Iris Eekhout. Missing data. Impressum Privacyverklaring Cookiebeleid Sitemap. Inloggen Uitloggen Bewerken. Deze website gebruikt cookies. The researcher should keep in mind that if the data are MCAR, then he may choose a pair-wise or a list-wise deletion of missing value cases.

If, however, the data are not MCAR, then imputation to replace them is conducted. The second form is missing at random MAR. This form is more common than the previous one. The non-ignorable missing value is the most problematic form which involves those types of missing values that are not randomly distributed across the observations.

In this case, the probability cannot be predicted from the variables in the model. This can be ignored by performing data imputation to replace them. There are estimation methods in SPSS that provide the researcher with certain statistical techniques to estimate the missing values. These are namely regression, maximum likelihood estimation, list-wise or pair-wise deletion, approximate Bayesian bootstrap, multiple data imputation, and many others.

Glas, C. Modeling nonignorable missing data in speeded tests. Educational and Psychological Measurement, 68 6 , Graham, J. Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60 , The closest you will come is to change the system-missing value to a user-missing value. This can be accomplished with a recode command, as is shown below. The keyword sysmis can be used on the recode command, and it stands for the system-missing value. We would expect that it would do the computations based on the available data, and omit the missing values for each pair of variables.

Because two variables are necessary to compute each correlation.

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Here is an example program. The output of this command is shown below.

Note how the missing values were excluded. For each pair of variables, corr used the number of pairs that had valid data. For the pairing of trial1 and trial3 there were four valid pairs, and likewise there were four valid pairs for trial2 and trial3. Since this used all of the valid pairs of data, this is often called pairwise deletion of missing data.

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## SPSS tutorials

It is possible to specify that the correlations run only on observations that had complete data for all of the variables listed on the var subcommand. You might want the correlations of the reaction times just for the observations that had non-missing data on all of the trials. This is called listwise deletion of missing data meaning that when any of the variables are missing, the entire observation is omitted from the analysis. As you see in the results below, the N for all the simple statistics is the same, 3, which corresponds to the number of cases with complete non-missing data for trial1 , trial2 and trial3.

### 1. Introduction

Since the N is the same for all of the correlations i. It is important to understand how SPSS commands used to analyze data treat missing data. To know how any one command handles missing data, you should consult the SPSS manual. Here is a brief overview of how some common SPSS procedures handle missing data.

## Missing Values in Data - Statistics Solutions

An assignment expression may appear on a compute or an if command. It is important to understand how missing values are handled in assignment statements. Consider the example shown below.

The list below illustrates how missing values are handled in assignment statements. The variable avg is based on the variables trial1 trial2 and trial3 , and the variable avgr is based on the variables trialr1 trialr2 and trialr3. If any of the component variables were missing, the value for avg or avgr was set to missing. This means that both were missing for observations 2, 3 and 4.

As a general rule, computations involving missing values yield missing values, as shown below.

Whenever you add, subtract, multiply divide etc. An exception is a value that is defined regardless of one of the values, for example zero divided by missing is zero. In our reaction time experiment, the average reaction time avg is missing for thee out of six cases.

### Imputation vs Removing Data

We could try just averaging the data for the non-missing trials by using the mean function as shown in the example below. The results below show that avg now contains the average of the non-missing trials, even if there is only one. Also, if we wanted to get the sum of the times instead of the average, then we could just use the sum function instead of the mean function. The syntax of the sum function is just like the mean function, but it returns the sum of the non-missing values.

Finally, you can use the nvalid function to determine the number of non-missing values in a list of variables, as illustrated below. As you see below, observations 1, 5 and 6 had three valid values, observations 2 and 3 had two valid values, and observation 4 had only one valid value.

These results are the same regardless of the type of missing value. You might feel uncomfortable with the variable avg for observation 4 since it is not really an average at all. We can use the mean.