Research Methods Psychology Pack: Year 1, Exams of Psychology

Download Research Methods Psychology Pack: Year 1 and more Exams Psychology in PDF only on Docsity!

Research Methods

Psychology Pack:

Year 1

Contents Page

• Revision Checklist/ Specification Page 3-
• Experimental methods (Aims, experimental and correlational hypothese, IV/DV ) Page 8 -
• Operationalisation Page
• Experimental methods: lab and field experiments Page
• Experimental methods: Quasi and Natural experiments Page
• Control of variables Page
• Reliability and validity Page
• Experimental design Page 1 6 -
• Non experimental methods: Self report – Interviews & questionnaires (inc. designing) Page 1 8 -
• Non experimental methods: Observational techniques (inc. designing) Page 22-
• Non experimental methods: Case studies Page
• Non experimental methods: Correlation Page 27-
• Non experimental methods: Content and Thematic Analysis Page
• Sampling Page 31 -
• Pilot studies and Ethics Page 34-
• Peer Review Page 36 -
• Psychology & the economy Page
• Qualitative & quantitative data Page
• Primary & secondary data and meta-analysis Page
• Descriptive Statistics Page 40 -
• Presentation & display of data Page 43 -
• Mathematical content (Percentages, decimals, fractions, ratios) Page 47 -
• Statistical testing: The sign test Page 50 -
• Design a study Page 54-

Correlational analysis

 What is a correlational study?

 What is the difference between correlations and experiments

 What is a positive/negative correlation?

 What are the disadvantages of a correlational study?

 What are the disadvantages of correlational study?

 How to design a correlation

Observational techniques

 What is an observational study?

 What is a naturalistic observation? (+advantages and disadvantages)

 What is a controlled observation? (+advantages and Disadvantages)

 What is a covert and overt observation? (+ advantages and disadvantages)

 What is a participant and non-participant observation? (+ advantages and

disadvantages)

 How to design an observation

Self report techniques

 What is a questionnaire? (+ advantages and disadvantages)

 What is an interview?

 What is a structured interview? (+ advantages and disadvantages)

 What is an unstructured interview? (+ advantages and disadvantages)

 How to design questionnaires and interviews.

Case studies

 What is a case study?

 What are the advantages of case studies?

 What are the disadvantages of case studies?

Content analysis

 What is a content analysis?

 What are the advantages of a content analysis?

 What are the disadvantages of a content analysis?

Investigation design

 Aims

• stating aims,
• difference between aims and hypotheses

 Hypotheses

• Directional/one tailed hypothesis
• Non-directional/ two tailed hypothesis
• Null hypothesis
• Operationalised hypotheses

 Sampling

• The difference between population and sample
  • Opportunity sample (+advantages and disadvantages)
  • Random sample (+advantages and disadvantages)
  • Volunteer sample (+advantages and disadvantages)
  • Systematic sample(+advantages and disadvantages)
  • Stratified sample (+advantages and disadvantages)

 Pilot studies and the aims of pilot studies

 Experimental design

• Independent groups (+advantages and disadvantages)
• Repeated measures (+advantages and disadvantages)
  • Matched pairs (+advantages and disadvantages)

 Observational design

• Time and event sampling (+ advantages and disadvantages) - Behavioural categories? (you must be able to create operationalised behavioural categories)

 Questionnaire construction

• The use of open and closed questions (+advantages and disadvantages)

 Designing interviews

 Variables

• Independent variable
• Dependent variable
• Co-variables (correlation)
• Operationalisation of variables
• Extraneous variables (and how they can be controlled)
• Confounding variables (and how they can be controlled)

Introduction

Psychology is often defined as ‘the science of mind and behaviour’. In order for Psychology to be considered a science (and therefore a legitimate academic subject) it has to follow the rules of science. This means that psychologists can’t just come up with ideas that they believe are true, or essentially opinions. Psychologists aim to gather evidence about behaviour whilst trying to remain objective and free from bias (personal views). One way this is achieved is through using the scientific method. The scientific method is a way of gathering evidence in an orderly, structured manner that can enable psychologists to develop theories and draw conclusions about behaviour. Research methods are a vital component of studying Psychology and this pack will take you through the various methods that are used within Psychological research, what kind of data can be gathered and how the data is analysed. Get ready for the journey!

