Hi! It’s been weeks since I try to figure out how to manage missing value in SPSS and R. what I have read is that there are types of missing value and few approaches to manage the missing value. But I was unable to find the right answers.
My experiment is, there are two factors which are spacing (plot) and fertilizer (subplot) treatment for Terminalia seedlings. The experiment has three replications, each subplot has nine seedlings. The experimental design is RCBD. Unfortunately, there are some seedlings that are died in the experiment.
So, my questions are the dead seedlings is count as missing value? Do I have to exclude the dead seedlings data from the dataset for further analysis such as normality test, ANOVA, Post Hoc and Regression?
I have read that in SPSS there are options for missing values but I just wondering is that the correct method to manage the dead seedlings data. Are there any references to manage data of dead seedlings in the experiment? I try to find it but had no luck.
Please help me.
Dear Ade Esah Azzahra Jobin
If there is any missing value or data of dead seedlings, it can be excluded as a whole without affecting statistical analysis. But there should be enough dataset (at least 3 replicates) to represent the treatment effect.
In your study, the most important thing is you have only three replicates. If you exclude one out of three then there might be only two replicates that are not truly representative of the treatment effect, i.e., different fertilizer and/or spacing effect on Terminalia seedlings.
Best wishes!
Ishfaq
Muhammad Ishfaq was right in the sense that you have replications. At the end of the day, you need averages to estimate your experimental error, treatment effects and to also suggest if there are significant differences among the treatments.
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