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In this video, I demonstrated How to Correct the Data that Violates the Parametric Assumption of Normality in SPSS using three different transformation methods, such as: 1. Log Transformation, 2. Square Root Transformation and 3. Inverse Transformation. The conditions for applying the different transformations were greatly highlighted and the procedure demonstrated was comprehensively simplified for easy follow-up and understanding. So, you are highly encouraged to see or watch this video to the end. Please, kindly like and share the link of this video for others to see and also benefit. Thank you. For your data analysis, interpretation of results, and SPSS tutorials, please reach me via: [email protected] You may also like the following videos: The Best ANCOVA Analysis Procedure in SPSS: • The Best ANCOVA Analysis Procedure in SPSS Interpretation of ANCOVA Results in SPSS: • Interpretation of ANCOVA Results in SPSS How to Test Data for Assumption of Binary Logistic Regression in SPSS: • How to Test Data for Assumptions of Binary... Analysis of Variance in SPSS (Effect Size and Interpretations): • Analysis of Variance in SPSS (Effect Size ... One-Way MANOVA in SPSS (Part-1): A Multivariate Data Analysis: • One Way MANOVA in SPSS (Part 1): A Multiva... Introduction of SPSS Software for Beginners (Part-1): • Introduction to SPSS Software for Beginner... Regression Analysis in SPSS (Part-1): • Regression Analysis in SPSS (Part 1) Two Way ANOVA in SPSS (Part-1): Data Analysis: • Two Way ANOVA in SPSS (Part-1): Data Analysis Two Way ANOVA in SPSS (Part-2): Results Interpretations: • Two Way ANOVA in SPSS (Part-2): Results In... Titocan Mark Solutions (TMS) is a YouTube Channel that improves the knowledge of how to do things in many endeavours. Please, encourage education and learning by liking, sharing and kindly subscribing to this channel. You will begin to receive notifications every time I publish new video content when you have subscribed. Please, SUBSCRIBE and share this video in the good interest of others. Thank you for your time and the subscription.