• Hypothesis tests (do this for each hypothesis) / and or revealing results about your expectation

• Briefly repeat hypothesis (“In H1, we hypothesized that […]”)
• Report the result in brackets - both the coefficient, and the significance level
• e.g., “Indeed, the effect of A on B is statistically significant (beta = xx, p = .012)”
• e.g., “A increases B by x% (beta = xx, p = .025)“
• Explain the result (the “why?”)
• For a confirmed hypothesis, increase the intuition of your hypothesis (“apparently, as hypothesized, A leads to B because [repeat, etc.]…”)
• For an unconfirmed hypothesis, explain why you don’t find the effect (e.g., a conceptual reason (effect doesn’t exist, and yo u h ave an argument why it may not exist), or a measurement issue (e.g., data problems), etc.)
• Remaining variables

• Explain the effects of control variables here (“The control variables age and gender have face valid effects. For example, all else equal, age increases the intention to purchase (beta = xx, p = .12). Education turns out to be not a significant predictor of intention to purchase (beta = xx, p = .63). This may be the case because

Note: While hypothesis tests are usually carried out against a p value of .05 (or sometimes .10), you need to report the exact p value in the text and tables with, e.g., three digits after the decimal point (e.g., p = .049, instead of p < .05). If you cannot give an exact p value because of rounding (e.g., the true p value is .00001, but it would round to p = .000), you need to write it down as p < .001. The p needs to be printed in italic always.