1. AS Research Methods
Revision

2. Specification

3. Methods and techniques

4. The experimental method
Experiments are a particular type of research method where the IV is manipulated to investigate whether it has an effect on the DV.
In a ‘true’ experiment, extraneous variables are controlled and cause and effect relationships can be established.

5. Types of experiment
Laboratory experiments - Lab experiments don’t have to be carried out in a laboratory.  However, any experiment that is carried out in a special, tightly controlled environment is classed as laboratory.  Importantly, it is obvious to those taking part that that they are in an experimental procedure. 
Field experiments - An experiment (where the IV is manipulated) that is carried out in a natural setting, e.g. a school if learning behaviour is being studied.
Natural experiments – NOT necessarily in a natural setting (although they can be). This type of experiment is so called because it takes advantage of a naturally occurring IV, e.g. in Rutter’s study, the age at which the children were adopted.

6. Evaluation of laboratory experiments
STRENGTHS
High level of control over extraneous variables = high internal validity.
Easier to replicate than other types of experiment.
Can establish cause and effect relationships.
LIMITATIONS
Artificial environment = low ecological validity.
High risk of demand characteristics = lowered internal validity.

7. Evaluation of field experiments
STRENGTHS
Real life setting = good ecological validity = can generalise to real life settings.
Lower risk of demand characteristics than other types of experiment = higher internal validity.
Can establish cause and effect relationships because IV is manipulated.
LIMITATIONS
Higher risk of extraneous variables (than laboratory experiments) due to real life setting = lower internal validity.
Difficult to replicate = cannot check reliability easily.
Can be more expensive than other types of experiment due to cost of moving equipment to the ‘field’.

8. Evaluation of natural experiments
STRENGTHS
The most ethical type of experiment because the IV is not manipulated – sometimes the only one suitable (e.g. child abuse).
LIMITATIONS
Not a ‘true’ experiment because the IV is not manipulated = cause and effect cannot be established.
Extraneous variables are not controlled – lower internal validity.
Very difficult/impossible to replicate = cannot check reliability of findings.

9. Correlational analysis
A correlation is a relationship between two variables. A correlational design is a way to test the relationship between two variables. A participant provides data for both.
A correlational design allows us to test whether 2 or more phenomena (e.g. age and shoe size) are related, and if so how strongly.
In a correlational design, there are no independent or dependant variables, but co-variables.

10. Evaluation of correlational analysis
STRENGTHS
Allows us to investigate areas where an experiment would be unethical e.g. stress or privation.
Can be used as a preliminary research too, to investigate a relationship before an experimental method is used.
LIMITATIONS
Not an experimental method so cause and effect cannot be established, because we cannot say which variable is affecting the other, or if a third variable is affecting both
For example, does shyness cause depression or does depression cause shyness?
Alternatively, is there a third variable (e.g. bullying in childhood) that causes both?

11. Observational techniques
Naturalistic observations - People or animals are observed in their natural environment, without any sort of intervention or manipulation of variables.
Controlled observations - As the name suggests, the researcher in some way manipulates the behaviour of the observers or the observed.  Ainsworth’s Strange Situation is a good example, with researchers organising the behaviour of the mother and stranger to see how the child reacts. 

12. Observational techniques
Covert (undisclosed) observations –Participants know they are being observed.  This reduces ethical issues of consent and privacy but reduces validity due to increased demand characteristics.
Overt (disclosed) observations - Participants are unaware of the observation.  This raises ethical issues (privacy and consent) but increases validity by reducing demand characteristics. 

13. Evaluation of observational techniques
A naturalistic, covert observation can be extremely high in internal and ecological validity, due to no risk of demand characteristics and behaviour occurring in a real life environment.
On the other hand, a structured, overt observation may be no more valid than a laboratory experiment because participants may not behave as they would naturally.
Not an experimental technique, so cause and effect cannot be established.

14. Self report techniques
A self report is any method of data collection which involves asking a participant about their feelings, attitudes, beliefs and so on.
Questionnaires and interviews are the most common self report techniques.
One of the biggest problems with data collected in this way is that people may not answer honestly because they want to be seen in a good light (social desirability bias).  

15. Questionnaires and interviews
Questionnaires are a type of self report method which consist of a set of questions, usually in a highly structured written form.
Questionnaires can contain both open questions and closed questions and participants usually record their own answers.
Interviews are a type of spoken questionnaire where the interviewer records the responses. Interviews can be structured, whereby there is a predetermined set of questions or unstructured, whereby no questions are decided in advance.
In a semi structured interview, some questions are pre-planned, but the course of the interview can be changed to suit the needs of the interviewer (e.g. to allow him/her to explore areas of interest that arise).

