Bootstrapping:A powerful technique to handle the Non-Normal Data with Bollen Stine Method using Amos
Bootstrapping : A Powerful method to handle non normal data with Bollen Stine Method
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In this video, we will discuss about bootstrapping, a powerful technique to handle non-normal data. We will use an example of mediation analysis to illustrate how bootstrapping can be used to assess the fit of a model to non-normally distributed data.
We will first assess the normality of the data using the skewness and kurtosis statistics. If the data is not normally distributed, we will then perform bootstrapping to create a larger sample of data that is more likely to be normally distributed. We will use the Bollenstein bootstrap method to assess the fit of the model to the bootstrapped data.
The results of the bootstrapping analysis will show whether the model is a good fit for the data. If the p-value of the Bollenstein bootstrap test is greater than 0.05, we can conclude that the model is a good fit for the data.
This video will show you how to use bootstrapping to handle non-normally distributed data in mediation analysis. By the end of the video, you will be able to:
Assess the normality of data using skewness and kurtosis statistics
Perform bootstrapping to create a larger sample of data that is more likely to be normally distributed
Use the Bollenstein bootstrap method to assess the fit of a model to the bootstrapped data
I hope this video will help you in your data analysis.
The bootstrapping method is a powerful tool that can be used to assess the fit of a model to data, even when the data is not normally distributed.
The Bollenstein bootstrap method is a specific type of bootstrapping method that is often used in structural equation modeling.
The p-value of the Bollenstein bootstrap test is a measure of the probability that the model would fit the data by chance. If the p-value is greater than 0.05, we can conclude that the model is a good fit for the data.
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