3 replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
source | link

It is a very bad thing to remove people who dropped out from your data set. The problem is that you do not know whether dropping out is correlated with the effect that you are studying.

For an extreme example, consider a study on the effect of being shot at on soccer ability. In round 1, people play soccer, then they get shot at randomly with a gun that might or might not hit them, then they play round 2 of soccer, then they get shot at again, and then they play round 3 of soccer. Of course, anybody who actually gets hit when they are shot will probably drop out. If you eliminate those people, you will vastly underestimate how badly soccer players are affected by people shooting at them.

This may seem like a rather extreme example, but things very much like it happen frequently in biomedical or psychological studies, just with less obvious causal connections.

Report exactly what happened, and take the missing people into account when you are computing your effect size. If you need help on the technical aspects of that, you should ask on Cross-Validated.SECross-Validated.SE.

It is a very bad thing to remove people who dropped out from your data set. The problem is that you do not know whether dropping out is correlated with the effect that you are studying.

For an extreme example, consider a study on the effect of being shot at on soccer ability. In round 1, people play soccer, then they get shot at randomly with a gun that might or might not hit them, then they play round 2 of soccer, then they get shot at again, and then they play round 3 of soccer. Of course, anybody who actually gets hit when they are shot will probably drop out. If you eliminate those people, you will vastly underestimate how badly soccer players are affected by people shooting at them.

This may seem like a rather extreme example, but things very much like it happen frequently in biomedical or psychological studies, just with less obvious causal connections.

Report exactly what happened, and take the missing people into account when you are computing your effect size. If you need help on the technical aspects of that, you should ask on Cross-Validated.SE.

It is a very bad thing to remove people who dropped out from your data set. The problem is that you do not know whether dropping out is correlated with the effect that you are studying.

For an extreme example, consider a study on the effect of being shot at on soccer ability. In round 1, people play soccer, then they get shot at randomly with a gun that might or might not hit them, then they play round 2 of soccer, then they get shot at again, and then they play round 3 of soccer. Of course, anybody who actually gets hit when they are shot will probably drop out. If you eliminate those people, you will vastly underestimate how badly soccer players are affected by people shooting at them.

This may seem like a rather extreme example, but things very much like it happen frequently in biomedical or psychological studies, just with less obvious causal connections.

Report exactly what happened, and take the missing people into account when you are computing your effect size. If you need help on the technical aspects of that, you should ask on Cross-Validated.SE.

2 explain why death is a reasonably sized issue
source | link

It is a very bad thing to remove people who dropped out from your data set. The problem is that you do not know whether dropping out is correlated with the effect that you are studying.

For an extreme example, consider a study on the effect of being shot at on soccer ability. In round 1, people play soccer, then they get shot at randomly with a gun that might or might not hit them, then they play round 2 of soccer, then they get shot at again, and then they play round 3 of soccer. Of course, anybody who actually gets hit when they are shot will probably drop out. If you eliminate those people, you will vastly underestimate how badly soccer players are affected by people shooting at them.

This may seem like a rather extreme example, but things very much like it happen frequently in biomedical or psychological studies, just with less obvious causal connections.

Report exactly what happened, and take the missing people into account when you are computing your effect size. If you need help on the technical aspects of that, you should ask on Cross-Validated.SE.

It is a very bad thing to remove people who dropped out from your data set. The problem is that you do not know whether dropping out is correlated with the effect that you are studying.

For an extreme example, consider a study on the effect of being shot at on soccer ability. In round 1, people play soccer, then they get shot at randomly with a gun that might or might not hit them, then they play round 2 of soccer, then they get shot at again, and then they play round 3 of soccer. Of course, anybody who actually gets hit when they are shot will probably drop out. If you eliminate those people, you will vastly underestimate how badly soccer players are affected by people shooting at them.

Report exactly what happened, and take the missing people into account when you are computing your effect size. If you need help on the technical aspects of that, you should ask on Cross-Validated.SE.

It is a very bad thing to remove people who dropped out from your data set. The problem is that you do not know whether dropping out is correlated with the effect that you are studying.

For an extreme example, consider a study on the effect of being shot at on soccer ability. In round 1, people play soccer, then they get shot at randomly with a gun that might or might not hit them, then they play round 2 of soccer, then they get shot at again, and then they play round 3 of soccer. Of course, anybody who actually gets hit when they are shot will probably drop out. If you eliminate those people, you will vastly underestimate how badly soccer players are affected by people shooting at them.

This may seem like a rather extreme example, but things very much like it happen frequently in biomedical or psychological studies, just with less obvious causal connections.

Report exactly what happened, and take the missing people into account when you are computing your effect size. If you need help on the technical aspects of that, you should ask on Cross-Validated.SE.

1
source | link

It is a very bad thing to remove people who dropped out from your data set. The problem is that you do not know whether dropping out is correlated with the effect that you are studying.

For an extreme example, consider a study on the effect of being shot at on soccer ability. In round 1, people play soccer, then they get shot at randomly with a gun that might or might not hit them, then they play round 2 of soccer, then they get shot at again, and then they play round 3 of soccer. Of course, anybody who actually gets hit when they are shot will probably drop out. If you eliminate those people, you will vastly underestimate how badly soccer players are affected by people shooting at them.

Report exactly what happened, and take the missing people into account when you are computing your effect size. If you need help on the technical aspects of that, you should ask on Cross-Validated.SE.