Research Methods Flashcards
One tailed hypothesis (directional hypothesis)
A one-tailed directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable. E.g., adults will correctly recall more words than children. This would normally be used when there’s previous research available.
Two tailed hypothesis (non directional)
A two-tailed non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified. E.g., there will be a difference in how many numbers are correctly recalled by children and adults.
Null hypothesis
This is when you predict that there will be no change whatsoever in an experiment that has already been done but with a changed independent variable.
What is an aim
This is what the experimenter is trying to find out.
What is the acronym for the sign test
Sign Nominal o Repeated measures Differences
How to do the sign test
- State the hypothesis, directional hypothesis require a one-tailed test however, a non-directional would require a two-tailed test
- Record the data and work out the sign. Record each pair of data and record the difference using + and -.
- Find the calculated value. S is the symbol for the test statistic we are calculating. It is calculated by adding up the + and - and selecting the smaller value. E.g +10 and -3 and one 0. The less frequent sign is minus, so S =3. This is the calculated value.
- Find the critical value of S. N= the total number of scores(ignoring any zero values). If the hypothesis is directional then you use a one-tailed test. We use the table of critical values and the row that has our N value.
When do we use the sign test
This is a test that is used when looking at paired or related data.
The two related pieces f data could come from a repeated measures design, the same person is tested twice.
The sign test can also be used with matched pairs design because the participants are paired and therefore count, for the purpose of statistics, as one person tested twice.
Meta analysis
When you do an overview of a large amount of studies
Nominal data
This is data in the form of categories. E.g male and female, using the cognitive interview or standard interview.
Ordinal data
Data that can be ranked. E.g on a scale of 1 - 5 how happy are you
Interval data
This is interval level data. This Provides the precise measurements and is not subjective. e.g it’s 39 degrees
Independent variables
The variable that is manipulated by the experimenter. E.g changing the intensity of the light.
Dependent variable
The variable that is measured. E.g seeing the change in photosynthesis
What is meant by the term operationalisation
This is clearly stating or defining your variables for what they are measuring. E.g saying that the patients will improve on a scale of 1-10.
Random allocation
Randomly picking people to participate in the experiment from a target audience.
Counter balancing
A procedure that allows a researcher to control the effects of nuisance variables in designs where the same participants are repeatedly subjected to conditions, treatments or stimuli.
Randomisation
Randomising the materials in your experiment or variable within it
Standardisation
Following standard procedures and instructions with all participants.
Extraneous variables
Things that we need to control so they don’t effect our results. E.g taking a test in a cold room, the temperature of the room may affect the performance on the test and therefore be a extraneous variable.
You can control them by making sure that they have taken into account and the right procedures have been met to negate the factors.
Confounding variables
Variables that you can’t control. E.g someone having a migraine can affect your results.
Validity
A measure of how well a test measures what it claims to measure
Ecological validity
A measure of how test performance predicts behaviour in real world setting.
Population validity
Are the participants in your study representative of the population.
Temporal validity
Does the theory/research stand the test of time.
Reliability
Refers to the consistency of a research study or measuring test
Informed consent
This is the participant giving their consent to the researcher/experimenter. If they are under 18 then their parents have to give the consent.
Deception
This is where the participants are misled or wrongly informed about the aims of the research.
Right to withdraw
Participants should know that they can withdraw at any time.
Confidentiality
Keep your participants identity safe.