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Approaches to research Flashcards

1
Q

Independent variable

A

Variable that the experimenter is manipulating - ‘cause’

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2
Q

Dependent variable

A

Variable that the experimenter is measuring - ‘effect’

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3
Q

Quantitative research methods

A

Collecting numerical data that can be analysed using mathematics and statistics – experiments, field and natural experiments, quasi-experiments, correlational research, surveys

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4
Q

Qualitative research methods

A

Collecting worded data – case studies, naturalistic observations, interviews

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5
Q

Reductionist approach

A

Collecting data about specific factors/variables

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6
Q

Holistic approach

A

Collecting data about many different factors

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7
Q

Lab studies

A

Conducted in a laboratory/not naturalistic setting; situation can be controlled, and outside influences can be minimised

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8
Q

Field studies

A

Conducted in a naturalistic setting and involves observing behaviour in real life; high ecological validity, however ethical issues such as informed consent and debriefing, difficult to replicate and extraneous variables can have an impact

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9
Q

Retrospective procedures

A

Involve researcher asking participants about past behaviour to determine relationships or correlations between variables; reliant on memory, cannot verify response

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10
Q

Prospective procedures

A

Involve researcher measuring a variable at the beginning of a study and then watching the effect of the variable over time; not dependent on memory, take longer to carry out

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11
Q

Longitudinal research

A

Repeated observations of the same variable/s over time; depth of insight participant variables have low influence, however time consuming and dropouts likely

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12
Q

Cross-sectional research

A

Collecting data from different groups at a specific point in time; more efficient and dropouts less likely, however participant variables and outside influences can impact study

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13
Q

Validity

A

The extent to which the investigation measures what it intends to measure

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14
Q

Internal validity

A

The extent to which the changes in the dependent variable are caused by the independent variable, and not other extraneous variables

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15
Q

Construct validity

A

The extent to which the variables being studied can be measured or an agreed-upon definition of what is being studied

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16
Q

External validity

A

The extent to which the results of an investigation can be applied to other situations and people

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17
Q

Ecological validity

A

The extent to which results can be generalised to other situations; the extent to which the situation represents a real-life situation

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18
Q

Population validity

A

Whether you can generalise the findings to the population

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19
Q

Reliability

A

If a study is reliable, its results can be replicated

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20
Q

Experiments

A

Quantitative research method; goal is to determine whether there is a cause-and-effect relationship between the variables by manipulating the independent variable and keeping all other variable constant; standardised (can be replicated)

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21
Q

Operationalisation

A

To operationalise a variable means that the variable is stated in more specific terms

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22
Q

Research hypothesis

A

Prediction of how the independent variable will affect the dependent variable; can be one tailed (predict an effect in one direction or two tailed (predict an effect in either direction)

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23
Q

Null hypothesis

A

Prediction/statement that the independent variable will not affect the dependent variable, or that any change in the dependent variable will be due to chance

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24
Q

Quasi-experiments

A

Experiments where participants are grouped based on a trait or behaviour (gender, culture, age); does not show direct causation and can only imply there may be a causal relationship between IV and DV due to participant characteristics

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25
Natural experiments
Type of quasi-experiment which uses an IV that is naturally occurring and outside of the control of the researcher; does not show direct causation and can lack internal validity
26
Extraneous variables
Variables other than the IV that may have an influence on the DV
27
Confounding variables
Extraneous variables that have had an influence on the DV
28
Demand characteristics
Participants act differently simply because they know they’re in a study
29
Expectancy effect
Participant attempts to discern the researcher’s hypotheses and act in a way that ‘helps’ them (acting a certain way, giving a ‘correct’ answer)
30
Screw you effect
Participant attempts to discern the researcher's hypotheses to destroy the credibility of the study
31
Social desirability effect
Participant answers in a way that makes them look good to the researcher to avoid embarrassment or judgement
32
Researcher bias
Expectations of the researcher consciously or unconsciously affect the findings of the study; can be avoided using a double-blind procedure
33
Participant variability
Characteristics of the sample may affect the DV; can be minimised using random sampling/allocation
34
Artificiality
The situation of the experiment is unlikely to occur in reality
35
Correlational studies
Relationship between variables observed with no manipulation from the researcher, often when an experiment cannot be conducted; cannot determine a cause-and-effect relationship
36
Correlation
The measurement of the extent to which pairs of related values of two variables tend to change together
37
Positive correlation
If one variable increases, so does the other
38
Negative correlation
If one variable decreases, the other increases
39
Curvilinear relationship
If one variable increases, so does the other, but only up to a certain point after which one continues to increase and the other decreases
40
Questionnaires
A written set of questions (written interview) used for data collection
41
Surveys
A set of questions and the process of collecting, aggregating and analysing the responses from those questions
42
Independent measures design
Members of the sample are randomly allocated to one condition of the experiment and results from each of the conditions are then compared
43
IMD strengths and weaknesses
Strengths: no order effects, less time consuming, demand characteristics less likely Weaknesses: participant variability may influence results, more participants required
44
Repeated measures design
One sample of participants who are exposed to all the conditions of the experiment and results from each of the conditions are then compared for each participant
45
RMD strengths and weaknesses
Strengths: participant variables controlled, fewer participants needed Weaknesses: participants may demonstrate order effects or demand characteristics, may introduce another confounding variable
46
Matched pairs design
Members of the sample pre-tested regarding an important or potentially confounding variable then paired with others with similar scores and separated into conditions of the experiment
47
MPD strengths and weaknesses
Strengths: no order effects, minimises influence of participant variability Weaknesses: time consuming and therefore expensive, cannot match participants on all variables
48
Target population
Broader group that the researcher is interested in
49
Representative sample
Sample that represents many of the characteristics found in the target population, considering sample size
50
Opportunity/convenience sampling
Participants selected based on naturally occurring groups; easy and fast, but problematic when generalising
51
Self-selected/volunteer sampling
Participants volunteer, usually in response to an advertisement; highly motivated sample, but problematic when generalising
52
Snowball sampling
Participants recruit other participants for the study; saves time and helps build trust in researcher, but problematic when generalising
53
Purposive sampling
Participants selected according to predetermined criteria relevant to the research question; effective when target population is limited, less time consuming, but limited ability to generalise
54
Random sampling
Each member of the population has an equal chance of being selected; more representative sample therefore can generalise, but time consuming
55
Stratified sampling
Drawing random samples from each subpopulation within the target population; most representative sample therefore can generalise, but time consuming
56
Sampling bias
The sample does not accurately represent the characteristics of the population; participant variables in the sample may not be representative and may influence the outcome the study
57
Standardisation
Eliminates or minimises the influence of some extraneous variables, improving validity and reliability
58
Single-blind procedure
Leaves participant unaware of whether they are in the experimental or control group, therefore participant expectancies have less influence on the results
59
Double-blind procedure
Hides the experimental and control conditions from both the participant and the researcher
60
Raw data
Data that has not been analysed or interpreted, determines how statistical analysis will proceed
61
Analysing quantitative data
Uses descriptive statistics (summarise data) and inferential statistics (test statistical significance of data)
62
Measures of dispersion
Standard deviation: measures how closely values cluster around the mean Range: indicates how high and low the data goes (interquartile range used for skewed results)
63
Inferential statistics
Allow researchers to infer patterns in a sample, and know whether they can conclude that these patterns are ‘true’ or valid for the population
64
P value
Probability of a sample generating a set of data if the null hypothesis is true (probability of DV being influenced by extraneous variables instead of IV)

Psychology (3 decks)

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