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A-Level AQA Psychology Questions by Topic

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PAST PAPERS: RESEARCH METHODS: AQA A-LEVEL PSYCHOLOGY RESOURCES

Psychology aqa  a-level (7182)  unit 2: 7182/2.

Full model answers for all of these questions are  available here

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THE SYLLABUS

METHODS, TECHNIQUES & DESIGN

• Primary and secondary data, and meta-analysis. Quantitative and qualitative data
• Aims, operationalising variables, IV’s and DV’s
• Hypotheses - directional and non-directional
• Experimental design - independent groups, repeated measures, matched pairs
• Validity – internal and external; extraneous and confounding variables; types of validity and improving validity
• Control – random allocation, randomisation, standardisation
• Demand characteristics and investigator effects
• Reliability; types of reliability and improving reliability
• Pilot studies
• Correlation analysis – covariables and hypotheses, positive/negative correlations
• Observational techniques – use of behavioural categories
• Self-report techniques – design of questionnaires and interviews
• Case studies
• Content analysis & thematic analysis

PARTICIPANTS; ETHICS; FEATURES OF SCIENCE & SCIENTIFIC METHOD; THE ECONOMY

• Selecting participants and sampling techniques
• The British Psychological Society (BPS) code of ethics and ways of dealing with ethical issues
• Forms and instructions
• Peer review
• Features of science: objectivity, empirical method, replicability and falsifiability, paradigms and paradigm shifts
• Reporting psychological investigations
• The implications of psychological research for the economy

DESCRIPTIVE STATISTICS

• Analysis and interpretation of quantitative data. Measures of central tendency - median, mean, mode. Calculating %’s. Measures of dispersion – range and standard deviation (SD)
• Presentation and interpretation of quantitative data – graphs, histograms, bar charts, scattergrams and tables
• Analysis and interpretation of correlational data; positive and negative correlations and the interpretation of correlation coefficients
• Distributions: normal and skewed

INFERENTIAL STATISTICS

• Introduction
• Factors affecting choice of statistics test: Spearman’s rho, Pearson’s r, Wilcoxon, Mann-Whitney, related t-test, unrelated t-test, Chi-Squared test
• Levels of measurement – nominal, ordinal, interval
• Procedures for statistics tests
• Probability and significance: use of statistical tables and critical values in interpretation of significance; Type I and Type II errors
• Introduction to statistical testing: the sign test

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SPECIMEN PAPER 1 ( Psychology A-level revision)

Read the item and then answer the questions that follow.

A psychologist wanted to see if verbal fluency is affected by whether people think they are presenting information to a small group of people or to a large group of people.

The psychologist needed a stratified sample of 20 people. She obtained the sample from a company employing 60 men and 40 women.

The participants were told that they would be placed in a booth where they would read out an article about the life of a famous author to an audience. Participants were also told that the audience would not be present, but would only be able to hear them and would not be able to interact with them.

There were two conditions in the study, Condition A and Condition B.

Condition A: 10 participants were told the audience consisted of 5 listeners.

Condition B: the other 10 participants were told the audience consisted of 100 listeners.

Each participant completed the study individually. The psychologist recorded each presentation and then counted the number of verbal errors made by each participant.

(a) Identify the dependent variable in this study. (2 marks)

(b) Write a suitable hypothesis for this study. (3 marks)

(c)  Identify one extraneous variable that the psychologist should have controlled in the study and explain why it should have been controlled. (3 marks)

(d) Explain one advantage of using a stratified sample of participants in this study. (2 marks)

(e) Explain how the psychologist would have obtained the male participants for her stratified sample. Show your calculations. (3 marks)

(f) The psychologist wanted to randomly allocate the 20 people in her stratified sample to the two conditions. She needed an equal number of males in each condition and an equal number of females in each condition. Explain how she would have done this. (4 marks)

Mean number of verbal errors and standard deviations for both conditions

(g)  What conclusions might the psychologist draw from the data in the table? Refer to the means and standard deviations in your answer. (6 marks)

(h) Read the item and then answer the question that follows.

The psychologist had initially intended to use the range as a measure of dispersion in this study but found that one person in Condition A had made an exceptionally low number of verbal errors.

Explain how using the standard deviation rather than the range in this situation, would improve the study. [3 marks]

(i) Name an appropriate statistical test that could be used to analyse the number of verbal errors in Table 1. Explain why the test you have chosen would be a suitable test in this case. [4 marks]

(j) The psychologist found the results were significant at p<0.05. What is meant by ‘the results were significant at p<0.05’? [2 marks]

(k) Briefly explain one method the psychologist could use to check the validity of the data she collected in this study. [2 marks]

(l)  Briefly explain one reason why it is important for research to undergo a peer review process. [2 marks]

(m) Read the item and then answer the question that follows.

The psychologist focused on fluency in spoken communication in her study. Other research has investigated sex differences in non-verbal behaviours such as body language and gestures

Design an observation study to investigate sex differences in non-verbal behaviour of males and females when they are giving a presentation to an audience.

In your answer you should provide details of:

• The task for the participants
• The behavioural categories to be used and how the data will be recorded
• How reliability of the data collection might be established
• Ethical issues to be considered.

(Total 12 marks)

SPECIMEN PAPER 2 ( A-level Psychology revision)

Researchers were interested in the spatial awareness skills of motorists. They decided to investigate a possible relationship between different aspects of spatial awareness. Motorists who had between ten and twelve years of driving experience and held a clean driving licence with no penalty points were asked to complete two sets of tasks.

Set 1: To follow a series of instructions and using a map, to identify various locations correctly. This provided a map reading score for each motorist with a maximum score of 20.

Set 2: To complete a series of practical driving tasks accurately. This involved tasks such as driving between cones, driving within lines and parking inside designated spaces. Each motorist was observed completing the Set 2 tasks by a single trained observer who rated each performance by giving the driver a rating out of 10.

The following results were obtained.

Table 1: The map reading scores and driver ratings of motorists

(a) Should the hypothesis be directional? Explain your answer. (2 marks)

(b) Write a suitable hypothesis for this investigation. (3 marks)

(c) Identify a suitable graphical display for the data in Table 1 and briefly explain why this display would be appropriate. (2 marks)

(d) Using the data in Table 1, comment on the relationship between the map reading scores and the driver rating scores of the participants. [3 marks]

(e) Briefly outline one problem of using a single trained observer to rate the participants’ driving skills in the practical task. Briefly discuss how this data collection method could be modified to improve the reliability of the data collected. (6 marks)

(f) The researchers decided to analyse the data using a Spearman’s rho test. Explain why this is a suitable choice of test for this investigation. (3 marks)

(g) After analysis of the data the researchers obtained a calculated value of r s = 0.808. Using the information in Table 2 above, what conclusion can the researchers draw about the relationship between the map reading and driving skills of the motorists? Explain your answer. [4 marks]

(h) Distinguish between a Type I error and a Type II error. (4 marks)

When the researchers looked at the data collected more closely they noticed possible gender differences in the results.

(i) What do the mean and standard deviation values suggest about the male and female performances in the investigation? (4 marks)

In a replication of the part of the study in which map reading skills were investigated, 20 men and 20 women completed the original map reading task and the researchers obtained the following data:

(j) The mean map reading score for both groups together was 12.23. What percentage of the male group scored above the mean score and what percentage of the female group scored above the mean score? Show your calculations. (4 marks)

(k) Using your answers to both 2 previous questions, comment on the performances of the male and the female participants in this study. (2 marks)

(l) Briefly explain one reason why it is important for research to be replicated. (2 marks)

(m) Imagine you have been asked to design a study to investigate possible gender differences in card sorting behaviours. You decide you will ask participants to sort a shuffled pack of playing cards into their suits of hearts, clubs, diamonds and spades. You decide you will time the participants as they do this using a stop watch.

Discuss the following aspects of this investigation:

• with reference to the card sorting task, explain how you would ensure that this is made the same task for all participants
• one methodological issue you should take into account when obtaining suitable participants for this study and explain how you would deal with this issue
• how you would ensure that the experience of your participants is ethical.

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SPECIMEN PAPER 3 ( AQA A-level Psychology revision)

Following previous research indicating the social benefits of green space in urban areas, two psychology students decided to observe social behaviour in public spaces. They focused on two neighbouring towns, Greensville where most public spaces were planted with flowers and vegetables, and Brownton where most public spaces were paved with concrete.

The students compared the instances of considerate behaviours in the two towns.

Considerate behaviour categories included putting litter in the bin, having a dog on a lead and riding a bike with care.

The observations were carried out in four different areas of a similar size in each town on weekdays between the hours of 4.30pm and 6.00pm. The students worked together to ensure inter-observer reliability, recording each target behaviour whenever it occurred.

(a) Should the hypothesis for this research be directional or non-directional? Explain your answer. (2 marks)

(b) Before the observation could begin, the students needed to operationalise the behaviour category ‘riding a bike with care’.

Explain what is meant by operationalisation and suggest two ways in which ‘riding a bike with care’ could have been operationalised. (4 marks)

(c) The students thought that having a dog on a lead was a useful measure of considerate behaviour because it had face validity. Explain what is meant by face validity in this context. (3 marks)

(d) Identify and briefly outline two other types of validity in psychological research. (4 mark

(e) Identify the behaviour sampling method used by the students. Shade one box only.

• Time sampling
• Pair sampling
• Event sampling
• Target sampling

(f) Explain how inter-observer reliability could be ensured by working as a pair. (3 marks)

The students noted that overall more considerate behaviours occurred in Greensville than in Brownton.

(g) Calculate the ratio of considerate behaviours observed in Greensville to considerate behaviours observed in Brownton. Show your workings and present your answer in the simplest form. (3 marks)

(h) The students carried out a Chi-square test on their data.  Explain why the Chi-square test was an appropriate test to use in this case. (3 marks)

(i) In order to interpret the results of the Chi-square test the students first needed to work out the degrees of freedom. They used the following formula.

Degrees of freedom (df) = (r–1) x (c–1)

r = number of rows and c = number of columns

Calculate the degrees of freedom for the data in Table 1. Show your workings.

(j) The calculated value of Chi-square was 6.20. Referring to Table 2 below, state whether or not the result of the Chi-square test is significant at the 0.05 level of significance. Justify your answer. (3 marks)

To be significant at the level shown the calculated value of Chi Square must be equal to or greater than the critical/table value

(k) In the discussion section of their report of the investigation the students wanted to further discuss their results in relation to levels of significance.

