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Pysch > research meth rev > Flashcards

research meth rev Flashcards

1
Q

primary data definition

A

data collected first hand by the researcher. It is collected specifically to match the aims of the investigation

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2
Q

primary data strnegth

A

high validity because it is collected by the researcher with his/her aims in mind so it will be more useful to get valid conclusions.​

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3
Q

primary data limitation

A

consuming and more costly because the researcher has to find participants and carried out the research to collect data. ​

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4
Q

secondary data definition

A

data that it is already published by other researchers so it is not specifically collected to match the researcher’s aims.

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5
Q

secondary data strength

A

it is less time consuming so it is cheaper to collect because the data is already available to the researcher to be analysed.​

useful when research cannot be carried out because of ethical issues. An example of this type of data is meta-analysis and content analysis where the data is published in reports, newspaper, books, academic journals etc

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6
Q

secondary data limitation

A

may not be valid because it is not specifically collected having in mind the aims of the researcher. ​

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7
Q

quanitiative data defintion

A

numerical data which means that it is objective. For example, scores from a test or rating in a questionnaire.​

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8
Q

quantataive data strnegth

A

objective data because it is numerical, it allows the researcher to make objective comparisons without the need to make biased interpretations of the data collected. This increases reliability.​

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9
Q

quantative data limitation

A

numerical data , it does not tell the researcher how and why the behaviour occurred. It only shows what behaviour occurred and how often. This decreases validity.​

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10
Q

qualitative data definition

A

data collected in a narrative form it provides in-depth information about the behaviour being investigated.​

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11
Q

qualitative data strength

A

As the participants can expand on their answers and give reasons for their behaviour, the researcher gains a better understanding of how and why the behaviour happened. This increases the validity of the findings. ​

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12
Q

qualitative data limitation

A

As the researcher makes annotations of what is being said, the analysis of the data can be more difficult to compare and the interpretation can lead to bias conclusions. This decreases the reliability of the findings as it will be difficult to replicate to find similar findings.

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13
Q

levels of measurement

A

interval data
ordinal data
nominal data

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14
Q

what is interval data

A

quantitative data which is objective. For example, time taken to complete a puzzle, heart beat, blood pressure, temperature.

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15
Q

strnetgh of interval data

A

easy to collect and categorise because it is numerical data. It is objective data as researchers do not need to interpret this data as the gaps between values are equally measured

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16
Q

what is ordinal data

A

quantitative data and categorical data with a set of order or scale to it. For example, scores in a test or ratings in a questionnaire. ​

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17
Q

ordinal data strength

A

easy to collect and categorise because it is numerical data. It is more informative than nominal data because it indicates relative values on a linear scale. Median can be calculated to make comparisons. ​

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18
Q

ordinal data limitation

A

subjective because the gaps between values are not equally measured. For example, in a rating scale on happiness form 1-5 the gaps between each scale are not equal. The mean cannot be used to assess central tendency.​

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19
Q

what is nomical data

A

Collection of information that is divided into groups. For example, age, size, race, gender.​

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20
Q

strength of nominal data

A

easy to analyse and reliable.

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21
Q

limitationa of nominal daat

A

subjective data because it does not have standardised intervals so numerical operations cannot be performed. The only central tendency that can be used with this data is the mode. Therefore, this data is not very useful.​

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22
Q

defintion of ppilot study

A

small-scale investigation
test out their methodology and make minor changes.

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23
Q

formation of pilot study

A

A pilot study involves running the planned methodology but with a much smaller number of participants

The participants will often be a small opportunity sample such as classmates or colleaguues

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24
Q

function of pilot study

A

modification of methodology
or whether a procedure is effective (e.g. does it take too long, are the instructions too complicated for participants to understand, or have any vital steps been left out).​

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25
Q

advantages of pilot studied

A

A pilot study allows a researcher to decide whether or not it will be worthwhile to conduct a planned study on a larger scale.​
A pilot study also provides the researchers with practice of running the study before the full data gathering begins, allowing all aspects of the study to go more smoothly

