Research Methods Flashcards

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1
Q

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A

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2
Q

What is the experimental method?

A

A scientific method involving the manipulation of variables to determine cause and effect. Participants are organised and allocated in a certain way. All procedures are standardised so that they are the same for all pps

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3
Q

What is a variable?

A

Any object, event or characteristic varying in some way

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4
Q

What is an independent variable?

A

The thing or factor that is manipulated in an experiment

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5
Q

What is a dependent variable?

A

The thing or factor that is measured by the researcher to see if the IV affected it

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6
Q

What is a control variable?

A

The variables which are held constant or limited in a research study because they could influence the outcomes of the experiment

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7
Q

What are extraneous variables?

A

Any other variables that could affect the DV, so we try to control these so that they don’t vary across any experimental conditions or between pps, e.g. participant variables (age), situational variables (noise levels) and experimenter variables (gender of experimenter)

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8
Q

What are confounding variables?

A

When an extraneous variable is not controlled, it confuses the results by affecting the dependent variable

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9
Q

What does it mean to operationalise a variable?

A

To clearly define a variable so that the IV can be manipulated and the DV can be measured. Some variables are very difficult to operationalise and therefore measure. Sometimes operationalising a variable means that we are only measuring one aspect, however, without accurate operationalisation, results can be unreliable and replicating the study to observe the validity becomes difficult

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10
Q

What are demand characteristics?

A

When conducting research, pps are not always passive, and spend time making sense of the situation. This is an extraneous variable and is difficult to control. Features of a study can help pps guess what the study is and the expected outcome

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11
Q

What can demand characteristics mean?

A

The pps guess the purpose of the study and try to please the researcher - the “please you” effect The pps guess the purpose of the study and try to upset it - the “screw you” effect The pps act unnaturally due to nervousness and fear of evaluation The pps act unnaturally due to social desirability bias

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12
Q

How can we avoid demand characteristics?

A

Single blind technique - pps are “blind” to the research aims with no idea of which condition they are in Double blind technique - both the pps and the researcher are blind to the research aims, reducing subconscious researcher cues/clues for certain behaviours, reducing investigator effects

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13
Q

What are investigator effects and give examples

A

When the investigator can subconsciously influence the results of the research - Physical characteristics (age, gender, ethnicity) - Personal characteristics (tone or volume of voice) - Unconscious bias in interpretation of data to support what they expected to find

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14
Q

What is a research aim?

A

A precise statement of why a study is taking place and what is being studied. It will describe the purpose of the research “The aim of the study is to investigate the effect of caffeine on reaction times”

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15
Q

What is a hypothesis?

A

A precise, testable prediction of what is expected to happen in an experiment

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16
Q

What is the experimental/alternative hypothesis?

A

This predicts that differences in the IV will lead to a change in the DV “Increasing levels of caffeine consumption will significantly affect reaction time”

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17
Q

What is a null hypothesis?

A

This predicts that changes in the IV will not affect the DV “Increasing levels of caffeine consumption will have no effect on reaction time”

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18
Q

What is a directional (one tailed) hypothesis?

A

Predicts the direction of the results/the actual direction of any change caused by the IV - uses words such as “smaller”, “greater”, “increase” and “decrease” “There will be a significant reduction in reaction times as a result of increased caffeine consumption”

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19
Q

What is a non-directional (two tailed) hypothesis?

A

Predicts that there will be a difference but does not predict the direction of the results/predicts that a change will be caused by the IV, but the direction of the change is unknown - uses words such as “lead to”, “change”, “difference”, “will have an effect on” “There will be a significant difference in the speed of reaction times as a result of caffeine consumption

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20
Q

Describe what a laboratory experiment is

A

Take place in controlled conditions/environments using standardised processes

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21
Q

What are the strengths of laboratory experiments?

A
  • High levels of control of all variables - IV and DV are precisely operationalised and measured, increasing accuracy - If all extraneous variables are controlled, it is easier to establish cause and effect - the change in the DV must be due to the IV - High level of reliability because the experiment can be replicated
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22
Q

What are the weaknesses of laboratory experiments?

A
  • Low ecological validity because it is not a real life environment - high levels of control make the situation artificial - Demand characteristics are more likely because pps are aware they are being tested - their behaviour may change unconsciously or consciously - Operationalising variables may mean they are less applicable to real life
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23
Q

Describe what a field experiment is

A

Take place in a real world setting rather than artificial environments. The IV is manipulated by the experimenter

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24
Q

What are the strengths of field experiments?

A
  • High ecological validity because findings are applicable to real life settings - Avoids pp effects because they are not aware of the study
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25
Q

What are the weaknesses of field experiments?

A
  • Low levels of reliability - it would be difficult to get the same findings in a natural environment because it has a low level of control to it - Harder to establish cause and effect
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26
Q

What is a natural experiment?

A

Takes place in a natural setting but the IV already exists naturally - the variable would have changed in real life whether or not it was being researched

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27
Q

What are the strengths of a natural experiment?

A
  • High ecological validity as it is happening in the natural environment - No demand characteristics, especially if participants are unaware of the experiment - Practical and ethical reasons may mean this is the only method that can be used
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28
Q

What are the weaknesses of natural experiments?

A
  • No control over extraneous variables - No direct manipulation of the IV, therefore it is more difficult to establish cause and effect - Difficult to replicate as conditions are unlikely to be exactly the same - Potential sample bias as groups are not selected in particular ways
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29
Q

What is a quasi experiment?

A

The IV simply exists and is based on a difference between people. It has not been manipulated; it simply exists. Pps are not randomly allocated to conditions - they fit that category by default. These differences can be tested in laboratory conditions

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30
Q

What are the strengths of quasi experiments?

A

Often carried out under controlled conditions and therefore share almost the same strengths and weaknesses as laboratory experiments. For example, there may be higher controlled studies with low ecological validity

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31
Q

What are the weaknesses of quasi experiments?

A

Because the allocation to the IV is not completely random, we cannot be certain that the IV has caused any change to the DV

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32
Q

What is meant by “experimental design”?

A

The way in which participants in a study are organised into conditions

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33
Q

What is a population?

A

The whole of a target group

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34
Q

What is a sample?

A

Part of a larger population - we test a sample as normally it is extremely difficult or maybe impossible to test an entire population

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35
Q

When is a sample biased or unrepresentative?

A

A sample may be selected in a way that means the selection is somehow biased to selecting certain pps and not others. This means that it may not represent the target population. This will be as a result of the sampling technique used

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36
Q

What is generalisability?

A

How effectively we can transfer the results from a sample to the entire population. If a sample is biased or unrepresentative of the target population, then the results may be considered less generalisable

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37
Q

What is a random sample?

A

Where each member of a population has an equal chance of being selected. For example, names could be selected by random number generators or out of a hat to avoid bias

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38
Q

What are the strengths of a random sample?

A
  • Unbiased selection, meaning it should be more representative - A representative sample creates results that are more generalisable to the population
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39
Q

What are the weaknesses of random samples?

