Have you plotted the data? Impossible to tell much from a simple
regression analysis; especially without any definition of the two
variables. If I were compelled to guess, I'd suppose that BEHPROBS
(your dependent variable) was the number of behavioral problems
reported, probably over some defined time span (perhaps the week
mentioned with respect to "how often the parent has spanked the
child", which I presume to be the dependent variable SPANK9235?).
But if you haven't even _looked_at_ the bivariate relationship, you
can't tell whether a _linear_ functional relation makes any sense.
On Wed, 19 Jan 2000, steinberg wrote:
> I am asking whether corporal punishment of children is associated
> with behavior problems.
Controlling for what other variables? The analysis you report
below shows none; but surely there are many that need to be controlled
(such as propensity for administering punishment at all, propensity for
corporal versus other kinds of punishment, whether corporal punishment
is administered by only one parent or by both, the severity of the
(alleged?) behavior problems, ...
> I am using data from the National
> Longitudinal Survey of Youth. I am interested in the results of a
> question that asks how often the parent has spanked the child in
> the last week. This data is extremely right skewed with some
> extreme outliers. Most of the responses are zeros and ones.
> Square root and log transforms have very little effect on the
> right skew. (I added 1 to each score and took the log to avoid
> zeros.)
But the important question is, what effect (if any) do these
transformations have on the bivariate relationship? Does it look
more (or less) linear in one form than in the others?
> The regression (output below) shows such a small R-squared that
> there would appear to be no meaningful association, although the
> slope is significantly different from zero.
Again: If you haven't examined the scatterplot, you cannot tell whether
there is an association or not. It is not at all clear that a simple
linear association is to be expected; especially if your respondents
include parents who refuse to use corporal punishment at all, however
great the behavioral provocation, as well as parents who believe firmly
in the dictum "Spare the rod and spoil the child".
With 1100 degrees of freedom, quite small effects can be found
formally significant; but your analysis reports r = .226.
> ... However, on general principle: Is there some way to properly
> transform such skewed data?
Sounds as though you've reasonably well addressed that, at least at the
simple level of bivariate regression, insofar as one can without looking
at the data.
> If not, can it still be used in a regression?
Certainly.
> Of what errors must I be aware if I were to use it?
Mainly, oversimplified models, I should think. You might profitably
spend some time thinking about how the data you have might have arisen,
and what other variables will affect the relationship you wish to
consider. AND you might also think about whether you've got the
relationship the right way round. You're using number of spankings in a
week to predict (number of?) behavior problems; it would not be
unreasonable, from one point of view, to predict the number of spankings
from the number (or intensity?) of the problems.
An assumption embedded in your analysis is that it makes sense to
think of spanking as inducing (or causing) behavior problems. Parents
who spank, if asked, will ordinarily claim that they are trying to reduce
or prevent behavior problems, and that spanking is a response to overt
behavior problems, not a cause of them.
> ============================
>
> Dep Var: BEHPROBS N: 1107 Multiple R: 0.226
> Squared multiple R: 0.051
>
> Adjusted squared multiple R: 0.050
> Standard error of estimate: 14.780
>
> Effect Coefficient Std Error Std Coef Tolerance t P
>
> CONSTANT 102.839 0.538 0.000 . 191.289 0.000
> SPANK9235 1.381 0.179 0.226 1.000 7.719 0.000
>
> Analysis of Variance
>
> Source Sum-of-Squares df Mean-Square F-ratio P
>
> Regression 13015.793 1 13015.793 59.583 0.000
> Residual 241384.857 1105 218.448
------------------------------------------------------------------------
Donald F. Burrill [EMAIL PROTECTED]
348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED]
MSC #29, Plymouth, NH 03264 603-535-2597
184 Nashua Road, Bedford, NH 03110 603-471-7128
