Insignificant regression explanation-HELP!!!

As part of my dissertation I am doing a regression analysis in which I am examining if gender, age, Internet use, education and occupational status have an influence of harinf information about online ethics. Basically the dependent variable is Sharing information and the Independent variables are age, gender, education, occupational status and Internet use. My results have show that the regression is insignificant. I do not care to make it significant!! I just care how to explain it!.Why did it come insignificant? What are the possible reasons? Do the variables have to do anything about it? How do you interpret in general insignificant regressions? What explanation would you give for example?
If I have understood you correctly, your analysis is telling you that there is no convincing evidence that any of the IVs have an effect on your DV. Said more carefully, it means that the data are compatible with the DV values being drawn randomly from a distribution which is not affected by any of the IVs. I'm sorry if that ruins your disertation, but that's what insignificance means.
Thank you for your reply.
Yes indeed this is it what it means, that the INV do not have a statistical significant effect on the DV.But my question is this? Does it mean that IF I remove some variables it might become significant? Is there any chance that the variables have an effect on each other (although the literature review does not mention something like that) ans as such they whole regression is insignificant ? Does it mean that the reality is more complex than what originally thought? Or is the reality just explained by other factors? Is th reality more sophisticated? Basically I am trying to fins possible answers because I cannot just write that the INV do not have an effect on the DV and that is what an insignificant regression means.

What kind of multiple regression did you run (i.e., standard, stepwise)?

You should double-check the bivariate correlations of the IVs with the DV and the IVs with each other. If several of your IVs are correlated with the DV at the bivariate level the fact they didn't enter the multiple regression could be concerning. As standard regression only looks at an IVs unique contribution to the prediction of the DV if several of the IVs were correlated with the DV at the bivariate level and the IVs were highly correlated to each other it could just mean they are all predicting "the same thing" about the DV and not enough unique variance on their own.

You might consider running a stepwise regression (although be warned that a stepwise regression can overfit the data). If you believe some of your IVs are more important than other IVs you could also perform a hierarchical regression.

I hope this helps and good luck.
It is theoretically possible that an F-test shows a more significant reduction in variance for fewer IVs than for more IVs, but that is a rare circumstance. You're welcome to remove some IVs from your regression to see if this happens.

But even if that does happen, it is not justifiable to say you have found a significant result. The reason is that by "tuning" your model to maximize significance you are making it much more likely to get a false positive. The only justifiable way to proceed is to do this as an exploratory analysis with a subset of the data, then, once you have fixed your model, do a straight-up test of significance on different data. If your model fails that test, then you don't get to tune further until it doesn't.

The situation which ocgiraffe addresses is the significance of individual regression coefficients (whether the confidence interval for them includes zero), not the significance of the overall fit (which is generally done via a single F-test). Individual regression coefficients might indeed look insignificant due to multi-colinearity effects, but the overall significance of a fit is not impacted by multi-colinearity.
Ok. Thank you very much.

The point is that I do not know statistics and this is my first time I am doing such a thing. I do not know how to do all these things that you mentioned and I do not even know how to interpret them. I just want an explanation in simple plain english, a narration basically, in which I will be saying that eg. the regression is insignificant because the INV are affecting one each other maybe, or because reality is more complex. That kind of stuff basically. Any suggestions or possible explanations would be nice.