A
  • Impractical - it is difficult to get full details of a target population, and not all members want to participate - Unbiased selection does not guarantee an unbiased sample, for example, by chance, all females could be selected
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40
Q

What is an opportunity sample?

A

Selecting pps who are available and willing to take part, such as asking people in a street, a class from school, or most commonly in university studies, undergraduates for convenience

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41
Q

What are the strengths of opportunity samples?

A
  • Easy to create with people who are available - In a natural experiment, the researcher often has to select those who are naturally available
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42
Q

What are the weaknesses of an opportunity sample?

A
  • The sample is likely to be biased and therefore unrepresentative as it may exclude certain pps. Therefore, it is hard to generalise results to the target population - Through self-selection, pps have the opportunity to decline, In this case, the sampling technique becomes self-selected
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43
Q

What is a volunteer (or self selected) sample?

A

People volunteer or self select to take part as pps. This is often by responding to an advert

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44
Q

What are the strengths of a volunteer (self selected) sample?

A
  • The sample is easy to form as, other than advertising, the pps volunteer themselves - Pps are presumably keen to take part and therefore less likely to sabotage the study (screw you effect)
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45
Q

What are the weaknesses of a volunteer (self selected) sample?

A
  • Volunteers are likely to be a certain type of person and therefore the sample is likely to be biased and unrepresentative. This may make results less generalisable - Volunteers may be eager to please, which increases the chance of demand characteristics
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46
Q

What is a systematic sample?

A

Involves taking every “nth” person from a list. The population size is calculated to work out the sampling interval

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47
Q

What are the strengths of systematic sampling?

A
  • Unbiased selection and therefore more representative - Generalisable results (unless by chance every nth person has certain characteristics)
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48
Q

What are the weaknesses of systematic sampling?

A

Periodic traits - if by chance every nth person has a particular trait, the sample will not be representative

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49
Q

What is stratified sampling?

A

This attempts to overcome the potential issues with other techniques by creating a small scale reproduction of a population by dividing it into characteristics that are important for the research, such as age, social class and level of education, and then the population is randomly sampled within each stratum (category)

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50
Q

What are the strengths of a stratified sample?

A
  • The sample should be representative as it is attempting to select from sub-groups within a population - Should be unbiased as the sample is randomly taken from the sub groups
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51
Q

What are the weaknesses of stratified sampling?

A
  • This needs detailed knowledge of the population characteristics, which may be difficult to obtain or not always available - To divide a population into stratums and then randomly selecting from each can be very time consuming
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52
Q

Explain repeated measures

A

The same pps are used in both conditions

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53
Q

What are the strengths of repeated measures?

A

No participant variables

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54
Q

What are the weaknesses of repeated measures?

A
  • Increased risk of demand characteristics - Order effects - Practice effects
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55
Q

Explain independent groups

A

The pps are allocated to two different groups, each only experiencing one condition

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56
Q

What are the strengths of independent groups?

A
  • Less risk of demand characteristics - No order effects - No practice effects
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57
Q

What are the weaknesses of independent groups?

A

Risk of participant variables

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58
Q

Explain matched pairs

A

Pairs of participants are closely matched (using key characteristics/criteria, like age or gender) and are then randomly allocated to one of the experimental conditions. The perfect pair would be monozygotic twins

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59
Q

Describe the process of creating a matched pairs experiment

A
  1. Pps are tested on key criteria 2. 2 pps are matched based on these characteristics to form a matched pair 3. The pair are then separated - one is put into condition A and the other into condition B 4. This is then repeated for all of the selected pps in the sample 5. A group in condition A and a group in condition B are made as similar as possible based on the key characteristics initially tested
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60
Q

What are the strengths of matched pairs?

A
  • Really makes the groups equal - Takes away pp variables
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61
Q

What are the weaknesses of matched pairs?

A
  • Time consuming as pre-testing is needed - Difficult to match pps exactly - monozygotic twins are hard to find
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62
Q

What are order effects?

A

When an experiment is affected by the order in which a pp experiences the conditions - they may get bored or tired and so perform worse in the second condition

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63
Q

What are practice effects?

A

When a pp gets better in the second conditions due to the fact that they have had practice in the first condition

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64
Q

Describe random allocation as a method of control in experimental design

A

Randomly placing pps in either condition in an independent groups experiment, e.g. names out of a hat

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65
Q

How do you randomly allocate by pulling names out of a hat?

A
  1. Get a hat 2. Cut piece of paper into strips of the number of pps you have in the study 3. Write the name of one of the pps onto one of the strips of paper 4. Fold up the strip of paper so that you can’t see the name and place it in the hat 5. Repeat steps 3 and 4 until all of the pp names are in the hat 6. Shake the hat to mix up the names 7. Close your eyes and reach into the hat and pick out a name. This person goes into condition A 8. Repeat step 7, but this pp goes into condition B 9. Repeat this process of allocation until all of the pps have been allocated to a condition
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66
Q

How would you use counterbalancing as a method of control in experimental design?

A

Used to counteract order or practice effects in repeated measures experiments In the first test, Group A would do condition 1 whilst Group B did condition 2 In the second test, Group B would do condition 1 whilst Group A did condition 2 Essentially the two groups are tested on the same two conditions in different orders We test each group on one condition first and then swap them over for the second test

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67
Q

Explain observations as a non-experimental method

A
  • No manipulation of variables - Observations are not technically classed as experiments - A researcher simply observes behaviour and looks for patterns - We cannot draw cause and effect relationships - Observations are used as a method or a technique in psychological research
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68
Q

Describe observations as a research technique

A

This is when observations are used as a part of another research method such as a lab study or a field study. Many areas in psychology involve some aspect of observation

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69
Q

What is observational research?

A

This is where the entire study is an observation

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70
Q

What is a naturalistic observation?

A

Behaviour is studied in a natural situation where everything has been left as it normally is

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71
Q

What is a controlled observation?

A

Some variables are controlled by the researcher, reducing the naturalness of the behaviour being studied. Pps are likely to know that they are being observed and the study may take place in a lab

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72
Q

What is a participant observation?

A

The observer is a pp who takes part in the observation. It may be difficult for the observer to record everything as they have to wait for an opportune moment to write down their observations. However, they may gain a deeper insight into behaviour

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73
Q

What is a non-participant observation?

A

A person who is not involved in the observation is the observer. It is easier to record data and the observer can remain unbiased

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74
Q

What is an overt observation?

A

The pp knows that they are being observed, e.g. by a physical person in the room

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75
Q

What is a covert observation?

A

The pp does not know that they are being observed, e.g. by hidden camera

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76
Q

What data may a researcher collect from an observation?

A

Qualitative data - non-numeric or Quantitative data - numeric data Depending on the method of observation, the researcher may collect either one of or both types of data

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77
Q

What is a coding system?

A

When behaviour is operationalised by being broken into different categories. Observers agree on a grid or coding sheet on which to record behaviour being studied. Rather than writing descriptions of behaviour observed, it is easier to code/rate behaviour using previously agreed scales

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78
Q

How should data be recorded in a continuous experiment and what is the problem with this?

