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…)
If you are a SPSS, SAS, or Stata user who finds yourself needing to use R (I mean, it’s free), I just found this great website: http://statmethods.net/index.html.
Regression models are just a subset of the General Linear Model, so you can use GLM procedures to run regressions. It is what I usually use.
But in SPSS there are options available in the GLM and Regression procedures that aren’t available in the other. How do you decide when to use GLM and when to use Regression?
GLM has these options that Regression doesn’t: (more…)
In addition to the five listed in this title, there are quite a few other options, so how do you choose which statistical software to use?
The default is to use whatever software they used in your statistics class–at least you know the basics.
And this might turn out pretty well, but chances are it will fail you at some point. Many times the stat package used in a class is chosen for its shallow learning curve, (more…)
SPSS Variable Labels and Value Labels are two of the great features of its ability to create a code book right in the data set. Using these every time is good data analysis practice.
SPSS doesn’t limit variable names to 8 characters like it used to, but you still can’t use spaces, and it will make coding easier if you keep the variable names short. You then use Variable Labels to give a nice, long description of each variable. On questionnaires, I often use the actual question.
There are good reasons for using Variable Labels right in the data set. I know you want to get right to your data analysis, but using Variable Labels will save so much time later.
1. If your paper code sheet ever gets lost, you still have the variable names.
2. Anyone else who uses your data–lab assistants, graduate students, statisticians–will immediately know what each variable means.
3. As entrenched as you are with your data right now, you will forget what those variable names refer to within months. When a committee member or reviewer wants you to redo an analysis, it will save tons of time to have those variable labels right there.
4. It’s just more efficient–you don’t have to look up what those variable names mean when you read your output.
Variable Labels
The really nice part is SPSS makes Variable Labels easy to use:
1. Mouse over the variable name in the Data View spreadsheet to see the Variable Label.
2. In dialog boxes, lists of variables can be shown with either Variable Names or Variable Labels. Just go to Edit–>Options. In the General tab, choose Display Labels.
3. On the output, SPSS allows you to print out Variable Names or Variable Labels or both. I usually like to have both. Just go to Edit–>Options. In the Output tab, choose ‘Names and Labels’ in the first and third boxes.
Value Labels
Value Labels are similar, but Value Labels are descriptions of the values a variable can take. Labeling values right in SPSS means you don’t have to remember if 1=Strongly Agree and 5=Strongly Disagree or vice-versa. And it makes data entry much more efficient–you can type in 1 and 0 for Male and Female much faster than you can type out those whole words, or even M and F. But by having Value Labels, your data and output still give you the meaningful values.
Once again, SPSS makes it easy for you.
1. If you’d rather see Male and Female in the data set than 0 and 1, go to View–>Value Labels.
2. Like Variable Labels, you can get Value Labels on output, along with the actual values. Just go to Edit–>Options. In the ‘Output Labels’ tab, choose ‘Values and Labels’ in the second and fourth boxes.
SPSS has a nice little feature for adding and averaging variables with missing data that many people don’t know about.
It allows you to add or average variables, while specifying how many are allowed to be missing.
For example, a very common situation is a researcher needs to average the values of the 5 variables on a scale, each of which is measured on the same Likert scale.
There are two ways to do this in SPSS syntax.
Newvar=(X1 + X2 + X3 + X4 + X5)/5 or
Newvar=MEAN(X1,X2, X3, X4, X5).
In the first method, if any of the variables are missing, due to SPSS’s default of listwise deletion, Newvar will also be missing.
In the second method, if any of the variables is missing, it will still calculate the mean. While this seems great at first, the researcher may wish to limit how many of the 5 variables need to be observed in order to calculate the mean. If only one or two variables are present, the mean may not be a reasonable estimate of the mean of all 5 variables.
SPSS has an option for dealing with this situation. Running it the following way will only calculate the mean if any 4 of the 5 variables is observed. If fewer than 4 of the variables are observed, Newvar will be system missing.
Newvar=MEAN.4(X1,X2, X3, X4, X5).
You can specify any number of variables that need to be observed.
(This same distinction holds for the SUM function in SPSS, but the scale changes based on how many are being averaged. A better approach is to calculate the mean, then multiply by 5).
This works the same way in the syntax or in the Transform–>Compute menu dialog.
First Published 12/1/2016;
Updated 7/20/21 to give more detail.
