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psychology y1 > research methods y1&2 > Flashcards

research methods y1&2 Flashcards

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1
Q

what is an experimental method?

A
  • manipulation of IV
  • so that the IV can have an effect on the DV
  • which is measured and stated in results
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2
Q

what are the 4 types of experimental methods?

A
  • quasi
    -laboratory
    -field
    -natural
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3
Q

what is an independent variable

A

the variable that the experimenter changes / manipulates

  • e.g : temperature of the room ( experimenter changes this , to see the change in maths scores )
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4
Q

what is a dependant variable

A
  • variable being tested and measured in an experiment
  • it is “dependant” on the independent variable

e.g : measuring the maths scores of participants in different temp conditions

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5
Q

Aim

A

general statement that the researcher intends to investigate

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6
Q

Hypothesis

A

A detailed statement which is clear, precise and testable that states the relationship between variables being tested.

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7
Q

Directional hypothesis

A

The researcher makes it clear what difference is anticipated between the 2 conditions or groups.

Clear effect of iv on dv
(One tailed).
e.g “ “The more sleep a participant has the better their memory performance.”

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8
Q

Non-directional hypothesis

A

Simply states that there is a difference but not what the difference will be.

e.g : “The difference in the amount of hours of sleep a participant has will have an effect on their memory performance, which will be shown by the difference in the memory test scores of the participants.”

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9
Q

Why must factors that effect the DV be controlled?

A
  • extraneous variables
  • confounding variables
  • to make sure that the effect on the DV is purely due to the independant variable
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10
Q

How would you test the effect of an IV

A

Compare the different experimental conditions:
- Control condition (e.g no energy drink/water) = used to determine whether the IV affected the DV.

  • Experimental condition (e.g energy drink)
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11
Q

Operationalisation

A

Clearly defining variables on terms of how they can be measured = makes the hypothesis clear + testable.

Example: After drinking 500ml of energy drink, participants speak more words in the next 5 minutes than participants who drink 500ml of water.
(even more operationalised : number of words said)

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12
Q

Extraneous variables

A

Any unwanted variables outside of the IV that will impact the DV.

  • Researcher should minimise the influence (control) or remove these variables.

e.g : lighting of lab or age of participants

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13
Q

Confounding variable

A

An uncontrolled extraneous variable that change systematically with the IV and affect the DV, so results won’t show the effect of the intended IV.

e.g : time of day
to control : all participants take test same time of day

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14
Q

state 3 types of extraneous variables

A
  • Participant variables
  • Situational variables
  • Investigator effects
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15
Q

Outline examples of participant variables

A
  • Personality
  • Age
  • Intelligence
  • Gender
  • Participant reactivity
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16
Q

Explain how to control Participant variables

A

-Sample: Use random sampling to gain a representative sample from the population.

-Design: Use repeated measures or matched pairs

Allocation: Randomly allocate them to conditions

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17
Q

Outline examples of situational variables

A
  • Time of day
  • Heat
  • Demand characteristics
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18
Q

Explain how to control Situational variables

A
  • Standardise: Keep everything the same for each participant (procedures and instruction)
  • Counterbalance: Reduces effect of situational variables
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19
Q

Definition and examples of investigator effects

A

Subtle cues from a researcher that may affect the performance of participants in studies:
- Body language
- Tone/voice
- Bias

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20
Q

Explain how to control Investigator effects

A
  • Double blind: Neither researcher nor participants knows which condition they’re in.
  • Inter-rater: Independent raters rate the same behaviour as the researchers and check for agreements.
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21
Q

Outline the definition of counterbalancing

A
  • participant sample is divided into a half
  • one half completing the two conditions in one order
  • the other half completing the conditions in the opposite order
  • used to deal with order effects e.g when using a repeated measures design
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22
Q

Demand characteristics

A

A cue that makes participants unconsciously aware of the aims of a study and helps them work out what the researcher expects them to find.

  • May behave in an unnatural way and over/under-perform to please the researcher = affects results/DV
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23
Q

How to control demand characteristics

A
  • Deception: Use distractor questions and lie about the aim.
  • Single blind: Participant is unaware of which condition they’re in.
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24
Q

What can demand characteristics cause?

A

Please-U effect : may act in a way they think the researcher wants them

Screw-U effect : intentionally underperform to sabotage the study’s results

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25
Q

Participant reactivity

A

when the responses and/or behaviours of study participants are affected by their awareness that they are part of a study

can lead to :
- demand characteristics
-investigator effects

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26
Q

Randomisation

A

used in the presentation of trials in an experiment to avoid any systematic errors that might occurs as a result of the order in which the trials take place.

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27
Q

Standardisation

A

using the exact same formalised procedures and instructions for every single participant involved in the research process

-eliminates non standardised instructions as being possible extraneous variables

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28
Q

Laboratory experiment

A

Conducted in a highly controlled environment, where the researcher manipulates the IV and records the effect on the DV.