A

The observer should record every instance of the behaviour being studied, however, in practicality, there would be too much data to record, so instead we use a systematic method of sampling

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79
Q

What is event sampling?

A

Counting each time a particular behaviour is observed in a target individual

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80
Q

What is time sampling?

A

Recording data at particular intervals, e.g. every 30 seconds

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81
Q

What is a structured interview?

A

Involves identical closed questions being read to pps with the interviewer writing down answers. Interviewers do not need much training as such interviews are easy to conduct

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82
Q

What is an unstructured interview?

A

Involves an informal discussion on a particular subject. Interviewers can explore interesting answers by asking follow up questions. Interviewers need considerable training and skill to conduct such interviews

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83
Q

What is a semi structured interview?

A

Combines both structured and unstructured interviews. Might have a series of prepared questions, but the interviewer might alter the interview as it goes along, adding new questions as information arises. It’s often called a clinical trial as it is like an interview a doctor will give you

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84
Q

What are the advantages of interviews?

A
  • Complex issues can be dealt with in face-to-face interviews, putting pps at ease to talk - particularly true of unstructured interviews - Can ease misunderstandings that may arise about the questions which can be adapted so they can be understood by all pps - Data analysis - semi-structured interviews produce both qualitative and quantitative data, which can be used to complement each other. Structured interviews produce quantitative data that can be easily analysed Replication - the more standardised or structured the easier to replicate - unstructured data can still be reviewed
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85
Q

What are the disadvantages of interviews?

A
  • Interviewer effects - interviewers may unconsciously bias answers, like by their appearance - women less likely to talk about sex with male interviewers - Subject to demand characteristics and social desirability bias - Interviewer training - a lot of skill required to carry out unstructured interviews, especially concerning sensitive issues and these interviewers are hard to find - Ethical issues - pps may not know purpose of interview an may reveal more than they wish - Pp answers - interviews are not suited to pps who have difficulty putting their feelings and opinions into words
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86
Q

Why should gender and age be considered when selecting an interviewer?

A

The sex and age of interviewers affect pp answers when topics are of a sensitive sexual nature

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87
Q

Why should ethnicity be considered when selecting an interviewer?

A

Interviewer may have difficulty interviewing people from different ethnic groups to themselves. Word et al (1974) found that white interviewers spent 25% les time interviewing black pps than white pps

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88
Q

Why should personal characteristics/adopted roles be considered when selecting an interviewer?

A

Interviewers can adopt different roles within and interview setting, and use of formal language, accent and appearance can also affect how the interviewer comes across to the interviewee

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89
Q

What are questionnaires?

A
  • Consist of pre-set questions - The same for everyone in the sample - Can use open or closed questions - A self-report method - Can use scales like the Likert scale, e.g. strongly agree - strongly disagree
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90
Q

What are closed questions?

A

Questions with a set number of possible answers - multiple choice/rating scales for example. Allow for statistical/quantitative data E.g. do you like chocolate?

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91
Q

What are open questions?

A

Questions that allow for in depth answers where pps choose their own response and are not limited. Answers are difficult to place into categories E.g. what is your favourite song?

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92
Q

What are the strengths of questionnaires?

A
  • Can be used to access people’s behaviours, thoughts and feelings - Easy to administer to a large sample quickly - Highly cost effective - All pps are asked the same questions so patterns and trends can be seen - Can easily be repeated so results can gain reliability
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93
Q

What are the weaknesses of questionnaires?

A
  • Subject to social desirability bias and lack of honesty - Unreliable - if pps misunderstand questions in closed questionnaires, they might not be able to give the answer they want - Qualitative data is not easy to collate into categories - Do not account for individual differences - same questions - individual answers cannot be explored
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94
Q

What is the difference between correlational studies and experimental studies?

A

Correlational studies measure the strength and direction of relationships between co-variables, often naturally occurring events, whereas experimental studies look for differences between different conditions of an IV

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95
Q

What is a positive correlation and what does it look like?

A

Occurs when one co-variable increases as the other co-variable increases

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96
Q

What is a negative correlation and what does it look like?

A

Occurs when one co-variable increases as the other co-variable decreases

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97
Q

What does the rating line of correlation look like?

A

-1.0 —- 0 —- +1.0 -1.0 = perfect negative correlation - all data falls on a downward slope line +1.0 = perfect positive correlation - all the data falls on an upward slope line

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98
Q

It ain’t great if it ain’t….

A

0.8!!! To be statistically significant, the correlation strength has to be at least -0.8 or +0.8

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99
Q

What are the strengths of correlational studies?

A
  • Allows predictions to be made, once a correlation can be established between two co-variables - It shows the strength and direction of a relationship between the co-variables - this is important in positive and negative correlations - No manipulation of variables is required and so correlational studies can be carried out where operationalisation of variables is difficult or an experiment would be unethical
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100
Q

What are the weaknesses of correlational studies?

A
  • It is difficult to establish cause and effect - they are not carried out under controlled conditions and so correlations do not show causality - it is difficult to confirm that one co-variable has caused another, e.g. coffee and anxiety - Extraneous relationships are at play - when another variable may also influence the effect, e.g. coffee and anxiety - Only works for linear relationships - correlations only show a linear relationship and not a curvilinear one (inverted u)
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101
Q

What is the BPS code of conduct?

A
  • The British Psychological Society published a code of ethics that all psychologists should follow - If it becomes clear that there are negative consequences resulting from the research, the research should be stopped and efforts made to correct the negative consequences
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102
Q

What is informed consent as one of the criteria of the code of ethics?

A

Pps should be given sufficient details of an investigation that they can make a considered choice if they want to participate. Parental consent should be given for children under 16 and informed consent cannot be given by those under the influence of drugs or alcohol or if they are mentally unfit

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103
Q

What is avoidance of deception as one of the criteria of the code of ethics?

A

Withholding information/misleading pps is unacceptable if pps are likely to object or be uneasy once debriefed. Intentional deception over the purpose and nature of investigations should be avoided and pps are not to be misled without medical or scientific justification or consent of an informed nature cannot be obtained. In cases where purpose deception is required to get realistic results, deception must be dealt with ethically. If deception is used, pps must be told immediately after the study has finished and offered to withdraw their data

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104
Q

What is presumptive consent?

A

Gained from people of similar background to the pps - if they say they would have been willing to participate, it is deemed that the pps would too

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105
Q

What is prior general consent?

A

This involves pps agreeing to be deceived but not knowing how/consenting to everything that could take place - this can affect behaviour

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106
Q

What is retrospective consent?

A

Asking pps for consent after participation

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107
Q

What is adequate briefing/debriefing as one of the criteria of the code of ethics?

A

Relevant details of a study should be explained to pps before and afterwards. A debrief is important if deception was used. Pps should leave the study in no worse state than when they started. Debriefing is not justification for unethical aspects of a study

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108
Q

What is protection of participants as one of the criteria of the code of ethics?

A

Investigators have responsibility to protect pps from physical and mental harm during the investigation. Risk of harm must be no greater than in ordinary life

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109
Q

What is the right to withdraw as one of the criteria of the code of ethics?

