Hypotheses Flashcards
Null Hypothesis
The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to the manipulation of the independent variable.
It states results are due to chance and are not significant in terms of supporting the idea being investigated.
Nondirectional Hypothesis
A non-directional (two-tailed) hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified. It just states that there will be a difference.
E.g., there will be a difference in how many numbers are correctly recalled by children and adults.
Directional Hypothesis
A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e. greater, smaller, less, more)
E.g., adults will correctly recall more words than children.
independent variable
The independent variable is the variable the experimenter manipulates or changes, and is assumed to have a direct effect on the dependent variable. For example, allocating participants to either drug or placebo conditions (independent variable) in order to measure any changes in the intensity of their anxiety (dependent variable).
OPPORTUNITY SAMPLING
The sample is those members of the target population who happen to be available at the time.
Opportunity sampling involves getting hold of the nearest and most convenient people: your friends, neighbours and passers-by. It’s easy to use, but there’s a big risk of experimenter bias, because only people near to you or known to you have any chance of being in the study.
VOLUNTEER SAMPLING
The sample is those members of the target population who select themselves.
Volunteer sampling involves asking for volunteers – for example, advertising your study on a notice board or on Facebook and using anyone who signs up.
Because they have volunteered, this sort of sample might be more committed than a sample that has to be approached and asked. That might be important if the research involves tasks that are stressful or boring.
This produces a much more varied sample than opportunity sampling because there’s no experimenter bias. However, the sample may still be unrepresentative because you only get certain sorts of people volunteering (the ones interested in Psychology, people with a lot of free time). You could think of this as participant bias instead of experimenter bias. What’s more, people have to see and understand the advert to have a chance of being in the sample, so that might rule out participants who speak other languages, don’t read newspapers or who aren’t online.
It also takes longer (because you have to wait for the volunteers to show up).
RANDOM SAMPLING
The sample is members of the target population selected without any bias.
This sounds like an ideal method, because you use an unbiased way of identifying people in the target population, for example by pulling their names out of a hat. However, it’s often very difficult to get a complete list of everyone in the target population, so this sampling technique is normally only used when the target population is very small; for example randomly selecting people from out of your class.
Also, just because the sample was selected in an unbiased way, it doesn’t mean it must be representative. You could selected people randomly and still find you had an unrepresentative sample that was all-boys
STRATIFIED SAMPLING
The sample is members of the target population selected in an unbiased way but guaranteed to be representative in certain ways.
Strata are sub-groups within the target population (like boys and girls or different age groups). This technique involves working out the strata you need in your sample and how many people there should be in each, then filling the strata through random sampling.
