OptinMon

Today let’s re-create two variables and see how to plot them and include a regression line. We take height to be a variable that describes the heights (in cm) of ten people. (more…)

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Lately, I’ve gotten a lot of questions about learning how to run models for repeated measures data that isn’t continuous.

Mostly categorical. But once in a while discrete counts.

A typical study is in linguistics or psychology where (more…)

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Just last week, a colleague mentioned that while he does a lot of study design these days, he no longer does much data analysis.

His main reason was that 80% of the work in data analysis is preparing the data for analysis.  Data preparation is s-l-o-w and he found that few colleagues and clients understood this.

Consequently, he was running into expectations that he should analyze a raw data set in an hour or so.

You know, by clicking a few buttons.

I see this as well with researchers new to data analysis.  While they know it will take longer than an hour, they still have unrealistic expectations about how long it takes.

So I am here to tell you, the time-consuming part is preparing the data.  Weeks or months is a realistic time frame.  Hours is not.

(Feel free to send this to your colleagues who want instant results.)

There are three parts to preparing data: cleaning it, creating necessary variables, and formatting all variables.

Data Cleaning

Data cleaning means finding and eliminating errors in the data.  How you approach it depends on how large the data set is, but the kinds of things you’re looking for are:

• Impossible or otherwise incorrect values for specific variables
• Cases in the data who met exclusion criteria and shouldn’t be in the study
• Duplicate cases
• Missing data and outliers (don’t delete all outliers, but you may need to investigate to see if one is an error)
• Skip-pattern or logic breakdowns
• Making sure that the same value of string variables is always written the same way (male ≠ Male in most statistical software).

You can’t avoid data cleaning and it always takes a while, but there are ways to make it more efficient. For example, rather than search through the data set for impossible values, print a table of data values outside a normal range, along with subject ids.

This is where learning how to code in your statistical software of choice really helps.  You’ll need to subset your data using IF statements to find those impossible values.

But if your data set is anything but small, you can also save yourself a lot of time, code, and errors by incorporating efficiencies like loops and macros so that you can perform some of these checks on many variables at once.

Creating New Variables

Once the data are free of errors, you need to set up the variables that will directly answer your research questions.

It’s a rare data set in which every variable you need is measured directly.

So you may need to do a lot of recoding and computing of variables.

Examples include:

• Creating change scores
• Creating indices from scales
• Reverse coding scale items
• Combining too-small-to-use categories of nominal variables
• Centering variables
• Restructuring data from wide format to long (or the reverse)

And of course, part of creating each new variable is double-checking that it worked correctly.

Formatting Variables

Both original and newly created variables need to be formatted correctly for two reasons:

First, so your software works with them correctly.  Failing to format a missing value code or a dummy variable correctly will have major consequences for your data analysis.

Second, it’s much faster to run the analyses and interpret results if you don’t have to keep looking up which variable Q156 is.

Examples include:

• Setting all missing data codes so missing data are treated as such
• Formatting date variables as dates, numerical variables as numbers, etc.
• Labeling all variables and categorical values so you don’t have to keep looking them up.

All three of these steps require a solid knowledge of how to manage data in your statistical software.  Each one approaches them a little differently.

It’s also very important to keep track of and be able to easily redo all your steps.  Always assume you’ll have to redo something.  So use (or record) syntax, not only menus.

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I have recently worked with two clients who were running generalized linear mixed models in SPSS.

Both had repeated measures experiments with a binary outcome.

The details of the designs were quite different, of course. But both had pretty complicated combinations of within-subjects factors.

Fortunately, both clients are intelligent, have a good background in statistical modeling, and are willing to do the work to learn how to do this. So in both cases, we made a lot of progress in just a couple meetings.

I found it interesting, through, that both were getting stuck on the same subtle point. It’s the same point I was missing for a long time in my own learning of mixed models.

Once I finally got it, a huge light bulb turned on. (more…)

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Author: Trent Buskirk, PhD.

In my last article, we got a bit comfortable with the notion of errors in surveys. We discussed sampling errors, which occur because we take a random sample rather than a complete census.

If you ever had to admit error, sampling error is the type to admit. Polls admit this sort of error frequently by reporting the margin of error. Margin of error is the sampling error multiplied by a distributional value that can be used to create a confidence interval.

But there are some other types of error that can occur in the survey context that, while influential, are a bit more invisible. They are generally referred to as non-sampling error.

These types of errors are not associated with sample-to-sample variability but to sources like selection biases, frame coverage issues, and measurement errors. These are not the kind of errors you want in your survey.

In theory, it is possible to have an estimator that has little sampling error associated with it. That looks good on the surface, but this estimator may yield poor information due to non-sampling errors.

For example, a high rate of non-response may mean that some participants are opting out and biasing estimates.

Likewise, a scale or set of items on the survey could have known measurement error. They may be imprecise in their measurement of the construct of interest or they may measure that construct better for some populations than others. Again, this can bias estimates.

Frame coverage error occurs when the sampling frame does not quite match the target population. This leads to the sample including individuals who aren’t in the target population, missing individuals who are, or both.

A perspective called the Total Survey Error Framework allows researchers to evaluate estimates on errors that come from sampling and those that don’t. It can be very useful in choosing a sampling design that minimizes errors as a whole.

So when you think about errors and how they might come about in surveys, don’t forget about the non-sampling variety – those that could come as a result of non-response, measurement, or coverage.

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Author: Trent Buskirk, PhD.

As it is in history, literature, criminology and many other areas, context is important in statistics.  Knowing from where your data comes gives clues about what you can do with that data and what inferences you can make from it.

In survey samples context is critical because it informs you about how the sample was selected and from what population it was selected.   (more…)

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