  • Strict control is maintained over extraneous variables
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29
Q

Strength of Laboratory experiment

A
  • High internal validity due to control over extraneous variables = Researcher can ensure any effect on the DV is due to their manipulation of the IV + proves cause and effect. ( high degree of control )
  • Results are more replicable = Results are valid and generalisable :
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30
Q

Limitations of Laboratory experiments

A
  • Lacks ecological validity = Not true to real world so can’t be generalised
  • Hawthorne effect = behaviour is altered due to awareness of the study.
  • Demand characteristics = participants are aware of study due to lab conditions so behaviour is unnatural.
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31
Q

Field experiment

A

Conducted in a natural environment where the researcher manipulates the IV and records the effect on the DV.

e.g Hoflings hospital study on obedience

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32
Q

Strength of field experiments

A
  • High mundane realism = Environment is more natural, so behaviour is more valid + authentic.
  • High external validity = Participants are unaware of study.
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33
Q

Limitations of field experiments

A
  • Lack of informed consent = ethical issues, invasion of privacy.
  • Increased realism also increases extraneous variables = Cause and effect of IV + DV is harder to establish and precise replication won’t be possible.
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34
Q

Definition of Natural experiment

A

Researcher takes advantage of a pre-existing IV.

  • Natural as the variable would’ve changed regardless of the researchers interest in it.

e.g : biological explanations of bullying

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35
Q

Strengths of natural experiment

A
  • Provide research opportunities for studies that can’t be conducted due to ethical/practical reasons.
  • High external validity = involves study of real situations
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36
Q

Limitation of natural experiment

A
  • Naturally occurring event is rare = limits scope for generalising results to other similar situations.
  • Participants may not be allocated randomly to experimental conditions = less clarity that the effect on DV is due to the IV.
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37
Q

Quasi experiment

A

IV isn’t determined by anyone, but is not manipulated and an existing difference between people. (age , gender, phobias)

e.g A memory task with a group of clinically depressed participants compared to a control group of non-depressed participants

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38
Q

Strength of Quasi experiments

A
  • High internal validity due to control over extraneous variables = Researcher can ensure any effect on the DV is due to their manipulation of the IV + proves cause and effect.
  • Conducted under highly controlled conditions = replicable, reliable, generalisable results.
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39
Q

Limitation of quasi experiments

A
  • Can’t randomly allocate participants to conditions = possibility of confounding variables
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40
Q

Population

A

A group of people from whom samples are drawn

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41
Q

Generalisation

A

The extent to which research results can be applied to the population.

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42
Q

Sample

A

A group of people who take part in a research investigation, chosen from a population.

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43
Q

Bias

A

When certain groups may be over or under-represented within the selected sample

= limits extent of generalisation.

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44
Q

Random sampling

A
  • Obtain a list of the population and assign numbers
  • Randomly choose sample via lottery method (random number generator or names in a hat)
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45
Q

Strengths of random sampling

A
  • No researcher bias = unable to choose samples to support their hypothesis.
  • Everyone has an equal chance of being chosen
  • Laws of probability = likely to be representative
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46
Q

Limitation of random sampling

A
  • Difficult and time consuming to obtain a list of the population.
  • Possibility of unrepresentative sample = doesn’t reflect the distribution of characteristics in the population.
  • Participants may refuse to participate = becomes a volunteer sample.
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47
Q

Systematic sampling

A
  • Sampling frame is produced = list of population in alphabetical number.
  • Sampling system is made = every nth member of the population is chosen.
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48
Q

Strength of systematic sampling

A

No researcher bias = researcher has no influence over chosen people after the sampling system is chosen.
- Fairly representative

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49
Q

Limitation of systematic sampling

A
  • Complete representation isn’t possible = doesn’t reflect all differences in people.
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50
Q

Stratified sampling

A
  • Identify the different strata within the population + work out the proportion needed from each strata to be representative.
  • Use random sampling to choose people from each strata.
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51
Q

Strength of stratified sampling

A
  • No researcher bias = sample from strata is random and uninfluenced by the researcher.
  • Representative = designed to accurately reflect composition of the population, so generalisation is possible.
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52
Q

Limitation of stratified sampling

A
  • Complete representation isn’t possible = can’t reflect all the ways people are different.
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53
Q

Opportunity sampling

A
  • Select anyone willing + available by asking anyone around at the time of their study. (e.g the streets)
  • participants available at the time of study
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54
Q

Strength of opportunity sampling

A
  • Convenient : easy method of recruitment
  • Saves time and effort
  • Less costly
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55
Q

Limitation of opportunity sampling

A
  • 2 forms of Bias:
    = Researcher bias as they have complete control over the selection.
    = Unrepresentative as chosen from a specific area so can’t be generalised.
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56
Q

Volunteer sampling

A
  • Participants select themselves to be apart of the sample (e.g raising hands/advert)
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57
Q

strength of volunteer sampling

A
  • Requires minimal researcher input : willing participants , more likely to co-operate
  • Less time-consuming
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58
Q

Limitation of volunteer sampling

A

Volunteer bias = attracts helpful, curious people so generalisation may be difficult.
- motivation like money could be driving participants so participants may not take study as seriously

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59
Q

independant group design

A

Two separate groups experience two different conditions of the experiment.