Write a short paragraph the students could use to do this. (4 marks)

(l) As a follow-up to their observation the students decided to interview some of their peers about inconsiderate behaviours in their 6th Form Centre. The interviews were recorded.

Explain how the students could develop their interview findings by carrying out a content analysis and why content analysis would be appropriate in this case. (3 marks

(m) Suggest one inconsiderate behaviour that the students might focus on in their content analysis. (1 mark)

(n) Design an experiment to investigate the effect of indoor plants on mood in office workers. For your measure of mood, you should devise a measure that would give data suitable for testing at the ordinal level of measurement.

• Design – include reference to the experimental design, variables and controls
• Materials/Apparatus – describe any special materials required
• Data analysis that could be used – include reference to descriptive and inferential analysis.

Justify your choices. (12 marks)

2017 ( AQA A-level Psychology revision guide)

A psychologist wanted to test whether listening to music improves running performance.

The psychologist conducted a study using 10 volunteers from a local gym. The psychologist used a repeated measures design. Half of the participants were assigned to condition A (without music) and half to condition B (with music).

All participants were asked to run 400 metres as fast as they could on a treadmill in the psychology department. All participants were given standardised instruction. All participants wore headphones in both conditions. The psychologist recorded their running time in seconds. The participants returned to the psychology department the following week and repeated the test in the other condition.

(a) Identify the type of experiment used in this study.

(b) Identify the operationalised dependent variable in the study. (2 marks)

The results of the study are given in Table 1 below.

Table 1. Mean number of second taken to complete the 400m run and the standard deviation for both conditions.

(c) Explain why a histogram would not be an appropriate way of displaying the means shown in Table 1. (2 marks)

(d) Name a more appropriate graph to display the means shown in Table 1. Suggest appropriate X (Horizontal) and Y (vertical) axis labels for your graph choice. (3 marks)

Name of graph

X axis label

Y axis label

(e) What do the mean and standard deviation values in Table 1 suggest about the participants’ performances with and without music? Justify your answer. (4 marks)

(f) Calculate the percentage decrease in the mean time it took participants to run 400 metres when listening to music. Show your workings. Give your answer to three significant figures. (4 marks)

The researcher used a directional hypothesis and analysed the data using a related t-test. The calculated value of t where degrees of freedom (df) = 9 was 1.4377. He decided to use the 5% level of significance.

Table 2. Table of critical values of t

Calculated value of t must be equal to or greater than the critical value in this table for significance to be shown.

(g) Give three reasons why the researcher used a related t-test in this study and, using Table 2, explain whether or not the results are significant (5 marks)

(h) What is meant by a Type II error? Explain why psychologists normally use the 5% level of significance in their research. (3 marks)

(i) Identify one extraneous variable that could have affected the results of this study. Suggest why it would have been important to control this extraneous variable and how it could have been controlled in this study. (3 marks)

(j) The report was submitted for peer review and a number of recommendations were advised.

Describe the process and purposes of peer review. (6 marks)

People’s perception of how they spent their time at the gym is often not very accurate. Some spent more time chatting than on the treadmill. A psychologist decides to observe the actual behaviour of an opportunity sample of gym users at a local gym.

(k) Explain why it is more appropriate for the psychologist to use an observation than a questionnaire in this case. (3 marks)

(l) Design an observational study to investigate how people spent their time at the gym.

In your answer you will be awarded credit for providing appropriate details of

• Type of observation with justification
• Operationalised behaviour categories
• Use of time and/or event sampling with justification
• How reliability of data collection could be assessed.

2018 ( A-level Psychology resources)

A psychologist was reading an article about typical dream themes in adults.

Figure 2 shows the main dream themes identified in the article.

Figure 2 Main dream themes

(a) Using Figure 2, estimate the percentage of dreams that were reported to be about being chased. Shade one box only. [1 mark]

A           4%

B           12%

C           27%

D           42%

The psychologist was interested in finding out whether dream themes differed between box males and females, particularly in terms of social interaction. She decided to conduct a pilot study. Twenty undergraduate students (8 male and 12 female) volunteered for the study. For a six-week period the students were interviewed at 9 am each morning when they arrived at university. Interviewers, who did not know the purpose of the study, carried out and recorded the dream interviews.

(a) What is meant by a pilot study? Explain one possible reason why the psychologist decided to conduct a pilot study for this investigation. [3 marks]

(b) The interviews produced qualitative data. What is meant by qualitative data? Give one strength of collecting qualitative data in this study. [2 marks]

(c) What are investigator effects? Suggest one way in which they could have been minimised during the dream interviews. [3 marks]

Another researcher, who did not know the purpose of the study, carried out a content analysis of the interview data.

(d) Explain how this content analysis could have been conducted. [4 marks]

The psychologist wanted to assess the reliability of the content analysis.

(e) Explain how the reliability of the content analysis could be assessed. [4 marks]

When comparing the data for males and females, the psychologist found that there was a difference in the proportion of friendly and aggressive social interactions. This is shown in Table 2.

Table 2: Percentage of friendly and aggressive social interactions in dreams reported by males and females

A total of 375 dreams reported by males included social interaction.

(f) Use the data in Table 2 to calculate how many of these dreams reported by males were classified as aggressive. Show your workings. [2 marks]

(g) Draw a suitable graphical display to represent the data in Table 2 box. Label your graph appropriately. [4 marks]

The psychologist decided to conduct an experiment to investigate the effect of watching box horror films before going to bed.

A volunteer sample of 50 university students consented to take part in the experiment.

The 50 students were randomly split into two groups. Group 1 watched a horror film before going to bed each night for the first week then a romantic comedy before going to bed each night for the second week. Group 2 watched the romantic comedy in the first week and the horror film in the second week.

When the students woke up each morning, each student received a text message that asked if they had had a nightmare during the night. They could respond ‘yes’ or ‘no’.

(h) Write a brief consent form that would have been suitable for use in this experiment. The consent form should:

• include some detail of what participants might expect to happen
• refer to ethical issues
• be in an appropriate format/style

(i) The psychologist proposed a directional hypothesis.

Write a directional hypothesis for this experiment. [3 marks]

The psychologist used a repeated measures design in this experiment.

(j) Explain why it was important to use a repeated measures design in this case.  [2 marks]

The psychologist used counterbalancing in this experiment.

(k) Explain why it was appropriate to use counterbalancing in this experiment. [2 marks]

(l) Explain how the box psychologist could have randomly split the sample of 50 students into the two groups. [3 marks]

The psychologist collated the responses of all the participants over the two-week period and calculated the mean and standard deviation for each condition.

Table 3: Mean number of nightmares reported and the standard deviation for each condition

(m) What do the mean and standard deviation values in Table 3 suggest about the effect of the type of film watched on the occurrence of nightmares? Justify your answer. [4 marks]

The psychologist found that the difference in the number of nightmares reported in the two conditions was significant at p<0.05.

(n) Explain what is meant by ‘significant at p<0.05’ in the context of this experiment. [2 marks]

(o) The psychologist was concerned about the validity of the experiment.

Suggest one possible modification to the design of the experiment and explain how this might improve validity. [3 marks]

2019 ( AQA A-level Psychology resources)

(a) Which of the following does reliability refer to?

Shade one circle only. [1 mark]

A           The accuracy of the data

B           The consistency of the data

C           The levels of the data

D           The validity of the data

(b) Which of the following is not a role of peer review in the scientific process?

A           To determine whether to award research funding

B           To ensure only significant results are published

C           To make sure research has high validity and reliability

D           To retain the integrity of psychological research

(c) Give one reason why it is important for scientific reports to include a referencing section? [1 mark]

It was recently reported in a newspaper that time spent playing team sports increases happiness levels. A researcher was keen to find out whether this was due to participating in a team activity or due to participating in physical activity, as he could not find any published research on this.

The researcher used a matched-pairs design. He went into the student café and selected the first 20 students he met. Each student was assigned to one of two groups.

Participants in Group A were requested to carry out 3 hours of team sports per week. Participants in Group B were requested to carry out 3 hours of exercise independently in a gym each week. All participants were told not to take part in any other type of exercise for the 4-week duration of the study.

All participants completed a happiness questionnaire at the start and end of the study. The researcher then calculated the improvement in happiness score for each participant.

(d) Which of the following is correct?

A           Groups A and B are conditions of the dependent variable and happiness is the independent variable.

B           Groups A and B are conditions of the independent variable and happiness is the dependent variable.

C           Groups A and B are the controls and happiness is the experimental condition.

D           Groups A and B are the experimental conditions and happiness is the control.

(e) Would a directional or non-directional hypothesis be more suitable for the researcher to use? Explain your answer. 2 marks]

(f) Write a suitable hypothesis for this experiment. [3 marks]

(g) Identify the type of sampling method used in this experiment. Explain one limitation of using this sampling method in this study. [3 marks]

(h) Identify one variable on which participants should be matched in this matched-pairs design. Explain how the researcher could assign matched participants to either Group A or Group B. [4 marks]

(i) Explain one strength of using a matched-pairs design rather than a repeated-measures design. [2 marks]

The results of the study are given in Table 2 below.

               Table 2 Improvement in happiness scores

The researcher decided to use the Sign Test to see whether there was a significant difference in the improvement in the scores between the two groups at the 5% level of significance.

(j) Calculate the value of S in this study. Show your workings. [2 marks]

Significance is shown if the calculated value of S is equal to or less than the critical value.

(k) Explain whether or not there was a significant difference in the improvement in the scores between the two groups. Use your answer to Question 22 and Table 3. [2 marks]

The validity of the data was questioned when the researcher presented his results. The researcher explained that he chose to use the happiness questionnaire because it had high concurrent validity.

(l) Explain what it means for a test to have high concurrent validity. [2 marks]

The questionnaire had high concurrent validity.

Validity was still a concern because the researcher knew which participants were in each experimental group.

(m) Explain how this could have affected the validity of the study. [4 marks]

(n) Using your answer to Question m, suggest one way in which the researcher could modify the study to improve the internal validity of the study? Justify your answer. [4 marks]

A psychology teacher read the researcher’s study on sport and happiness. She considered whether setting group tasks could improve her students’ level of happiness. She decided to conduct an independent groups experiment with 30 students taking A-level Psychology using the same happiness questionnaire.

(o) Suggest an appropriate statistical test the psychology teacher could use to analyse the data. Justify your choice of test. [4 marks]

(p) Design an independent groups experiment that the psychology teacher could conduct.

• the aim of the experiment
• identification and manipulation of variables including details of the task
• controls to minimise the effects of extraneous variables
• data handling and analysis – use of descriptive statistics and/or data presentation.

