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IB Psychology HL > Research methods > Flashcards

Research methods Flashcards

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1
Q

Quantitative research

A

Aim:

  • nomothetic approach (derive universally applicable rules)
  • these rules may be applied to the behaviour of large groups of individuals

Focus: behavioural manifestations (operationalisations)

Data: Numbers

Objectivity: more objective- the researcher is eliminated from the studied reality

Types:

  • experiment
  • quasi-experimental
  • correlational study
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2
Q

Qualitative research

A

Aim:

  • idiographic approach (in-depth understanding of a particular case or phenomenon)
  • obtained knowledge isn’t a universal law, but it’s deeper in the sense that a particular case is understood more holistically

Focus: Human experiences, interpretations, meanings

Data: Texts

Objectivity: more subjective- researcher is included in the studied reality
- researcher is an integral part of the procedure and a “tool of measurement”

Types:

  • observation
  • interview
  • focus group
  • case study
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3
Q

Sample

A

a group of individuals taking part in the research study

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4
Q

Define sampling

A

process of recruiting individuals for participation

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5
Q

Define credibility

A

extent to which results of the study can be trusted to reflect the reliability
- study is credible when there are reasons to believe that its findings are true

Qualitative research study:
Credibility = trustworthiness

Quantitative research study:
Credibility = internal validity

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6
Q

Define bias

A
  • characterises various distortions introduced to the findings by the researcher, research procedure, mistakes in process of measurement etc.
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7
Q

Define generalisability

A

extent to which results of the study can be applied beyond sample and setting used in the study itself

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8
Q

Sampling in quantitative research

A

Experimental studies and correlational studies

  • random sampling
  • stratified sampling
  • self-selected sampling
  • opportunity sampling
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9
Q

Sampling in qualitative research

A
  • quota sampling
  • purposive sampling
  • theoretical sampling
  • snowball sampling
  • convenience sampling
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10
Q

Two types of quantitative research

A
  1. Experimental studies

2. Correlational studies

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11
Q

Generalisability in quantitative research

A

Experimental studies:

  • External validity: ecological validity and population validity
  • Construct validity

Correlational studies:

  • population validity
  • construct validity
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12
Q

Generalisability in quantitative research

A
  • sample-to-population generalisation
  • case-to-case generalisation
  • theoretical generalisation
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13
Q

Credibility in quantitative research

A

Experimental studies:

  • referred to as internal validity
  • ways to improve this: controlling confounding variables; eliminating or keeping constant in all conditions

Correlational studies:

  • referred to as credibility
  • ways to improve: using reliale ways to measure the variables; avoid biases in interpreting results
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14
Q

Credibility in qualitative research

A
  • referred to as credibility/trustworthiness
  • ways to improve this: triangulation, establishing a rapport, iterative questioning, reflexivity, credibility checks, thick descriptions
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15
Q

Bias in qualitative research

A

Experimental studies- Threats to internal validity:

  • selection, history and maturation
  • testing effect and instrumentation
  • regression to the mean
  • experimenter mortality
  • experimenter bias
  • demand characteristics

Correlational studies:

  • While measuring variables: depends on the method of measurement
  • While interpreting findings: curvilinear relationships, third variable problem, spurious correlations
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16
Q

Bias in qualitative research

A

Participant bias:

  • acquiescence
  • social desirability
  • dominant respondent
  • sensitivity

Researcher bias:

  • confirmation bias
  • leading questions bias
  • question order bias
  • sampling bias
  • biased reporting
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17
Q

Variable

A

any characteristic that is objectively registered and quantified

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18
Q

Construct

A

any theoretically defined variable eg. violence, attraction, memory, anxiety
- constructs need to be operationalised

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19
Q

What does it mean to operationalise a construct?