16. Evaluation of questionnaires
STRENGTHS
Cheap and easy to distribute, so lots of people can be tested quickly = larger potential sample = improved population validity.
If closed questions are used, data can be analysed quantitatively – quicker and cheaper.
Can be anonymous – no ethical issues regarding confidentiality and less chance of social desirability bias reducing internal validity.
LIMITATIONS
Low return rate = sample may not be representative = lower population validity.
Care must be taken to ensure that questions are well designed = unambiguous and not leading, otherwise results may not be valid.
Does not provide as rich data as interviews – not all views may be represented by closed questions.
Social desirability bias = participants will not answer truthfully = lower internal validity.

17. Evaluation of interviews
STRENGTHS
Generates a large amount of rich data = improved internal validity.
Semi/unstructured interviews may allow interviewer to change questions to explore areas of interest = increased internal validity.
Difficult to replicate unless structured = may not allow comparison.
LIMITATIONS
Can be time consuming and costly to conduct interview and analyse data, particularly if open questions used.
Cost of training interviewers makes it more expensive than questionnaires.
Social desirability bias may be more of a problem than questionnaires because an interview is likely to be face to face.

18. Case studies
A case study is an in depth study of a person or a small group of people (e.g. Genie or Clive Wearing).
It can be short term, but is often carried out over a long period of time (longitudinal).
A case study can use a variety of methods, such as questionnaires, interviews and observations, and may involve friends and family of those being studied, as well as the participant themselves.

19. Evaluation of case studies
STRENGTHS
They provide a wide variety of in-depth and detailed information that would be impossible to acquire using heavily controlled situations such as experiments.  
A case study may sometimes be the only ethical way of studying a particular phenomenon or situation.
Gathering data from more than one source (e.g. interviews with child, parents and teachers) means it can be compared and reliability can be claimed if data seems to verify each other.
LIMITATIONS
Low population validity – can’t be generalised because limited sample.
Often use retrospective data (looking back on the past) which may be unreliable due to poor memory.
Researcher bias may occur due to high degree of involvement with the participant (e.g. Susan Curtiss with Genie).
Time consuming and expensive.
Ethical issues – confidentiality can be an issue so pseudonyms are often used to protect the identity of participants.

20. Investigation design

21. Aims The aim is a general statement of the area of investigation.
When carrying out a piece of research it is essential that you have an aim in mind.  This needs to be reasonably precise, for example ‘I’m gonna study memory’ would not be sufficiently precise. 
However, the aim is broader, or less precise than a hypothesis.  A suitable aim for memory might be ‘to see if age affects the duration of STM.’ 

22. Hypotheses
A testable, predictive statement.   This statement is tested by researchers to see if it is true.  
The hypothesis either states a predicted difference between an independent and dependent variable (an experimental hypothesis), or it states a predicted relationship between variables (in the case of a correlational analysis). 
Hypothesis should be fully operationalised (more on this later).

23. Hypotheses A hypothesis can be either directional or non directional.
Directional – a directional hypothesis states the direction of the predicted difference e.g. teenagers will sleep for significantly more hours in a week more than adults aged
Non directional – a non directional predicts a difference between two conditions but does not specify in which direction the difference will be e.g. there will be a significance difference between teenagers and adults aged in the number of hours they sleep in a week.

24. Hypotheses
Generally, studies which are repeating research that have been carried out before (or similar research) will have some idea of what they expect to find and therefore they will choose a DIRECTIONAL hypothesis. If this is new research, or if the psychologist is unsure of what the affect will be, they will choose a NON DIRECTIONAL one.

25. Experimental design There are three types of experimental design:
Independent groups – two separate groups of participants participate in one condition of the IV each.
Repeated measures – the same group of participants take part in both conditions of the IV, one after the other.
Matched pairs – two separate groups, matched in terms of important characteristics (e.g. eyesight on a visual memory test) each take part in one condition of the IV.

26. Evaluation of experimental designs
There are four main factors to consider when asked to evaluate an experimental design:
Time and cost – How long will take to find suitable participants? The longer the time, the more expensive this method will be.
Demand characteristics – In a repeated measures design, the participants may guess the aims of the research more easily because they take part in both conditions of the IV.
Individual differences - In an independent groups design, there may be chance differences between the groups that can affect the DV. E.g. differences in eyesight on a memory test shown visually.

27. Evaluation of experimental designs
Order effects - In a repeated measure design, the effects that the order of presenting the conditions has on the dependent variable. Order effects may occur due to either:
Practice effect (improvement in performance due to repeated practice with a task)
Fatigue effect (decline in performance as the research participant becomes tired or bored while performing a sequence of tasks).  