Justify your design choices. [12 marks]

2020 ( A-level Psychology notes)

A study into the relationship between recreational screen time and academic achievement was conducted. Students were asked to self-report the number of hours spent watching TV, playing on their mobile phones or video games (daily recreational screen time) and their end-of-year test performances (academic performance).

The results of the study are shown in Figure 2.

(a) In which section(s) of a scientific report would you expect to find reference to the results/findings of the investigation?

A           The abstract and the results sections only

B           The abstract, the discussion and the results sections only

C           The results and the discussion sections only

D           The results section only

(b) Which of the following correlation co-efficients best describes the data represented in Figure 2?

A           –0.80

B           –0.25

C           +0.25

D           +0.80

(c) Identify the type of graph shown in Figure 2 and explain why this is an appropriate graph to use for the data collected. [3 marks]

(d) Explain why it would not be appropriate for the researchers to conclude that increased recreational screen time reduces academic performance. [2 marks]

A psychologist reads a review of a meta-analysis confirming the relationship between recreational screen time and academic performance.

(e) What is meant by the term meta-analysis? [2 marks]

The psychologist decided to design an experiment to test the effects of recreational screen time on children’s academic performance.

The psychologist randomly selected four schools from all the primary schools in her county to take part in the experiment involving Year 5 pupils. Three of the four schools agreed to take part. In total, there were 58 pupils whose parents consented for them to participate. The 58 pupils were then randomly allocated to Group A or Group B.

For the two-week period of the experiment, pupils in Group A had no recreational screen time. Pupils in Group B were allowed unrestricted recreational screen time. At the end of the experiment all pupils completed a 45-minute class test, to achieve a test score.

(f) Complete Table 1 by ticking the statement that best describes the population and the sample in the psychologist’s experiment.

Place one tick in each column. [2 marks]

(g) Briefly explain why a directional hypothesis would be most suitable for this experiment. [1 mark]

(h) Write an appropriate hypothesis for this experiment. [3 marks]

The results obtained from the experiment are summarised in Table 2.

Table 2  Descriptive statistics for the test performance scores for Group A and Group B

(i) Using the data in Table 2, explain how the distribution of scores in Group A differs from the distribution of scores in Group B. [4 marks]

(j) What do the mean and standard deviation values in Table 2 suggest about the effect of the recreational screen time on test performance? Justify your answer. [4 marks]

(k) The psychologist wanted to test the statistical significance of the data.

Identify the most appropriate choice of statistical test for analysing the data collected and explain three reasons for your choice in the context of this study. [7 marks]

One criticism of the study is that the pupils were not matched on their typical recreational screen time.

(l) Explain how the psychologist could have matched pupils on their typical recreational screen time across the experimental conditions. [4 marks]

(m) Identify one other variable for which the psychologist could have matched the pupils. Explain how this might have affected the test performance if it was not controlled. [2 marks]

The feedback from one of the schools was that recreational screen time affected pupils’ social interactions. The psychologist decided to investigate this further by using an observation of social interaction during playtime at the school.

(n) Design the observation to investigate pupils’ social interaction in the playground.

In your answer you will be awarded credit for providing appropriate details of:

• type of observation, with justification
• choice of time sampling or event sampling, with justification
• dealing with one relevant ethical issue
• assessing reliability of the data through inter-observer reliability.

2021 ( AQA A-level Psychology notes)

A researcher placed an advert in a university psychology department asking for third year students to participate in a sleep experiment.

Each student had a sleep tracker watch to wear at home for the two-week study. Each morning they were asked to open the sleep tracker app to view their sleep quality data on their mobile phones. The students were unaware that the sleep data they could see on their phones had been manipulated by the researcher. Over the two weeks of the study, each student saw that he or she had had poor sleep quality for seven random nights of the experiment and good sleep quality for the remaining nights.

Every morning, after viewing the sleep data, each student completed a questionnaire about the previous night’s sleep. One of the questions asked the students to rate how well rested they felt, on a scale from 1–10, after the previous night’s sleep. Apart from this, students were asked to continue their normal everyday activities.

(a) Which of the following best describes the experimental method used in this study?

Shade one box only. [1 mark]

A           Field experiment

B           Laboratory experiment

C           Natural experiment

D           Quasi-experiment

(b) Write a directional hypothesis the researcher might use for this study. [3 marks]

(c) Which of the following best describes the sampling method used in this study?

A           Opportunity sampling

B           Stratified sampling

C           Systematic sampling

D           Volunteer sampling

(d) Explain one strength and one limitation of using this sampling method in this study. [4 marks]

The researcher collected quantitative data about how well rested the students felt.

(e) Explain one strength of collecting quantitative data in this study. [2 marks]

One ethical issue in this study is deception, as the students were unaware that the sleep data they could see on their phones had been manipulated by the researcher.

(f) Explain one way in which the researcher might deal with the deception in this study. [2 marks]

Apart from the question about how well rested the students felt, the researcher’s questionnaire contained nine other questions. The responses to these questions were not analysed.

(g) Explain one reason why the researcher decided to include these additional questions on the questionnaire. [2 marks]

(h) Explain one limitation of assessing sleep quality using a rating scale of 1–10. [2 marks]

The researcher believed that the actual number of hours slept by the students could have affected the results of the study.

(i) Suggest one other extraneous variable that could have affected the results of this study. Explain why it would have been important to control this extraneous variable and how it could have been controlled in this study. [4 marks]

In a follow-up study, the researcher investigated whether there was a correlation between the number of hours slept and how well rested the students felt.

The researcher randomly selected 18 participants from first-year students at the university.

On the day of the study, each student participant was asked, ‘How many hours did you sleep last night?’ They then had to rate on a scale of 1 to 5 how well rested they felt.

The researcher hypothesised that there would be a positive correlation between the two co-variables.

(j) Outline one reason why it was appropriate to conduct a correlation rather than an experiment in this case. [2 marks]

(k) Describe how the researcher could have used random sampling to obtain the students for this study. [3 marks]

The researcher used Spearman’s rho statistical test to analyse the data from this study.

(l) Explain why Spearman’s rho was a suitable test for this study. Refer to the description of the study in your answer. [4 marks]

The researcher chose to use the 5% level of significance and the calculated correlation coefficient for the Spearman’s rho test was 0.395

(m) Identify the appropriate critical value from Table 1. Explain your choice. [4 marks]

(n) Explain whether the researcher’s hypothesis should be accepted. Refer to the critical value identified in Question 21 in your answer. [2 marks]

(o) Explain why the researcher decided to use the 5% level of significance rather than the 1% level in this study. [2 marks]

When the researcher compared the calculated and critical values of rho, he began to wonder if he might have made a Type II error.

(p) Explain what is meant by a Type II error in the context of this study. [2 marks]

(q) Discuss features of science. Refer to one or more examples of psychological research in your answer. [8 marks]

2022 ( A-level Psychology revision notes)

A controlled observation was designed to compare the social behaviours of pre-school children of working parents and pre-school children of stay-at-home parents. The sample consisted of 100 children aged three, who were observed separately. Half of the children had working parents and the other half had stay-at-home parents.

The observation took place in a room which looked like a nursery, with a variety of toys available. In the room, there were four children and one supervising adult. Their behaviour was not recorded.

Each child participant was brought into the room and settled by their parent. The parent then left to sit outside. Each child participant’s behaviour was observed covertly for five minutes while they played in the room.

The observation was conducted in a controlled environment and a standardised script was used when the children and their parents arrived.

(a) Explain why the researcher used a controlled observation and a standardised script in this study. [4 marks] 

(b) Identify one limitation of controlled observations. [1 mark]

The researcher used two trained observers to record the social behaviours of each child during the observation.

(c) Give two behavioural categories that the observers could have used in the observation to assess the pre-school children’s social behaviour. Explain why your chosen categories are appropriate. [4 marks]

(d) Describe how the observers could use time sampling to record the social behaviour of each child during the five-minute period. [4 marks]

(e) Explain one strength and one limitation of using time sampling for this observation. [4 marks]

(f) Explain how the reliability of the controlled observation could be assessed through inter-observer reliability. [4 marks]

The data from the observation was summarised by converting the number of agreed observations into a total social behaviour score for each child.

The researcher then conducted a statistical test to identify whether there was a significant difference between the social behaviour scores for the children of stay-at-home parents and those of working parents.

(g) Identify an appropriate statistical test that the researcher could use to analyse the social behaviour scores in this study. Explain three reasons for your choice in the context of this study. [7 marks]

(h) Explain one reason why collecting quantitative data could reduce the validity of this study. [2 marks]

The findings of this study might have implications for the economy.

(i) Explain one or more possible implications of this study for the economy. [3 marks]

This study was written up as a scientific report.

(j) Describe features of the abstract section in a scientific report. [3 marks]

A new TV programme has been developed to increase positive social behaviours in pre-school children.

There is a proposal to carry out an experiment to compare the effects of the new TV programme and an existing TV programme, on positive social behaviours in pre-school children.

A sample of 500 pre-school children and their parents is available for the experiment. The parents have given consent for their children to take part in this experiment.

The experiment will take place over an 8-week period. Data on the children’s social behaviours will be gathered from the parents using a self-report method.

(j) Design the experiment to investigate whether watching the new TV programme leads to an increase in positive social behaviours in the children, compared with watching the existing TV programme.

In your answer you will gain credit for providing appropriate details of the following:

• the type of experimental design, with justification
• a self-report method of data collection, with justification
• how to control one extraneous variable, with justification as to why this would need to be controlled.

A-level Psychology AQA Revision Notes

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Revision Notes

Paper 1 : AS and A-Level

Social Influence

Paper 1 : A-Level

Psychopathology

Biopsychology

Research Methods

Paper 3 : Compulsory

Issues and Debates

Relationships

Cognitive Development

Schizophrenia

Eating Behaviour

Forensic Psychology

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Research methods exam questions and answers, research methods exam questions and answers (24 marks), research methods exam questions and answers (48 marks).

There are three assessment objectives assessed in each examination: 

There may be one, two, or all (only in the extended writing 16-mark question). It is important to understand how assessment objectives are allocated to each type of question to maximize your chance of obtaining full marks.

AO1 : Demonstrate knowledge

• Demonstrate knowledge and understanding of scientific ideas, processes, techniques, and procedures.
• Show knowledge and understanding of psychological theories, terminology, concepts, studies, and methods.