A

means expressing the construct in terms of observable behaviour

A good operationalisation will:

  • capture the essence of the construct
  • be clearly measurable
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20
Q

Independent variable

A

variable that is manipulated by the experimenter

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21
Q

Dependent variable

A

variable that changes as a result of the manipulation by the experimenter- the one that is measured

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22
Q

Confounding variables

A 
other variables (other than IV and DV) that can interfere in the relationship between the IV and the DV
- this is to ensure that it is the change in the IV that causes the change in the DV
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23
Q

Target population

A

the group of people to which the findings of the study are expected to be generalised
- sample: group of people that take part in the experiment; a sub-set of the target population

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24
Q

Target population and generalisability

A
  • results of quantitative research need to be able to to be generalised from the sample to the target population
  • for this to be possible, sample must be representative of the target population
  • a sample is representative if it reflects all of the essential characteristics of the target population
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25
Q

Sampling techniques used in quantitative research

A
  1. Random sampling
  2. Stratified sampling
  3. Convenience (opportunity) sampling
  4. Self-selected sampling
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26
Q

Random sampling

  • What it is
  • Advantages
  • Disadvantages
A

What it is:

  • create a list of all members of the target population and randomly select a sub-set
  • this way every member of the target population has an equal chance of being a part of the sample

Advantages:
If sample size is sufficient, researchers may be certain that even unexpected characteristics are fairly represented in the sample

Disadvantages:
It’s practically impossible to carry out truly random sampling
eg. target population may be geographically dispersed

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27
Q

Stratified sampling

  • What it is
  • Advantages
  • Disadvantages
A

What it is:

  • decide on the list of essential characteristics of the population that the sample has to reflect
  • then study the distribution of these characteristics in the target population
  • then recruit participants randomly, but in a way that keeps the same proportions in the sample as observed in target population

Advantages:

  • allows researchers to control representativeness of some key characteristics without relying on chance
  • useful when: researcher is certain about which characteristics are essential; sample sizes aren’t large

Disadvantages:

  • requires more knowledge about the characteristics of the target population
  • harder to implement
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28
Q

Convenience (opportunity) sampling

  • What it is
  • Advantages
  • Disadvantages
A

What it is:
- recruiting participants that are easily available

Advantages:

  • useful when financial sources are limited
  • in some studies, there may be a reason to believe that people aren’t that different
  • useful when the generalisation of findings isn’t the primary purpose of the study

Disadvantages:
- generalisation is very limited due to sampling bias

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29
Q

Self-selected sampling

  • What it is
  • Advantages
  • Disadvantages
A

What it is:

  • recruiting volunteers
  • anyone that wants to participate is included in the sample

Advantages:

  • a quick and easy method to recruit participants
  • at the same time has wide coverage

Disadvantages:

  • representativeness and generalisation are limited
  • a typical volunteer is more motivated than the average participant from a bigger population
  • volunteers could pursue monetary incentives for their participation
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30
Q

3 types of experimental design

A
  • based on how the independent variable is manipulated
  1. Independent measures
  2. Matched pairs
  3. Repeated measures
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31
Q

Independent measures design

  • advantages
  • disadvantages
  • how to overcome the disadvantages
A
  • IV is manipulated by randomly allocating participants into different groups
  • rationale behind random group allocation: all potential confounding variables cancel each other out

Advantages:

  • can have multiple groups
  • participants only take part in one condition, so no order effect and more difficult for them to figure out true aim of the study

Disadvantages:
- participant variability: different people are used, likely that participants in the groups won’t be completely equivalent at the start of the study

How to overcome the disadvantages:
- when allocation into groups is random and groups are large enough, it’s likely that pre-existing individual differences will cancel each other out and groups on average will be equivalent

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32
Q

Matched pairs design

  • advantages
  • disadvantages
  • how to overcome the disadvantages
A
  • researchers use matching to allocate participants into different groups
  • participants are assessed on a matching variable
  • all participants are ranked according to the matching variable and allocated randomly into groups pairwise as we move along the ranks
  • the participant then allocates each pair randomly into groups, until all participants have been allocated
  • this way researcher ensures that the two groups are equivalent in terms of this one variable, and all other characteristics are kept random

Advantages:

  • useful when researcher is particularly careful about certain confounding variables and wants to keep them constant in all groups
  • useful when sample size isn’t large and there’s a chance that random allocation will end up producing groups that aren’t equivalent

Disadvantages:

  • more difficult to implement because matching variables need to be measured first
  • theory-driven: researcher needs to know what variables are likely to be confounding

How to overcome disadvantages:

  • keeping the experiment simple
  • matching is easier to implement when there’s one matching variable and 2 groups
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33
Q

Repeated measures design

  • advantages
  • disadvantages
  • how to overcome the disadvantages
A
  • same group of participants is exposed to 2 (or more) conditions and the conditions are compared
  • this way participants are compared to themselves (also called “within-subject” designs)

Advantages:

  • participant variability isn’t a problem as participants are compared to themselves
  • means that sample sizes can be smaller

Disadvantages:

  • order effects: fatigue or practice
  • participants take part in more than one condition, increases the chances that they’ll figure out the true aim of the study

How to overcome the disadvantages:

  • counterbalancing
  • however, this is difficult when there are many conditions
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34
Q

How is the quality of experiments determined?