  • One group takes part in condition A and the other group takes part in condition B.
  • Performances of the groups are compared
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60
Q

Limitation and resolution of independant group design

A
  • Researcher can’t control participant variables = different abilities of participants

Randomly allocate participants to conditions so the participant variables are distributed evenly.

  • Needs more participants
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61
Q

Strengths of independent group design

A

no order effects : e.g practise effect or boredom effec
- Can use the same test for both groups = faster
- Participants are less likely to guess aim

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62
Q

Repeated measures design

A

Only one group of participants and they take part in both conditions

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63
Q

Limitation and resolutions of repeated measures design

A
  • Order effect may affect performance (participants may perform better/worse in the second condition due to practice/boredom)

= Use 2 different tests to reduce practice effect, so the order effect is dealt with via counterbalancing.

  • During the second test, participants may guess the aim of the experiment which affects their behaviour

= Use deception and lie about the aim + use distractor questions

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64
Q

Strength of repeated measures design

A
  • Limits the variability between participants
  • Fewer participants needed
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65
Q

Matched pairs design

A

Two separate groups, but they’re matched into pairs for certain qualities before splitting (age,gender,intelligence).

each person from a pair goes into a different experimental condition

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66
Q

Limitations and resolution of matched pair design

A
  • Very time consuming + difficult to match participants based on key variables = Restrict number of variables to match
  • Not possible to control all participant variables = Conduct a pilot study to consider key variables that may be important when matching.
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67
Q

Strengths of matched pair design

A
  • Reduces participant variables as researcher has paired the participants, so each condition has people with similar abilities + characteristics.
  • Avoids order effects, so no counterbalancing is needed.
    demand characteristics less of a problem
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68
Q

hawthorne effect

A

When an individual modifies an aspect of their behaviour, due to their awareness of being observed.

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69
Q

Mudane realism

A

The extent to which the materials + procedures involved in a study are similar to events that occur in the real world.

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70
Q

External validity

A

Extent of results being applicable to other experiments, settings, people, and times.

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71
Q

Naturalistic observation

A

Watching and recording behaviour in a setting where it would normally occur.

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72
Q

Strength of naturalistic observation

A
  • High ecological validity = results can be generalised to everyday life as behaviour is observed in a natural env
  • No researcher influence
  • People are less likely to alter their behaviour as they’re unaware of the observation + it’s not controlled
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73
Q

Limitation of naturalistic obervation

A
  • Low internal validity = no control over extraneous variables, so it confounds + difficult to judge behaviour patterns.
  • Hard to replicate = due to no control and extraneous variables.
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74
Q

Controlled obervation

A

Watching and recording behaviour within a structured environment, where variables are controlled

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75
Q

Strengths of controlled observation

A
  • Replicable = easy to show reliability + generalisable after repeating.
  • No confounding variables due to control
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76
Q

Limitation of controlled observation

A
  • Reducing naturalness of environment + behaviour = due to regulating variables.
  • Demand characteristics + low ecological validity = Participants know they’re being studied
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77
Q

Covert observation

A

Participants behaviour is watched and recorded WITHOUT their knowledge/consent.

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78
Q

Strengths of covert observation

A
  • High ecological validity
  • Removes participant reactivity = behaviour is natural as they’re unaware of the observation.
  • Authentic
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79
Q

limitation of covert observation

A
  • Unethical = lack of informed consent + deception
  • Intrusive = lack of privacy
  • No control over variables
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80
Q

Overt obervation

A

Participants behaviour is watched and recorded WITH their knowledge/consent.

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81
Q

Strength of overt observation

A
  • Ethically acceptable = informed consent + the right to withdraw is given.
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82
Q

limitation of overt observation

A
  • Hawthorne effect = altered behaviour due to awareness of observation.
  • Demand characteristics affect data
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83
Q

Participant observation

A

Researcher becomes a member of the group being watched and recorded.

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84
Q

Limitation of participant observation

A
  • Subjective and biased = observation made by someone that actively participated in the activity being observed.
  • Researcher ‘goes native’= line between researcher and participant is blurred
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85
Q

Non-participant observation

A

Researcher doesn’t become involved with the group being watched and recorded.

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86
Q

eval of Non-participant observation

A
  • Lack of direct involvement = objective and less likely to ‘go native’
  • Easier to observe and record data.
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87
Q

limitation of non participant observation

A

Loss of valuable insight = may miss some things

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88
Q

Self reporting techniques

A

A method where a person is asked to state their own feelings, opinions and experiences related to a topic:
Example: questionnaires, interviews

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89
Q

Questionaires

A

A set of pre-written questions used to collect data by assessing a person’s thought or experiences.
- May be used to assess the DV
- Always structured

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90
Q

Advantages of questionnaires

A

Directly observing intentions/feelings = Reduces assumption.
- Can be distributed to a large sample = good for generalisation.
- May be more willing to share personal information.

