Research Methods Flashcards

Students should demonstrate knowledge and understanding of the following research methods, scientific processes and techniques of data handling and analysis, be familiar with their use and be aware of their strengths and limitations:

1
Q

Define the “aim” of a study

A

The area of research that you are interested in studying
- Purpose of the study
- “To investigate…”

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2
Q

Define the “hypothesis” of a study & define the different types of hypothesis

A

Prediction of the experiment’s results
- Written in the future tense (i.e. there will be…)

Alternative hypothesis: Predicting a difference/correlation

Directional hypothesis: Predicts specific DIRECTION of results (increase or decrease)

Non-directional hypothesis: Predicts a difference/correlation but does NOT include a specific direction

Null hypothesis: Predicting that there will be NO DIFFERENCE

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3
Q

Outline the different variables

A

Independent variables are MANIPULATED
Dependent variables are MEASURED
- In order to establish cause and effect, all variables must be OPERATIONALISED, otherwise results cannot be compared as everyone has different ideas of sizes of amounts

Nuisance variables:
Extraneous variables - UNPREDICTED variables which effect EVERY PARTICIPANT in the same way so the impact on the DV is LESS SERIOUS

Confounding variables - UNPREDICTED variables which effect only SOME PARTICPANTS so the impact on the DV is (MORE) SERIOUS

Examples:
Demand characteristics - The participants GUESS THE AIM of the experiment and CHANGE BEHAVIOUR (either to support or sabotage the study)

Investigator effects - The verbal and non-verbal behaviours of the PSYCHOLOGIST which INFLUENCE the participants’ behaviour

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4
Q

How can nuisance variables be controlled for?

A

Pilot studies: Small TRIAL RUN study before the full experiment; used to IDENTIFY NUISANCE variables so they can be FIXED before the actual study - saving on TIME and MONEY

Control groups: Not exposed to the manipulation of the IV; provide a COMPARISON (“before”) data set

Single blind trials: PARTICIPANTS are not told the AIM - reduces DEMAND CHARACTERISTICS

Double blind trials: Participant AND research unaware of AIM (3rd party researcher who is aware carries out the study) - reduces INVESTIGATOR EFFECTS

Randomisation: Use of CHANCE to decide which participants take part in which group
- E.g. using a random online generator, pulling names out of a hat

Standardisation: Keep all aspects of the experiment the SAME for all participants
- E.g. the room used, time of arrival, instructions read, materials used

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5
Q

What is reliability (and inter-rater reliability) and how is it measured?

A

Reliability means CONSISTENCY

Measured using REPEATED studies to see whether results are SIMILAR

Inter-rater reliability: Consistency between TWO OR MORE psychologists, 80% agreement needed (if it is under 80%, there is no inter-rater reliability)

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6
Q

What is validity?

A

Validity means ACCURACY (i.e. are the results truthful)

Internal validity: How well CONTROLLED the experiment is

External validity: How REALISTIC the experiment is (mundane realism/task realism)
- Ecological validity: Is the setting relevant in everyday life?
- Temporal validity: Is the time period still relevant today?
- Population validity: Is the sample representative of people (in everyday life)?

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7
Q

Define experimental design and outline and evaluate the different types of experimental design

A

The way in which we allocate participants to different groups/conditions

Independent groups: Group A and B in SEPARATE conditions
- Strength: No order effects (boredom, fatigue) = Less likely to guess the aim
- Weakness: Individual differences = Strangers cannot be fairly compared (different ages, IQ, etc)

Repeated measures: ONE group experience ALL conditions
- Strength: No individual differences
- Order effects (boredom, fatigue)

Matched pairs: Participants are matched, then SPLIT into different groups/conditions to compare them more accurately (as they are put into conditions where the people are similar in some way)
- Strength: A more fair comparison (individual differences are balanced out)
- Weakness: Time consuming and costly (hard to do as there are too many traits)

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8
Q

What is sampling? Outline and evaluate the different types of sampling

A

How you select people from the target population to be participants in your sample

Opportunity: Selecting people who are AVAILABLE and convenient to the researcher (friends, family, public spaces)
- Strength: Quick and easy, cheap
- Weakness: Most biased as the researcher chooses who to ask and all from the same area

Random: Using CHANCE to select participants - all names from target population go into a hat/generator
- Strength: Less bias = More representative = Can be generalised
- Weakness: Time consuming as it is difficult to identify everyone in the target population