AO 2: Application of knowledge

• in a practical context
• when handling qualitative data
• when handling quantitative data
• in a theoretical context
• This skill area tests knowledge of research design and data analysis, and applying theoretical understanding of psychology to everyday/real-life examples.

AO3: Analyse, interpret and evaluate

Analyse, interpret, and evaluate scientific information, ideas, and evidence, including in relation to issues, to:

• make judgements and reach conclusions
• develop and refine practical design and procedures.

Examples of how you can score AO3 marks

• Whether or not theories are supported or refuted by valid research evidence : After describing a theory go on to describe a piece of research evidence saying, ‘X’s study supports/refutes this theory…’ and then describe the research study.
• Contextualising how the topic in question relates to broader debates and approaches in Psychology : For example, would they agree or disagree with a theory or the findings of the study?
• Animal Research : This raises the issue of whether it’s morally and/or scientifically right to use animals.The main criterion is that benefits must outweigh costs. Animal research also raises the issue of extrapolation. Can we generalize from studies on animals to humans as their anatomy & physiology is different from humans?
• General criticisms and/or strengths of theories and studies : E.g. ‘Bandura’s Bobo Doll studies are laboratory experiments and therefore criticizable on the grounds of lacking ecological validity’.To gain marks for criticising study’s methodologies the criticism must be contextualised: i.e. say why this is a problem in this particular study.‘Therefore, the violence the children witnessed was on television and was against a doll not a human’.

10% of the examination will consist of mathematical questions at the GCSE level. These questions will cover basic arithmetic, data, and graphs. There is no need to be worried if you have a GCSE pass grade of 5 or higher, as you will be familiar with these concepts.

To do well, you must get organized and plan your time logically and rationally to make sure you cover everything on the syllabus in an adequate amount of depth.

What are the most effective ways of revising subject knowledge?

• Ask ‘How” and ‘Why’ questions  when revising and try to connect ideas (this method is called ‘elaboration’)
• No cramming : Distribute your revision over time and use a spaced system of repetition
• Switch topics regularly  when revising (this is called ‘ interleaving, ‘ and it will help you to identify connections between different topics)
• Words and visuals . Combine words and visual representations to create two ways of remembering key ideas (this is called ‘dual coding’)

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AQA A-level Psychology Research Methods

This section provides revision resources for AQA A-level psychology and the Research Methods chapter. The revision notes cover the AQA exam board and the new specification. As part of your A-level psychology course, you need to know the following topics below within this chapter:

• A-Level Revision
• AQA Psychology
• Research Methods
• We've covered a number of the topics within research methods already in our GCSE psychology research methods content since there is significant overlap between GCSE and A-level. This section will look to cover both AS and A level psychology content for research methods.

The Experimental Method

The AQA AS/A level psychology specification states you need to know the following for research methods:

• Experimental method. Types of experiment, laboratory and field experiments; natural and quasi-experiments.

The experimental method is a scientific method that involves the manipulation of variables to determine cause and effect.

Participants are usually randomly allocated without bias to different testing groups which results in the groups being fairly similar. The procedures within the experiment should also be standardised, which means they are kept the same for all participants.

Within the experiment, the researcher will manipulate an independent variable (IV) to see if this has an effect on the dependent variable (DV). For example, the consumption of a stimulant such as coffee (IV) may be manipulated to see its effect on reaction time (DV).

Within an experiment, variables need to be operationalised so they can be manipulated and measure the effect. Some variables are more difficult to operationalise and in turn only allow one aspect of a variable to be measured. Without the ability to operationalise variables, the results will be unreliable and impossible to replicate to determine their validity.

Types of Experiment

Research methods for A level psychology identifies 4 different types of experiments we need to know about which are:

• Laboratory experiments,
• Field experiments,
• Natural experiments 
• Quasi-experiments.

We've covered many of these already in our GCSE psychology content which we have linked to at the beginning of this chapter (scroll to the top, in yellow) but for ease of use, we've copied over the main elements.

Laboratory Experiments

"Laboratory experiments are experiments that are conducted in a controlled setting , usually a research laboratory where participants are aware of being observed and part of a study. Laboratory experiments tend to have high internal validity because researchers can control all the variables so the main differences between the experimental condition and control group are only the independent variable whose effect is being monitored. This allows researchers to more confidently assume that any differences between the conditions are due to the independent variable."

Source: Laboratory Experiments (GCSE Psychology)

Field Experiments

A field experiment is conducted in a more natural or everyday environment , unlike the laboratory experiment where the behaviour being measured is more likely to occur. The field experiment can be conducted anywhere in real-world settings with researchers manipulating an independent variable to measure its impact on the dependent variable. A field experiment can include confederates that participants are unaware of also being involved to test their response in the field setting. One key difference between a field experiment compared to a laboratory experiment, are participants may not be aware of being observed or studied. This is in an attempt to generate more realistic behaviour or responses from them that can generalise to real-world settings.

Source: Field Experiments (GCSE Psychology)

Natural Experiments

"A natural experiment is conducted when ethical or practical reasons to manipulate an independent variable (IV) are not possible. It is therefore said that the IV occurs 'naturally'. The dependent variable (DV), may however, be tested in a laboratory, for example, the effects of institutionalisation in some form, which may occur naturally due to imprisonment or disruption of attachment through the care system and how it may affect psychological development such as intellect or emotional development. Another good example of a natural experiment is the study by Charlton et al. (2000) which measured the effects of television. Prior to 1995, the people of St. Helena, a small island in the Atlantic had no access to TV however it's arrival gave the researchers to examine how exposure to western programmes may influence their behaviour. The IV in this case was the introduction of TV which was not controlled by researchers and something they took advantage of would be practically difficult to control. The DV was measures of pro or anti-social behaviours that were assessed through the use of questionnaires, observations and psychological tests. These types of experiments would either impractical or unethical to implement and therefore cases where this occurs naturally due to normal circumstances may be examined through natural experiments."

Source: Natural Experiments (GCSE Psychology)

Quasi-Experiments

In quasi-experiments, the independent variable (IV) is naturally occurring, similar to a natural experiment, however, the dependent variable (DV) may be measured in a laboratory. The key feature of a quasi-experiment is that the IV has not been created by anyone. An example where an IV might be occurring naturally would be a study of gender where males and females are compared.

Quasi-experiments are often used when it might be unethical to manipulate an IV and a common feature of such experiments is that random allocation of participants is not possible.

Strengths and Weaknesses of Quasi-Experiments

• A weaknesses of quasi-experiments is randomisation is not used with the samples. This limits the study's ability to draw a causal association between cause and effect but also rule out confounding variables which are more likely to occur. This would make results less reliable and difficult to replicate due to the lack of control over the IV.
• Another issue with quasi-experiments using non-random samples is this increases the possibility of having groups that are not comparable due to significant differences in the samples. This means the results may be due to these significant differences rather than the IV that is being measured which would mean study's lack internal validity and may not be measuring what they intended to.
• A strength of quasi-experiments is it allows researchers to test a naturally occurring IV that may be unethical to test in the context of an experiment. This avoids ethical issues that would prevent such an experiment from taking place.
• Another strength of quasi-experiments is they can be argued to be more realistic and have ecological validity as they look to test something that is naturally occurring. Therefore, the behaviours observed should also be more realistic and have validity.

Observational Techniques

• Observational techniques. Types of observation: naturalistic and controlled observation; covert and overt observation; participant and non-participant observation.

During observational study, a researcher will watch or listen to participants engaging in whatever behaviour is being studied and record these observations. An important aspect of observations is they are often used in an experiment as a way to measure the dependent variable. Therefore, observations are less of a research method and more of a technique that is used in conjunction with other research methods.

There are different types of observational techniques that are used which we will explore.

Naturalistic and Controlled Observations

In a naturalistic observation, behaviour is studied in a natural situation where everything has been left as it would be normally without interference from the researcher. Examples of naturalistic observations might include children playing in their normal environment i.e. a nursery or an animal being observed in an environment that is natural to them such as a zoo (if raised in captivity) or the wild. 

During such observations, researchers will normally take great care to not to intrude or interfere with the behaviour they are observing to ensure the behaviour is realistic.

Controlled observations involve variables in the environment being altered by the researcher which would reduce the 'naturalness' of the environment. This could therefore alter the naturalness of the behaviour being studied too. Participants are also more likely to be aware of being observed as the study may be conducted in a laboratory setting.

Controlled observations allow researchers to investigate the effects of one variable on another more directly (the IV on the DV) and also allows researchers to randomly assign participants to different groups for comparison.

Evaluating Naturalistic and Controlled Observations

• A strength of naturalistic observations is the behaviour observed is more realistic as it occurs in a natural habitat and therefore the findings are seen to be more valid and applicable to generalisation. Further strengths of naturalistic observation includes the ability to study something that may be unethical or difficult to setup as the independent variable is naturally occurring. For example, observing animals in their natural habitat would be difficult to setup in an artificial setting, or predatory behaviour animals engage in that might be deemed unethical to recreate.
• Weaknesses of naturalistic observation include the inability to manipulate variables which makes it difficult to establish causal relationships with certainty. As researchers are unable to isolate an independent variable on its own with naturalistic observations, it is possible that the dependent variable observed may be a consequence of other confounding variables that haven't been controlled for. 
• Another weakness is the information gathered may be subjective and based on the researchers own interpretations and observer biases. As the study lacks control to isolate the independent variable, the researcher observes the behaviour and makes recording, sometimes against defined criteria. An issue with this is, it may still be subject to interpretations, mistakes and biases. 
• A strength of controlled observations is they give researchers the ability to isolate an independent variable more directly through laboratory settings. This greater control allows researchers to measure how the IV affects the DV with greater certainty and limit extraneous variables from influencing the results. 
• A weakness of controlled observations is they are less realistic and lack ecological validity due to the artificial setting which is normally a laboratory setting. This means the behaviour by participants may not be indicative of real world behaviour as the nature of the experiment is artificially setup. 
• Another issue with controlled observations is the risk of demand characteristics. Observers are aware they are being observed and may engage in behaviour that either looks to please researchers and their expectations or be different to what would normally happen in the real world. Due to this, the results collected may not generalise to real world behaviour and be invalid.

Covert and Overt Observations

Covert observation refers to studies where participants are unaware that they are being observed by researchers. This might involve naturalistic experiments conducted in everyday environments for participants or animals.

Overt observation refers to studies where participants are aware they are being observed. This usually involved a controlled environment such as a laboratory setting.

Participant and Non-Participant Observation

Participant observation involves the observers/researchers becoming actively involved in the situation being studied to gain a more 'hands-on' perspective. An example of participant observation would be Milgram's Obedience study.

