1. BY MUZZAMMIL MOHYUDDIN
AS PSYCHOLOGY (9990)
BY MUZZAMMIL MOHYUDDIN

2. Experiments
a scientific procedure undertaken to make a discovery, test a hypothesis, or demonstrate a known fact.

3. Experimental Method
The prime method of inquiry in science is the experiment. The key features are control over variables, careful measurement, and establishing cause and effect relationships.
An experiment is an investigation in which a hypothesis is scientifically tested. In an experiment, an independent variable (the cause) is manipulated and the dependent variable (the effect) is measured; any extraneous variables are controlled.
An advantage is that experiments should be objective. The views and opinions of the researcher should not affect the results of a study. This is good as it makes the data more valid, and less bias.

4. . Laboratory / Controlled Experiments
This type of experiment is conducted in a well-controlled environment – not necessarily a laboratory – and therefore accurate measurements are possible.
The researcher decides where the experiment will take place, at what time, with which participants, in what circumstances and using a standardized procedure. Participants are randomly allocated to each independent variable group.
An example is Milgram’s experiment on obedience or Loftus and Palmer's car crash study.
Strength: It is easier to replicate (i.e. copy) a laboratory experiment. This is because a standardized procedure is used.
Strength: They allow for precise control of extraneous and independent variables. This allows a cause and effect relationship to be established.
Limitation: The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e. low ecological validity. This means it would not be possible to generalize the findings to a real life setting.
Limitation: Demand characteristics or experimenter effects may bias the results and become confounding variables.

5.  Field Experiments
Field experiments are done in the everyday (i.e. real life) environment of the participants. The experimenter still manipulates the independent variable, but in a real-life setting (so cannot really control extraneous variables).
An example is Holfing’s hospital study on obedience.
Strength: Behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e. higher ecological validity than a lab experiment.
Strength: There is less likelihood of demand characteristics affecting the results, as participants may not know they are being studied. This occurs when the study is covert.
Limitation: There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

6. Natural Experiments
Natural experiments are conducted in the everyday (i.e. real life) environment of the participants, but here the experimenter has no control over the IV as it occurs naturally in real life.
For example, Hodges and Tizard's attachment research (1989) compared the long term development of children who have been adopted, fostered or returned to their mothers with a control group of children who had spent all their lives in their biological families.
Strength: Behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e. very high ecological validity.
Strength: There is less likelihood of demand characteristics affecting the results, as participants may not know they are being studied.
Strength: Can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g. researching stress.
Limitation: They may be more expensive and time consuming than lab experiments.
Limitation: There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

7. Experiment Terminology

8. Ecological validity
The degree to which an investigation represents real-life experiences.
Experimenter effects
These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.
Demand characteristics
The clues in an experiment that lead the participants to think they know what the researcher is looking for (e.g. experimenter’s body language).
Independent variable (IV)
Variable the experimenter manipulates (i.e. changes) – assumed to have a direct effect on the dependent variable.
Dependent variable (DV)
Variable the experimenter measures. This is the outcome (i.e. result) of a study.
Extraneous variables (EV)
All variables, which are not the independent variable, but could affect the results (DV) of the experiment. EVs should be controlled where possible.

9. Confounding variables
Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.
Random Allocation
Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.
The principle of random allocation is to avoid bias in the way the experiment is carried out and to limit the effects of participant variables.
Order effects
Changes in participants’ performance due to their repeating the same of similar test more than once. Examples of order effects include:
(i) practice effect: an improvement in performance on a task due to repetition, for example because of familiarity of the task;
(ii) fatigue effect: a decrease in performance of a task due to repetition, for example because of boredom or tiredness.

10. Research Variables

11. A variable is anything that can vary, i. e
A variable is anything that can vary, i.e. changed or be changed, such as memory, attention, time taken to perform a task, etc. Variable are given a special name that only applies to experimental investigations. One is called the dependent variable and the other the independent variable.
In an experiment, the researcher is looking for the possible effect on the dependent variable that might be caused by changing the independent variable. 
• Independent variable (IV): Variable the experimenter manipulates (i.e. changes) – assumed to have a direct effect on the dependent variable.
• Dependent variable (DV): Variable the experimenter measures, after making changes to the IV that are assumed to affect the DV.
For example, we might change the type of information (e.g. organized or random) given to participants to see what effect this might have on the amount of information remembered.
In this particular example the type of information is the independent variable (because it changes) and the amount of information remembered is the dependent variable (because this is being measured).

