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Nick Cox
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Rank versus boxcoxBox-Cox transformation

I'm attempting to assess the relationship between two quantitative variables, but the DV is highly skewed (and so are the residuals). I work among biologists who tend to favor non-parametric techniques (e.g., Mann Whitney, Kruskall WallaceKruskal Wallis). In one part of the paper, a Mann Whitney is used to assess group differences. Although I generally do the boxcoxBox-Cox when doing simple (or multiple) regression, for continuity I decided to rank transform the DV. Alas, it was all noise (i.e., small effect sizes and non-significant p-values). Out of curiosity, I decided to use a boxcoxBox-Cox transformation. With that, the p-values became significant and the effect sizes increased.

So, with that background, a couple of questions:

  1. Am I to interpret the discrepancy as due to random fluctuations (i.e., the boxcoxBox-Cox is committing a Type I error)?

  2. Am I to interpret the rank transformation as more conservative? (i.e., the rank transformation is committing a type II error).

  3. (Related to #1 and #2) Does one transformation tend to be more conservative than the other? Or does it depend on the dataset?

Thanks!

Rank versus boxcox transformation

I'm attempting to assess the relationship between two quantitative variables, but the DV is highly skewed (and so are the residuals). I work among biologists who tend to favor non-parametric techniques (e.g., Mann Whitney, Kruskall Wallace). In one part of the paper, a Mann Whitney is used to assess group differences. Although I generally do the boxcox when doing simple (or multiple) regression, for continuity I decided to rank transform the DV. Alas, it was all noise (i.e., small effect sizes and non-significant p-values). Out of curiosity, I decided to use a boxcox transformation. With that, the p-values became significant and the effect sizes increased.

So, with that background, a couple of questions:

  1. Am I to interpret the discrepancy as due to random fluctuations (i.e., the boxcox is committing a Type I error)?

  2. Am I to interpret the rank transformation as more conservative? (i.e., the rank transformation is committing a type II error).

  3. (Related to #1 and #2) Does one transformation tend to be more conservative than the other? Or does it depend on the dataset?

Thanks!

Rank versus Box-Cox transformation

I'm attempting to assess the relationship between two quantitative variables, but the DV is highly skewed (and so are the residuals). I work among biologists who tend to favor non-parametric techniques (e.g., Mann Whitney, Kruskal Wallis). In one part of the paper, a Mann Whitney is used to assess group differences. Although I generally do the Box-Cox when doing simple (or multiple) regression, for continuity I decided to rank transform the DV. Alas, it was all noise (i.e., small effect sizes and non-significant p-values). Out of curiosity, I decided to use a Box-Cox transformation. With that, the p-values became significant and the effect sizes increased.

So, with that background, a couple of questions:

  1. Am I to interpret the discrepancy as due to random fluctuations (i.e., the Box-Cox is committing a Type I error)?

  2. Am I to interpret the rank transformation as more conservative? (i.e., the rank transformation is committing a type II error).

  3. (Related to #1 and #2) Does one transformation tend to be more conservative than the other? Or does it depend on the dataset?

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dfife
dfife
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Rank versus boxcox transformation

I'm attempting to assess the relationship between two quantitative variables, but the DV is highly skewed (and so are the residuals). I work among biologists who tend to favor non-parametric techniques (e.g., Mann Whitney, Kruskall Wallace). In one part of the paper, a Mann Whitney is used to assess group differences. Although I generally do the boxcox when doing simple (or multiple) regression, for continuity I decided to rank transform the DV. Alas, it was all noise (i.e., small effect sizes and non-significant p-values). Out of curiosity, I decided to use a boxcox transformation. With that, the p-values became significant and the effect sizes increased.

So, with that background, a couple of questions:

  1. Am I to interpret the discrepancy as due to random fluctuations (i.e., the boxcox is committing a Type I error)?

  2. Am I to interpret the rank transformation as more conservative? (i.e., the rank transformation is committing a type II error).

  3. (Related to #1 and #2) Does one transformation tend to be more conservative than the other? Or does it depend on the dataset?

Thanks!