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91
Q

Disadvantages of questionnaires

A
  • People may be untruthful = due to social desirability bias.
  • Completed by people that can read/write = limits sample.
  • ‘Eager sample’ = filled by people who have time/want to fill them so the sample is biased.
  • Acquiscence bias = tendency to agree to content without reading q’s properly.
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92
Q

State 3 guiding principles of questionnaires

A

The validity, objectivity and scientific nature is dependant on the design of the questionnaire:
- Clarity
- Bias
- Analysis

93
Q

Describe the effects of Clarity in Questionnaires

A

Questions must be clear, high in face validity, shouldn’t be ambiguous and no use of double negatives:
- Double-barrel questions reduce clarity (e.g how satisfied are you with school AND your grades)

94
Q

Describe the effects of Bias in Questionnaires

A

Any bias in a question may produce a biased answer:
- e.g how effective was the obnoxious president trump

95
Q

Describe the effects of Analysis in Questionnaires

A

Questions must be simple enough to analyse.

96
Q

Strengths and limitations of Open questions

A
  • Allows people to elaborate on answers and be more detailed.
  • Qualitative data BUT difficult to analyse
97
Q

Strengths and limitations of Closed questions

A

Easy to analyse

  • Lacks detail + forces you to choose an option (yes/no) = lacks validity.
98
Q

State 3 ways to improve questionnaires

A
  • Filler questions = have random unrelated questions embedded to reduce demand characteristics.
  • Sequence of questions = easiest to hardest questions reduces resistance and anxiety in the person.
  • Sampling techniques = alternative sampling method to overcome volunteer/eager sample.
99
Q

Interviews

A

A method where data is collected face to face by an interviewer:
- Asks questions to assess interviewee’s thoughts/experiences.

100
Q

Structured interview

A

Made up of a pre-determined set of questions that are asked in a fixed order.

101
Q

Strengths of Structured interviews

A
  • Easily repeatable = questions are standardised so different answers can be compared.
102
Q

Limitation of structured interviews

A
  • Validity of interview may be compromised = investigator effects due to their behaviour towards different interviewees.
  • Less detail obtained.
103
Q

Unstructured interview

A

Questions are developed during the course of the interview:

  • Aims that a certain topic will be discussed + free flowing.
  • Interviewee is encouraged to expand + elaborate on answer.
104
Q

Strengths of Unstructured interviews

A

Higher validity due to more detail = due to follow-up questions

105
Q

Limitations of Unstructured interviews

A
  • Requires trained/skilled interviewers due to coming up with questions on the spot = expensive
  • No pre-determined questions = questions may lack objectivity.
  • Researcher has to sift through irrelevant info and draw conclusions = difficult and time-consuming.
  • Social desirability = untruthful answers
106
Q

Semi structured interview

A

Begins with pre-determined questions but follow-up questions are asked when appropriate.

107
Q

State 3 guiding principles of Interviews

A
  • Recording the interview
  • Effect of the interviewer
  • Interviewer bias
108
Q

Describe the effects of Recording in Interviews

A
  • Record audio + video and have the interview scribed.
  • If the writer is required to write answers down, their listening may interfere with writing + vice versa.
  • Writing/non-writing = may come across as intimidating or that the interviewee made a mistake.
109
Q

Describe the effects of the interviewer in Interviews

A
  • Presence of interested interviewer = yields fruitful + detailed answers = interviewers must be mindful on their non-verbal communication. (body language/posture)
  • Mindful of verbal communication = knowing when to speak and not to speak.
110
Q

Describe the effects of Interviewer bias in Interviews

A

When the interviewers expectations are unconsciously communicated and has an effect on the interviewees behaviour.

111
Q

State 2 ways to improve interviews

A
  • Avoid repeating questions
  • Avoid probing and asking suggestive questions
    = e.g. ‘are you sure?’
112
Q

Describe 3 types of closed questions for Questionnaires

A

Likert scale = when the respondent indicates their agreement on a scale of strongly agree to strongly disagree.

Rating scales = when the respondent identifies a value that represents the strength of their feelings towards the subject from 1 to 5.

Fixed choice option = when the respondent is given a list of options and they tick what applies to them.

113
Q

Describe 2 factors of designing an interview

A

Interview schedule = a list of questions that the interviewer intends of covering, this should be standardised between each participants to avoid investigator effect.

One to one interviews = increases the likelihood of the interviewee opening up, should start with neutral qs to get them relaxed and constantly reminded that everything they share is confidential.

114
Q

State 3 methods for writing BAD questions

A
  • Overuse of jargon
  • Emotive language and leading questions
  • Double-barrelled questions and double negatives
    -ambiguous
115
Q

Explain the effect of the Overuse of jargon

A

Overusing technical terms that are only familiar to people specialised in that field = confuses the respondent as it’s unnecessarily complex.

116
Q

Explain the effect of Emotive language and leading question

A
  • The author’s attitude to a topic may become clear through the way the question is phrased = ‘barbaric, destroyed, crashed’
  • Leading questions guide the respondent to a particular answer.
117
Q

Explain the effect of the Double-barrelled question and double negatives

A
  • Contains 2 questions in 1
  • The issue is that respondents may agree with half the question and not the other.
  • Double negatives may be difficult to interpret
    (I am not unhappy with my job agree/disagree)
118
Q

Definition and evaluation of Qualitative data

A

Data expressed in words + non-numerical:
- More detail than quantitative

-Greater external validity = provides more insight to participants view.