12. Operationalising Variables
It is very important in psychological research to clearly define what you mean by both your IV and DV.
Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study.
For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.
Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15 minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room’.

13. In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define "young", “old” or "memory"? "Participants aged between will recall significantly more nouns from a list if twenty than participants aged between " is operationalized.
The key point here is that we have made it absolutely clear what we mean by the terms as they were studied and measured in our experiment. If we didn’t do this then it would be very difficult (if not impossible) to compare the findings of different studies into the same behavior.
Operationalization has the great advantage that it generally provides a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability.

14. Extraneous Variables
When we conduct experiments there are other variables that can affect our results, if we do not control them. The researcher wants to make sure that it is the manipulation of the independent variable that has changed the changes in the dependent variable.
Hence, all the other variables that could affect the DV to change must be controlled. These other variables are called extraneous or confounding variables.
Extraneous variables – These are all variables, which are not the independent variable, but could affect the results (e.g. dependent variable) of the experiment.
Extraneous variables should be controlled were possible. They might be important enough to provide alternative explanations for the effects.

15.  Four types of extraneous variables:
1. Situational Variables
These are aspects of the environment that might affect the participant’s behavior, e.g. noise, temperature, lighting conditions, etc. Situational variables should be controlled so they are the same for all participants.
Standardized procedures are used to ensure that conditions are the same for all participants. This includes the use of standardized instructions
2. Participant / Person Variable
This refers to the ways in which each participant varies from the other, and how this could affect the results e.g. mood, intelligence, anxiety, nerves, concentration etc.
For example, if a participant that has performed a memory test was tired, dyslexic or had poor eyesight, this could effect their performance and the results of the experiment. The experimental design chosen can have an affect on participant variables.
Situational variables also include order effects that can be controlled using counterbalancing, such as giving half the participants condition 'A' first, while the other half get condition 'B' first. This prevents improvement due to practice, or poorer performance due to boredom.
Participant variables can be controlled using random allocation to the conditions of the independent variable.

16. 3. Experimenter / Investigator Effects
The experimenter unconsciously conveys to participants how they should behave - this is called experimenter bias.
The experiment might do this by giving unintentional clues to the participants about what the experiment is about and how they expect them to behave. This affects the participants’ behavior.
The experimenter is often totally unaware of the influence which s/he is exerting and the cues may be very subtle but they may have an influence nevertheless.
Also, the personal attributes (e.g. age, gender, accent, manner etc.) of the experiment can affect the behavior of the participants.
4. Demand Characteristics
these are all the clues in an experiment which convey to the participant the purpose of the research.
Participants will be affected by: (i) their surroundings; (ii) the researcher’s characteristics; (iii) the researcher’s behavior (e.g. non-verbal communication), and (iv) their interpretation of what is going on in the situation.
Experimenters should attempt to minimize these factors by keeping the environment as natural as possible, carefully following standardized procedures. Finally, perhaps different experimenters should be used to see if they obtain similar results.

17. Correlation

18. Correlation means association - more precisely it is a measure of the extent to which two variables are related.
If an increase in one variable tends to be associated with an increase in the other then this is known as a positive correlation. An example would be height and weight. Taller people tend to be heavier.
If an increase in one variable tends to be associated with a decrease in the other then this is known as a negative correlation. An example would be height above sea level and temperature. As you climb the mountain (increase in height) it gets colder (decrease in temperature).
When there is no relationship between two variables this is known as a zero correlation. For example their is no relationship between the amount of tea drunk and level of intelligence. .

19. When you draw a scattergram it doesn't matter which variable goes on the x-axis and which goes on the y-axis. Remember, in correlations we are always dealing with paired scores, so the values of the 2 variables taken together will be used to make the diagram. Decide which variable goes on each axis and then simply put a cross at the point where the 2 values coincide.
Strictly speaking correlation is not a research method but a way of analysing data gathered by other means. This might be useful, for example, if we wanted to know if there were an association between watching violence on T.V. and a tendency towards violent behaviour in adolescence (Variable B = number of incidents of violent behaviour observed by teachers).
Another area where correlation is widely used is in the study of intelligence where research has been carried out to test the strength of the association between the I.Q. levels of identical and non-identical twins.

