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

At first, my data showed not a normality, so I transformed to log10 and became good normal distribution.

Note that this automatically transforms the effects. See this questionthis question on the same transformation you did. You would have to discuss if you rather want a generalized linear model (not "general linear model").

It is generally not a good idea to transform in order to keep the distributional assumptions. If the assumptions are not met, you will miss your type-I-error rate, which is bad, but is healed often by large sample sizes. On the other hand, if you transform it in a way that you cannot interpret the effects in terms of your experiment, it is worse.

Variance homogeneity (homoskedasticity) is usually not so big a problem any more as there are procedures which can deal with it (I think they are even implemented in SPSS). In your case, data are even perfectly balanced (N=23), so there is even an exact procedure for it. I would generally suggest to use a procedure supporting homoskedasticity. If your choice of method depends on the result of a variance homogenity test, you are in fact using a two-staged procedure that behaves differently than the single staged procedures.

At first, my data showed not a normality, so I transformed to log10 and became good normal distribution.

Note that this automatically transforms the effects. See this question on the same transformation you did. You would have to discuss if you rather want a generalized linear model (not "general linear model").

It is generally not a good idea to transform in order to keep the distributional assumptions. If the assumptions are not met, you will miss your type-I-error rate, which is bad, but is healed often by large sample sizes. On the other hand, if you transform it in a way that you cannot interpret the effects in terms of your experiment, it is worse.

Variance homogeneity (homoskedasticity) is usually not so big a problem any more as there are procedures which can deal with it (I think they are even implemented in SPSS). In your case, data are even perfectly balanced (N=23), so there is even an exact procedure for it. I would generally suggest to use a procedure supporting homoskedasticity. If your choice of method depends on the result of a variance homogenity test, you are in fact using a two-staged procedure that behaves differently than the single staged procedures.

At first, my data showed not a normality, so I transformed to log10 and became good normal distribution.

Note that this automatically transforms the effects. See this question on the same transformation you did. You would have to discuss if you rather want a generalized linear model (not "general linear model").

It is generally not a good idea to transform in order to keep the distributional assumptions. If the assumptions are not met, you will miss your type-I-error rate, which is bad, but is healed often by large sample sizes. On the other hand, if you transform it in a way that you cannot interpret the effects in terms of your experiment, it is worse.

Variance homogeneity (homoskedasticity) is usually not so big a problem any more as there are procedures which can deal with it (I think they are even implemented in SPSS). In your case, data are even perfectly balanced (N=23), so there is even an exact procedure for it. I would generally suggest to use a procedure supporting homoskedasticity. If your choice of method depends on the result of a variance homogenity test, you are in fact using a two-staged procedure that behaves differently than the single staged procedures.

1
source | link

At first, my data showed not a normality, so I transformed to log10 and became good normal distribution.

Note that this automatically transforms the effects. See this question on the same transformation you did. You would have to discuss if you rather want a generalized linear model (not "general linear model").

It is generally not a good idea to transform in order to keep the distributional assumptions. If the assumptions are not met, you will miss your type-I-error rate, which is bad, but is healed often by large sample sizes. On the other hand, if you transform it in a way that you cannot interpret the effects in terms of your experiment, it is worse.

Variance homogeneity (homoskedasticity) is usually not so big a problem any more as there are procedures which can deal with it (I think they are even implemented in SPSS). In your case, data are even perfectly balanced (N=23), so there is even an exact procedure for it. I would generally suggest to use a procedure supporting homoskedasticity. If your choice of method depends on the result of a variance homogenity test, you are in fact using a two-staged procedure that behaves differently than the single staged procedures.