• tables
• scattergrams
• bar charts
• histograms

 Distributions

• normal and skewed distributions
• characteristics of normal and skewed distributions

 Analysis and interpretation of correlation, correlation co-efficients

 Qualitative data analysis - content analysis and thematic analysis

 Introduction to statistical testing

• calculating of the sign test
• interpreting the significance of the sign test

Research Methods

Experimental Methods

Psychologists use the experimental method to identify the "effect" one variable has on other variables. In

other words….does one variable "cause" other variables to change. This is called cause and effect. Being

able to conduct research that establishes cause and effect is a key feature of scientific research. However establishing cause and effect is not easy and requires researchers to conduct studies that not only follow the scientific method, but also classify as "true experiments". In a true experiment, there must be a control condition and an experimental condition and participants must be randomly assigned to these conditions. This is so that the researcher can make fair comparisons between the two groups. A researcher manipulates the independent variable (IV) in order to test its effect on the dependent variable (DV). Everything else is kept the same ( controlled) between the two conditions. If there is a significant difference in the results of the two groups, we can conclude that the independent variable caused the change in the dependent variable (cause and effect). Independent and dependent variables In an experiment, a researcher manipulates the independent variable (IV) and measures the effect of this on the dependent variable (DV). All other variables that might potentially affect the DV should remain constant. This means the researcher can be confident that the effect on the DV, was due to the change in the IV and nothing else. Independent Variable: A variable that is manipulated by the researcher – or changes naturally. Dependent Variable : The variable that is measured by the researcher. Any effect on the DV should be caused by changes in the IV. Levels of the IV In order to test the effect of the IV we need different experimental conditions. The control condition and the experimental condition.

 The control condition provides a baseline measure of behaviour without experimental treatment.

 The experimental condition is the one in which there has been researcher manipulation. This is the

condition in which the researcher is particularly keen to see if a difference in behaviour has occurred. Operationalisation Psychologists are interested in a range of behaviour; intelligence, aggression, social anxiety etc. It’s important when studying them that they are defined. Operationalisation is clearly defining variables so they can be measured. A variable is anything that can vary, i.e. changed or be changed, such as memory, attention, time taken to perform a task, etc.

How to write an experimental hypothesis: In order to write a hypothesis there are a few factors you need to be clear on before you can begin.

1. What are the IV and the DV?
2. How is the IV manipulated e.g. what are the levels of the IV
3. How has the DV been measured exactly? E.g. how has it been operationalised?
4. Should the hypothesis be one tailed or two tailed?
5. Write your hypothesis- Put it all together! Worked example: The aim is to investigate whether a new drug (axocalm) reduces anxiety in patients with phobias. No previous research has been conducted on the effectiveness of this drug. Step 1: Identify the DV- what is being measured? The answer is anxiety. Identify the IV- what is being manipulated? The answer is whether they are given the drug or not Step 2: In order to test the effect of the IV we need different experimental conditions. If we simply gave some participants the drug, how would we know if it reduced their anxiety? We need a comparison. We could either:  Compare participants anxiety levels before and after talking the drug  Compare two groups of participants- those who take the drug and those who do not take the drug Step 3: Operationalise the DV - how exactly is anxiety being measured? In this example, they have not specified how anxiety is measured so we need to come up with a sensible way to measure anxiety e.g. a self report scale. “On a scale of 1-10 (1=not anxious at all and 10= highly anxious) how do you feel now?” Sometimes exam questions will give you information about how the DV has been measured so you need to identify this from the scenario and include it in your hypothesis. Step 4: Identify from the information you have been given if you should write a one-tailed or two-tailed hypothesis. In this case, they have said that no previous research has been conducted so that informs us that we should write a non-directional (two tailed) hypothesis. Step 5: Put all of this information together into a written testable statement. Below is an example of a template you can use to write nearly every non-directional hypothesis: There will be a difference in ________ (DV), measured by ___________( operationalised DV) for participants who ________( IV - condition 1) compared to those who __________( IV - condition 2). The answer is on the next page.

Answer: There will be a difference in anxiety , (measured on a self-report scale where 1=not anxious and 10= very anxious) for participants who are given the drug compared to those who are not given the drug. Writing a directional (one-tailed) hypothesis: Follow steps 1-3, then you will identify in step 4 that previous research has been conducted that has demonstrated the direction the researcher is likely to go in e.g. the drug does reduce anxiety. You will have to make sure you include in your answer which group will be more/less anxious. Template to use for directional hypotheses: Participants who _________ (IV - cond. 1 ) will be more/less ___________ ( operationalised DV ) than participants who ___________ (IV - cond. 2). Your answer would then look like this: The participants who are given the drug will feel less anxious (measured on a self-report scale where 1=not anxious and 10= very anxious) than participants who are not given the drug. Writing correlational hypotheses The difference when writing a correlational hypothesis is simple. We are no longer investigating a difference between two conditions , like in an experiment, but we are looking at a relationship between two co-variables. There is no IV or DV in a correlation. Correlational hypotheses can still be directional or non-directional. The Co-variables must still be clearly operationalised. Worked example: The aim is to investigate whether there is a correlation between the price of chocolate and how tasty it is. Template: There will be a correlation between __________(co variable 1) and __________(co variable 2). Non directional example- There will be a correlation between the price of a chocolate bar and its tastiness rating (out of 20). Directional example - There will be a positive/negative correlation between the price of a chocolate bar and its tastiness rating (out of 20). NB: Notice that when you write a directional correlational hypothesis you predict whether the correlation between the co-variables will be positive or negative.