Volunteer: Researcher will ADVERTISE the study and people will contact them
- Strength: Quick and easy, cheap, less bias
- Weakness: Only certain ‘types’ of people will volunteer - more extroverted, confident people

Systematic: Selecting EVERY NTH from target population
- Strength: Easy to do, less biased
- Weakness: Larger target population = More difficult and time consuming

Stratified: Identify the STRATA (sub-groups), then work out proportional RATIOS of groups to each other, then select participants randomly from each sub-group
- Strength: Most representative, least biased, best to generalise
- Weakness: Most difficult to do, time consuming

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9
Q

Define an experiment. Outline and evaluate the different types of experiment/experimental methods

A

Experiment: Difference between IV and DV, IV is directly manipulated, ‘cause and effect’

Laboratory experiment: Highly controlled, Artificial environment, IV is manipulated, DV is measured
- Strength: Control of nuisance variables = High internal validity = Can establish ‘cause and effect’
- Weakness: Artificial environment = Low ecological validity = Lacks mundane realism = Cannot be applied/generalised to the real world

Field experiment: Natural environment where the participants would usually be, IV is manipulated, DV is measured
- Strength: Real life setting = Can generalise to real world = High ecological validity
- Weakness: Cannot control for nuisance variables = Low internal validity

Natural experiment: Societal world (e.g. war) or weather event (e.g. hurricane), IV is NOT manipulated, DV is measured
- Strength: Researcher does not manipulate IV = More ethical
- Weakness: No control over nuisance variables = Impossible to establish ‘cause and effect’

Quasi experiment: Participant trait (illness, talent), IV is NOT manipulated, DV is measured
- Strength: Researcher does not manipulate IV = More ethical
- Weakness: No control over nuisance variables = Impossible to establish ‘cause and effect’

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10
Q

Outline and evaluate the types of observation/observational techniques

A

Naturalistic observation: Observation in the setting the behaviour would normally occur
- Strength: High external validity
- Weakness: Lacks control of situation = Uncontrolled extraneous variables = Cannot replicate

Controlled observation: Variables are managed/controlled
- Strength: Less extraneous variables = Easier to replicate
- Weakness: Low external validity = Findings cannot be applied to real world settings

Covert observation: Behaviour is watched and recorded without consent
- Strength: Ensures all behaviour will be natural
- Weakness: Ethics are questioned

Overt observation: Behaviour is watched and recorded with consent
- Strength: More ethically acceptable
- Weakness: Knowledge of being observed may influence behaviour - The observer effect

Participant observation: Researcher becomes a member of the study
- Strength: Validity increased (The researcher is a direct participant in the study)
- Weakness: May lose objectivity - “going native” (The researcher’s results may be biased as they become a member of the group)

Non-participant observation: Researcher is separate from the group being studied
- Strength: Results are more objective
- Weakness: May lose valuable insight

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11
Q

Define the ‘observer effect’

A

Individuals change their behaviour because they know they are being observed

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12
Q

Define ‘observer bias’

A

When a researcher’s expectations/opinions influence what they record

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13
Q

Define ‘behavioural categories’

A

Observations which need to be operationalised (precisely defined and made measurable)

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14
Q

Outline the two sampling methods

A

Event sampling: Tallying/counting the number of times a behavioural category is seen

Time sampling: Tallying behaviour at a set interval/time frame
- Nothing outside of the interval is recorded

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15
Q

Outline and evaluate self-report techniques

A

Self-report techniques:
- Strength: Give you the person’s own perspective
- Weakness: Results may be inaccurate due to social desirability bias (try to look better than they really are to be socially accepted)

Questionnaires: Pre-set list of written questions used to assess the DV
- Strength: Completed without the researcher present reduces effort involved; cost effective (gather larger amount of data quickly), statistical analysis available
- Weakness: The answer choices provided may not be an accurate reflection of how the participants actually feel; social desirability bias can lead people to respond in a way that makes them look better than they really are; response bias as questions may be misread

Interviews: Face to face interaction between an interviewer and an interviewee
Structured interview: Pre-determined set of questions in a fixed order
- Strength: Easy to replicate due to standardised format
- Weakness: Interviewer cannot deviate from the topic or elaborate points
Unstructured interview: No set questions; free flowing interaction
- Strength: Can follow up points to gain deeper insight = Much more flexibility = High construct validity (detail)
- Weakness: Drawing conclusions may be difficult as there is more irrelevant information; social desirability bias

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16
Q

Define the different types of closed questions

A

Likert scales: Respondent indicates their agreement on a scale of usually 5 points (1 = strongly agree, 5 = strongly disagree)