A

Quality of an experiment is characterised by its:

  • construct: characterises generalisability of results
  • internal validity: credibility of the experiment
  • external validity: characterises generalisability of results
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35
Q

Construct validity

A

a characteristic of the quality of operationalisations

  • operationalisations express constructs in terms of observable behaviour
  • is high if operationalisation provides sufficient coverage in the construct
  • relates to the overarching concept of generalisability; it characterises generalisability of findings to the theory
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36
Q

External validity

A
  • a characteristic of generalisability of findings to other people and other situations

Two types:

  • population validity
  • ecological validity
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37
Q

Population validity

A
  • the extent to which findings can be generalised from the sample to the target population
  • it depends on how representative the sample is
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38
Q

Ecological validity

A
  • the extent to which the findings can be generalised from the experiment to other settings or situations
  • depends on how artificial the experimental procedure is

Lab experiments:

  • participants find themselves in situations that don’t normally occur in their daily lives
  • this can change their behaviour, making it less natural

The more closely the experimental procedure approximates real-life situations, higher the ecological validity of the experiment

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39
Q

Internal validity

A
  • a characteristic of the methodological quality of an experiment
  • it relates to credibility of the research study
  • it’s high when confounding variables have been controlled and are certain that it was the change in the IV that caused the change in the DV
  • it links directly to bias
  • the less bias there is, higher the internal validity of teh experiment
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40
Q

Internal validity and ecological validity

A

Usually there is an inverse relationship between internal validity and ecological validity

  • when internal validity is high, ecological validity is low
  • when ecological validity is high, internal validity is low
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41
Q

Threats to internal validity (RED TIME)

A
  1. Regression to the mean
    - becomes a threat when initial score on DV is extreme (v. low or v. high)
    - Counteracted: a control group w/ same starting score on the DV, but no experimental manipulation
  2. Experimental mortality
    - occurs when some participants drop out of the experiment; is only a problem when rate of dropping out isn’t the same in every experimental condition
    - Counteracted: Design experimental condition so participants don’t feel discomfort causing them to withdraw
  3. Demand characteristics
    - occurs when participants understand true aim of the experiment and alter their behaviour (unintentional/ intentional) as a result
    - a bigger problem in repeated measures design as participants take part in more than one condition
    - Counteracted: deception to conceal true aim of the study (but ethical considerations arise); post-experimental questionnaires to investigate extent to which participants could guess true aim of the study
  4. Testing effect
    - first measurement of DV may affect subsequent measurements; sometimes in independent measures, DV is measured twice eg. before and after experiment
    - in repeated measures designs testing effect is a special case of order effects
    - Counteracted: in independent measures designs there must be a control group, the same test and retest, but no experimental manipulation; in repeated measures, counterbalancing must be used
  5. Instrumentation
    - occurs when instrument measuring DV changes slightly between measurements, compromising standardisation of measurement process
    - Counteracted: standardise measurement conditions as much as possible across all comparison groups and all observers
  6. Maturation
    - natural changes that participants go through in the course of the experiment eg. fatigue or growth (if procedure is extended in time)
    - Counteracted: have a control group; if we can assume that rates of maturation are the same in both groups, comparison won’t be affected
  7. Experimenter bias
    - occurs when researcher unintentionally influences participants behaviour and results of the study
    - Counteracted: use a double-blind design; neither participants nor experimenter knows who has been assigned to what condition
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42
Q

What are the different types of experiments?