A

Pps should be aware that they can leave a study at any time, and can even withdraw their data after the study has finished

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110
Q

What is confidentiality/anonymity as one of the criteria of the code of ethics?

A

Data should not be disclosed to anyone unless agreed so in advance. Numbers are used instead of names in published research papers. In confidentiality, data can be traced back to names and so is preferable when pps may be followed up, but in anonymous data, no names are collected at all

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111
Q

What is observational research as one of the criteria of the code of ethics?

A

Observations should only be made in public places where people might expect to be observed by strangers

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112
Q

What does the code of ethics say about incentives to participate?

A

Pps should not be offered bribes or promised rewards for their participation in a study as this puts pressure on them to take part

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113
Q

What are the implications of psychological research for the economy?

A
  • Practical everyday applications are created for the betterment of society through conducting research - Psychological research substantially contributes to the economy, e.g. creation of effective therapy for mental disorders - 10% of the time in a mental institute sees 1/3 people receive treatment for a mental problem - Effective therapies make huge savings in financial costs as many can return to work and contribute more fully to the economy - Koran et al 2000 - Olanzapine and Fluoxetine = reduced OCD - sufferers could return to work, earn money, pay tax and not incur long term financial costs upon the health service, benefitting the economy - Brosnan and Thorpe 2006 - study on systematic desensitisation
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114
Q

What is a pilot study?

A

A small scale “practice” piece of research, taking place before conducting the real research. It allows the researcher to identify any issues with the design, method or analysis so they can be changed. Pps can also suggest possible changes, e.g. if they experienced demand characteristics. It is used to discover if there is a chance of significant results being found

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115
Q

What is a peer review?

A
  • After conducting research, psychologists need to disseminate it - As many experts as possible need to publicly read, evaluate and comment on it - An integral aspect of science - Once research is written up as a report, it is submitted for publication to a journal - Journals are publications that appear several times a year containing collections of reports - Reports are not automatically published - go through a peer review process - Editorial boards select reports based on relevance and quality and send them to other specialists in the field
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116
Q

What are three things that can happen to a report based on the anonymous evaluation of specialists?

A

It can be… - Accepted for publication - Revised before publication - Rejected for publication

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117
Q

What are the criticisms of peer reviews?

A
  • Peer review is more likely to accept research with existing theory than novel and controversial research that is not mainstream - Peer review is subject to bias, especially if the reviewers hold views that are contradictory to the research - Research is more likely to be accepted if it accepts the experimental hypothesis rather than the null hypothesis, i.e. it has actually proven something
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118
Q

Define the term “validity”

A

The extent to which a test measures what it claims to measure

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119
Q

What is internal validity?

A
  • Refers to the things that happen “inside” the study - Concerned with whether we can be certain that it was the IV which caused the change in the DV - If aspects of the experimental situation lack internal validity, the results are meaningless and we can make no meaningful conclusions from the study
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120
Q

What can internal validity be affected by?

A

Internal validity can be affected by a lack of mundane realism - the tests used are not anything like what we would do in “normal life”. This could lead the pps to act in a way which is unnatural, thus making the results less valid. Internal validity can also be affected by extraneous variables which may have had an effect on the DV

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121
Q

What is external validity?

A

How well the results of the study can be generalised beyond the experimental setting

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122
Q

What is ecological validity?

A

How well the experimental situation reflects real life and therefore how well the results can be generalised to other places and settings. Can be assessed by looking at the context of the experiment. A field study, for example, takes place in the pp’s own environment, leading to the study having high ecological validity. Some studies are artificial but make efforts to be like a real situation, increasing ecological validity. If the pps are asked to do artificial things in the experiment, the study would have low external validity and lacks ecological validity

123
Q

What is temporal validity?

A

The extent to which the results of a study can be generalised over time

124
Q

What is population validity?

A

The extent to which the results of a study can be generalised to other people

125
Q

Describe validity of psychological measures (how valid the tool we use to measure is)

A
  • When designing an experiment in psychology, we need to decide how to measure our variables - If we cannot physically easily measure something (i.e. if we can’t weigh it), these psychological concepts need to be turned into numbers that can be measured and compared - they need to be operationalised - By breaking down a concept into numerical form, we can lose validity by not measuring what we intended, which is an issue
126
Q

What types of measures can we use in psychology?

A
  • A test which is given to pps, producing a score - A questionnaire or interview - A checklist where pp behaviour can be recorded using a coding system - A biological response (e.g. body temperature, hormone levels)
127
Q

Define the term “reliability”

A

Refers to the consistency of a measure - considered reliable if we get the same results repeatedly - if we can repeat the research method and get the same results, it is considered to be reliable. If a test can be replicated but the results are not consistent, then it is an unreliable test. If we cannot replicate a test, it is immediately unreliable as we cannot check the consistency of the results

128
Q

What is internal reliability?

A

Concerns the extent to which something is consistent within itself

129
Q

What is external reliability?

A

Concerns the extent to which a test measures consistently over time

130
Q

How can we measure reliability using the test-retest method?

A

The measure is administered to the same group of people twice or multiple times. If the results on each test are similar, we can assume the test is reliable

131
Q

How can we measure reliability using the inter-rater method?

A

If the measure depends upon interpretation of behaviour, we can compare the results from two or more raters. If there is high agreement between the raters, the measure is reliable

132
Q

How can we measure validity using face validity?

A

This is a simple way of assessing validity and involves the idea that on the “face of it” a test measures what it is supposed to measure (or doesn’t!)

133
Q

What is concurrent validity?

A

How well does the measure agree with existing and well-established measures? We can ensure concurrent validity by testing pps with both the new test and the established test, and so, if the new test has concurrent validity, there should be high agreement between the scores on both measures

134
Q

What is predictive validity?

A

Is our measure associated with future behaviour? We can investigate this by following up our pps to see if future performance is similar to performance on our measure

135
Q

How can we improve validity?

A
  • Using a control group to assess whether a change in the DV occurred due to the changes in the IV - the closer the match of the control group, the more valid - Standardise procedures - reduction of “participant reactivity” - Techniques such as the single and double blind reduces demand characteristics and investigator effects, increasing validity
136
Q

What is a case study?

A

In depth, detailed investigation of unique, rare or atypical individuals or groups, usually including biographical info, behavioural info and experiences of interest. They allow researchers to examine individuals in depth. Explanations of behaviour are outlined in subjective ways, describing what an individual feels of believes about particular issues

137
Q

What are the strengths of case studies?

A
  • Rich detail - provide great depth and understanding about individuals and acknowledge human diversity. About real people and so provide a sense of truth. Information relates to a real person, not an average gathered from many - The only possible method - allows psychologists to study unique behaviours or experiences that could not have been studied in any other way. Also allows “sensitive” issues to be explored where other methods would be unethical, e.g. effects of sexual abuse - Useful for theory contradiction - just one case study can contradict a theory
138
Q

What are the weaknesses of case studies?