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26
Q

mean strength

A

Most representative of all ​
the measures of central ​
tendency because it is ​
comprised of the whole ​
data set.​

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27
Q

mean limitation

A

most sensitive measure as outliers​
can distort the mean. It can be ​
very misrepresentative of the data set if there are extreme scores present. Therefore, it can only be used with ordinal and interval​
data.​

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28
Q

median strength

A

not distorted by extreme ​
scores. Therefore, when there are extreme values in a data set, the median is used rather than the mean, so the data is more representative, increasing validity

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29
Q

strength of mode

A

Not distorted by extreme ​
scores. The only method ​
which can be used with ​
nominal data. ​

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30
Q

limitation of mean

A

Most sensitive measure as outliers​
can distort the mean. It can be ​
very misrepresentative of the data set if there are extreme scores present. Therefore, it can only be used with ordinal and interval​
data.​

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31
Q

limitation of median

A

Does not reflect all scores in the data set. Therefore, it is not the most representative of the data set and it should only be used when there are outliers.

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32
Q

limitation of mode

A

there can be more than one mode so it is ​
not always a useful measure of central ​
tendency. ​

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33
Q

ethical issues definition

A

onsiderations that researchers need to consider before, during and after the research is conducted
welfare of the participants, the integrity of the research and the use of the data.

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34
Q

5 main guidlines from BPS

A

-Deception​
 Right to withdraw​
 Informed consent​
 Privacy and confidentiality​
 Protection from harm​

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35
Q

deception

A

nformation is​
deliberately withheld from​
participants
prevents participants from giving fully informed consent
fully​
debriefed and told the true aim​ and nature of the research.
contact details of the​
experimenter should be given if​ participants have any further​ questions or queries.​

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36
Q

right to withdraw

A

researcher must​
destroy any data or​
information collected.​
may feel unnecessary or undue stress and are therefore not protected from harm. ​
fully debriefed and told the true aim and nature of the research.

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37
Q

infromed consent

A

their consent must be fully informed
rmed which means the aims of the research should be made clear before they agree to participate. ​
part in research that goes against their wishes or beliefs

38
Q

confidentiality

A

personal information is protected by law under the Data Protection Act
data may be used by other parties against the participant’s wishes.
ake name, number or initials to protect their identity and assure anonymity

39
Q

privscy

A

right of individuals to decide how information about them will be communicated to others. ​
which could be an invasion of privacy and the participant may later feel ashamed or embarrassed. ​
informed consent and the right to withdraw
xplain to participants the way(s) in which their information will be protected and kept confidential,

40
Q

protection from harm

A

hysical or psychological harm, including stress. The risk of harm must be no greater than that which they are exposed to in everyday life. ​
may suffer from long‐term effects that could impact their lives in future. ​
may be referred to counselling. ​

41
Q

Types of Research Methods​

A

Experiments​
Observations​
Self-report​
Correlation​
Pilot studies​
Case studies​
Content analysis

42
Q

Types of observations:​

A

Naturalistic vs controlled​
Covert vs overt​
participant vs non-participant​

43
Q

Naturalistic:

A

conducted in the natural environment where the behaviour occurs.

44
Q

Controlled:

A

conducted in a controlled environment. The researcher controls variables

45
Q

Covert:

A

those observations in which the participants are unaware they are the focus of study because the observer is not visible to them. (no consent)​

46
Q

Overt:

A

The participants are aware the observer is observing them because the observer is visible. (participants consent).​

47
Q

Participant:

A

The observer becomes part of the group they are studying.​

48
Q

Non-participant:

A

the researcher remains separate from those they are studying and records data in a more objective way​

49
Q

naturalistic strengths and limitation

A

high external validity. This means that the findings can be generalised because the behaviour is studied within the environment it would normally occur.​

replication is difficult. This is because of the lack of control over the research situation. Therefore, uncontrolled extraneous variables will make the judge of any pattern of behaviour difficult make. This reduces reliability.​

50
Q

Controlled strength limitation

A

replication is high. This is because this type of observations are carried out in a controlled setting, so the extraneous variables are controlled.

ow external validity. This is because the observation is in a very controlled setting, so the behaviour does not occur in its natural environment. Therefore, the findings may be difficult to apply to real-life settings.​

51
Q

Covert strength limitation

A

they remove participants reactivity (demand characteristics) because they don’t know they are being observed. Therefore, The behaviour observed is more natural and this increases internal validity of the data gathered.

t has ethical issues. This is because even it the observation is in public, participants may not want to have their behaviour noted down.