A
  1. True experiment
  2. Quasi-experiment
  3. Laboratory experiment
  4. Field experiment
  5. Natural experiment
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43
Q

True experiment

A
  • allocation into experimental groups is done randomly
  • researchers can assume that IV is the only difference between the two groups
  • allows researcher to interpret results of the study as a cause-effect relationship (IV influences the DV)
44
Q

Quasi-experiment

A
  • allocation into groups is done on the basis of pre-existing differences
  • researchers can’t be sure that the groups are equivalent in all other characteristics
  • hence, because IV isn’t manipulated by the researcher, cause-effect inference can’t be made
  • in the way they’re made, quasi-experiments resemble experiments (they involve a comparison of groups)
  • but, from the POV of possible inferences, they are correlational studies
45
Q

Laboratory experiment

A
  • conducted in highly controlled, artificial conditions
  • since confounding variables are better controlled this way, it increases internal validity but compromises ecological validity
46
Q

Field experiment

A
  • conducted in real-life settings
  • researcher manipulates IV; but, as participants are in their natural setting many confounding variables can’t be controlled
  • this increases ecological validity and decreases internal validity
47
Q

Natural experiment

A
  • conducted in participants’ natural environments
  • IV isn’t manipulated by the researcher but occurs naturally
  • advantage is there’s ecological validity; also they can be used when it’s unethical to manipulate the IV
  • disadvantage is internal validity due to a large no. of confounding variables that are impossible to control
48
Q

Correlational studies

A
  • no variable is manipulated by the researcher
  • cause-effect inferences can’t be made
  • 2 or more variables are measured and the relationship between them is mathematically quantified
  • cause-effect relationships can’t be made
49
Q

Correlation

A

a measure of linear relationship between 2 variables

  • correlation coefficient can vary from -1 to +1
  • correlation close to 0 means there’s no relationship between 2 variables

Negative correlation: there’s an inverse relationship between 2 variables, higher the A, lower the B

Positive correlation: means a direct relationship; higher the A, higher the

Characterised by 2 parameters:

  • effect size
  • statistical significance
50
Q

Effect size

A

the absolute value of the correlation coefficient (no. from 0 to 1)
- shows how large the correlation is

51
Q

Correlation coefficient effect size (r) and its interpretations

A

Less than 0.10 = negligible

  1. 10-0.29 = small
  2. 30-0.49 = medium
  3. 50 and larger = large
52
Q

Statistical significance

A

shows likelihood that a correlation of this size has been obtained by chance
- if this likelihood is less than 5%, correlation is accepted as statistically significant

53
Q

Probability that the result is due to random chance and its interpretation

A

p = n. s result is non-significant
p < 0.5 result is statistically significant
p = < 0.01 result is very significant
p = < 0.001 result is highly significant

54
Q

Credibility and bias in correlational research

A
  • bias in correlational research can occur on level of variable measurement and interpretation of findings
  • less bias there is in a correlational research study, the more credible it is
  • credibility in correlation research is the same idea as internal validity in experimental research
  • term ‘internal validity’ is NOT used in correlational research
55
Q

Bias on the level of variable measurement

A
  • depending on method used to measure variables, bias may be inherent in measurement procedure
  • ## bias isn’t specific to correlational research; will occur in any other research study using same variable measured in the same way
56
Q

Sources of bias in interpretation of findings

A
  • curvilinear relationships between variables
  • the third variable problem
  • spurious correlations
57
Q

Curvilinear relationships between variables

A
  • in calculating correlation between 2 variables, we assume that the relationship is linear
  • formula of a correlation coefficient = formula of a straight line
  • but, curvilinear relationships can’t be captured in a standard correlation coefficient

Counteracted: if suspected, curvilinear relationships should be investigated graphically

58
Q

The third variable problem

A
  • there’s always a possibility that a 3rd variable exists that correlates w/ both A and B and explains their correlation
  • if you only measure A and B, you’ll observe a correlation between them; doesn’t mean they’re related directly

Counteracted: consider potential ‘3rd variables’ in advance, include them in research study to explicitly investigate links between A and B, and these ‘3rd variables’

59
Q

Spurious correlations

A

correlations obtained by chance

  • becomes an issue if research study includes multiple variables and computes multiple correlations between them
  • if 100 correlations are measured, there’s a chance that a small no. will be significant, even if in reality the variables aren’t related

Counteracted:

  • results of multiple correlations should be interpreted w/ caution
  • effect sizes need to be considered together w/ level of statistical significance
60
Q

Sampling and generalisability in correlational studies

A

Sampling strategies: random, stratified, convenience and self-selected

Generalisability:

  • depends on how representative sample is of the target population; representativeness of sample depends on sampling strategy
  • random and stratified samples are more representative than opportunity and self-selected samples; similar to idea of population validity in experimental research
  • construct validity is important in considering generalisability
61
Q