Rating scales: Indicates their feeling about the topic (e.g. 1 = very entertaining, 5 = not at all entertaining - put yourself where you see fit on that scale)

Fixed choice: List of possible options, select all that apply to them

17
Q

Outline and evaluate open and closed questions

A

Open questions: Provides qualitative data (language based)
- Strength: More detailed responses = High construct validity
-Weakness: Long and difficult as responses differ; unable to replicate to test reliability

Closed questions: Provides quantitative data (numerical)
- Strength: Easy to quantify (analysis = quick and easy); easy to replicate to test for reliability
- Weakness: Less detailed response = Lower construct validity

18
Q

What effects the success of interviews or questionnaires? (in terms of the way that the questions are written)

A

Overuse of jargon: Using technical terms in questions only familiar to those specialised in the field/area

Emotive language: The author’s attitude is presented through the words and phrases used in the question

Leading questions: Guides the respondent towards a particular, desired answer

Double-barrelled questions: Contain two questions in one (may agree with only one half of the question)

Double negatives: “I am not unhappy with my job” could simply be said as “I am happy with my job”

19
Q

Define correlations and the different types of correlations

A

Correlation: Relationship between two existing continuous variables (co-variables)

Positive correlation: As one variable increases so does the other

Negative correlation: As one co-variable increases the other decreases

Zero correlation: There is no relationship between the co-variables

20
Q

Define correlation coefficient

A

Calculate how strong the relationship between two co-variables is
-1 = strong negative, (-) 0 = weak negative
+1 = strong positive, (+) 0 =weak positive

21
Q

What is the difference between an experiment and a correlation?

A

Experiments look for a difference between an IV and a DV

Correlations simply look for a relationship between two existing co-variables
- There is no manipulation of the IV so we cannot establish cause and effect

22
Q

Evaluate correlations (strengths and weaknesses)

A

Strengths:
- More ethical way of studying behaviour as IV is NOT manipulated
- Quantifiable measure of how two variables are related
- Assess possible patterns before experimental study
- Quantitative data = easy analysis
- Less time/money consuming as pre-existing secondary research can be used

Weaknesses:
- Does not tell us why variables are related –> Correlation is not equal causation so it is not scientific (less valid)
- Cannot establish cause and effect because we do not know which variable causes the other to change
- Third variable problem –> two variables are influenced by an unseen third variable, leading to misleading interpretations
- Lack of control of variables = Hard to replicate = Reliability is unknown

23
Q

Outline the meta analysis

A
  • Combination of results from existing studies to produce effect size
  • Overall ‘effect size’ is the size of IV’s effect on the DV
  • Effect size 5% = You are allowed 5% wrong (extraneous variables), i.e. the change in DV was due to less than 5% chance
  • Establish cause and effect
24
Q

Outline and evaluate the different types of data

A

Qualitative: Words, thoughts, feelings, opinions; interpretation of language from interview or unstructured observation
- Strength: High construct validity (detail)
- Weakness: Difficult to replicate and analyse; time consuming to collect

Quantitative: Numerical; gather in form of individual scores; open to be analysed statistically; can be conversted to graphs
- Strength: Standardised responses = easier analysis (descriptive statistics); easily replicable
- Weakness: Lacks construct validity (less detail)

Primary: Original data collected specifically for the purpose of the investigation; first hand from participants
- Strength: High quality assurance (conducted yourself)
- Weakness: High cost of time and money (conducted yourself)

Secondary: Collected by someone else; already exists before investigation begins; already been subject to statistical testing (significance is known)
- Strength: Low cost of time and money
- Weakness: Low quality assurance (may not meet your standard or aim)