Non-participant observation means observers/researchers will not become actively involved in the behaviour being studied. An example of this would be Ainsworth's Strange Situation study.

Self-Report Techniques

Psychologists attempt to understand behaviour and self-reporting techniques require participants to report on themselves. This is typically done by having participants answer questions or respond in some way to statements. Two principle methods for self-reporting include the use of questionnaires and interviews which may be structured interviews or unstructured interviews.

Questionnaires

A questionnaire is a list of predetermined questions to which participants are required to respond. 

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Overview – Research Methods

Research methods are how psychologists and scientists come up with and test their theories. The A level psychology syllabus covers several different types of studies and experiments used in psychology as well as how these studies are conducted and reported:

• Types of psychological studies (including experiments , observations , self-reporting , and case studies )
• Scientific processes (including the features of a study , how findings are reported , and the features of science in general )
• Data handling and analysis (including descriptive statistics and different ways of presenting data ) and inferential testing

Note: Unlike all other sections across the 3 exam papers, research methods is worth 48 marks instead of 24. Not only that, the other sections often include a few research methods questions, so this topic is the most important on the syllabus!

Example question: Design a matched pairs experiment the researchers could conduct to investigate differences in toy preferences between boys and girls. [12 marks]

Types of study

There are several different ways a psychologist can research the mind, including:

• Experiments
• Observation
• Self-reporting

Case studies

Each of these methods has its strengths and weaknesses. Different methods may be better suited to different research studies.

Experimental method

The experimental method looks at how variables affect outcomes. A variable is anything that changes between two situations ( see below for the different types of variables ). For example, Bandura’s Bobo the doll experiment looked at how changing the variable of the role model’s behaviour affected how the child played.

Experimental designs

Experiments can be designed in different ways, such as:

• Independent groups: Participants are divided into two groups. One group does the experiment with variable 1, the other group does the experiment with variable 2. Results are compared.
• Repeated measures: Participants are not divided into groups. Instead, all participants do the experiment with variable 1, then afterwards the same participants do the experiment with variable 2. Results are compared.

A matched pairs design is another form of independent groups design. Participants are selected. Then, the researchers recruit another group of participants one-by-one to match the characteristics of each member of the original group. This provides two groups that are relevantly similar and controls for differences between groups that might skew results. The experiment is then conducted as a normal independent groups design.

Types of experiment

Laboratory vs. field experiment.

Experiments are carried out in two different types of settings:

• E.g. Bandura’s Bobo the doll experiment or Asch’s conformity experiments
• E.g. Bickman’s study of the effects of uniforms on obedience

Strengths of laboratory experiment over field experiment:

The controlled environment of a laboratory experiment minimises the risk of other variables outside the researchers’ control skewing the results of the trial, making it more clear what (if any) the causal effects of a variable are. Because the environment is tightly controlled, any changes in outcome must be a result of a change in the variable.

Weaknesses of laboratory experiment over field experiment:

However, the controlled nature of a laboratory experiment might reduce its ecological validity . Results obtained in an artificial environment might not translate to real-life. Further, participants may be influenced by demand characteristics : They know they are taking part in a test, and so behave how they think they’re expected to behave rather than how they would naturally behave.

Natural and quasi experiment

Natural experiments are where variables vary naturally. In other words, the researcher can’t or doesn’t manipulate the variables . There are two types of natural experiment:

• E.g. studying the effect a change in drug laws (variable) has on addiction
• E.g. studying differences between men (variable) and women (variable)

Observational method

The observational method looks at and examines behaviour. For example, Zimbardo’s prison study observed how participants behaved when given certain social roles.

Observational design

Behavioural categories.

An observational study will use behavioural categories to prioritise which behaviours are recorded and ensure the different observers are consistent in what they are looking for.

For example, a study of the effects of age and sex on stranger anxiety in infants might use the following behavioural categories to organise observational data:

Rather than writing complete descriptions of behaviours, the behaviours can be coded into categories. For example, IS = interacted with stranger, and AS = avoided stranger. Researchers can also create numerical ratings to categorise behaviour, like the anxiety rating example above.

Inter-observer reliability : In order for observations to produce reliable findings, it is important that observers all code behaviour in the same way. For example, researchers would have to make it very clear to the observers what the difference between a ‘3’ on the anxiety scale above would be compared to a ‘7’. This inter-observer reliability avoids subjective interpretations of the different observers skewing the findings.

Event and time sampling

Because behaviour is constant and varied, it may not be possible to record every single behaviour during the observation period. So, in addition to categorising behaviour , study designers will also decide when to record a behaviour:

• Event sampling: Counting how many times the participant behaves in a certain way.
• Time sampling: Recording participant behaviour at regular time intervals. For example, making notes of the participant’s behaviour after every 1 minute has passed.

Note: Don’t get event and time sampling confused with participant sampling , which is how researchers select participants to study from a population.

Types of observation

Naturalistic vs. controlled.

Observations can be made in either a naturalistic or a controlled setting:

• E.g. setting up cameras in an office or school to observe how people interact in those environments
• E.g. Ainsworth’s strange situation or Zimbardo’s prison study

Covert vs. overt

Observations can be either covert or overt :

• E.g. setting up hidden cameras in an office
• E.g. Zimbardo’s prison study

Participant vs. non-participant

In observational studies, the researcher/observer may or may not participate in the situation being observed:

• E.g. in Zimbardo’s prison study , Zimbardo played the role of prison superintendent himself
• E.g. in Bandura’s Bobo the doll experiment and Ainsworth’s strange situation , the observers did not interact with the children being observed

Self-report method

Self-report methods get participants to provide information about themselves. Information can be obtained via questionnaires or interviews .

Types of self-report

Questionnaires.

A questionnaire is a standardised list of questions that all participants in a study answer. For example, Hazan and Shaver used questionnaires to collate self-reported data from participants in order to identify correlations between attachment as infants and romantic attachment as adults.

Questions in a questionnaire can be either open or closed :

• >8 hours
• E.g. “How did you feel when you thought you were administering a lethal shock?” or “What do you look for in a romantic partner and why?”

Strengths of questionnaires:

• Quantifiable: Closed questions provide quantifiable data in a consistent format, which enables to statistically analyse information in an objective way.
• Replicability: Because questionnaires are standardised (i.e. pre-set, all participants answer the same questions), studies involving them can be easily replicated . This means the results can be confirmed by other researchers, strengthening certainty in the findings.

Weaknesses of questionnaires:

• Biased samples: Questionnaires handed out to people at random will select for participants who actually have the time and are willing to complete the questionnaire. As such, the responses may be biased towards those of people who e.g. have a lot of spare time.
• Dishonest answers: Participants may lie in their responses – particularly if the true answer is something they are embarrassed or ashamed of (e.g. on controversial topics or taboo topics like sex)
• Misunderstanding/differences in interpretation: Different participants may interpret the same question differently. For example, the “are you religious?” example above could be interpreted by one person to mean they go to church every Sunday and pray daily, whereas another person may interpret religious to mean a vague belief in the supernatural.
• Less detail: Interviews may be better suited for detailed information – especially on sensitive topics – than questionnaires. For example, participants are unlikely to write detailed descriptions of private experiences in a questionnaire handed to them on the street.

In an interview , participants are asked questions in person. For example, Bowlby interviewed 44 children when studying the effects of maternal deprivation.

Interviews can be either structured or unstructured :

• Structured interview: Questions are standardised and pre-set. The interviewer asks all participants the same questions in the same order.
• Unstructured interview: The interviewer discusses a topic with the participant in a less structured and more spontaneous way, pursuing avenues of discussion as they come up.

Interviews can also be a cross between the two – these are called semi-structured interviews .

Strengths of interviews:

• More detail: Interviews – particularly unstructured interviews conducted by a skilled interviewer – enable researchers to delve deeper into topics of interest, for example by asking follow-up questions. Further, the personal touch of an interviewer may make participants more open to discussing personal or sensitive issues.
• Replicability: Structured interviews are easily replicated because participants are all asked the same pre-set list of questions. This replicability means the results can be confirmed by other researchers, strengthening certainty in the findings.

Weaknesses of interviews:

• Lack of quantifiable data: Although unstructured interviews enable researchers to delve deeper into interesting topics, this lack of structure may produce difficulties in comparing data between participants. For example, one interview may go down one avenue of discussion and another interview down a different avenue. This qualitative data may make objective or statistical analysis difficult.
• Interviewer effects : The interviewer’s appearance or character may bias the participant’s answers. For example, a female participant may be less comfortable answering questions on sex asked by a male interviewer and and thus give different answers than if she were asked by a female interviewer.

Note: This topic is A level only, you don’t need to learn about case studies if you are taking the AS exam only.

Case studies are detailed investigations into an individual, a group of people, or an event. For example, the biopsychology page describes a case study of a young boy who had the left hemisphere of his brain removed and the effects this had on his language skills.

In a case study, researchers use many of the methods described above – observation , questionnaires , interviews – to gather data on a subject. However, because case studies are studies of a single subject, the data they provide is primarily qualitative rather than quantitative . This data is then used to build a case history of the subject. Researchers then interpret this case history to draw their conclusions.

Types of case study

Typical vs. unusual cases.

Most case studies focus on unusual individuals, groups, and events.

Longitudinal

Many case studies are longitudinal . This means they take place over an extended time period, with researchers checking in with the subject at various intervals. For example, the case study of the boy who had his left hemisphere removed collected data on the boy’s language skills at ages 2.5, 4, and 14 to see how he progressed.

Strengths of case studies:

• Provides detailed qualitative data: Rather than focusing on one or two aspects of behaviour at a single point in time (e.g. in an experiment ), case studies produce detailed qualitative data.
• Allows for investigation into issues that may be impractical or unethical to study otherwise. For example, it would be unethical to remove half a toddler’s brain just to experiment , but if such a procedure is medically necessary then researchers can use this opportunity to learn more about the brain.

Weaknesses of case studies:

• Lack of scientific rigour: Because case studies are often single examples that cannot be replicated , the results may not be valid when applied to the general population.
• Researcher bias: The small sample size of case studies also means researchers need to apply their own subjective interpretation when drawing conclusions from them. As such, these conclusions may be skewed by the researcher’s own bias and not be valid when applied more generally. This criticism is often directed at Freud’s psychoanalytic theory because it draws heavily on isolated case studies of individuals.

Scientific processes

This section looks at how science works more generally – in particular how scientific studies are organised and reported . It also covers ways of evaluating a scientific study.