Credibility in qualitative research

A
  • ‘trustworthiness’

- a measure of the extent to which the experiment test what it’s intended to test

62
Q

Measures to increase credibility in qualitative research

A
  1. Triangulation
  2. Establishing a rapport
  3. Iterative questioning
  4. Reflexivity
  5. Credibility checks
  6. Thick descriptions (rich descriptions)
63
Q

Triangulation

A

Combining and comparing different approaches to collecting and interpreting data

Four types:

  • method triangulation: combining different methods
  • data triangulation: using data from a variety of accessible sources
  • researcher triangulation: combing and comparing observations of different researchers
  • theory triangulation: using multiple perspectives or theories to interpret the data
64
Q

Establishing a rapport

A
  • building a relationship of trust w/ participant; emphasise necessity to be honest; right to withdraw and that there are no good or bad answers
  • the above prevents participants from altering their behaviour in the presence of the researcher
65
Q

Iterative questioning

A
  • returning to the topic later in the process of interaction w/ participant and rephrasing the question
  • allows a deeper investigation of sensitive topics
66
Q

Reflexivity

A
  • taking into account possibility that the researcher’s own biases may be affecting the results of the study
  • doesn’t necessarily allow researchers to avoid bias, but allows them to identify findings possibly affected by bias

Two types:

  1. Epistemological reflexivity: taking into account strengths and limitations of the methods used to collect data
  2. Personal reflexivity: taking into account personal beliefs and expectations of the researcher that might have resulted in bias
67
Q

Credibility checks

A
  • checking accuracy of data by asking participants themselves to read transcripts of interview or field notes of observation
  • get them to confirm that the notes/ transcript reflect correctly what the participant said or did
68
Q

Thick descriptions

A
  • describing the observed behaviour in sufficient detail so it can be understood holistically and in context
  • contextual details should be sufficient to make description meaningful to an outsider who never observed this behaviour first-hand
69
Q

Bias in qualitative research

A
  • sources of bias can be associated both w/ researcher and participant
  • there are 2 groups of biases: participant bias and researcher bias
70
Q

Acquiescence bias

A
  • tendency to give +ve answers whatever the questions
  • may occur due to participant’s natural agreeableness or because they feel uncomfortable disagreeing w/ RQ

To overcome:

  • be careful not to ask leading questions
  • questions should be open-ended and neutral
  • should be clear that there are no right or wrong answers
71
Q

Social desirability bias

A
  • participant’s tendency to respond in a way they believe will make them more liked/accepted
  • intentionally/ unintentionally, participants may produce a certain impression instead of natural behaviour; especially true for sensitive topics

To overcome:

  • questions are phrased in a non-judgemental way
  • good rapport should be established
  • questions can be asked about a 3rd person
72
Q

Sensitivity bias

A

tendency of participants to answer regular questions honestly but distort their responses to questions on sensitive topics

To overcome:

  • build good rapport
  • create trust
  • reinforce ethical considerations eg. confidentiality
  • increase sensitivity of questions gradually
73
Q

Types of participant bias in qualitative research

A
  • acquiescence bias
  • social desirability bias
  • dominant respondent bias
  • sensitivity bias
  • confirmation bias
  • leading questions bias
  • question order bias
  • biased reporting
74
Q

Dominant respondent bias

A
  • occurs in a. group interview setting when one participant influences behaviour of others
  • other participants may be intimidated or feel like they will be compared to dominant respondent

To overcome:

  • keep dominant respondent in check
  • try to provide everyone w/ equal opportunity to speak
75
Q

Confirmation bias

A
  • occurs when researcher has a prior belief and uses research to confirm this belief (intentional/ unintentional)
  • may manifest as selectivity of attention or tiny differences in non-verbal behaviour that may influence participants’ behaviour

To overcome:

  • unavoidable as in qualitative research, human observer is an integral part of the process
  • bias is recognised and taken into account through process of reflexivity
76
Q

Leading question bias

A

occurs when questions in an interview are worded in a way that encourages a certain answer

To overcome:
- researchers are trained in asking open-ended, neutral questions

NB/ issue in an interview not in an observation

77
Q

Question order bias

A

occurs when response to one question influences particpants responses to subsequent questions

To overcome:

  • bias can’t be avoided but can be minimised by asking general questions before specific ones
  • asking positive questions before negative ones
  • aksing behaviour-related questions before attitude-related ones
78
Q