A
  • Not representative - no two case studies are alike and so results cannot be generalised to others - Researcher bias - researchers conducting case studies may be biased in their interpretations or method of reporting, making findings suspect - Reliance on memory - case studies often depend upon pps having full and accurate memories
139
Q

In a reporting method, what is an abstract?

A

A short summary of the research (150-250 words), usually 2 sentences of each of the following: - previous research on the subject - aims/hypotheses - methodology - results - conclusions - suggestions for future research

140
Q

In a reporting method, what should be in an introduction?

A
  • Why the study was conducted, overview of theories it is based on - Previous findings of similar studies and any controversial findings so far - Funnel technique is used - only relevant research is discussed
141
Q

In a reporting method, what should be in the method section?

A

Split into several subsections: - design - participants - apparatus/materials - standardised procedure - controls

142
Q

In a reporting method, what should be included in the findings/results?

A

Presents what was found in terms of data collected. Summary of the raw data written with words and supported by tables/graphs etc. Only include tables and graphs you actually talk about in the report. - Descriptive statistics - Inferential statistics

143
Q

In a reporting method, what is discussion?

A

Explains what the results mean and is broken down into several sections: - explanation of findings - all findings presented - relationship to background research - discuss what you found in relation to what has already been found - limitations/modifications - possible sources of error - implications and suggestions for future research - further ideas of how to research the theory and suggestions for how research could be improved in the future

144
Q

In a reporting method, what is a conclusion?

A

A concise paragraph that summarises conclusions drawn from your study

145
Q

In a reporting method, what should be in the references section?

A

A full list of all of the references cited in the report - enables others to look at the studies you have spoken about to see the original report

146
Q

What is the process of scientific investigation?

A
  1. Make an observation about what has already been done 2. Develop questions and form a hypothesis to test 3. Conduct an experiment and analyse results
147
Q

What does empirical mean?

A

Supported with tested evidence

148
Q

What is replicability?

A

Being able to test/check the reliability and validity of the results

149
Q

What is objectivity?

A

Observations/investigations made without bias

150
Q

What is falsification?

A

Scientific statements are capable of being proven wrong - it must be able to be tested to see if it is false

151
Q

What did Karl Popper say about falsification in 1969?

A
  • It is a hallmark of science - Using induction, there is no way for theories to be proven as true, so scientists should aim to demonstrate that they are wrong by ruling out alternative explanations - Falsification is the only way to be certain
152
Q

What is theory construction/hypothesis testing?

A

The idea that theories generate hypotheses

153
Q

How is scientific research carried out in psychology?

A

Observation -> Induction -> deduction -> evaluation -> testing -> back to observation

154
Q

What is a paradigm?

A

The current, accepted theories/facts/explanations “A framework containing the basic assumptions, ways of thinking and methodology that are commonly accepted by members of a scientific community”

155
Q

How do scientific advancements happen?

A

Occasionally paradigms are replaced with new paradigms, often emerging from a minority. Scientific advancements happen through paradigm shifts, not steady progress

156
Q

What were Thomas Kuhn’s views on paradigms?

A

“The shared set of assumptions about the subject matter of a discipline and the methods appropriate to its study” To be a science, the paradigm must be unified Believed Popper’s ideas of the scientific methods were not accurate of how science works

157
Q

What is quantitative data?

A

Reports on data in numerical form quaNtitative

158
Q

Give some examples of quantitative data

A
  • Percentages - Mean, mode, median - Range
159
Q

Give some examples of types of experiments which could produce quantitative data

A
  • From a lab experiment (e.g. measuring testosterone) - Structured questionnaire/interview with closed questions (tally) - National statistics, e.g. data on crime rates
160
Q

What are the strengths of quantitative data?

A
  • Large sample = generalisable - Higher chance of establishing cause and effect (objective) - Easy to analyse - Easier to make comparisons and see patterns and trends - Can repeat to test reliability
161
Q

What are the weaknesses of quantitative data?

A
  • Statistics sometimes distort the truth and therefore it may lack the validity - Does not give context, i.e. conclusions may lack depth and detail
162
Q

State a question that would collect quantitative data

A
  • Your patient has felt better since having physiotherapy following their stroke - strongly agree -> strongly disagree (take tally of answers) - On average, how many hours of physiotherapy does the patient receive per week
163
Q

What is qualitative data?

A

Reporting anything in word form with written language and context quaLitative

164
Q

What types of experiment would produce qualitative data?

A
  • Transcripts of unstructured interviews with pps - Collation of answers from open ended questions in a questionnaire - Written report of free flowing observation
165
Q

What are the strengths of qualitative data?

A
  • Depth gives detailed insight by allowing a range of responses/behaviours - increases validity - Taking the context into account makes results more valid - Chance for new or unexpected info with open ended questions and unstructured interviews
166
Q

What are the weaknesses of qualitative data?

A
  • Harder to analyse/interpret - Behaviour in observations and interviews are open to interpretation (subjective) - Can be unreliable in terms of exactly how info is gathered - Unrepresentative if sample size is very small
167
Q

Give a question which would collect qualitative data

A

Since having their physiotherapy, what effects have you noticed in your patient?

168
Q

What is primary data?

A

Data collected first hand from the pps (original data) specifically towards a research aim which has not been published before

169
Q

What research methods would produce primary data?

A
  • Interviews - Observations - Questionnaires - Physical testing - Diary extracts (from pps)
170
Q

What is a strength of primary data?

A

More reliable and valid than secondary data as it’s not been manipulated in any way

171
Q

What is secondary data?

A

Data originally collected towards another research aim which has been published previously

172
Q

What research methods would produce secondary data?

A
  • Websites - Journal articles - Books - Government publications - Diary extracts (published)
173
Q

What is a strength of secondary data?

A

If drawn from several sources, can help to give a clearer insight into a research area

174
Q

What is a meta-analysis?

A

The collation and review of the results/findings of multiple research studies in the same area of study

175
Q

How do we conduct a meta-analysis?

A

We do not replicate the chosen studies, but trust in and review the findings

176
Q

What are the strengths of a meta-analysis?

A
  • Allows for the identification of trends and patterns that would not be possible to see in smaller individual studies - Can improve reliability of findings because sample sizes can increase greatly. This in turn can increase validity
177
Q

What is content analysis?

A

A method that quantifies qualitative data, for example, creating a tally - translating words into numbers

178
Q

What types of qualitative data can content analysis quantify?

A
  • Spoken interactions (conversations) - Written forms (texts/emails) - Media (books, magazines, TV)
179
Q

How is content analysis carried out?

A
  • Identify key themes within the info and categorise data into meaningful units based on what you are trying to answer - sometimes known as “coding units”, e.g. references to positive behaviour in the info you are analysing - Re-read or listen to the info several times - Count the number of times a particular word or phrase is present, i.e. producing a tally, giving you quantitative data, e.g. if looking at stereotyping against the mentally ill, analysing the number of times terms like “crazy” or “mad” are used in the media
180
Q

What are coding units?

A

Required to categorise analysed material For example: - If the unit is word, a researcher might count the number of slang words used - If the unit is theme, a researcher might count the amount of violence on TV - If the unit is character, a researcher might count the number of female commentators in sports programmes on TV - If the unit is time and space, a researcher might count the amount of time (TV) and space (newspapers) dedicated to mental health

181
Q

What are the strengths of content analysis?