  • Difficult to analyse + subjective interpretations due to bias and the researcher may have preconceptions about what they’ll find
119
Q

Definition and evaluation of Quantitative data

A

Data expressed numerically:
- Simple to analyse = comparisons between data are easy to find.
- More objective + less open to bias
- May fail to represent real life
Statistical test

120
Q

Definition and evaluation of Primary data

A

Info that has been obtained 1st hand by the researcher:
- Authentic data that can be designed to specifically target info the researcher requires (e.g interviews)
- Requires more time and effort

121
Q

Definition and evaluation of Secondary data

A

Info that has already been collected by someone else:
- Inexpensive + easily accessed with minimal effort
- May be variation in quality, outdated or incomplete

122
Q

Describe how to work out the Mean and state a strength

A

Add all the values up, then divide by how many values there are.

  • Includes all the values in the calculation = representative of all data.
123
Q

Describe how to work out the Mode

A

The most frequently occurring value in a set of data

124
Q

Describe how to work out the Range

A

Subtract the lowest value from the highest value

125
Q

Definition of Standard deviation

A

A measure of how much scores deviate from the mean score:

  • Low SD = data is tightly clustered around the mean, so all participants responded in similar ways.
  • Large SD = not all participants were affected by the IV in the same way as data is widely spread + some anomalous.
126
Q

interpretative validity

A

this is the extent to which the researcher’s interpretation of events matches
those of their participants.

127
Q

Single blind procedure

A

participants are not made aware of the aims of the study until they have taken part

unaware of which experimental condition they. are in

(to reduce the effect of demand characteristics on their behaviour)

128
Q

Double blind procedure and strengths

A

a third party conducts the investigation without knowing its main
purpose either

(which reduces both demand characteristics and investigator effects and
thus improves validity)

129
Q

Internal validity

A
  • this is whether the outcomes
    observed in an experiment are due to the
    manipulation of the IV and not any other factor
130
Q

Ecological validity

A

This is the extent to
which findings can be generalised to other
situations and settings.

131
Q

Temporal validity

A

Generalisability to other
historical times and eras

132
Q

Population validity

A

Generalisability to different populations of various ages

133
Q

Face validity

A
  • this is when a measure is
    examined to determine whether it
    appears to measure what it is supposed
    to. This can be done either through simply
    looking at it or passing it to an expert to
    check.
134
Q

Concurrent validity

A

this refers to the
extent to which a psychological measure
compares to a similar existing measure.
The results obtained should either match or
be closely similar to the results of the well
established and recognised test.

135
Q

Predictive validity

A

this refers to how well
a test can predict future events or
behaviours

E.g. how childhood attachment measured
using the strange situation are able to predict
how the child will grow up to behave in
adulthood (from Attachment topic).

136
Q

Validity

A

refers to the extent to which results of a research study are legitimate. There are various
types of validity and ways of assessing them:

137
Q

Meta-analysis

A

this is when a
researcher combines results
from many different studies
and uses all the data to form
an overall view of the subject
they are investigating.

138
Q

strength of meta analysis

A

More generalisability is
possible as a larger amount of
data is studied.
- The researcher is able to
view the evidence with more
confidence as there is a lot of
it.

139
Q

weakness of meta analysis

A

Publication bias such as the
file drawer problem may be
presented- this is when the
researcher intentionally does
not publish all the data from
the relevant studies but
instead chooses to leave out
the negative results. This
gives a false representation of
what the researcher was
investigating

140
Q

Summarising data in a table

A

data has been converted into descriptive statistics

141
Q

Bar Charts

A
  • discrete data ( whole values e.g 3 cats not 3.5 cats)

-data that has been divided into categories
-do not touch each other which shows that we are dealing with separate conditions
freq : y-axis
categories : x-axis

142
Q

Histograms

A

-continous data (doesnt have to be full numbers e.g 3.75ml of water)

freq: y-axis
catgories : x-axis

143
Q

Line graphs

A
  • continious data
    y-axis : DV
    x-axis : IV
144
Q

scattergrams

A
  • associations betweeen co-variables
    -rather than differences hence we
    came across them in the correlations topic

-Either of the co-variables can occupy the x-axis or
the y-axis,

145
Q

normal distributions

A

a symmetrical pattern of frequency data that forms a bell-shaped pattern

146
Q

skewed distribution

A

a spread of frequency data that is not symmetrical, instead the data
all clusters to one end.

positive : distr of data is concentrated on the right

negative : distr of data is concentrated on left

147
Q

Define Peer review

A

the assessment of scientific work by experts in the same field

  • it is done to make sure that all research intended to eventually be published is of high quality.
148
Q

What are the main purposes of peer review?

A
  • to know which research is worthwhile so that funding can be allocated towards it
  • to validate the relevance and quality of research : prevent fraudulent research from being publicised

-to suggest possible improvements for the study

149
Q

what are real life examples of why papers are peer reviewed?

A
  • peer review contributes to research rating of university departments

-public journals must be peer reviewed before publication

150
Q

Limitations of peer review

A

Publication bias: editors tend to prefer to publish positive results or “headline grabbing” results as opposed to any negative results :
file drawer problem , where negative results are intentionally not published
- negative findings are important so replications can be made to check validity

Once a study has been it is often difficult to retract , even when proven to be wrong : once in public domain the damage is done

It can be difficult to find an expert.
Smith (1999) argues that because of this a lot of poor
research is passed as the reviewer didn’t really understand the work.

