# How to report SPSS OLS output?

Consider the model below:

In many research papers, significance of statistical results are indicated by *, **, and *** as a significant value at respectively the 1%, 5%, or 10% level.

Does a Sig. value of .047 (forninstance, LAW_LE) mean a significane at the 4,7% level? (So that I may report LAW_LE 0.313**)?

Second, I see many times the Std.Error is also reported in papers. How do I need to interpret the standard error in combination with the significance? For instance, COM_BIK has a standard error of 0.045 and a significance of 0.021; whereas HOF_MAS has a low standard error (0.002) but a significance of .416. What can be said about these two variables based on this information?

It depends on the field, but I think this is the most common approach:

• Report coefficients that have a p-value that is less than 0.05 but greater than 0.01 with one asterisk (*) -- equivalent to the 5% level of significance;
• Report coefficients that have a p-value that is less than 0.01 but greater than 0.001 with two asterisks (**);
• Report coefficients that have a p-value that is less than 0.001 with three asterisks (*)

So the LAW_LE would get one asterisk. Usually/often the standard error is also reported, as you suggest. Again, this varies according to the specific field of study, it is fairly standard to see values in a table as the coefficient, the significance level and the standard error in parentheses. So for your LAW_LE you would have

LAW_LE $0.075^{*}$ $(0.037)$

(unless you wanted to report the standardized coefficients in which case $0.075$ would be replaced with $0.313$ -- the decision as to whether you report standardized or unstandardized coefficients is another question and has probably been covered here).

Most journals won't want to see all of these numbers, and which numbers are preferred vary from journal to journal. You're right that "Sig." is the $p$-value, so a $p$-value of 0.047 would indicate that you should reject the null hypothesis at a confidence level of 0.05

In general, I prefer showing the coefficient values and the $t$-statistic because it contains information on both the standard error and the $p$-value. Most readers have a good intuition that $t$ values above 1.9 are generally significant, and if the reader really wants to see the standard error, it's easy for them to calculate $t/\beta$.

$\textbf{Answer to part 2}$: If your null hypothesis is that the coefficient on each variable is zero, you can relate the standard error, $t$, and $beta$ as $t=beta/SE(beta)$. A rule of thumb for the significance of coefficient (at 10 %) under two tailed test is that $t$ should be >=2.

$\textbf{Answer to part 1}$: Most of the academic papers report the non-standardized coefficient, so you may want to write $LAW_{LE}= 0.075^{**}$. Also, 1% significance implies significance at $p<=0.01$ and 5 % significance implies significance at $p<=0.05$.