A
  • Easy to perform, inexpensive, non-invasive with no direct contact with pps - Complements other methods - verifies results from other methods, particularly being useful in longitudinal methods detecting changes over time - Reliable - very easy to replicate
182
Q

What are the weaknesses of content analysis?

A
  • Lacks descriptiveness - “what” but not “why” - Flawed results - if limited material is available then the analysis may not reflect reality, for example, negative events receive more coverage than positive ones in the media
183
Q

What is thematic analysis?

A

Purely a qualitative analytical method identifying, analysing and reporting themes (patterns)

184
Q

What does a thematic analysis do?

A
  • Organises, describes and interprets data - A psychologist uses these themes to address the research or say something about an issue. This is much more than simply summarising the data; a good thematic analysis interprets and makes sense of it
185
Q

What are the 6 stages of carrying out thematic analysis?

A
  • Familiarisation with the data - Coding - Searching for themes - Reviewing themes - Defining and naming themes - Writing up
186
Q

What is familiarisation with the data as a stage in thematic analysis?

A

Intense reading of data to become immersed in content

187
Q

What is coding as a stage in thematic analysis?

A

Generating codes (labels) that identify features of the data important to answering the research question

188
Q

What is searching for themes as a stage in thematic analysis?

A

Examining the codes and data to identify patterns of meaning (to find potential themes)

189
Q

What is reviewing themes as a stage in thematic analysis?

A

Checking the potential themes against the data to see if they explain it and answer the research theme

190
Q

What is defining and naming themes as a stage in thematic analysis?

A

Detailed analysis of each theme creating an informative name for each one

191
Q

What is writing up as a stage in thematic analysis?

A

Combining together the information gained from the analysis

192
Q

How do you calculate percentage decrease?

A

Calculate the difference between the starting point and how much it goes down by, then divide the difference by the original number and multiply by 100 to get a percentage E.g. percentage decrease in mean no of hours worked before and after Xmas - hours before = 30, hours after = 13 30-13 = 17 17/30 x 100 = 57%

193
Q

How do you calculate percentage increase?

A

Calculate the difference between the end point and the start point, then divide by the original number (which is the lower number) and multiply by 100 to get your percentage For example: Percentage increase in hours worked at Xmas - hours before = 13, hours at Xmas = 30 30-13 = 17 17/13 x 100 = 131%

194
Q

In central tendency, how do you calculate the mean?

A

It is the average score of the data - to calculate, add all of the scores together and divide by the total number of scores

195
Q

In central tendency, how do you calculate the median?

A

It is the central/middle number in a list of rank ordered scores - odd number median = middle number, even number median = midpoint of the 2 middle scores (this may not be one of the original scores)

196
Q

In central tendency, how do you calculate the mode?

A

It is the most common/frequent in a set of scores

197
Q

What are the advantages of taking a mean?

A
  • Uses all data in the calculation - Most accurate measure of central tendency - uses internal level of measurement when the units of measurement are all of equal size, for example, seconds in time
198
Q

What are the disadvantages of taking a mean?

A
  • Less useful if there is an extreme score which will then skew the data - The mean score could be one that isn’t actually listed in your data set, which can create difficulties when applying to people, e.g. you can’t have .1 of a person
199
Q

What are the different types of statistical test?

A
  • Sign test - Chi-squared - Spearman’s Rho - Pearson’s R - Wilcoxon - Related T-test - Mann-Whitney - Unrelated T-test
200
Q

Which is the only statistical test you need to be able to calculate?

A

The Sign Test

201
Q

Why do psychologists have to do statistical tests once they have collected their data?

A
  • Research produces data which psychologists have to analyse to make sense of - You can purely describe what the data shows in several ways (e.g. mean, median, mode, graphs, tables, etc) - A more sophisticated form of analysis is to carry out inferential statistical tests
202
Q

What is meant by the term “inferential (statistical) testing”?

A

A psychologist can make informed decisions about whether differences or relationships in data are significant ones, i.e. beyond the boundaries of chance, and can then be applied to whole target populations that the sample represents

203
Q

What questions should you ask to determine which test you should choose?

A
  1. Is the research looking for a difference or a relationship (correlation)? HINT: IF IT SAYS IT’S LOOKING FOR A RELATONSHIP, IT’S A CORRELATION - What experimental design has been used? (repeated measures, matched pairs, independent groups) 3. What type of data did the psychologist collect? (nominal/ordinal/interval)
204
Q

“A psychologist wanted to test whether listening to music improves running performance” Is the research looking for a difference or a relationship (correlation)?

A

A difference (between running performance with or without music)

205
Q

“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 instructions. All participants wore headphones in both conditions. The psychologist recorded their running times in seconds. The participants returned to the psychology department the following week and repeated the test in the other condition.” What experimental design has been used? (repeated measures, matched pairs, independent groups)

A

Repeated measures (participants ran both with and without music)

206
Q

How can “types of data” also be phrased?

A

“Levels of measurement”

207
Q

What are the three types of data?

A
  • Nominal - Ordinal - Interval
208
Q

What is nominal data?

A

Each item can only appear in one category (discontinuous) It is the lowest level of measurement/the simplest data collection and is the most uninformative type of data

209
Q

Give an example of nominal data

A

Blood groups

210
Q

What data is produced by nominal data?

A

Qualitative data - it cannot be measured numerically

211
Q

How is nominal data represented?

A

In a bar chart

212
Q

What is ordinal data?

A

You can automatically rank this data into place order A problem with the data is that the gap between each data point may be different

213
Q

Give an example of ordinal data

A

Finishing places in athletics (tells us which athletes are better than others but doesn’t say how much better 1st place is to 2nd) Use of the Likert scale (strongly agree - strongly disagree)

214
Q

What data is produced by ordinal data?

A

Quantitative and qualitative data can be used

215
Q

How can ordinal data be represented?

A

By ranking/scales

216
Q

What is interval data?

A

Standardised measurement UNITS like weight, temperature, time and distance - units like g, degrees C, s, m “Highest level of measurement” Most informative as the distance between each data point (or the interval) is exactly the same - most informative when drawing conclusions

217
Q

Give an example of interval data

A

How long, heart rate, etc

218
Q

What type of data is produced by interval data?

A

Quantitative only

219
Q

How can interval data be represented?

A

On a number line

220
Q

What type of data is time taken to sort cards into categories?

A

Interval data as time has a unit

221
Q

What type of data is people’s choice of the Sun, The Times or the Guardian?

A

Nominal data as people can only go in one category, the Sun, the Times or the Guardian

222
Q

What type of data is participants’ sense of self-worth, estimated on a scale of 1-10?

A

Ordinal data as pps’ estimations of self worth can be ranked

223
Q

What type of data is judges in a dancing competition giving marks for style and presentation?

A

Ordinal data as the marks given by the judges can be ranked

224
Q

What type of data is participants’ reaction to aversive stimuli measured using a heart rate monitor?