151
Q

Outline real life consequences of peer review

A

MMR VACCINE : (Andrew wakefield 1998) the vaccine leads to autism: had implications as number of measles cases increased : later found research was fraudulent
- however rumours about MMR continue to persist

11+ EXAM

152
Q

How is psychology and the economy linked?

A

how what we learn from psychological research
influences our country’s economic prosperity

mental healthy: £22 spent on MH annually in the UK e.g stress anxiety
For such problems psychology research has been able to present solutions to them and this expresses why psychology research is important for the economy.

153
Q

economical implications : psychopathology

A

Treatments - Cognitive Behavioural Therapy and Rational Emotive Behavioural Therapy for depression, drug therapy for OCD.

Economy - workers able to return to work

154
Q

economical implications : Attachment

A

Role of the father - Tiffany
Field (1978) found that fathers
can take on the role of being a
primary caregiver.

Economy - - Mothers can return to work.
- More flexible working arrangements within families.
- Can maximise their income and effectively contribute to the economy.

155
Q

economical implications : social influence

A

Social influence leading to social change -Minority influence, appealing to NSI, disobedient models.

Economy :
-Health campaigns.
- Unions strike- make working conditions better.
- Environmental campaigns like getting companies to reduce their waste and use of non-renewable energy.

156
Q

economical implications : memory

A

Eyewitness testimony - How leading questions or post event discussion can affect
eyewitness testimony.

Economy - Led to police using the
cognitive interview which reduces wrongful convictions hence reduces waste of money and space in jail.

157
Q

Case studies

A

detailed study into the life of a person which covers great detail into their background

  • builds up a case history hence providing qualitative data
158
Q

Examples an example of case studies

A

case of HM : memory:could tie shoelaces but couldn’t remember stroking a dog : procedural memory intact but episodic wasn’t

159
Q

Strengths of Case studies

A
  • Detailed so able to gain in depth insight
  • Forms basis for future research.
  • From studying unusual cases you are able to
    infer things about normal usual behaviour of
    humans.
  • Permits investigation of situations that would
    be otherwise unethical or impractical.
160
Q

Limitation of case studies

A
  • Not generalisable to wider populations as
    data is only gathered from one person.
  • Various interviewer biases are presented like
    social desirability bias (from the unique
    person’s side) and interpretative bias ( from
    the researcher’s side).
  • They are time consuming and difficult to
    replicate.
161
Q

content analysis

A

qualitative research tool or technique widely used to analyze content and its features.

  • This allows us to have insight into the
    structured values, beliefs and prejudices of our society.
162
Q

how to conduct a content analysis

A

● Identify hypothesis that you will investigate.

● Create a coding system depending on what you are investigating e.g. 1= male, 2= female.

● Gather resources.

● Conduct content analysis and record data in a table.

● Analyse data which is descriptive and qualitative e.g. using ‘thematic analysis’- allows
themes, patterns and trends to emerge in data.

● Write up a report in the format of a scientific report.

163
Q

Strengths of content analysis

A
  • Strong external validity as the data is already
    in the real world so it has high mundane
    realism.
  • Produces large data set of both quantitative
    and qualitative data that is easy to analyse.
  • Easy replication.
  • Ethical issues like ‘right of privacy,
    confidentiality, informed consent’ are avoided
    as data is already in the public domain.
164
Q

Limitations of content analysis

A
  • Observer bias is presented but it can be
    eliminated by achieving inter-observer
    reliability.
  • Content of choice to analyse can be biased
    by researcher.
  • Interpretative bias - the researcher may
    ignore some things but pay extra attention to
    others.
165
Q

what are the 3 levels of measurement of quantitative data

A
  • nominal
  • ordinal
  • interval
  • ratio
166
Q

Nominal data

A
  • type of data that is in the form of categories.
  • Qualitative data
  • discrete- one item can only appear in one category.
  • difference between categories have no further meaning

It does not enable sensitive analysis as it does not yield a numerical result for each participant

167
Q

Ordinal data

A
  • data represented in a ranking form e.g 1 = hates maths 10= loves maths
  • no equal intervals between each unit
  • Qualitative data

weakness : lacks precision : based on subjective opinions of people

168
Q

interval data

A

-named and ordered
based on numerical scales which include equal units of precisely defined size
- Quantitive data
-Continuous data

169
Q

Ratio

A
  • same characteristic as interval data
  • however has a meaningful ZERO point
    e.g time taken to undertake a task
170
Q

Outline appropriate measures for each level of data

A

Nominal: Mode, no measure of disperesion
Ordinal: Median , range
Interval: Mean , SD

171
Q

Scientific report

A
  • writing up of a research for publication
  • has a specific series of sections
172
Q

Outline the sections of a scientific report

A

Title : what is the report about?

