Does comparing two p-values make sense? Does comparing two p-values make sense? 
For example, the p-value of factors willingness to pay and the number of owned cars is 0.3. 
The p-value of willingness to pay and the number of owned pets is 0.6.
Can I claim that 
the number of owned cars has a stronger relationship with willingness to pay 
and the number of owned cars explains willingness to pay 
more than the number of owned pets does?
I know that p-value with less than 0.05 is significant but not sure if the p-value is larger then 0.05 we can compare two p-values.
 A: Absent requested clarifications, I can only make generic comments on
the proper uses of P-values. 
If a chi-squared goodness-of-fit test or test for independence has a
statistic $Q$ that is approximately distributed as $\mathsf{Chisq}(\text{df} = 5),$
then the critical critical values for tests at the 5% and 1% levels, respectively, are $c = 11.07$ and $c = 15.07.$ You can find these values
on row 5 of the table to which you linked; I have found them using R statistical
software below: 
qchisq(c(.95, .99), 5)
[1] 11.07050 15.08627

So if your computed value of the test statistic is $Q = 12.33,$ you can
reject the null hypothesis at the 5% level, but not at the 1% level.
Nowadays, most statistical software gives P-values instead of dealing
with specific fixed levels of significance. Software can do that because it
can find more detailed information about a particular distribution
(for example, $\mathsf{Chisq}(\text{df} = 5)$) than is convenient to print
in a published table. 
Specifically, the P-value 0.0305 corresponding to $Q = 12.33$ is the area under
the density function for $\mathsf{Chisq}(\text{df} = 5)$ to the right of
of 12.33. You would reject at the 5% level because $0.0305 < 0.05,$ but not
at the 1% level because $0.0305 > 0.01.$
1 - pchisq(12.33, 5)
[1] 0.03053538

Thus given the P-value, a person can choose their own significance level, and
make a determination whether the test shows a significant result at that level.
So it is fair to say that small P-values are useful to determine the result
of a test, and that a tiny P-value such as 0.0003 indicates stronger evidence
against $H_0$ than does a larger one such as 0.045--even though both P-values lead
to rejection at the 5% level.
However, it is not generally useful to make distinctions between the
'information contained' in larger P-values such as 0.3 and 0.6. That is
because, assuming $H_0$ to be true, the P-value is a random variable 
that is approximately uniform on the interval $(0,1).$ For a continuous
test statistic, such as $Z$ in a normal test or $T$ in a t test, one can
prove that P-values are precisely $\mathsf{Unif}(0,1).$ For most discrete
test statistics P-values are roughly, but not exactly uniform. (One
usually explores the distributions of such P-values through simulation.)
The test statistic $Q$ for a chi-squared goodness-of-fit statistic is discrete,
because its values are based on integer counts. A simple example is to
see what happens in repeated tests whether a die is fair. If a die is rolled
$n = 600$ times, then we ought to see each of the six faces "about 100" times.
The purpose of the chi-squared statistic is to assess whether the actual
face counts are sufficiently close to the expected 100 to say results are
consistent with a fair die.
The R code below simulates 100,000 such 600-roll experiments and finds the test
statistic
$Q = \sum_{i=1}^6 \frac{(X_i-100)^2}{100}$ for each experiment. Then we can
make a histogram of the 100,000 values of $Q$ and also a histogram of the
corresponding 100,000 P-values.
set.seed(1234)
m = 10^5;  n = 600;  E = n/6; die = 1:6;  q = numeric(m)
for (i in 1:m) {
  faces = sample(die, 600, rep=T)
  X = rle(sort(faces))$lengths
  q[i] = sum((X-E)^2/E)  }

mean(q >= 11.07)
[1] 0.04864

pv = 1 - pchisq(q, 5)
mean(pv <= .05)
[1] 0.04864

Because rolls of fair dice are simulated, it is not surprising to see that
$Q > 11.07$ for about 5% of the 600-roll experiments. Equivalently, about 5% of the  P-values are below 0.05.
From the histogram we can see that $Q$ has approximately the target chi-squared
distribution, rejecting for values to the right of the vertical broken line.
Also, the P-values are approximately normally distributed, rejecting for
values to the left of the vertical line. 

The point of this demonstration is that the uniform distribution of P-values
makes it difficult to say that particular P-values such as .3 and .6 are
more remarkable or meaningful than others. Ordinarily, we only care about whether P-values
are small enough to lead to rejection at our chosen significance level.