52
Q

Overt strength limitation

A

The participants know they are being observed so their behaviour may not be natural. This will decrease the validity of the findings.

They are more ethically acceptable as the participants know they are being observed.

53
Q

Participants strength and limitations

A

n this type of observations, the researcher can experience the situation as the participants do, giving them increased insight into the lives of the people being studied. This increases the validity of the findings.

The researcher may come to identify too strongly with those they are studying and lose objectivity. This will reduce the validity of the findings.

54
Q

Non-participants strengths and lim

A

This type of observations will allow the researcher to maintain an objective psychological distance from the participants so there is no danger for their findings to become blurred. This will increase validity.

However, the researcher may lose the valuable insight to be gained in a participant observation as they are too far removed from the people and behaviour they are studying. This will decrease validity.

55
Q

How to assess inter-observer reliability​

A

Two observers discuss the behavioural categories they want to observe and agree on what they are looking for. The categories must be specific, measurable, observable and clear.​
Then they observe the behaviours at the same time but they tally separately.​
They count their tallies and compare for similarity. ​
Then they run a correlation test to see if their tallies correlate. If they have 80% or above, then the observation seems to be reliable. ​

56
Q

observation designs

A

Structured:
Unstructured:

57
Q

structured

A

refers to how we collect the data. The researcher watches the behaviours and collects the data in a structured way: operationalised

58
Q

Unstructured:

A

Where there is no checklist (pre-categories) so every behaviour seen is written down in an much detail as possible.​

59
Q

structured strengths

A

recording of data easier and more systematic. Easy to replicate which increases reliability. The data produced is more likely to be numerical (Quantitative). Therefore, analysing and comparing the behaviours observed is more straight forward. There is less observer bias and this increases validity​

60
Q

strucutred limitations

A

ose depth and understanding of why that behaviour is happening because they only record the times a particular behaviour occurs or what behaviours are happening. This decreases ecological validity.​

61
Q

unsrtucutred strenghts

A

is will give the researcher a better understanding of the participants behaviour. This will increase ecological validity of the findings. ​

62
Q

unstrucutred limitations

A

qualitative data which may be more difficult to record and analyse
record those behaviours that catch their eye and this may not be the most important or useful. This will decrease validity as it be a risk of observer bias.​
​

63
Q

sampling methods in structured observations​

A

Event sampling
Time sampling

64
Q

Event sampling:

A

Involves counting the number of times a particular behaviour (event) occurs in a target individual or group. For example, the number of times a child cries when the mother leaves the room.​

65
Q

Time sampling:

A

behaviour, as specified on a predetermined checklist, is observed and recorded at specific time intervals (e.g. every 10 minutes for a period of 15 seconds​

66
Q

adv and disadv time sampling

A

t is easier to focus on specific behaviour and tally as you only observe one behaviour in that time slot

some behaviours will be missed outside the intervals. Therefore, observations may not be representative.

67
Q

adv and disadv of event sampling

A

Behaviours will not be missed as the researcher can tally the behaviours as they happen

if too many observations happen at once, it may be difficult to record everything.