Natural experiments:

A natural experiment is where the researcher takes advantage of a naturally occurring IV and the effect it has on the DV. The experimenter has not manipulated the IV directly; the IV would vary naturally whether or not the researcher was interested. The researcher cannot randomly allocate participants to conditions and/or has no control over the IV. This is not a ‘true’ experiment. Note: it is the IV that is natural, not necessarily the setting. Example: Romanian Orphan studies (Attachment topic). IV = adoption before or after the age of 6 months (naturally occurring/varying)

Quasi – experiments:

Studies that are ‘almost’ experiments. The IV is not something that varies at all – it is simply a difference between people that exists. The researcher records the effects of this ‘quasi-IV’ on the DV. The researcher cannot randomly allocate participants to conditions and/or has no control over the IV. This is also not a ‘true’ experiment. Examples: Experiments where the IV is a variable such as age, gender, locus of control etc. Summary of key differences Type of experiment Environment conducted in: Independent variable: Lab Controlled Controlled Field Natural Controlled Quasi Controlled Naturally occurring natural Natural Naturally occurring Strengths of Natural experiment :  Provides opportunities for research that may not be otherwise conducted due to practical/ethical reasons e.g. does smoking when pregnant lead to behavioural problems in infants?  They have high external validity because they involve the study of real-life. Limitations of Natural experiments :  A naturally occurring event may happen, rarely limiting generalisation to other situations.  Participants may not be randomly, allocated to conditions. Strengths of Quasi- experiments :  Carried out under controlled conditions & share the strengths of a lab experiment. Limitations of Quasi- experiments:  Participants are aware of being tested – possible demand characteristics.  Participants cannot be randomly allocated and therefore there may be confounding variables.

Control of variables

The key to an experiment is that the independent variable (IV) is manipulated (changed) to see how this affects the dependent variable (DV). Remember, the researcher only wants the IV to affect the DV. If however, there are other variables that may influence the IV or DV (and these are unwanted) these are extraneous variables. Extraneous variables: Any variable, other than the independent variable (IV), that may have an effect on the dependent variable (DV) if it is not controlled. These variables can come from the participant (e.g. age, intelligence), the experimental situation (e.g. noise levels, temperature) or the experimenter (e.g. personality, appearance or conduct of the researcher) Confounding variables: Any variable, other that the independent variable (IV), that has not been controlled so do affect the DV. Therefore we cannot be sure of the true reason for the changes to the DV/difference found. Demand characteristics: Participants are not ‘passive’ in experiments and they may work out what is going on and change their behaviour to please the experimenter or even act negatively. Demand characteristics occur when a participant may receive a ‘cue’ from the researcher or the situation and so the participant changes their behaviour as a result. Investigator effects: Any effects of the investigator’s behaviour (conscious or unconscious) on the research outcome (DV). This may include everything from the design of the study, to the selection of and interaction with the participants during the research process.

Ways to minimise extraneous/confounding variables:

Randomisation: Randomisation is the use of ‘chance’ in order to control for the effects of bias i.e. in a memory experiment that may involve participants recalling words from a list. The order of the list should be randomly generated so that the position of each word is not decided by the experimenter. Standardisation: This is using exactly the same procedures for all participants, such as the same environment, instructions and experience.

Experimental Design

Experimental design refers to how the participants in an experiment will be used. A researcher can arrange his/her participants in one of three ways. Independent groups , repeated measures or matched pairs.

Independent groups:

An independent groups design is when two separate groups of participants experience two different conditions of the experiment.

Repeated groups:

A repeated measures design is where all participants take part in both the conditions.

Strengths: Limitations:

1. Participant variable problems are avoided because all participants take part in both conditions. Therefore, it doesn’t matter if they have different IQs or memory abilities because they are kept constant through both conditions.
2. This experimental design requires fewer participants because the same group is re-used. 1. Order effects are very likely to occur; participants may become bored, aware of aims or tired because they carry out a task twice. They would need to control for this by using counterbalancing (see notes below). 2. Demand characteristics are more likely to occur because participants have been exposed to both conditions of the experiment and therefore may pick up on cues or figure out the aim of the experiment. 3. The researcher will need to ensure they have different test materials for condition1 and 2. For example, they would not be able to use the same list of words in a memory test in both conditions. To control for this they have to use a different set of words but make sure they are of similar difficulty.