A

Interval data as heart rate has a standardised unit

225
Q

What type of data is a set of medical records classifying patients as either chronic, acute or “not yet classified”?

A

Nominal data as patients can only go in one category - chronic, acute, or “not yet classified”

226
Q

Give a mnemonic for remembering the statistical test table

A

Cats Share Carrots Meanwhile Wobbly Shrek Unleashed Rabbid Parrots

227
Q

A psychologist is testing the effect the number of hours of sleep has on cognitive function. He tests two groups of pps in a simple word recall task. Pps in group 1 sleep 6 hours per night, whereas pps in group 2 sleep 10 hours. State the correct statistical test to analyse the data collected and justify your choice (4 marks)

A

The study is testing a difference between effect on cognitive function after having 6 hours or 10 hours of sleep The study is using an independent groups design as participants only experience one of the conditions - they either sleep for 6 hours or 10 hours The study is collecting ordinal data as the word recall scores produced by the participants can be ranked from best to worst Therefore the statistical test needed to analyse the data collected is the Mann-Whitney test

228
Q

During an investigation on schizophrenia a psychologist aims to measure the relationship between pps’ numbers of delusions per months and the time of onset for their symptoms. He uses a content analysis method to gain info from local hospitals State the correct statistical test to analyse the data collected and justify your choice (3 marks)

A

The study is testing a correlation between participants’ numbers of delusions per month and the time of onset for their symptoms The study is collecting ordinal data as the numbers of delusions can be ranked from most to least Therefore the statistical test needed to analyse the data collected is Spearman’s Rho

229
Q

What do you do if there are two types of data collected in a study?

A

Always go to the lowest data level of the two, e.g. if the two were interval and ordinal, you would go with ordinal as it is the lowest level

230
Q

A researcher is conducting a study to find out if a positive view towards Donald Trump correlated with a negative view towards Mexicans in society. He uses interview methods to compare pps’ views in each case State the correct statistical test to analyse the data collected and justify your choice (3 marks)

A

The study is testing a correlation between positive views on Donald Trump and negative views on Mexicans in society The study is collecting nominal data as each participant can only fit into one category - they either have a positive or a negative view towards Donald Trump and a positive or negative view towards Mexicans in society and there are no cases of pps being in between categories Therefore the statistical test needed to analyse the data collected is the Chi-Squared test

231
Q

Can researchers ever be 100% certain they have found statistical significance?

A

NO - it is usually up to 5% possible that the wrong hypothesis may be accepted

232
Q

What is a Type I error?

A
  • When the null hypothesis is rejected and the experimental hypothesis is accepted when it should have been the other way around as the null hypothesis is true - Referred to as an optimistic error or false positive as the researcher claims to have found a significant difference or correlation when one does not exist
233
Q

What is a Type II error?

A
  • When the null hypothesis is accepted but it should have been the experimental hypothesis as the experimental hypothesis is true - This is a pessimistic error or false negative
234
Q

When are we likely to make a Type I error?

A

If the significance level is too lenient, e.g. 0.1 or 10%

235
Q

When are we likely to make a Type II error?

A

If the significance level is too stringent, e.g. 0.01 or 1% as potentially significant values may be missed

236
Q

Which significance level do psychologists favour and why?

A

The 5% level - it best balances the risk of making a type I or II error

237
Q

What is probability?

A

A measurement of the likelihood that an event will occur, i.e. chance - this can be represented numerically

238
Q

What does it mean if the probability is 0?

A

There is no chance it will happen, i.e. statistically impossible (a person getting to the moon in a second) - also represented as 0%

239
Q

What does it mean if the probability is 1?

A

Statistically certain (Christmas will happen on 25th December) - also represented as 100%

240
Q

What is meant by the term “significance level”?

A

The point at which a null hypothesis can be accepted or rejected - psychologists ideally want to be able to reject the null hypothesis and accept their experimental one

241
Q

How is the accepted significance level (0.05) represented and what does this translate to?

A

p < 0.05 The probability that the results of the experiment are due to chance is less than 5% You are 95% certain that the results show a significant difference/relationship

242
Q

How is the 0.01 significance level represented and what does this translate to?

A

p < 0.01 The probability that the results were due to chance is less than 1% You can be 99% certain there was a significant difference - this is VERY high

243
Q

How is the 0.1 significance level represented and what does this translate to?

A

p < 0.10 The probability that the results were due to chance is less than 10% You can only be 90% certain there was a significant difference - this is too uncertain or lenient

244
Q

Why might a researcher in a clinical trial of a drug (which can only be done once due to side effects) use the 1% level of significance?

A

They can be 99% certain that they have found a significant difference between symptoms after using the drug vs not using the drug (or a placebo), and as it can only be done once, there is not room for uncertainty. The findings can have far reaching consequences for a person’s health and so being 95% certain is just too uncertain here

245
Q

Which two values do you need to be able to work out in the sign test?

A

S (the calculated value) N (number of pps showing a significant difference)

246
Q

What is the first step in using the sign test?

A

Control result - experimental result Record the sign produced - if the value is positive, write a +, if it is negative, write a -, and if it is the same result, write a 0

247
Q

How do you calculate the S value in the sign test?

A

Count up the number of times the + and - occurs - the least frequent sign is the S value (calculated value)

248
Q

How do you calculate the N value in the sign test?

A

Count the total number of + and - signs (don’t count the 0s) - the result is the N value

249
Q

What is the fourth step of the sign test?

A

Determine if the hypothesis is one or two tailed

250
Q

How do you get your critical value in the sign test?

A

Look at the level of significance column (usually 0.05 unless otherwise stated) under the heading of the hypothesis used in the experiment (one or two tailed) and follow it down to meet your value of N. The value here is your critical value

251
Q

How do you determine if a result is significant using the sign test?

A

Compare your S value (calculated value) to the critical value obtained from the table - the S value must be equal to or less than the critical value to be significant

252
Q

What is the final step of calculating the sign test?

A

State the conclusion - state the calculated value of S, if it is less, more or equal to the critical value, state the critical value, determine if the finding is significant or not, state which hypothesis you accept and which you reject, write conclusion of whether the researcher’s hypothesis was correct or not (e.g. if using a proposed worksheet will improve exam scores or not)

253
Q

What 3 factors do you need to determine which critical value is the correct one to use from a critical values table (not sign test)?

A
  • What kind of hypothesis was used (one tailed or two tailed/ directional or non-directional) - Number of participants (the N value) or degrees of freedom (the df value) - Level of significance (usually 0.05 unless otherwise stated)
254
Q

How do you determine your critical value from a table other than the sign test one?

A

Look at the column of the significance level under the heading of the type of hypothesis used and match this to the number of participants (N) or the df - the value crossing both is the critical value - them compare your calculated (S) value to the critical value

255
Q

What is the formula to calculate df?

A

(rows - 1) x (columns - 1) - substitute in the number of rows and columns in the results table where raw data has been entered and then use the formula

256
Q

What is the rule of R?