Biased reporting

A

occurs when some findings of the study aren’t equally reported in the research report

To overcome:

  • reflexivity
  • independent researchers may be asked to review the results (researcher triangulation)
79
Q

Types of generalisability in qualitative research

A
  1. Sample-to-population generalisation
  2. Theoretical generalisation
  3. Case-to-case generalisation (transferability)
80
Q

Sample-to-population generalisability

A
  • applying results of the study to a wider population
  • depends on how representative the sample is
  • best way to ensure representativeness of sample is to sample randomly
  • nature of sampling in qualitative research is non-probabilistic, this generalisation is a weak point

Equivalent in quantitative research:
- population validity

81
Q

Theoretical generalisation

A
  • generalising results of particular observations to a broader theory
  • theory plays a much greater role in qualitative research
  • can generalise to a broader theory if data saturation has been achieved

Equivalent in quantitative research:

  • similar idea to construct validity
  • as it refers to “leap” from observable operationalisations to unobservable construct
82
Q

Case-to-case generalisation (transferability)

A
  • applying findings fo a study to a different group of people or a different setting or context
  • this is the responsibility of the researcher and the reader of the research report
  • researcher ensures that thick descriptions are provided so reader has sufficient information
  • reader decides whether new context is similar enough to one described in report for findings to be applicable

Equivalent in quantitative research:
- ecological validity (generalising from experimental settings to real-life settings)

83
Q

Data saturation

A

a point when further data doesn’t add anything new to the already formulated conclusions and interpretations

84
Q

Sampling in qualitative research

A
  • is non-probabilistic
  • doesn’t aim to ensure representativeness in relation to a. target population
  • aims to ensure that participants recruited for the study have the characteristics that are of interest to RQ
85
Q

Types of sampling in qualitative research

A
  • quota sampling
  • purposive sampling
  • theoretical sampling
  • snowball sampling
  • convenience sampling
86
Q

Quota sampling

A
  • decided prior to research how many people to include in the sample and which characteristics they should have
  • decision is driven by RQ
  • various recruitment strategies are then sued to meet the quota; isn’t important how many people are sampled
  • it’s important that people in sample have characteristics of interest to researcher
  • approach is completely theory-driven; all characteristics of the sample are defined in advance based on RQ
87
Q

Purposive sampling

A
  • recruit participants that are of interest to researcher
  • sample size and proportions of participants within sample aren’t defined in advance
  • target characteristics of participants are defined in advance, but composition of sample is not
88
Q

Theoretical sampling

A
  • a sampling method that stops when data saturation is reached
  • whether information is “new” or not is defined on the basis of the background theory
89
Q

Snowball sampling

A
  • small no. of participants is invited and asked to invite people they know also w/ these characteristics of interest
  • can be used in combination w/ other sampling strategies
  • convenient w/ groups of people who are difficult to reach
90
Q

Convenience sampling

A
  • using the sample that is readily available

- most cost-efficient method, but also most superficial

91
Q

Observation

A

Reasons to choose observation:

  • focus of research is on how people interact in a natural setting; most other methods would require placing participant in an artificially created environment
  • meaningful knowledge in a research area can’t be easily articulated; so, observing behaviour is preferable to asking participants for their interpretations
  • observation allows researcher to gain first-hand experiences w/ phenomenon under study

Limitation:

  • researcher is strongly involved in generation of data through selective attention and interpretation
  • but, this is the case w/ most qualitative research methods- why reflexivity is especially important
92
Q

Naturalistic observations

A

Carried out in real-life settings that haven’t been arranged for the purposes of the study

Pros:

  • sometimes is the only option eg. when it’s unethical to encourage a particular behaviour in a lab eg. violence
  • participants behaviour isn’t influenced by artificiality of research procedure

Cons:
- may be time-consuming because behaviour of interest only occurs at certain times

93
Q

Laboratory observations

A

Carried out in specially designed environments
- participants are invited to lab and most of the time know they’re participating in psychological research

Pros:

  • it’s possible to recreate situations that don’t frequently emerge in real life
  • it’s possible to isolate behaviour of interest more efficiently

Cons:
- artificiality of procedure may influence behaviour of participants

94
Q

Interview

A

Reasons to choose interview:

  • may be the only way to get an insight into participant’ subjective experience and interpretations; these phenomena are unobservable, only option is to rely on verbal reports
  • can be used to understand participants’ opinions, attitudes and meanings they attach to certain events
  • only way to understand how participants respond to past events is through self-report; can’t recreate those experiences
  • in-depth individual interviews are useful when topic is too sensitive for people to discuss in a group setting

Interview data: comes in form of an audio or video recording that’s subsequently converted to an interview transcript

95
Q

Covert observations

A

Occurs when researcher doesn’t inform members of the group about the reasons for their presence

Pros:
- participants don’t suspect they’re being observed, so they behave naturally

Cons:
- often participants don’t consent to being observed, which raises ethical issues

96
Q

Structured interview

A

Includes a fixed list of questions that need to be asked in a fixed order

Pros:

  • useful when research project involves several interviews
  • it’s essential to ensure that they all conduct interview in a standardised way

Cons:
- some participants may have unique circumstances/opinions that can’t be accommodated in a structured interview

97
Q

Semi-structured

A

Don’t specify an order or a particular list of questions
- interview guide is like a checklist; interviewer knows there are some questions that must be asked, but there’s flexibility to ask additional follow-up questions

Pros:

  • fits natural flow of conversation better
  • better suited for smaller research projects
  • more effective in studying the unique experiences of each participant

Cons:
- less comparability across researchers and participants

98
Q

Unstructured

A
  • is participant-driven
  • eery next question is determined by interviewee’s answer to the previous one

Pros:
- very effective for investigating unique cases or cases where no theoretical expectations exist that would inform wording of the questions

Cons:

  • most “qualitative” of all 3 types
  • more time-consuming
  • results are more difficult to analyse and interpret
99
Q

Focus group

A

a special type of semi-structured interview that is conducted simultaneously w/ a small group of people (6-10)

  • encourages participants to interact w/ each other; creates group dynamics that are observed and analysed by the researcher
  • interviewer acts as facilitator who keeps interaction focused on research question

Reasons to choose focus group:

  • participants interact w/ each other rather than the researcher; makes behaviour more natural
  • interaction between participants may reveal more aspects than would be revealed in a one-on-one conversation w/ researcher
  • is easier to respond to sensitive questions when in a group
  • multiple perspectives are discussed, allows researcher to obtain a more holistic understanding of the topic

Limitations of focus groups:

  • dominant respondents can disrupt group dynamics; their assertiveness may affect and distort behaviour of other participants
  • it’s more difficult to preserve confidentiality in a group
  • especially demanding in terms of sampling and creating interview transcripts
100
Q

Case study

A

an in-depth investigation of an individual or a group

  • involve a variety of methods eg. interviews, observations, questionnaires
  • deepen understanding of an individual or a group of interest

Reasons to choose a cases study:

  • useful to investigate phenomena that can’t be studied otherwise
  • can contradict established theories; in this way urge scientists to develop new ones

Limitations of case studies:

  • researcher bias and participant base are problems; researcher interacts w/ participant for prolonged periods of time- may compromise impartiality and influence how natural participants’ behaviour is
  • generalisation of findings from a single case to other setting or a wider population is particularly problematic
  • is difficult to protect confidentiality of participants and their data
101
Q

Alternative/addition for structured interview

A

Alternative/ addition: semi-structured interview

Reason:

  • fixed questions in the fixed order might force some participants to respond unnaturally
  • so, some aspects of their experiences were missed
102
Q

Alternative/addition for a correlational study

A

Alternative/ addition: experiment

Reason:

  • correlational studies don’t show causation, but causation in the context of a study is important
  • if one the variables can be manipulated, then the study can be conducted as an experiment
103
Q

Alternative/addition for an overt non- participant observation

A

Alternative/ addition: covert participant observation

Reason:
- the fact that participants knew they were being observed altered their behaviour, leading to participant bias

104
Q

Alternative/addition for an experiment: repeated measures design

A

Alternative/ addition: experiment, independent measures design

Reason:

  • in repeated measures, participants take part in more than one condition
  • increases chance they’ll figure out true aim of the study, which could lead to demand characteristics
105
Q

Unstructured observation

A

Observers simply register whatever behaviour they find noteworthy- there is no checklist that was prepared in advance

Pros:
- more flexible; researcher isn’t limited by prior theoretical expectations

Cons:
- less structured, means less comparable across researchers and across participants

IB Psychology HL (2 decks)

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