Study features and design

Studies will usually have an aim . The aim of a study is a description of what the researchers are investigating and why . For example, “to investigate the effect of SSRIs on symptoms of depression” or “to understand the effect uniforms have on obedience to authority”.

Studies seek to test a hypothesis . The experimental/alternate hypothesis of a study is a testable prediction of what the researchers expect to happen.

• E.g. “That SSRIs will reduce symptoms of depression” or “subjects are more likely to comply when orders are issued by someone wearing a uniform”.
• E.g. “That SSRIs have no effect on symptoms on depression” or “subject conformity will be the same when orders are issued by someone wearing a uniform as when orders are issued by someone bot wearing a uniform”

Either the experimental/alternate hypothesis or the null hypothesis will be supported by the results of the experiment.

It’s often not possible or practical to conduct research on everyone your study is supposed to apply to. So, researchers use sampling to select participants for their study.

• E.g. all humans, all women, all men, all children, etc.
• E.g. 10,000 humans, 200 women from the USA, children at a certain school

For example, the target population (i.e. who the results apply to) of Asch’s conformity experiments is all humans – but Asch didn’t conduct the experiment on that many people! Instead, Asch recruited 123 males and generalised the findings from this sample to the rest of the population.

Researchers choose from different sampling techniques – each has strengths and weaknesses.

Sampling techniques

Random sampling.

The random sampling method involves selecting participants from a target population at random – such as by drawing names from a hat or using a computer program to select them. This method means each member of the population has an equal chance of being selected and thus is not subject to any bias.

Strengths of random sampling:

• Unbiased: Selecting participants by random chance reduces the likelihood that researcher bias will skew the results of the study.
• Representative: If participants are selected at random – particularly if the sample size is large – it is likely that the sample will be representative of the population as a whole. For example, if the ratio of men:women in a population is 50:50 and participants are selected at random, it is likely that the sample will also have a ratio of men to women that is 50:50.

Weaknesses of random sampling:

• Impractical: It’s often impractical/impossible to include all members of a target population for selection. For example, it wouldn’t be feasible for a study on women to include the name of every woman on the planet for selection. But even if this was done, the randomly selected women may not agree to take part in the study anyway.

Systematic sampling

The systematic sampling method involves selecting participants from a target population by selecting them at pre-set intervals. For example, selecting every 50th person from a list, or every 7th, or whatever the interval is.

Strengths of systematic sampling:

• Unbiased and representative: Like random sampling , selecting participants according to a numerical interval provides an objective means of selecting participants that prevents researcher bias being able to skew the sample. Further, because the sampling method is independent of any particular characteristic (besides the arbitrary characteristic of the participant’s order in the list) this sample is likely to be representative of the population as a whole.

Weaknesses of systematic sampling:

• Unexpected bias: Some characteristics could occur more or less frequently at certain intervals, making a sample that is selected based on that interval biased. For example, houses tend to be have even numbers on one side of a road and odd numbers on the other. If one side of the road is more expensive than the other and you select every 4th house, say, then you will only select even numbers from one side of the road – and this sample may not be representative of the road as a whole.

Stratified sampling

The stratified sampling method involves dividing the population into relevant groups for study, working out what percentage of the population is in each group, and then randomly sampling the population according to these percentages.

For example, let’s say 20% of the population is aged 0-18, and 50% of the population is aged 19-65, and 30% of the population is aged >65. A stratified sample of 100 participants would randomly select 20x 0-18 year olds, 50x 19-65 year olds, and 30x people over 65.

Strengths of stratified sampling:

• Representative: The stratification is deliberately designed to yield a sample that is representative of the population as a whole. You won’t get people with certain characteristics being over- or under-represented within the sample.
• Unbiased: Because participants within each group are selected randomly , researcher bias is unable to skew who is included in the study.

Weaknesses of stratified sampling:

• Requires knowledge of population breakdown: Researchers need to accurately gauge what percentage of the population falls into what group. If the researchers get these percentages wrong, the sample will be biased and some groups will be over- or under-represented.

Opportunity and volunteer sampling

The opportunity and volunteer sampling methods:

• E.g. Approaching people in the street and asking them to complete a questionnaire.
• E.g. Placing an advert online inviting people to complete a questionnaire.

Strengths of opportunity and volunteer sampling:

• Quick and easy: Approaching participants ( opportunity sampling) or inviting participants ( volunteer sampling) is quick and straightforward. You don’t have to spend time compiling details of the target population (like in e.g. random or systematic sampling ), nor do you have to spend time dividing participants according to relevant categories (like in stratified sampling ).
• May be the only option: With natural experiments – where a variable changes as a result of something outside the researchers’ control – opportunity sampling may be the only viable sampling method. For example, researchers couldn’t randomly sample 10 cities from all the cities in the world and change the drug laws in those cities to see the effects – they don’t have that kind of power. However, if a city is naturally changing its drug laws anyway, researchers could use opportunity sampling to study that city for research.

Weaknesses of opportunity and volunteer sampling:

• Unrepresentative: The pool of participants will likely be biased towards certain kinds of people. For example, if you conduct opportunity sampling on a weekday at 10am, this sample will likely exclude people who are at work. Similarly, volunteer sampling is likely to exclude people who are too busy to take part in the study.

Independent vs. dependent variables

If the study involves an experiment , the researchers will alter an independent variable to measure its effects on a dependent variable :

• E.g. In Bickman’s study of the effects of uniforms on obedience , the independent variable was the uniform of the person giving orders.
• E.g. In Bickman’s study of the effects of uniforms on obedience , the dependent variable was how many people followed the orders.

Extraneous and confounding variables

In addition to the variables actually being investigated ( independent and dependent ), there may be additional (unwanted) variables in the experiment. These additional variables are called extraneous variables .

Researchers must control for extraneous variables to prevent them from skewing the results and leading to false conclusions. When extraneous variables are not properly controlled for they are known as confounding variables .

For example, if you’re studying the effect of caffeine on reaction times, it might make sense to conduct all experiments at the same time of day to prevent this extraneous variable from confounding the results. Reaction times change throughout the day and so if you test one group of subjects at 3pm and another group right before they go to bed, you may falsely conclude that the second group had slower reaction times.

Operationalisation of variables

Operationalisation of variables is where researchers clearly and measurably define the variables in their study.

For example, an experiment on the effects of sleep ( independent variable ) on anxiety ( dependent variable ) would need to clearly operationalise each variable. Sleep could be defined by number of hours spent in bed, but anxiety is a bit more abstract and so researchers would need to operationalise (i.e. define) anxiety such that it can be quantified in a measurable and objective way.

If variables are not properly operationalised, the experiment cannot be properly replicated , experimenters’ subjective interpretations may skew results, and the findings may not be valid .

Pilot studies

A pilot study is basically a practice run of the proposed research project. Researchers will use a small number of participants and run through the procedure with them. The purpose of this is to identify any problems or areas for improvement in the study design before conducting the research in full. A pilot study may also give an early indication of whether the results will be statistically significant .

For example, if a task is too easy for participants, or it’s too obvious what the real purpose of an experiment is, or questions in a questionnaire are ambiguous, then the results may not be valid . Conducting a pilot study first may save time and money as it enables researchers to identify and address such issues before conducting the full study on thousands of participants.

Study reporting

Features of a psychological report.

The report of a psychological study (research paper) typically contains the following sections in the following order:

• Title: A short and clear description of the research.
• Abstract: A summary of the research. This typically includes the aim and hypothesis , methods, results, and conclusion.
• Introduction: Funnel technique: Broad overview of the context (e.g. current theories, previous studies, etc.) before focusing in on this particular study, why it was conducted, its aims and hypothesis .
• Study design: This will explain what method was used (e.g. experiment or observation ), how the study was designed (e.g. independent groups or repeated measures ), and identification and operationalisation of variables .
• Participants: A description of the target population to be studied, the sampling method , how many participants were included.
• Equipment used: A description of any special equipment used in the study and how it was used.
• Standardised procedure: A detailed step-by-step description of how the study was conducted. This allows for the study to be replicated by other researchers.
• Controls : An explanation of how extraneous variables were controlled for so as to generate accurate results.
• Results: A presentation of the key findings from the data collected. This is typically written summaries of the raw data ( descriptive statistics ), which may also be presented in tables , charts, graphs , etc. The raw data itself is typically included in appendices.
• Discussion: An explanation of what the results mean and how they relate to the experimental hypothesis (supporting or contradicting it), any issues with how results were generated, how the results fit with other research, and suggestions for future research.
• Conclusion: A short summary of the key findings from the study.
• Book: Milgram, S., 2010. Obedience to Authority . 1st ed. Pinter & Martin.
• Journal article: Bandura, A., Ross, D. and Ross, S., 1961. Transmission of Aggression through Imitation of Aggressive Models . The Journal of Abnormal and Social Psychology, 63(3), pp.575-582.
• Appendices: This is where you put any supporting materials that are too detailed or long to include in the main report. For example, the raw data collected from a study, or the complete list of questions in a questionnaire .

Peer review

Peer review is a way of assessing the scientific credibility of a research paper before it is published in a scientific journal. The idea with peer review is to prevent false ideas and bad research from being accepted as fact.

It typically works as follows: The researchers submit their paper to the journal they want it to be published in, and the editor of that journal sends the paper to expert reviewers (i.e. psychologists who are experts in that area – the researchers’ ‘peers’) who evaluate the paper’s scientific validity. The reviewers may accept the paper as it is, accept it with a few changes, reject it and suggest revisions and resubmission at a later date, or reject it completely.

There are several different methods of peer review:

• Open review: The researchers and the reviewers are known to each other.
• Single-blind: The researchers do not know the names of the reviewers. This prevents the researchers from being able to influence the reviewer. This is the most common form of peer review.
• Double-blind: The researchers do not know the names of the reviewers, and the reviewers do not know the names of the researchers. This additionally prevents the reviewer’s bias towards the researcher from influencing their decision whether to accept their paper or not.

Criticisms of peer review:

• Bias: There are several ways peer review can be subject to bias. For example, academic research (particularly in niche areas) takes place among a fairly small circle of people who know each other and so these relationships may affect publication decisions. Further, many academics are funded by organisations and companies that may prefer certain ideas to be accepted as scientifically legitimate, and so this funding may produce conflicts of interest.
• Doesn’t always prevent fraudulent/bad research from being published: There are many examples of fraudulent research passing peer review and being published (see this Wikipedia page for examples).
• Prevents progress of new ideas: Reviewers of papers are typically older and established academics who have made their careers within the current scientific paradigm. As such, they may reject new or controversial ideas simply because they go against the current paradigm rather than because they are unscientific.
• Plagiarism: In single-blind and double-blind peer reviews, the reviewer may use their anonymity to reject or delay a paper’s publication and steal the good ideas for themself.
• Slow: Peer review can mean it takes months or even years between the researcher submitting a paper and its publication.

