Reaserch methods

Independent variable

Manipulated or changed by the researcher to investigate whether it consequently brings change in another variable, the DV

Dependent variable

Measured by the researcher to see if it has changed as a consequence of the manipulation of the IV

Levels of IV

Different conditions in an experiment. Made up of an experimental group and control group

Operationalisation

To make something measurable

Directional hypothesis

The sort of difference between two variables / conditions / people is made clear. It include words like higher/lower, faster/slower.

Non directional hypothesis

States that there will be a difference between conditions or groups of people, but not what the difference will be

Importance of control

Allows us to be sure that it is the manipulation of the IV that causes changes to the DV and allow cause and effect to be established.

Extraneous variable

Any variable other than the IV that might affect the DV and thus affect the results. For example age of participants. Many EVs are straightforward to control as they do not tend to vary systematically in the IV. Where extraneous variables are important enough to cause a change in the DV, they become confounding variables.

Situational variable

Variables connected with the research situation. E.g temperature or time of the day. They are controlled through standardisation, ensuring that the only thing which differs between the two groups is the IV. E.g. making sure that the temperature/ time of day etc. is the same for both groups.

Participant variables

Variables connected with the reasearch participants. E.g. age, gender, intelligence etc. They are controlled through the experimental design, such as matched pairs design, or by randomly allocating participants to conditions (evenly distribute participant’s characteristics), which helps to reduce bias.

Counterbalancing

Order effects may occur in a design where participants are involved in a number of different conditions, in such cases, researchers can counterbalance the order of the condtions to balance these effects. The sample is split in half with one half completing the two conditions in one order and the other half completing the conditions in the reverse order.

Randomisation

The use of chance wherever possible in an investigation, to reduce the researcher’s influence on the design. This is an attempt to control investigator effects.

Standardisation

All participants should be subjected to the same envioronment, information and experience. To ensure this, all procedures are standardised, and investigations use standardised instructions that are read to each participants. This means that any changes in the DV can be attibuted to manipulation of the IV

Demand characteristics

The clues of the experimental situation that help the participant to second guess the experimenters’ intentions as well as the aims of the study. Participants are not passive within experiments and are likely to try and make sense of the new situation they find themselves in. They may look for clues about how to behave, and act in a way that they think is expected and over-perform to please the experimenter (the ‘please-U effect’), or they may deliberately under-perform to sabotage the results of the study (the ‘screw-U effect’).This means the behaviour is no longer natural, an extraneous variable that may affect the DV and reduce the internal validity of the experiment.

Investigator effects

Investigator effects (which can be conscious or unconscious) are any unwanted influence of the investigator on the research outcome. This can include ‘expectancy effects’ and ‘unconscious cues’, whereby small signals (an expression or gesture) indicate something to the participant. It can also include actions of the researcher relating to the study’s design, such as selection of participants, materials, instructions etc. Leading questions are also an example of the power of investigator effects.

Population

A group of people who are the focus of the researcher’s interest, from which smaller sample is drawn.

Sample

A group of people who take part in a reasearch investigation. The sample is drawn from a target population and is presumed to be representative of that population.

Bias

When certain groups are over or under represented within the sample selected. For example, too many young people in a sample. This limits the extent to which generalisations can be made to the target population.

Generalisation

The extent to which findings and conclusions from a particular investigation can be broadly applied to the population. This is possible if the sample of participants is representative of the target population.

Random sample

Create a sampling frame, which is a list of everyone in a target population

Each participants are given a number, they are then selected entirely by chance through lottery methods.

Pros : Reduces bias, which means that confounding variables or extraneous variables can be equally divided between different groups, enhancing internal validity

Cons : Can be difficult and time consuming to conduct. A complete list of the target population may be extremely difficult to obtain. In addition, selected participants may refuse to take part.

Systematic sample

Participants are selected with a fixed numerical system in a sampling frame. For example, they might select every 10th person on the list.

Pros : Highly objective and unbiased because the researcher has no influence over who is chosen

Cons : Time consuming and participants may refuse to take parts

Opportunity sample

The researcher selects people who are available and willing to take part at the time the research is being conducted

Pros : convenient, saves time and money

Cons : unrepresentative so cannot be generalised

Stratified sample

The researcher divides the population into subgroups based on relevant characteristics, such as gender or age. The sample has the same proportions as the population. Participants are then selected randomly from each stratum.

Pros : representative because it is designed to accurately reflect the composition of the population. This means that generalisation of finding becomes possible.

Cons : the identified strata cannot reflect all the ways that people are different, so complete representation of the target population is not possible

Volunteer sample

Participants choosing to be part of a sample. The researcher could use advertisement

Pros : less time consuming and participants may be more coorperative

Cons : volunteer bias