Computing Cronbach’s Alpha in SPSS with Missing Data

July 16th, 2010 by

I recently received this question:

I have scale which I want to run Chronbach’s alpha on.  One response category for all items is ‘not applicable’. I want to run  Chronbach’s alpha requiring that at least 50% of the items must be answered for the scale to be defined.  Where this is the case then I want all missing values on that scale replaced by the average of the non-missing items on that scale. Is this reasonable? How would I do this in SPSS?

My Answer:

In RELIABILITY, the SPSS command for running a Cronbach’s alpha, the only options for Missing Data (more…)

Missing Data: Criteria for Choosing an Effective Approach

May 20th, 2009 by

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…)

Seven Ways to Make up Data: Common Methods to Imputing Missing Data

February 4th, 2009 by

There are many ways to approach missing data. The most common, I believe, is to ignore it. But making no choice means that your statistical software is choosing for you.

Most of the time, your software is choosing listwise deletion. Listwise deletion may or may not be a bad choice, depending on why and how much data are missing.

Another common approach among those who are paying attention is imputation. Imputation simply means replacing the missing values with an estimate, then analyzing the full data set as if the imputed values were actual observed values.

How do you choose that estimate?  The following are common methods: (more…)