- What percentage of missing data is acceptable?
- What if data is not missing at random?
- What is monotone missing pattern?
- How do I know if my data is missing at random?
- How do you select cases in SPSS with two variables?
- How do I get rid of missing data?
- How do you manipulate data in SPSS?
- What is data missing at random?
- How do you present missing data?
- What if Little’s MCAR test is significant?
- How do you treat missing data in SPSS?
- How do I recode missing values in SPSS?

## What percentage of missing data is acceptable?

Statistical guidance articles have stated that bias is likely in analyses with more than 10% missingness and that if more than 40% data are missing in important variables then results should only be considered as hypothesis generating [18], [19]..

## What if data is not missing at random?

You may suspect that your data are not missing at random, but nothing in your data will tell you whether or not that’s the case. … For any data set, there are an infinite number of possible MNAR models. Nothing in the data will tell you which of those models is better than another.

## What is monotone missing pattern?

A missing data pattern is said to be monotone if the variables Yj can be ordered such that if Yj is missing then all variables Yk with k>j are also missing. This occurs, for example, in longitudinal studies with drop-out. If the pattern is not monotone, it is called non-monotone or general.

## How do I know if my data is missing at random?

If there is no significant difference between our primary variable of interest and the missing and non-missing values we have evidence that our data is missing at random.

## How do you select cases in SPSS with two variables?

You go to Data->Select Cases->and Click on ‘If condition is satisfied’ You then click on the ‘IF’ push button, highlight my variable, and click on the middle arrow to bring it over to the Expression box. You then specify ‘var=1’ AND ‘var=2’. When you do so, all the cases become unselected.

## How do I get rid of missing data?

Deletion. There are two primary methods for deleting data when dealing with missing data: listwise and dropping variables.

## How do you manipulate data in SPSS?

Sometimes, you would like to select only a specific group of cases for analysis. In SPSS, before the analysis can be performed, you SELECT CASES. This is done by going to “DATA” (on the menu bar) and then SELECT CASES. Sorting cases is a common tool in data manipulation, where data are sorted based on key variables.

## What is data missing at random?

When we say data are missing completely at random, we mean that the missingness is nothing to do with the person being studied. … When we say data are missing at random, we mean that the missingness is to do with the person but can be predicted from other information about the person.

## How do you present missing data?

Techniques for Handling the Missing DataListwise or case deletion. … Pairwise deletion. … Mean substitution. … Regression imputation. … Last observation carried forward. … Maximum likelihood. … Expectation-Maximization. … Multiple imputation.More items…•May 24, 2013

## What if Little’s MCAR test is significant?

The results of Little’s MCAR test appear in footnotes to each EM estimate table. The null hypothesis for Little’s MCAR test is that the data are missing completely at random (MCAR). … Because the significance value is less than 0.05 in our example, we can conclude that the data are not missing completely at random.

## How do you treat missing data in SPSS?

In SPSS, you should run a missing values analysis (under the “analyze” tab) to see if the values are Missing Completely at Random (MCAR), or if there is some pattern among missing data. If there are no patterns detected, then pairwise or listwise deletion could be done to deal with missing data.

## How do I recode missing values in SPSS?

From Transform Menu –> Recode into Same Variable –> Old and New Variables –> System Missing –> in value space add the value you want to replace the missing data with –> continue –> Ok. Done.