For example if the reliability for variable A is 070 and reliability for variable B is 080 then the reliability for the interaction variable A B is 070 080 056.

Example of hypothesis with moderating variable. Therefore we may not be able to find the interaction effects between A and B that actually exist. A moderating variable can either be categorical eg race or continuous eg weight and is used exclusively in quantitative rather than qualitative research. Since this method allows you to account for all potential confounding variables which is nearly impossible to do otherwise it is often considered to be the best way to reduce the impact of confounding variables.

For instance imagine researchers are evaluating the effects of a new cholesterol drug. The amount of sleep. Randomization example You gather a large group of subjects to participate in your study on weight loss.

Moderating variable are typically an interaction term in statistical models. Its likely that each extra hour of exercise causes resting heart rate to drop more for younger people compared to older people. The independent variable and the dependent variable.

For example in the diagram below you might find a simple main effect that is moderated by sex. I know that there should be at least 3 hypotheses. The language I assume that by hypothesis you mean null hypothesis.

If I drink Mountain Dew before bed then I will not sleep very much. Moderating variables are important in scientific analysis where the researchers want to determine the correlation between two variables. That is the relationship is stronger for women than for men.

One for each main effect and one for the interaction. Moderators specify when a relation will hold. For example 2 the hypothesis that can be formulated is.