Independent Measures Design
An independent measures design consists of using different participants for each condition of the experiment. If two groups in an experiment consist of different individuals then this is an independent measures design.
This type of design has an advantage resulting from the different participants used in each condition - there is no problem with order effects
The most serious disadvantage of independent measures designs is the potential for error resulting from individual differences between the groups of participants taking part in the different conditions. Also an independent groups design may represent an uneconomic use of those participants, since twice as many participants are needed to obtain the same amount of data as would be required in a two-condition repeated measures design.
Repeated Measures Design
A repeated measures design consists of testing the same individuals on two or more conditions
The key advantage of the repeated measures design is that individual differences between participants are removed as a potential confounding variable. Also the repeated measures design requires fewer participants, since data for all conditions derive from the same group of participants.
The design also has its disadvantages. The range of potential uses is smaller than for the independent groups design. For example, it is not always possible to test the same participants twice.
There is also a potential disadvantage resulting from order effects, although these order effects can be minimised. Order effects occur when people behave differently because of the order in which the conditions are performed. For example, the participant’s performance may be enhanced because of a practice effect, or performance may be reduced because of a boredom or fatigue effect.
Order effects act as a confounding variable but can be reduced by using counterbalancing. If there are two conditions in an experiment the first participant can do the first condition first and the second condition second. The second participant can do the second condition first and the first condition second and so on. Therefore any order effects should be randomised.
When carrying out experiments it is expected that the researcher will start with a hypothesis.
A hypothesis is a testable, predictive statement. The hypothesis will state what the researcher expects to find out. For example, participants who are tested at 10am will perform significantly better on a memory test than participants who are tested at 10pm.
It is important that the independent and dependent variables are clearly stated in the hypothesis.
When a hypothesis predicts the expected direction of the results it is referred to as a one-tailed hypothesis. For example the hypothesis above is stating that participants will perform better in the morning than the evening and is therefore a one-tailed hypothesis.
When a hypothesis does not predict the expected direction of the results it is referred to as a two-tailed hypothesis. For example a two tailed hypothesis might be that there will be a difference in performance on a memory test between participants who are tested at 10am and participants who are tested at 10pm
The hypothesis that states the expected results is called the alternate hypothesis because it is alternative to the null hypothesis. When conducting an experiment it is important that we have an alternate hypothesis and a null hypothesis. The null hypothesis is not the opposite of the alternate hypothesis it is a statement of no difference. A null hypothesis might be that there will be no significant difference on the performance on a memory test between participants who are tested at 10am and participants whom are tested at 10pm.
The reason we have a null hypothesis is that the statistical tests that we use are designed to test the null hypothesis.
However laboratory experiments are not always typical of real life situations. These types of experiments are often conducted in strange and contrived environments and the participants mat be asked to carry out unusual tasks. The behaviour of the participants may be distorted and not be like behaviour that would be carried out in the real world. Therefore, it should be difficult to generalise findings from experiments because they are not usually ecologically valid (true to real life).
A further difficulty with the experimental method is demand characteristics. Demand characteristics are all the cues which convey to the participant the purpose of the experiment. If a participant knows they are in an experiment they may seek cues about how they think they are expected to behave.
Another problem with the experimental method concerns ethics. For example, experiments often involve deceiving participants to some extent. However, it is possible to obtain a level of informed consent from participants. That is, the experimenter can provide information about what is going to happen without giving away the full aim of the study. This helps participants decide if they really want to take part.
It is recommended that participants in experiments are effectively debriefed and that the participants are clear that they can withdraw from the study at any time. It is important to recognise that there are very many areas of human life which cannot be studied using the experimental method because it would be simply too unethical to do so.
Before researchers carry out experiments they operationalise the variables and create hypotheses. A hypothesis is a testable, predictive statement.
Experiments produce quantitative data which can be analysed statistically. For this part of the course you need to be aware of descriptive statistics including measure of central tendency and bar charts.