Study evaluation

In psychological studies, ethical issues are questions of what is morally right and wrong. An ethically-conducted study will protect the health and safety of the participants involved and uphold their dignity, privacy, and rights.

To provide guidance on this, the British Psychological Association has published a code of human research ethics :

• Participants are told the project’s aims , the data being collected, and any risks associated with participation.
• Participants have the right to withdraw or modify their consent at any time.
• Researchers can use incentives (e.g. money) to encourage participation, but these incentives can’t be so big that they would compromise a participant’s freedom of choice.
• Researchers must consider the participant’s ability to consent (e.g. age, mental ability, etc.)
• Prior (general) consent: Informing participants that they will be deceived without telling them the nature of the deception. However, this may affect their behaviour as they try to guess the real nature of the study.
• Retrospective consent: Informing participants that they were deceived after the study is completed and asking for their consent. The problem with this is that if they don’t consent then it’s too late.
• Presumptive consent: Asking people who aren’t participating in the study if they would be willing to participate in the study. If these people would be willing to give consent, then it may be reasonable to assume that those taking part in the study would also give consent.
• Confidentiality: Personal data obtained about participants should not be disclosed (unless the participant agreed to this in advance). Any data that is published will not be publicly identifiable as the participant’s.
• Debriefing: Once data gathering is complete, researchers must explain all relevant details of the study to participants – especially if deception was involved. If a study might have harmed the individual (e.g. its purpose was to induce a negative mood), it is ethical for the debrief to address this harm (e.g. by inducing a happy mood) so that the participant does not leave the study in a worse state than when they entered.

Reliability

Study results are reliable if the same results can be consistently replicated under the same circumstances. If results are inconsistent then the study is unreliable.

Note: Just because a study is reliable, its results are not automatically valid . A broken tape measure may reliably (i.e. consistently) record a person’s height as 200m, but that doesn’t mean this measurement is accurate.

There are several ways researchers can assess a study’s reliability:

Test-retest

Test-retest is when you give the same test to the same person on two different occasions. If the results are the same or similar both times, this suggests they are reliable.

For example, if your study used scales to measure participants’ weight, you would expect the scales to record the same (or a very similar) weight for the same person in the morning as in the evening. If the scales said the person weighed 100kg more later that same day, the scales (and therefore the results of the study) would be unreliable.

Inter-observer

Inter-observer reliability is a way to test the reliability of observational studies .

For example, if your study required observers to assess participants’ anxiety levels, you would expect different observers to grade the same behaviour in the same way. If one observer rated a participant’s behaviour a 3 for anxiety, and another observer rated the exact same behaviour an 8, the results would be unreliable.

Inter-observer reliability can be assessed mathematically by looking for correlation between observers’ scores. Inter-observer reliability can be improved by setting clearly defined behavioural categories .

Study results are valid if they accurately measure what they are supposed to. There are several ways researchers can assess a study’s validity:

• E.g. let’s say you come up with a new test to measure participants’ intelligence levels. If participants scoring highly on your test also scored highly on a standardised IQ test and vice versa, that would suggest your test has concurrent validity because participants’ scores are correlated with a known accurate test.
• E.g. a study that measures participants’ intelligence levels by asking them when their birthday is would not have face validity. Getting participants to complete a standardised IQ test would have greater face validity.
• E.g. let’s say your study was supposed to measure aggression levels in response to someone annoying. If the study was conducted in a lab and the participant knew they were taking part in a study, the results probably wouldn’t have much ecological validity because of the unrealistic environment.
• E.g. a study conducted in 1920 that measured participants’ attitudes towards social issues may have low temporal validity because societal attitudes have changed since then.

Control of extraneous variables

There are several different types of extraneous variables that can reduce the validity of a study. A well-conducted psychological study will control for these extraneous variables so that they do not skew the results.

Demand characteristics

Demand characteristics are extraneous variables where the demands of a study make participants behave in ways they wouldn’t behave outside of the study. This reduces the study’s ecological validity .

For example, if a participant guesses the purpose of an experiment they are taking part in, they may try to please the researcher by behaving in the ‘right’ way rather than the way they would naturally. Alternatively, the participant might rebel against the study and deliberately try to sabotage it (e.g. by deliberately giving wrong answers).

In some study designs, researchers can control for demand characteristics using single- blind methods. For example, a drug trial could give half the participants the actual drug and the other half a placebo but not tell participants which treatment they received. This way, both groups will have equal demand characteristics and so any differences between them should be down to the drug itself.

Investigator effects

Investigator effects are another extraneous variable where the characteristics of the researcher affect the participant’s behaviour. Again, this reduces the study’s ecological validity .

Many characteristics – e.g. the researcher’s age, gender, accent, what they’re wearing – could potentially influence the participant’s responses. For example, in an interview about sex, females may feel less comfortable answering questions asked by a male interviewer and thus give different answers than if they were asked by a female. The researcher’s biases may also come across in their body language or tone of voice, affecting the participant’s responses.

In some study designs, researchers can control for demand characteristics using double- blind methods. In a double-blind drug trial, for example, neither the participants nor the researchers know which participants get the actual drug and which get the placebo. This way, the researcher is unable to give any clues (consciously or unconsciously) to participants that would affect their behaviour.

Participant variables

Participant variables are differences between participants. These can be controlled for by random allocation .

For example, in an experiment on the effect of caffeine on reaction times, participants would be randomly allocated into either the caffeine group or the non-caffeine group. A non -random allocation method, such as allocating caffeine to men and placebo to women, could mean variables in the allocation method (in this case gender) skew the results. When participants are randomly allocated, any extraneous variables (e.g. gender in this case) will be allocated evenly between each group and so not skew the results of one group more than the other.

Situational variables

Situational variables are the environment the experiment is conducted in. These can be controlled for by standardisation .

For example, all the tests of caffeine on reaction times would be conducted in the same room, at the same time of day, using the same equipment, and so on to prevent these features of the environment from skewing the results.

In a repeated measures experiment, researchers may use counterbalancing to control for the order in which tasks are completed.

For example, half of participants would do task A followed by task B, and the other half would do task B followed by task A.

Implications of psychological research for the economy

Psychological research often has practical applications in real life. The following are some examples of how psychological findings may affect the economy:

• Attachment : Bowlby’s maternal deprivation hypothesis suggests that periods of extended separation between mother and child before age 3 are harmful to the child’s psychological development. And if mothers stay at home during this period, they can’t go out to work. However, some more recent research challenges Bowlby’s conclusions, suggesting that substitutes (e.g. the father , or nursery care) can care for the child, allowing the mother to go back to work sooner and remain economically active.
• Depression : Psychological research has found effective therapies for treating depression, such as cognitive behavioural therapy and SSRIs. The benefits of such therapies – if they are effective – are likely to outweigh the costs because they enable the person to return to work and pay taxes, as well avoiding long-term costs to the health service.
• OCD : Similar to above: Drug therapies (e.g. SSRIs) and behavioural approaches (e.g. CBT) may alleviate OCD symptoms, enabling OCD sufferers to return to work, pay taxes, and avoid reliance on healthcare services.
• Memory : Public money is required to fund police investigations. Psychological tools, such as the cognitive interview , have improved the accuracy of eyewitness testimonies, which equates to more efficient use of police time and resources.

Features of science

Theory construction and hypothesis testing.

Science works by making empirical observations of the world, formulating hypotheses /theories that explain these observations, and repeatedly testing these hypotheses /theories via experimentation.

• E.g. A tape measure provides a more objective measurement of something compared to a researcher’s guess. Similarly, a set of scales is a more objective way of determining which of two objects is heavier than a researcher lifting each up and giving their opinion.
• E.g. Burger (2009) replicated Milgram’s experiments with similar results.
• E.g. The hypothesis that “water boils at 100°c” could be falsified by an experiment where you heated water to 999°c and it didn’t boil. In contrast, “everything doubles in size every 10 seconds” could not be falsified by any experiment because whatever equipment you used to measure everything would also double in size.
• Freud’s psychodynamic theories are often criticised for being unfalsifiable: There’s not really any observations that could disprove them because every possible behaviour (e.g. crying or not crying) could be explained as the result of some unconscious thought process.

Paradigm shifts

Philosopher Thomas Kuhn argues that science is not as unbiased and objective as it seems. Instead, the majority of scientists just accept the existing scientific theories (i.e. the existing paradigm) as true and then find data that supports these theories while ignoring/rejecting data that refutes them.

Rarely, though, minority voices are able to successfully challenge the existing paradigm and replace it with a new one. When this happens it is a paradigm shift . An example of a paradigm shift in science is that from Newtonian gravity to Einstein’s theory of general relativity.

Data handling and analysis

Types of data, quantitative vs. qualitative.

Data from studies can be quantitative or qualitative :

• Quantitative: Numerical
• Qualitative: Non-numerical

For example, some quantitative data in the Milgram experiment would be how many subjects delivered a lethal shock. In contrast, some qualitative data would be asking the subjects afterwards how they felt about delivering the lethal shock.

Strengths of quantitative data / weaknesses of qualitative data:

• Can be compared mathematically and scientifically: Quantitative data enables researchers to mathematically and objectively analyse data. For example, mood ratings of 7 and 6 can be compared objectively, whereas qualitative assessments such as ‘sad’ and ‘unhappy’ are hard to compare scientifically.

Weaknesses of quantitative data / strengths of qualitative data:

• Less detailed: In reducing data to numbers and narrow definitions, quantitative data may miss important details and context.

Content analysis

Although the detail of qualitative data may be valuable, this level of detail can also make it hard to objectively or mathematically analyse. Content analysis is a way of analysing qualitative data. The process is as follows:

• E.g. A bunch of unstructured interviews on the topic of childhood
• E.g. Discussion of traumatic events, happy memories, births, and deaths
• E.g. Researchers listen to the unstructured interviews and count how often traumatic events are mentioned
• Statistical analysis is carried out on this data

Primary vs. secondary

Researchers can produce primary data or use secondary data to achieve the research aims of their study:

• Primary data: Original data collected for the study
• Secondary data: Data from another study previously conducted

Meta-analysis

A meta-analysis is a study of studies. It involves taking several smaller studies within a certain research area and using statistics to identify similarities and trends within those studies to create a larger study.