68
Q

directional hypothesis

A

higher/lower

69
Q

non directional

A

will be a difference

70
Q

null

A

no difference

71
Q

sampling tecniques

A

Opportunity sampling​
Volunteer sampling​
Random sampling​
Systematic sampling​
Stratified sampling

72
Q

oppurtunity

A

who is available and willing to take part in the study at the time
convenient
quicker and easier since it requires less effort on behalf of the researcher.
economical technique.
issues of bias
specific area or location
who are not representative of the target population. ​
nvestigator bias
may select particular individuals or avoid others according to their own subjective preferences. This means that it is less representative of the target population

73
Q

volunteer

A

self‐selecting
responding to an advert.​
requires minimal effort and input on behalf of the researcher
quicker and easier,
useful if you are looking for a particular kind of participant (left handed people)
bias
particular type of person that is likely to take part
are more curious or inquisitive by nature may volunteer more readily.
not representative of the target population

74
Q

random

A

every member of the target ​
population has an equal chance of being selected.
such as pulling names from a hat, or using a computer software package which generates names/number randomly and without bias.
free from researcher bias.
his means that the sample is likely to be representative so can be generalised to the target population.
Ensuring that everyone in the ​target population has an equal chance of being selected is a difficult and time consuming task
who are picked may be unwilling to take part.

75
Q

systematic

A

With systematic sampling, a predetermined system is used to select participants. For example, every fifth person is chosen and the same interval
is free from researcher bias
following a predetermined system, this reduces any potential influence that the investigator may have over obtaining the sample.
may not be truly unbiased. It might be that every Nth person ​
has a particular characteristic in common,fairly unlikely and unlucky
Therefore, the sample generated may not be representative

76
Q

stratisfies

A

subgroups within a population are identified. Participants are obtained from each stratum (‘layer’ or category) in proportion to their occurrence within the population
largely free from researcher bias. In this technique, the sample is generated randomly once the subcategories/strata have been identified
likely to be representative
subgroups identified cannot possibly reflect all the individual differences that exist between those in the target population

77
Q

extraneous

A

any variable that you are not investigating that can influence the dependent variable.​

78
Q

confounidng

A

type of extraneous variable. ​
ffect both the independent and dependent variables. They influence the dependent variable directly and either correlate with or causally affect the independent variable.​

79
Q

demand characteristics

A

A feature of a procedure that influences a participant to try to guess what a study is about and look for clues as to how to behave.​

80
Q

controlling deman characterisitcs

A

deception- distractor q and lying abt aim
single blind

81
Q

investirgator effects

A

subtle cues or signals from an experimenter that affect the performance of participants in studies. ​
may be unconscious nonverbal cues, such as muscular tension or gestures. They may be vocal cues, such as tone of voice.​

82
Q

controls for investigator effects

A

double blind
randominsation

83
Q

standardising

A

keep everything the same for each participant​

procedure + instruction

84
Q

counterbalancing

A

Important control when using repeated measures. ​
It reduces order effects as half participants do condition A then B and the other half do condition B then A.​

85
Q

order effects

A

Carrying out a task repeatedly leads to changes in performance​
Boredom Effect: Deterioration of performance across conditions as PPs become tired or bored.​
Practice Effect: Improvement across conditions through familiarity of the task or environment. ​
This is a problem with repeated measures design​
Leave a long gap between conditions.​
Use independent groups or matched pairs.​
Counterbalanced design​

86
Q

matached oairs

A

There are no order effects as participants only take part in one condition.​
Participant variables between conditions are reduced as participants have been matched

It is time consuming and expensive to match up participants.​
Participants can never be matched exactly

87
Q

reoeated measures

A

Any differences between conditions are likely to be due to changes in the IV and not due to participant variables.​
Fewer participants need to be recruited, as they are used twice

Order effects may occur (e.g. practice, fatigue, boredom) as participants take part in all conditions.​
May see more demand characteristics as participants are more likely to work the aim if they take part in both conditions.​

88
Q

independent groups

A

No order effects (practice, fatigue, boredom) as participants only take part in one condition. ​
​
Less chance of demand characteristics.​

ny differences between conditions could be due to participant variables, e.g. one group could do better because they are more intelligent – control by randomly allocating to groups.​
Less economical as twice as many participants are needed in comparison to repeated measures.​
​

89
Q

Natural Experiment

A

The researcher takes advantage of a pre-existing IV which would have changed even if the experimenter was not studying it. Participants may be tested in the field or a lab.​

90
Q

quasi

A

Have an IV that is based on an existing difference between people, e.g. age or gender. No one has manipulated this variable, it simply exists.​

Pysch (17 decks)

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