repeated measures analysis.The traditional way is to treat it as a multivariate test–each response is considered a separate variable.The other way is to it as a mixed model.While the multivariate approach is easy to run and quite intuitive, there are a number of advantages to running a repeated measures analysis as a mixed model.

First I will explain the difference between the approaches, then briefly describe some of the advantages of using the mixed models approach. (more…)

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