28. Evaluation of independent groups design
STRENGTHS
No risk of order effects because each person only takes part in one condition of the IV.
Lower risk of demand characteristics than repeated measures because each participant only takes part in one condition of the IV, so is less likely to guess the aim of the experiment (than with repeated measures)
LIMITATIONS
Twice as many participants needed as repeated measures = higher cost.
Individual differences between groups may be an extraneous variable. For example, if one group is coincidentally more intelligent.

29. Evaluation of a repeated measures design
STRENGTHS
Fewer participants needed than independent groups – cheaper and easier.
No risk of individual differences between groups being an extraneous variable because same participants used in both conditions of IV.
LIMITATIONS
Order effects can be a problem and should be controlled by counterbalancing (splitting the sample in half and putting one half in condition A first and the other in condition B first – ABBA).
Demand characteristics are more likely than in other designs because P’s take part in both conditions of the IV so more likely to guess the aim.

30. Evaluation of matched pairs design
STRENGTHS
Less risk of individual differences between groups being an extraneous variable because groups are matched on important characteristics.
No risk of order effects because each person only takes part in one condition of the IV.
Lower risk of demand characteristics than independent groups because each person only takes part in one condition of the IV.
LIMITATIONS
The most time consuming and expensive sampling method because each participant must be matched with one in the other condition of the IV.

31. Design of naturalistic observations
IDENTIFY YOUR AIM
‘to investigate….’
DECIDE YOUR BEHAVIOURAL CATEGORIES
You must be objective, cover all possible component behaviours and be mutually exclusive
DECIDE ON YOUR SAMPLING PROCEDURE
You can’t observe everything! Choose from event, time or point sampling.
DECIDE HOW TO RECORD YOUR DATA- A CODING SYSTEM
You can create a ‘behavioural checklist’ with a code for each behaviour, or create a tally chart.
CONSIDER THE RELIABILITY ISSUE
You can overcome bias with ‘inter observer reliability’ by having more than one observer using the same coding system.
CONSIDER ETHICS
Not only the issues but also how to overcome them!

32. Quantitative and qualitative data
One of the key decisions to be made when designing a psychological study is whether you want to produce quantitative or qualitative data (or a mixture of both).
Quantitative data is numerical e.g. time in seconds, ratings on a stress scale.
Qualitative data is non numerical e.g. verbal accounts of how participants feel about something.

33. Design of questionnaires
Questions to consider:
Qualitative or quantitative data ?
Open or closed questions?
Are questions clear? (not ambiguous)
If closed, have you provided a suitable range of options?
Have you eliminated biased or leading questions?

34. Design of interviews Questions to consider:
Qualitative or quantitative data?
Development of interview schedule - structured, semi-structured or unstructured?

35. Variables A variable is simply something that can be varied (changed).
In experiments, the independent variable (IV) is manipulated (changed by the researcher) to investigate whether it has an effect on the dependent variable (DV).
IV= what is changed.
DV= what is found.

36. Operationalisation of variables
Operationalisation of variables means making them measurable/quantifiable.
For instance, we can’t really measure ‘happiness’ but we can measure how many times a person smiles within a two hour period.
By operationalising variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

37. Pilot studies
A pilot study is a small scale version of the actual study that is carried out to identify and resolve any problems with the design of the study. This should save time and money.
Examples of problems that might be identified include:
Unrepresentative sample
Problems with the questions used (interviews and questionnaires)
Low inter-observer reliability (observations)

38. Extraneous variables
An extraneous variable is anything other than the IV that could have an effect on the DV.
It is important that extraneous variables are controlled so that they do not affect (confound) the results.
Participant variables – those connected with the research participants e.g. intelligence, age and gender.
Situational variables – those connected with the research setting – e.g. temperature, time of day, lighting.

39. Control of extraneous variables
Participant variables can be controlled by careful selection of participants, and matching on important characteristics (matched pairs).
Situational variables can be controlled by standardising procedures, for example, ensuring that the room temperature is the same for each condition.

40. Reliability
Reliability is the extent to which research is consistent. It can be either internal or external:
Internal: the extent to which a measure is consistent within itself e.g. if all questions on a questionnaire measure the same thing.
External: the extent to which a measure is consistent over time. This means that it should produce the same results each time it is carried out.
Inter rater/observer: the extent to which all raters in an observation agree.

41. Validity
Internal validity is the extent to which we are measuring what we intend to (e.g. do intelligence tests really measure intelligence?)
External validity is concerned with the extent to which our findings are generalisable. It can be divided into:
Ecological validity – the extent to which the findings can be generalised to other situations.
Population validity – the extent to which the findings can be generalised to other groups of people.

42. BPS ethical guidelines
Protection of participants – P’s should experience no more distress than is likely in every day life.
Confidentiality – Data must be stored securely and identifying details should be removed.
Informed consent – P’s should be given enough information to be able to decide whether they want to take part.
Deception – Where possible, P’s should not be deceived about the purpose of the study.
Right to withdraw – P’s should be able to withdraw themselves and their data from the study at any time.
Debrief – P’s should be told the purpose of the study when it is complete, and be given the opportunity to ask questions. They may also be given advice/offered counselling if appropriate. A full debrief is particularly important if P’s have been deceived as to the purpose of the study.

