0
$\begingroup$

Being my dependent variable (0 = firm innovates incremental, 1 = company innovates radical), positive values of beta means greater effect on the propensity of firm innovates radically.

My question: what does negative coefficient estimates values mean? Increasing the propensity of the company to innovate incrementally?

In other words, can I interpret the results in the same way in both directions, negative and positive?

For example, my predictors are binary variables (0 = no, 1 = yes) that indicate whether the company cooperated with suppliers, customers, and universities. Since my dependent variable is 0 = innovated incremental, 1 = innovated radical, how to interpret these results:

enter image description here

$\endgroup$
  • $\begingroup$ there should not be negative values when using logit $\endgroup$ – Aksakal Oct 25 '17 at 19:41
  • 6
    $\begingroup$ What are you referring to: negative coefficient estimates, negative predicted log odds, or negative predicted probabilities? $\endgroup$ – gung - Reinstate Monica Oct 25 '17 at 19:55
  • 2
    $\begingroup$ The interpretation is no different than it would be for positive coefficients. If you are concerned about seeing a negative number, then change how you code suppliers: switch 1 to 0 and 0 to 1 in the dataset. Now the estimate will be +0.107. $\endgroup$ – whuber Oct 25 '17 at 21:14
1
$\begingroup$

I assume that by you mean: what does negative [betas -- i.e. coefficient] values mean? Increasing the propensity of the company to innovate incrementally?.

It means that the log odds that a "firm that innovates radical" is lower if a firm has higher value of covariate with a negative coefficient compared to another firm -- all other covariates being equal. You can flip this around and say that the firm has a higher log odds of being a "firm that innovates incremental".

| cite | improve this answer | |
$\endgroup$
0
$\begingroup$

When doing logistic regression, the output is reported in terms of the log-odds ratio, which is just an unexponentiated odds ratio.

Typically, when we interpret the results of a logistic regression, we aren't usually interested in those numbers (i.e., the numbers below Coef(b) in your output). I could tell you that a negative coefficient implies that the odds in the case when the corresponding binary variable = 1 are lesser than the odds in the case when that variable = 0, but that's beside the point, as the number itself has no real interpretation.

You can more easily infer the "direction" of the odds ratio after exponentiating the coefficients, as in e^{Coef}, or e^(-0.107) = 0.899. This number has a tangible interpretation; in your case, it is the ratio of the odds of innovating radically given that the company cooperated with its suppliers, compared to the odds of innovating radically for a company that did not cooperate with its suppliers, customers, or universities.

In other words, a company that cooperates with its suppliers has a 10% reduced odds of innovating radically compared to a company that does not cooperate with its suppliers, customers, or universities.

It is always important to remember that the odds ratio is a ratio: it has a numerator and a denominator. When interpreting it, you have to call attention to the numerator--the case "described" by a particular combination of variables-- as well as the denominator--the "reference case" (usually when all covariates are set =0, but this is an assumption on my part).

| cite | improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.