interactions lmer continuous and categorical fixed factor

Hi,

I am using the lmer function in lme4 to test a model like this:

DV ~ factor1 * factor2 (simplified for purposes of illustration, so
without random effects structure)

DV = continuous (Reaction time)
factor1 = continuous
factor2 = categorical (3 levels)

summary(model) will give me output like this:

factor2-level1 * Factor1 = xxx
factor2-level2 * Factor1 = xxx
factor2-level3 * Factor1 = xxx

If I try to get p-values for this model, however, I only get one p-value
for the interaction factor2 * factor 1.

What do you recommend to report in this case?
p-values with corresponding F-values and df, or the t-values found in
summary(model), without any p-values?

Thanks in advance,
Lotte

Dear Lotte,

I assume that the "one p-value for the interaction" is the p-value
from anova(model). Note that this tests a different hypothesis than
the hypothesis than summary(model) tests (without reporting
p-values).

IMHO, p-values of parameters estimates are not that relevant.
Confidence intervals of those parameter estimates are much more
relevant. I'd rather report those.

Best regards,

ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek /
Research Institute for Nature and Forest team Biometrie &
Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat
25 1070 Anderlecht Belgium

To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may
be able to say what the experiment died of. ~ Sir Ronald Aylmer
Fisher The plural of anecdote is not data. ~ Roger Brinner The
combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given
body of data. ~ John Tukey

2015-06-01 16:56 GMT+02:00 Lotte Schoot <Lotte.Schoot at mpi.nl>:

Hi,

I am using the lmer function in lme4 to test a model like this:

DV ~ factor1 * factor2 (simplified for purposes of illustration,
so without random effects structure)

DV = continuous (Reaction time) factor1 = continuous factor2 =
categorical (3 levels)

summary(model) will give me output like this:

factor2-level1 * Factor1 = xxx factor2-level2 * Factor1 = xxx 
factor2-level3 * Factor1 = xxx

If I try to get p-values for this model, however, I only get one
p-value for the interaction factor2 * factor 1.

What do you recommend to report in this case? p-values with
corresponding F-values and df, or the t-values found in 
summary(model), without any p-values?

Thanks in advance, Lotte

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