Abstract : brief summary of study

Introduction : background of study

Method : process of study

Results : summarise findings

Discussion : findings and their implications

**References ** : inform reader about sources of information used

Appendices: detailed info not in report

173
Q

Abstract

A
  • concise summary of report
    -key details of report
    -150-200 words long
  • includes : aim , hypothesis , method , results and conclusion
  • read to know whether research study is worth examining any further
174
Q

Introduction

A

information of past research on a similar topic whereby relevant theories , studies and concepts are mentioned

broad leading towards specific detail

allows reader to place the study in context

175
Q

Method and what is included

A

Description of how the study was conducted
- Must be enough information to be able to replicate the study

includes : design, sample collected , materials used and procedure , ethics etc

176
Q

Results

A

findings of study , presented with
- descriptive statistics : visual representation of differences between group

  • inferential : analysis of data and null hypothesis accepted or rejected
177
Q

Discussion

A

considering what the findings exactly mean and for psychological theories

-implications of the research such as on the society
- limitations and improvement

178
Q

Refrencing

A

List all of sources that were quoted or referred to in the report

179
Q

Appendicies

A

copy of all resources/ material used in the study , raw data and statistical calculations

(aids in peer review and replication)

180
Q

Statistical testing

A

provides a way of determining whether hypotheses should be rejected or accepted

  • tells us whether differences or relationships between variables that have been found during the xperiment are statistically significant or if they only occur due to chance
181
Q

Alternative hypothesis (H1)

A

there IS a statistical difference
- there is a difference between the conditions
- small probability that the results are due to chance

182
Q

Null hypothesis (H0)

A

there is NOT a statistical difference
-there is no difference between the conditions
- high probability that results are due to chance

183
Q

When to use (Sign test)

A
  • looking for difference not association
  • used a related experimental design - repeated measures design
    -collected nominal data
184
Q

How to find value for sign test

A
  • calculate difference between the two sets of data : minus them from eachother
    -state if theyre a - or +
  • amount of negative data = y
  • amount of positive data = x

x-y = sign value

N= no of participants , not including the participants whos difference is a 0
-FIND CR on table

185
Q

how to conduct sign test

A

1) hypothesis : alternative and null hypothesis

2) record data and work out the sign
e.g sign will be negative (-) if the
value has decreased in the second condition but positive (+) if it has increased. If the value
has stayed the same , this value will be ignored and the N adjusted to exclude it.

3) calculated value for sign test , S,
4) find critical value of S - use calculated N value and p<0.05 which means that theres less than 5% prob thhat results occured by chance
5) conclusion , refer back to hypothesis mentoning IV and DV in conclusion etc

186
Q

what are the 3 factors you must take into consideration when choosing an inferential statistical test?

A

Design of study:
- unrelated design ? - independant group design
- related design? - repeated measures or matched pair design

level of data collected during study: ordinal , nominal or interval

difference or correlation being measured

187
Q

when to use chi squared test

A

Nominal + unrelated : chi squared
Nominal + related : sign test
test for association/correlation

188
Q

What data’s should i use

A

Nominal + unrelated : chi squared
Nominal + related : sign test
test for association/difference : chi-squared

Ordinal + unrelated : Mann-whitney test
Ordinal + related : Wilcoxon test
test for association/difference : spearman’s Rho

Interval+unrelated : unrelated t-test
interval+ related= t-test
test for association/difference : Pearsons R

189
Q

critical value

A

the numerical boundary that stands between accepting or rejecting the null
hypothesis when a hypothesis is being tested

190
Q

event sampling

A

Event sampling is used to sample behaviour in observational research.

191
Q

Rule of R

A

If there is an R in the name of the statistical test the calculated value has to be
gReater or equal to the critical value for the result to be significant.

192
Q

what are the 2 types of errors that can occur during inferential statistical testing

A

Type I : incorrect rejection of null hypothesis (false positive)
- therefore findings were actually due to chance

Type II : incorrect accepting of null hypothesis and rejection of alternative
(false negative)
-therefore findings were actually significant

193
Q

paradigm and example

A

set of shared ideas and assumptions within a scientific discipline about a subject and its method of enquiry

e.g Kuhn argues that a subject can only be called a science if there is an agreed global theory
e.g evolution

194
Q

paradigm shift

A

a significant change in these central assumptions within a scientific
discipline, resulting from a scientific revolution.

195
Q

outline 3 stages of a science

A

pre science : no paradigm exists , much debate about what the subject is and its theoretical approach

normal science : generally accepted paradigm can explain and interpret all findings

scientific revoluution : evidence against the old paradigm reaches a certain point and there is a paradigm shift . Old paradigm is replaced by new one

196
Q

What scientific stage is psychology at?

A
  • pre science
  • too much disagreement and conflicting approaches
197
Q

Theory

A

set of general principles and laws which can be used to explain specific
events or behaviours

198
Q

Theory construction

A

gathering evidence from
direct observation during investigations.
- order and direction in science

199
Q

Deduction

A

deriving new hypotheses
from an already existing theory

e,g Baddeley and Hitch modifying WMM in 2000 as they added the episodic buffer to model

200
Q

Falsifiability

A

-principle that states a theory cannot be considered scientific unless it
allows itself to be proven untrue

  • (Popper)states that successful theories that
    have been constantly tested and supported simply haven’t been proven false yet

-‘pseudosciences’ : sciences that cant be proven wrong e.g Freud

the alternative hypothesis is always
accompanied by the null hypothesis.
-formulating hypotheses that can either be proved or disproved by experimentation.