Strengths: Limitations:

1. Order effects are avoided (when participants become aware of or bored with an experimental procedure).
2. There are less likely to be demand characteristics because participants only take part in one condition of the experiment and are therefore less likely to pick up on cues. 1. Individual differences between groups, otherwise called “participant variables”, may affect the results (what if one group has people who have a naturally higher IQ than people in the other group?) – to deal with this random allocation is used. 2. A larger amount of participants are needed in this experimental design.

Dealing with order effects: Counterbalancing Counterbalancing is an attempt to control order effects in which half the participants take part in condition A then B, and the other half take part in condition B then A. (ABBA technique). For example, Participant 1 A-B Participant 2 B-A Participant 3 A-B and so on…… Now, counterbalancing does not remove or prevent order effects, but attempts to balance out the effects of order between the two conditions. Matched pairs: A matched pairs design is where pairs of participants are first matched on a key variable/s (i.e. IQ). Then one member is assigned to condition A and the other assigned to condition B. Example of using matched pairs design in psychological research: Bandura et al. investigated the effect of observing aggressive and non- aggressive role models on children’s behaviour. Would they imitate the aggression they had seen? In order to control for naturally occurring aggression levels in the children (so it would not confound the DV) he got the children’s parents and teachers to rate their aggression on a 1-5 scale. He then matched the children on their aggression levels so each condition had the same number of highly aggressive children (5), medium aggression (4-2) and non aggressive children (1). Strengths: Limitations:

1. The issue of participant variables is greatly reduced. 2. Order effects are totally avoided. 3. Demand characteristics less likely. 1. It is pretty much impossible to match people exactly on every characteristic; unless maybe they are identical twins – and even then, it is usually just matching physical characteristics. 2. It is very time-consuming to find lots of people that match each other so closely.

Open and closed questions: Open questions do not have a fixed range of answers and respondents are free to answer in any way that they wish. Open questions tend to produce qualitative data (rich in depth, but difficult to analyse). For example, “Why do you enjoy the psychology A level course?” Closed questions offer a fixed number of responses and produce numerical data by limiting the answers respondents can give. They produce quantitative data (easy to analyse, but lacks the depth associated with open questions). For example, ‘Do you watch more than 10 hours per week of TV?’… ‘yes’ or ‘no’. Or respondents may be asked to rate how often they watch soap operas on TV on a scale of 1-5. 1 2 3 4 5 1= never 3=sometimes 5= every day

Evaluation of open and closed questions:

Open questions  Respondents can expand on their answers, which increases the amount of detailed information collected.  Open questions can reveal unexpected answers; therefore researchers can gain new insight into people’s feelings and attitudes.  They also provide qualitative data (non-numerical data) which although may be rich in information, it can be more difficult to summarise and/or detect patterns to draw conclusions. Vs Closed questions:  They have a limited range of answers and produce quantitative data (numerical data). This means the answers are easier to analyse using descriptive statistics (mean, mode, graphical representation).  However, respondents may be forced to select answers that don’t represent their true thoughts or behaviour, therefore the data collected may lack validity.

Designing questionnaires and interviews- What to avoid:

 Overuse of jargon (technical terms) that only those familiar with the field will understand e.g. “do you agree that maternal deprivation in infanthood inevitably leads to affectionless psychopathy in later life?”  Emotive language and leading questions (guiding the respondent to a particular response) e.g. Fox hunting is a barbaric sport and any sane person would want it banned (emotive) e.g. Is it not obvious that student fees should be abolished? (leading question)  Double barrelled questions and double negatives e.g. do you agree with this statement: Premier league footballers are overpaid and should give 20% of their wages to charity. (This contains two questions in one; respondents may agree with one half of the question but not the other and therefore would not know how to respond) e.g. I am not unhappy in my job (agree/disagree) This question is hard to decipher and could be written in a much clearer way!

Designing Interviews

Interviews are also a self-report method They are more likely to collect qualitative data than questionnaires, but certain types of interview will lead to quantitative data being gathered. A good interview will involve:- An interview schedule – a list of questions the interviewer intends to cover. This should be standardised for each interviewee to reduce interviewer bias. Recording – the interviewer may take notes throughout the interview (although this may interfere with listening skills). Alternatively, the interview may be audio recorded of videoed. Effect of interviewer – one of the strengths of interviews over questionnaires is that the presence of the interviewer who is interested in the interviewee may increase the amount of information provided, this is because it allows a rapport to be built with the interviewee. The interviewer needs to be careful with their non-verbal communication – not sitting with arms folded for example. Behaviour needs to be welcoming and encouraging i.e head nodding & leaning forward. A further consideration for the interviewer is listening skills – an experienced interviewer will know when and how to speak, i.e not interrupting or using negative language. Ethical issues – Respondents should be reminded that their answers will be treated confidentially. This is especially important if the interview includes topics that may be personal or sensitive.