We have looked at some examples of meta-analyses elsewhere in the course such as Van Ijzendoorn’s meta-analysis of several strange situation studies and Grootheest et al’s meta-analysis of twin studies on OCD .

A good meta-analysis is often more reliable than a regular study because it is based on a larger data set, and any issues with one single study will be balanced out by the other studies.

Descriptive statistics

Measures of central tendency: mean, median, mode.

Mean , median , and mode are measures of central tendency . In other words, they are ways of reducing large data sets into averages .

The mean is calculated by adding all the numbers in a set together and dividing the total by the number of numbers.

• Example set: 22, 78, 3, 33, 90
• 22+78+3+33+90=226
• The mean is 45.2
• Uses all data in the set.
• Accurate: Provides a precise number based on all the data in a set.

Weaknesses:

• E.g.: 1, 3, 2, 5, 9, 4, 913 <- the mean is 133.9, but the 913 could be a measurement error or something and thus the mean is not representative of the data set

The median is calculated by arranging all the numbers in a set from smallest to biggest and then finding the number in the middle. Note: If the total number of numbers is odd, you just pick the middle one. But if the total number of numbers is even, you take the mid-point between the two numbers in the middle.

• Example set: 20, 66, 85, 45, 18, 13, 90, 28, 9
• 9, 13, 18, 20, 28 , 45, 66, 85, 90
• The median is 28
• Won’t be skewed by freak scores (unlike the mean).
• E.g.: 1, 1, 3 , 9865, 67914 <- 3 is not really representative of the larger numbers in the set.
• Less accurate/sensitive than the mean.

The mode is calculated by counting which is the most commonly occurring number in a set.

• Example set: 7, 7, 20 , 16, 1, 20 , 25, 16, 20 , 9
• There are two 7’s, but three 20’s
• The mode is 20
• Makes more sense for presenting the central tendency in data sets with whole numbers. For example, the average number of limbs for a human being will have a mean of something like 3.99, but a mode of 4.
• Does not use all the data in a set.
• A data set may have more than one mode.

Measures of dispersion: Range and standard deviation

Range and standard deviation are measures of dispersion . In other words, they quantify how much scores in a data set vary .

The range is calculated by subtracting the smallest number in the data set from the largest number.

• Example set: 59, 8, 7, 84, 9, 49, 14, 75, 88, 11
• The largest number is 88
• The smallest number is 7
• The range is 81
• Easy and quick to calculate: You just subtract one number from another
• Accounts for freak scores (highest and lowest)
• Can be skewed by freak scores: The difference between the biggest and smallest numbers can be skewed by a single anomalous result or error, which may give an exaggerated impression of the data distribution compared to standard deviation .
• 4, 4, 5, 5, 5, 6, 6, 7, 19
• 4, 16, 16, 17, 17, 17, 18, 19 19

Standard deviation

The standard deviation (σ) is a measure of how much numbers in a data set deviate from the mean (average). It is calculated as follows:

• Example data set: 59, 79, 43, 42, 81, 100, 38, 54, 92, 62
• Calculate the mean (65)
• -6, 14, -22, -23, 16, 35, -27, -11, 27, -3
• 36, 196, 484, 529, 256, 1225, 729, 121, 729, 9
• 36+196+484+529+256+1225+729+121+729+9=4314
• 4314/10=431.4
• √431.4=20.77
• The standard deviation is 20.77

Note: This method of standard deviation is based on the entire population. There is a slightly different method for calculating based on a sample where instead of dividing by the number of numbers in the second to last step, you divide by the number of numbers-1 (in this case 4314/9=479.333). This gives a standard deviation of 21.89.

• Is less skewed by freak scores: Standard deviation measures the average difference from the mean and so is less likely to be skewed by a single freak score (compared to the range ).
• Takes longer to calculate than the range .

Percentages

A percentage (%) describes how much out of 100 something occurs. It is calculated as follows:

• Example: 63 out of a total of 82 participants passed the test
• 63/82=0.768
• 0.768*100=76.8
• 76.8% of participants passed the test

Percentage change

To calculate a percentage change, work out the difference between the original number and the after number, divide that difference by the original number, then multiply the result by 100:

• Example: He got 80 marks on the test but after studying he got 88 marks on the test
• His test score increased by 10% after studying

Normal and skewed distributions

Normal distribution.

A data set that has a normal distribution will have the majority of scores on or near the mean average. A normal distribution is also symmetrical: There are an equal number of scores above the mean as below it. In a normal distribution, scores become rarer and rarer the more they deviate from the mean.

An example of a normal distribution is IQ scores. As you can see from the histogram below, there are as many IQ scores below the mean as there are above the mean :

When plotted on a histogram , data that follows a normal distribution will form a bell-shaped curve like the one above.

Skewed distribution

Skewed distributions are caused by outliers: Freak scores that throw off the mean . Skewed distributions can be positive or negative :

• Mean > Median > Mode
• Mean < Median < Mode

Correlation

Correlation refers to how closely related two (or more) things are related. For example, hot weather and ice cream sales may be positively correlated: When hot weather goes up, so do ice cream sales.

Correlations are measured mathematically using correlation coefficients (r). A correlation coefficient will be anywhere between +1 and -1:

• r=+1 means two things are perfectly positively correlated: When one goes up , so does the other by the same amount
• r=-1 means two things perfectly negatively correlated: When one goes up , the other goes down by the same amount
• r=0 means two things are not correlated at all: A change in one is totally independent of a change in the other

The following scattergrams illustrate various correlation coefficients:

Presentation of data

For example, the behavioural categories table above presents the raw data of each student in this made-up study. But in the results section, researchers might include another table that compares average anxiety rating scores for males and females.

Scattergrams

For example, each dot on the correlation scattergram opposite could represent a student. The x-axis could represent the number of hours the student studied, and the y-axis could represent the student’s test score.

For example, the results of Loftus and Palmer’s study into the effects of different leading questions on memory could be presented using the bar chart above. It’s not like there are categories in-between ‘contacted’ and ‘hit’, so the bars have gaps between them (unlike a histogram ).

A histogram is a bit like a bar chart but is used to illustrate continuous or interval data (rather than discrete data or whole numbers).

Because the data on the x axis is continuous, there are no gaps between the bars.

For example, the line graph above illustrates 3 different people’s progression in a strength training program over time.

For example, the frequency with which different attachment styles occurred in Ainsworth’s strange situation could be represented by the pie chart opposite.

Inferential testing

Probability and significance.

The point of inferential testing is to see whether a study’s results are statistically significant , i.e. whether any observed effects are as a result of whatever is being studied rather than just random chance.

For example, let’s say you are studying whether flipping a coin outdoors increases the likelihood of getting heads. You flip the coin 100 times and get 52 heads and 48 tails. Assuming a baseline expectation of 50:50, you might take these results to mean that flipping the coin outdoors does increase the likelihood of getting heads. However, from 100 coin flips, a ratio of 52:48 between heads and tails is not very significant and could have occurred due to luck. So, the probability that this difference in heads and tails is because you flipped the coin outside (rather than just luck) is low.

Probability is denoted by the symbol p . The lower the p value, the more statistically significant your results are. You can never get a p value of 0, though, so researchers will set a threshold at which point the results are considered statistically significant enough to reject the null hypothesis . In psychology, this threshold is usually <0.05, which means there is a less than 5% chance the observed effect is due to luck and a >95% chance it is a real effect.

Type 1 and type 2 errors

When interpreting statistical significance, there are two types of errors:

• E.g. The p threshold is <0.05, but the researchers’ results are among the 5% of fluke outcomes that look significant but are just due to luck
• E.g. The p threshold is set too low (e.g. <0.01), and the data falls short (e.g. p=<0.02)

Increasing the sample size reduces the likelihood of type 1 and type 2 errors.

Key maths skills made easy!

Types of statistical test

Note: The inferential tests below are needed for A level only, if you are taking the AS exam , you only need to know the sign test .

There are several different types of inferential test in addition to the sign test . Which inferential test is best for a study will depend on the following three criteria:

• Whether you are looking for a difference or a correlation
• E.g. at the competition there were 8 runners, 12 swimmers, and 6 long jumpers (it’s not like there are in-between measurements between ‘swimmer’ and ‘runner’)
• E.g. First, second, and third place in a race
• E.g. Ranking your mood on a scale of 1-10
• E.g. Weights in kg
• E.g. Heights in cm
• E.g. Times in seconds
• Whether the experimental design is related (i.e. repeated measures ) or unrelated (i.e. independent groups )

The following table shows which inferential test is appropriate according to these criteria:

Note: You won’t have to work out all these tests from scratch, but you may need to:

• Say which of the statistical tests is appropriate (i.e. based on whether it’s a difference or correlation; whether the data is nominal, ordinal, or interval; and whether the data is related or unrelated).
• Identify the critical value from a critical values table and use this to say whether a result (which will be given to you in the exam) is statistically significant.

The sign test

The sign test is a way to calculate the statistical significance of differences between related pairs (e.g. before and after in a repeated measures experiment ) of nominal data. If the observed value (s) is equal or less than the critical value (cv), the results are statistically significant.

Example: Let’s say we ran an experiment on 10 participants to see whether they prefer movie A or movie B .

• n = 9 (because even though there are 10 participants, one participant had no change so we exclude them from our calculation)
• In this case our experimental hypothesis is two-tailed: Participants may prefer movie A or movie B
• (The null hypothesis is that participants like both movies equally)
• In this case, let’s say it’s 0.1
• The experimental hypothesis is two-tailed
• So, in this example, our critical value (cv) is 1
• In this example, there are 2 As, so our observed value (s) is 2
• In this example, the observed value (2) is greater than the critical value (1) and so the results are not statistically significant. This means we must accept the null hypothesis and reject the experimental hypothesis .

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Research Methods - Sampling

Last updated 5 May 2017

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This revision quiz on research methods for A Level Psychology explores the topic of sampling.

Core Topics Revision Flashcards for AQA A-Level Psychology

Printed Resource

• Opportunity Sampling
• Systematic sample
• Random Sampling
• Volunteer Sampling

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Sampling techniques.

Study Notes

A Level Psychology Topic Quiz - Research Methods

Observational techniques - introduction, minority influence - consistency and commitment, research control, research methods - demand characteristics, investigator effects and pilot studies, model answer for question 11 paper 2: as psychology, june 2016 (aqa).

Exam Support

Example Answer for Question 16 Paper 2: AS Psychology, June 2017 (AQA)

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