201
Q

Replicability

A

-the extent to which scientific methods and their results can be repeated by other researchers across other contexts and circumstances.

  • validity and reliiability of results
202
Q

Objectivity

A

all possible biases from the researcher are minimised so that they don’t influence or distort the research process.

empirical methods of investigation :
making direct observations and through direct experiences.

  • cant be scientific if not empirically tested and verified using either empirical or observational methods
203
Q

Strengths of psychology as a science

A
  • Scientific methods are used in many research
    studies giving them scientific credibility.
  • Findings from studies do positively impact
    society & individuals e.g. Cognitive behavioural
    Therapy to treat depression.
204
Q

Limitations of psychology as a science

A
  • Not all research is generalisable e.g. from
    case studies.
  • Psychologists do often make inferences of
    behaviour rather than directly measuring it , for
    example this is usual for cognitive
    psychologists that infer about cognitive
    processes from brain scans (Memory topic link
    here).
205
Q

Definition of ethics in psychology

A

A matter of balance between the rights of participants and the goal of research to produce valid data.

206
Q

Explain the BPS code of ethics

A

Instructs psychologists in the UK about acceptable and unacceptable behaviour when dealing with participants.

  • Based on 4 major principles: respect, competence, responsibility and integrity
207
Q

State 6 ethical issues in research: Can Do, Can’t Do With Participants

A
  • Informed Consent
  • Deception
  • Confidentiality
  • Debriefing
  • Right to withdraw
  • Protection from physical and psychological harm
208
Q

Explain ways to deal with Consent

A

Participants must be fully informed of the aims of the experiment, procedures, risks and their rights

-They’ll choose whether to participate without being coerced.

  • Consent forms should be used: under 16s and medically ill must be consented by guardians.
209
Q

Explain the use of Deception in research

A

Deliberately misleading/withholding information = invalid consent, but may be necessary in some cases

210
Q

Describe the use of Debriefing after Deception

A

After the study, participants should be fully informed of the true aims of the experiment and must be able to Withdraw their data.
- Should be allowed to express concerns/questions + be provided with counselling if subjected to stress.

211
Q

Describe the use of the Right to withdraw in research

A

Participants must be allowed to withdraw themselves and their data at any time during the study.

212
Q

Explain ways to deal with Confidentiality

A

Identities of the participants should remain private and unidentifiable in published research.
- Participants must be able to control information about themselves = right to privacy

213
Q

Explain ways to deal with Protection from physical and psychological harm

A

Participants shouldn’t be placed at any more risk than they would be in their daily lives.

  • Includes: embarrassment, stress and pressure
  • They should be constantly reminded of their right to withdraw, counselling and therapy in extreme situations.
214
Q

Measures of central tendancy

A

Mean
Mode
Median

215
Q

Measures of dispersion

A

Range
SD
Percentages
posittive neegative and zero calculattions

216
Q

Pilot study

A

small-scale version of an investigation which is done before the real
investigation is undertaken

-so that participants do not bias results in ways that they “think” they should act e.g demand characteristics

217
Q

Timed sampling

A

a method of sampling behaviour in an observation study and is where an observer records behaviour at prescribed intervals

218
Q

timed sampling evaluation

A

+ Less likely to miss behaviours as the researcher usually has a short time to focus on recording behaviour, therefore is more likely to be accurate.

-Behaviours that occur outside the time intervals are not accounted for, therefore may reduce validity as important behaviours may be missed.

219
Q

how to increase reliability of observations

A

Check inter-rater reliability
Conduct a pilot study to check behaviour categories

220
Q

controlled observational study

A

Watching and recording behaviour within a structured environment

i.e. one where some
variables are managed

221
Q

Controlled observational study eval

A

+ Replication is easy as less extraneous variables

-Cannot be applied to real life settings and low in ecological validity

222
Q

Naturalistic observation

A

Observation of behaviour in a natural setting

  • Investigator does not interfere, just observes + records
223
Q

Naturalistic observation evaluation

A

+ High in ecological validity
+ Used to generate ideas for experimental research / validate experimental findings

-No manipulation of variables = cannot infer cause+effect
-Lack of control = no replication
-Ethical problems = invasion of privac

224
Q

Behavioural categories

A

when psychologists must decide which specific behaviours should be examined

these should be observable/objectively defined/operationalised/unambiguous.

“behaviour checklist” affection : hugging kissing etc

225
Q

Efficient behavioural categories

A

must be observable efficient and self evident

226
Q

Event sampling

A

involves counting how the number of times a particular behaviour occurs within a target individual or group

227
Q

Sampling methods eval ( event and timed sampling)

A

+less time consuming : reducing the number of observations that have to be made

-if specified event is too complex the observer may overlook important details using event sampling

228
Q

test retest reliability

A

a way of assessing the external reliability of a research tool. It involves presenting the same participants with the same test or questionnaire on two separate occasions, and seeing whether there is a positive correlation between the two.

psychology y1 (7 decks)

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