A

Statistical tests with a letter “R” in their name are those in which the calculated value must be more than or equal to the critical value

257
Q

In which statistical tests must the calculated value be equal to or more than the critical value for there to be statistical significance?

A
  • Chi-squared - Unrelated T-test - Spearman’s Rho - Related T-test - Pearson’s R
258
Q

In which statistical test must the calculated value be equal to or less than the critical value for there to be statistical significance?

A
  • Sign Test - Wilcoxon - Mann-Whitney
259
Q

What is a correlation?

A

The relationship between two co-variables

260
Q

How is a correlation represented graphically?

A

The relationship is plotted on a scatter graph

261
Q

What is a correlation co-efficient?

A
  • Represents the strength of the correlation by placing a numerical value between +1 and -1 - +1 represents a perfect positive correlation and -1 represents a perfect negative correlation - The closer to 0 the weaker the correlation
262
Q

How could a weak correlation be statistically significant?

A

Due to a large data set

263
Q

How are correlation coefficients calculated?

A

Using a Spearman’s Rho or Pearson’s R statistical test

264
Q

What is the number above or equal to which a correlation is seen as strong?

A

+ or - 0.8 “It ain’t great if it ain’t 0.8”

265
Q

What are the strengths of correlations?

A
  • Can study relationship between variables that occur naturally (we can measure things that cannot be manipulated experimentally) - Can suggest trends that can lead to experiments
266
Q

What are the weaknesses of correlations?

A
  • It is not possible to say that one thing causes another - There may be another variable connecting the two variables
267
Q

What do descriptive statistics do?

A

Give us a way to summarise and analyse numerical data in order to draw meaningful conclusions

268
Q

What are the 3 measures of central tendency?

A
  • Mean - Median - Mode
269
Q

What are the strengths of using the median?

A
  • Not affected by extreme scores - Easy to calculate - Can be used with ordinal (ranked) data whereas the mean can’t
270
Q

What are the weaknesses of using the median?

A
  • It is less sensitive than the mean as not all scores are included in the final calculation - Can be un-representative of small sets of data in particular
271
Q

What are the strengths of using the mode?

A
  • Unaffected by extreme values - Only method that can be used when data are in categories
272
Q

What are the weaknesses of using the mode?

A
  • Loses meaning if there is more than one mode - It does not use all scores in its description
273
Q

What is a measure of dispersion?

A

Summarises and describes data by showing us the spread of a set of data - how far the scores vary from one another

274
Q

What are the two measures of dispersion?

A
  • Range - Standard deviation
275
Q

How do you calculate the range?

A

Largest value - smallest value

276
Q

What is a strength of using the range?

A

Easy to calculate

277
Q

What are the weaknesses of using the range?

A
  • Only takes into account the two most extreme values, which may be unrepresentative of the data set as a whole - Doesn’t indicate whether most numbers are closely grouped around the mean or spread out evenly
278
Q

What does it mean if you have a low range value?

A

Data is consistent

279
Q

What does it mean if you have a high range value?

A

Data is inconsistent

280
Q

What is standard deviation?

A

A more sophisticated measure of dispersion - a single value that tells you how far scores deviate (move away from) the mean

281
Q

What does a low standard deviation value mean?

A

Data is consistent (clustered around the mean) A smaller dispersion of data - all pps responded in a similar way to the IV

282
Q

What does a high standard deviation value mean?

A

Data is inconsistent (spread from the mean) A greater dispersion of data - pps not affected by IV in the same way

283
Q

What does a standard deviation of zero mean?

A

All of the data was the same

284
Q

What are the strengths of using standard deviation?

A
  • More sensitive than the range as includes all values in final calculation - Less likely to be distorted by a single extreme value
285
Q

What are the weaknesses of using standard deviation?

A
  • Harder to interpret if the data is not in a normal distribution curve - Hard to calculate
286
Q

What do you need to put in a summary table if you are asked to draw one?

A

You do not need to show the raw data, but you do need to calculate the mean, median/mode, range and show the totals for each condition

287
Q

What two things do you always need on a graph?

A
  • X and Y axis labelled and operationalised - A title operationalised
288
Q

How should you use a bar graph?

A
  • Used to compare categories - Uses discontinuous/discrete data - bars must be separate - X axis = IV (what you change) - Y axis = DV (what you measure) For example, data for different blood groups would be represented on a bar graph
289
Q

How should you use a histogram?

A
  • Bars touch each other - data is continuous rather than discrete - X axis = equal sized intervals of a single category (e.g. score) - Y axis = frequency within each interval - If there was zero frequency for one of the intervals, it remains without a bar
290
Q

How should you use a line graph?

A
  • Continuous data - Dot to mark middle top of where each bar would be and each dot is connected by a line - X axis = IV (what you change) - Y axis = DV (what you measure)
291
Q

How should you use a scattergram?

A
  • Used when doing correlational analysis - do not depict differences, but associations between co-variables - It does not matter which variables go on the X and Y axis - Each cross on the graph corresponds to the X and Y positions of the co-variables
292
Q

What are distributions?

A

A way of visually showing where the mean, median and mode lie

293
Q

What is a normal distribution?

A
  • Classic bell-shaped curve - The predicted distribution when considering an equally likely set of results
294
Q

How can we identify a normal distribution?

A
  • Mean, median and mode are all in exact midpoint - Distribution is symmetrical around this midpoint - Dispersion of scores/measurements either side of the midpoint = consistent and can be expressed in standard deviations
295
Q

What always goes on the Y axis of a distribution graph?

A

Frequency

296
Q

How are the measures of central tendency plotted on a skewed distribution graph?

A

Mode = at the top middle (peak) Median = in the middle of the mean and the mode (either to the left or right) Mean = at the bottom (either to the left or right)

297
Q

When does a skewed distribution occur?

A

When scores are not distributed equally around the mean - the distribution appears to lean to one side or the other

298
Q

What is a positive skew?

A

Where most of the distribution is concentrated towards the left of the graph (tail lies to the right) - occurs, for example, when students take a very difficult test and most people got low marks with only a handful getting to the higher end

299
Q

How can we identify a positive skew?

A
  • The tail lies at the positive (right) end of the number line - Mode has highest frequency (peak) - Median is next mark - Mean is to the right as it has been affected by the few high scores and pulled upwards
300
Q

What is a negative skew?

A

Where most of the distribution is concentrated towards the right of the graph (tail lies to the left) - would occur, for example, if students took a very easy test and most people got high marks with only a handful getting low marks

301
Q

How can we identify a negative skew?

A
  • The tail lies at the negative (left) end of the number line - Mode is the highest frequency (peak) - Median is the next mark - Mean is to the left as it has been affected by the few low scores and pulled downwards
302
Q

How do you calculate decimal places?

A
  • Count no of decimal places you need to go after the decimal point - this can include 0s - Look at number after the decimal place you are asked to go to - if it’s 5 or above, round up
303
Q

How do you calculate significant figures?

A
  • 1 sig fig - 1st no you count must not be zero, e.g. 0.145, the 1 is your first sig fig - Use same rounding rule - if the fig after is more than 5, round up - 2 sig fig - count along from first figure which is not a zero