25
Outline and evaluate the different measures of tendency
Mean: Most representative (accounts for every person); Use when no anomalies present (lower score and brings mean/average down = causes mean to be skewed) - Strength: Makes use of all the values = Most representative - Weakness: Easily disorted by extreme values Median: Better reflection of data than mean - Strength: Easy to calculate (unaffected by extreme values) - Weakness: Ignores most values = isn't as representative as mean Mode: Only important for distributions, otherwise never used - Strength: Unaffected by extreme values - Of little use when there are no repeat values or is bi-modal (multiple modes)
26
Outline and evaluate range and standard deviation
Range: - Strength: Easy to calculate - Weakness: Doesn't tell us about spread of values and is affected by extreme values Standard deviation: - Small standard deviation (low value) = Data is consistent; all participants reacted similarly (scores close) - Large standard deviation (high value) = Data is dispersed; participants affected differently (scores widely spread) - Strength: Includes all values = more precise measure of dispersion; can tell how each individual participant performed - Weakness: Difficult to calculate and can be skewed by anomalies
27
Define normal and skewed distributions
Normal distribution: Spread of frequency data which forms a bell curve (mean, median and mode all located at the highest peak) Skewed distribution: Spread of frequency data that clusters to one end - Positive skew: Long tail on the positive (right) side = most distribution - Negative skew: Long tail on the negative (left) side = most distribution
28
Outline the different types of graphs used to display quantitative data
Bar graphs: Used when data is divided into categories (discrete data) - Frequency of each variable is represented by height of the bars - x-axis = different categories (e.g. eye colour - each bar represents a different eye colour) - y-axis = frequency (e.g. how many people have the colour eyes) Histographs: Continous data rather than discrete (bar chart/graph) - x-axis = equal sized intervals of a single category (e.g. age - an example of continous data) - y-axis = frequency within each interval (e.g. number of children) Line graphs: Continous data; use points connected by lines to shown how something CHANGES in value (e.g. overtime) - x-axis = continous interval e.g. day (Mon, Tue, Wed, Thur, Fri, etc) - y-axis = frequency e.g. number of hotdogs sold (per day) Scattergrams: Depict associations rather than differences between co-variables (correlations); represents the strength and diection of a relationship between co-variables in a correlational analysis - x-axis = e.g. exam score - y-axis = e.g. number of hours spent revising (i.e. is there a relationship between the score achieved and the number of hours spent revising? Positive/negative/zero correlation?)
29
What is peer review and why is it used?
Peer review is part of the verification process which decides whether research is scientifcally acceptable or not. It consists of a system used by scientists to determine whether research findings can be published in scientific journals Aims of peer review: - To allocate funding (decide whether or not funding for a proposed research project should be awarded) - To validate the quality and relevance (re-run calculations for conclusions made) - To suggest ammendments or improvements - Evaluate ethical treatment of participants
30
Evaluate peer review
Finding an expert: - Experts should be objective and unknown to the researcher - Experts in the same particular field may try to sabotage because they need funding (in direct competetion for limited research fundings and do not want other people's research to get funding instead) Anonymity: - Peer (expert) remains anonymous for more honest appraisal - A minority of peers use anonymity to critise rival researchers because they are in direct competetion for limited research fundings, so in some journals where open review has taken place the names of the reviewer(s) are public Publication bias: - Editors want to publish significant "headline grabbing" findings and more positive results to increase credibility and circulation of publication - Therefore some research may be ignored (if it does not meet this criteria) which creates a false impression for psychology as journal editors are being selective in what they publish Buying ground-breaking research: - Peer review may suppress opposition to mainstream theories to maintain the status quo with particular scientific fields - Reviewers are more favourable to research which matches/supports their own - Established scientists are more lkely to be chosen by prestigious publishers so findings that support the current opinion will be passed whereas new and innovative research is ignored as it has not been peer reviewed because it challenges the established order - Therefore peer review slows down the rate of change
31
Outline the implications of psychological research for the economy
If psychologists understand why we behave and think the way we do, they can create methods which positively contribute to the population and the economy A third of all absences from work are due to mental health, including depression, anxiety and stress Research on drug therapies means: - Less absence from work - Less pressure on NHS - Less pressure on emergency services - People will be able to go into work = earn a better salary = better quality of life Research into the accuracy of eye-witness testimony means: - Less money spent on investigation - Less people wrongfully convicted (who would thus be more likely to sue) - Less crime as the right people are arrested
32
Statistical testing: What is the sign test and when do we use it?
The sign test is a statisical test to determine whether results are significant or not - We use probability to test is the results occurred by chance or not - Accepted level of probability = p<0.05, meaning there is less than 5% probability that the results occurred by chance (IV was responsible for at least 95% of the effect measured in DV) It is only appropriate to use the sign test when: - It is a test of difference (an experiment) - There is related design (repeated measures/matched pairs) - There is paired (nominal) data - each participant is categorised as either having a plus or minus sign
33
How do you conduct the sign test?
1.) Convert data to nominal data (paired) by finding out which particiapnts produced a high score in the 2nd column and which produced a lower score in the 2nd column. Make not of this with a + or - (if the first score is higher then +, if first score is lower then -, if scores stay the same then =) 2.) Add up the + and - signs respectively to get two totals. 3.) Work out your S-value