20. Some uses of Correlations
Prediction
If there is a relationship between two variables, we can make predictions about one from another.
Validity
Concurrent validity (correlation between a new measure and an established measure).
Reliability
Test-retest reliability (are measures consistent).
Inter-rater reliability (are observers consistent).
Theory verification
Predictive validity.

21. Differences between Experiments and Correlations
An experiment isolates and manipulates the independent variable to observe its effect on the dependent variable, and controls the environment in order that extraneous variables may be eliminated. Experiments establish cause and effect.
A correlation identifies variables and looks for a relationship between them. An experiment tests the effect that an independent variable has upon a dependent variable but a correlation looks for a relationship between two variables.
This means that the experiment can predict cause and effect (causation) but a correlation can only predict a relationship, as another extraneous variable may be involved that it not known about.

22. Strengths of Correlations
1. Correlation allows the researcher to investigate naturally occurring variables that maybe unethical or impractical to test experimentally. For example, it would be unethical to conduct an experiment on whether smoking causes lung cancer.
2. Correlation allows the researcher to clearly and easily see if there is a relationship between variables. This can then be displayed in a graphical form
Limitations of Correlations
1. Correlation is not and cannot be taken to imply causation. Even if there is a very strong association between two variables we cannot assume that one causes the other.
For example suppose we found a positive correlation between watching violence on T.V. and violent behavior in adolescence. It could be that the cause of both these is a third (extraneous) variable - say for example, growing up in a violent home - and that both the watching of T.V. and the violent behavior are the outcome of this.
2. Correlation does not allow us to go beyond the data that is given. For example suppose it was found that there was an association between time spent on homework (1/2 hour to 3 hours) and number of G.C.S.E. passes (1 to 6). It would not be legitimate to infer from this that spending 6 hours on homework would be likely to generate 12 G.C.S.E. passes

23. Aims and Hypotheses

24. Aim Types of Hypotheses
An aim identifies the purpose of the investigation. It is a straightforward expression of what the researcher is trying to find out from conducting an investigation.
The aim typically involves the word “investigate” or “investigation”.
For example:
Milgram (1963) investigated how far people would go in obeying an instruction to harm another person.
Bowlby (1944) investigated the long-term effects of maternal deprivation.
Types of Hypotheses
A hypothesis (plural hypotheses) is a precise, testable statement of what the researchers predict will be the outcome of the study.
This usually involves proposing a possible relationship between two variables: the independent variable (what the researcher changes) and the dependant variable (what the research measures).
In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

25. Briefly, the hypotheses can be expressed in the following ways:
The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). It states results are due to chance and are not significant in terms of supporting the idea being investigated.
The alternative hypothesis states that there is a relationship between the two variables being studied (one variable has an effect on the other). It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.
In order to write the experimental and null hypotheses for an investigation, you need to identify the key variables in the study. A variable is anything that can change or be changed, i.e. anything which can vary. Examples of variables are intelligence, gender, memory, ability, time etc.
A good hypothesis is short and clear should include the operationalized variables being investigated.

26. For Example
Let’s consider a hypothesis that many teachers might subscribe to: that students work better on Monday morning than they do on a Friday afternoon (IV=Day, DV=Standard of work).
Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and on a Friday afternoon and then measuring their immediate recall on the material covered in each session we would end up with the following:
The experimental hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
The null hypothesis states that these will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.
The null hypothesis is, therefore, the opposite of the experimental hypothesis in that it states that there will be no change in behavior.
At this point you might be asking why we seem so interested in the null hypothesis. Surely the alternative (or experimental) hypothesis is more important?
Well, yes it is. However, we can never 100% prove the alternative hypothesis. What we do instead is see if we can disprove, or reject, the null hypothesis.
If we can’t reject the null hypothesis, this doesn’t really mean that our alternative hypothesis is correct – but it does provide support for the alternative / experimental hypothesis.

27. One tailed or two tailed 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.
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.