I compared: x(+)y(+) and x(-)y(+) groups, and, x(+)y(-) and x(-)y(-) with p=0.04 for first, and p=0.0001 for second

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I compared: x(+)y(+) and x(-)y(+) groups, and, x(+)y(-) and x(-)y(-) with p=0.04 for first, and p=0.0001 for second

How large are your groups, and how exactely was the dependent variable measured?

With kind regards

Karabiner

With kind regards

Karabiner

I compared the decline rate of a scale that is used to assess patients with this disease = (how many points they lost / disease duration)

as, for example, if sb lost 1 point in 5 months time, his decline rate is 0,2, or if sb lost 5 points in 6 months, his decline rate is 0,83,

If the dependent variable can be considered as interval scaled, then an

analysis of variance with the factors "x" and "y" can be performed,

(without interaction). Regarding the normal distribution stuff, you

could search this forum or other sources why this is not important,

given your sample size.

With kind regards

Karabiner

analysis of variance with the factors "x" and "y" can be performed,

(without interaction). Regarding the normal distribution stuff, you

could search this forum or other sources why this is not important,

given your sample size.

With kind regards

Karabiner

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analysis of variance with the factors "x" and "y" can be performed,

(without interaction). Regarding the normal distribution stuff, you

could search this forum or other sources why this is not important,

given your sample size.

With kind regards

Karabiner

so I assume I can't use

Sorry, I didn't want to shout. I just thought it looked better that way.

With two-way ANOVA I've got:

factor 1: 0.005

factor 2: 0.000

factor 1 * factor 2: 0.668

so I hope that's enough for a proof that factor 1 irrespective of factor 2 has an impact on disease prognosis.

edit

However, if I perform t-student test between groups x(+)y(+) vs x(-)y(+) p value is 0.057. What stays in opposition to two-way ANOVA result.

With two-way ANOVA I've got:

factor 1: 0.005

factor 2: 0.000

factor 1 * factor 2: 0.668

so I hope that's enough for a proof that factor 1 irrespective of factor 2 has an impact on disease prognosis.

edit

However, if I perform t-student test between groups x(+)y(+) vs x(-)y(+) p value is 0.057. What stays in opposition to two-way ANOVA result.

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