43. Dealing with ethical issues
An ethical issue occurs when there is a dilemma between what the researcher wants to do and the rights and dignity of the participants.
Some ways that ethical issues can be dealt with are:
Debriefing can deal with a lot of ethical issues such as deception and protection of participants.
Informed consent for children (under 16) can be gained from parents.
Prior general consent can be gained to deal with deception and informed consent (agreeing to take part in a study but not being fully aware of the details/aims of the research.

44. Sampling techniques
The sampling technique is the method used to acquire participants for your study. There are three common techniques:
Opportunity sampling – Simply selecting those people that are available at the time. E.g. going up to people in cafés and asking them to be interviewed
Volunteer sampling – Also called a self-selecting sample. Individuals who have chosen to be involved in a study. E.g. people who responded to an advert for participants
Random sampling - Every member of a population has an equal chance of being selected, e.g. pulling names out of a hat.

45. Evaluation of opportunity sampling
STRENGTHS
Cheap and easy to get a good sample size = improved population validity
LIMITATIONS
Unlikely to be representative of the target population, so results may not be generalisable (e.g. if all white male college students used).

46. Evaluation of volunteer sampling
STRENGTHS
Cheap and easy to get a good sample size = improved population validity.
LIMITATIONS
A certain type of person is likely to volunteer for studies (e.g. interested in psychology, has time to spare, etc) so the sample is unlikely to be representative of the target population = less likely to be generalisable.

47. Evaluation of random sampling
STRENGTHS
The most representative sampling method = should be able to generalise findings to target population.
LIMITATIONS
More complicated and time consuming than other methods = more expensive.

48. Demand characteristics
Demand characteristics occur when the participants changes their behaviour because they know they are taking part in an investigation. There are two ways this could affect behaviour:
Social desirability bias - the tendency to provide socially desirable rather than honest answers on questionnaires and in interviews because of the desire to be viewed favourably by the researcher.
The ‘screw you’ effect – this is where the participant deliberately acts in a way to bias the experiment or invalidate the results.

49. Investigator effects
Investigator effects occur when the person carrying out the research (the investigator) influences the results in some way. This could be deliberate or accidental.
For example, even when using standardised instructions, the investigator may talk in a different tone of voice or make more eye contact with one group, thus influencing their behaviour and/or answers.

50. Data analysis and presentation

51. Presentation of quantitative data
Charts and graphs are useful for summarising data and making it easier to understand.
You need to be able:
Select and draw an appropriate graph for the data given.
Correctly label both axis.
Interpret information from graphs.

52. Bar charts
Bar charts are used for plotting discrete (or 'discontinuous') data. The types of data do not overlap in any way. E.g. types of birds, breeds of dog

53. Histograms
Similar to a bar chart, but the x-axis does not show discrete data; it is continuous. There are no gaps between the bars.

54. Frequency polygons
Very similar to a histogram. It has continuous variables on the x-axis and a continuous line connecting the points instead of bars

55. Tables
Putting data into a table can make it easier to see patterns or problems within the data. You should be able to interpret data presented in tables.

56. Analysis of correlational data
Correlations can be both positive and negative:
A positive correlation means that as one variable increases, the other also increases.
A negative correlation means that as one variable increases, the other decreases.
The strength of the correlation can be seen by looking at the data plotted on a scattergram (next slide).

57. Analysis of correlational data

58. Analysis of correlational data
After looking at the scattergram, if we want to be sure that a significant relationship does exist between the two variables, a statistical test can be conducted (you don’t have to worry about these until next year).
The test will give us a score, called a correlation coefficient. This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables.
This value can be both positive e.g or negative

59. Presentation of qualitative data
Qualitative data involved people’s meanings, experiences and descriptions. It is particularly good for researching attitudes, opinions and beliefs.
Data usually consists of verbal or written descriptions.
Most qualitative methods look for common themes within the data.
These may be illustrated with the use of direct quotes.

60. Content analysis
Content analysis is a very simple form of analysing qualitative data by converting it into a quantitative form by counting themes within the data.
Identify the material to be studied (e.g. interviews, adverts, etc.)
Identify coding units (categories/themes)
Count the number of times that each theme occurs in the data – using tally chart.

61. Evaluation of content analysis
STRENGTHS
A very simple and relatively quick method of analysing qualitative data.
LIMITATIONS
Some of the detail and richness of the data may be lost in converting to qualitative categories.
The research could be biased in their interpretation of the data – this can be reduced by having several raters and checking for inter-rater reliability.