Psychology - Research Methods
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- Created by: pbowman
- Created on: 13-05-17 18:30
Ethics
Moral guidelines used by psychologists studying human behaviour to protect their participants. The ethical guidelines are provided by the British Psychological Society.
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DCCOWPAD
Deception, Consent, Confidentiality, Observation, Withdrawal, Protection, Advice, Debriefing.
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Respect
Consent, Confidentiality, Withdrawal. Must gain permission from participants and allow them to withdraw. Must respect personal details and data by keeping it confidential.
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Competence
Advice. Psychologists must work within their own capabilities, and not giving advice if no qualified to do so.
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Responsibility
Protection, Debrief. Psychologist have a responsibility to protect participants and ensure they're fully debriefed.
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Integrity
Deception, Observation. Psychologists should have high standards regarding honesty, accuracy, clarity and fairness, and they must avoid deception.
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Sample and Sampling
Who is used in a study and how they are recruited.
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Target Population
The total group of individuals from which the sample might be drawn, e.g. students.
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Random Sampling
Target population has an equal chance of being chosen, e.g. by random number generator.
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Random sampling - Advantages
Non biased.
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Random Sampling - Disadvantages
Time consuming to make a list of all participants and some less or not willing to participate.
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Snowball Sampling
Asking participants to nominate another person who has the same characteristics being sampled.
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Snowball Sampling - Advantages
Find participants with niche traits or characteristics.
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Snowball Sampling - Disadvantages
Ethical issues, can lose control of participants and time consuming.
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Opportunity Sampling
Selecting people who are ready and avaliable.
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Opportunity Sampling - Advantages
It is convinient, cheap and quick.
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Opportunity Sampling - Disavantages
It can be biased and unrepresentative.
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Volunteer Sampling (Self-Selecting)
Participants select themseleves to participate.
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Volunteer Sampling - Advantages
The participants are willing and less likely to withdraw.
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Volunteer Sampling - Disadvantages
It is time consuming to gather participants and can be unrepresentative.
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Ethnocentrism
When a sample is taken from one area or culture.
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Ethnocentrism - Advantages
Easy to recruit.
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Ethnocentrism - Disadvantages
Not representative - cannot be generalised.
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Independent Variable (IV)
What the research looks for a difference between, what they are comparing (what they change) e.g. Gender, Age, Culture.
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Dependent Variable (DV)
The variable that the research measures, e.g. a score on a test or a timing.
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Extraneous Variable
Any variables controlled by the researcher, also known as controls, e.g. all participants have a drink of water before a memory test.
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Confounding Variable
Any variables not controlled by the researcher that could ruin the result e.g. temperature in a room during a memory test.
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Lab Experiment
When the environment is controlled and the conditions are manipulated.
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Lab - Advantages
Researcher has control, so there are fewer confounding variables. Can see if IV has an effect on DV.
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Lab - Disadvantages
Often low in ecological validity, and prone to demand characteristics.
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Field Experiment
Conducted where you would expect to see the that behaviour occur and there are manipulated IVs.
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Field - Advantages
High in ecological validity as it is natural behaviour, still have control over IVs to see if it has an effect on the DV.
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Field - Disadvantages
May have confounding variables that cant be controlled.
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Quasi Experiment
When the IV is naturally occuring - researcher hasnt manipulated them e.g. gender, age.
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Quasi - Advantages
High in ecological validity as IV is naturally occuring.
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Quasi - Disadvantages
Difficult to see cause and effect as the researcher hasnt manipulated the IV.
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Hypothesis - Experiments
These look for a difference.
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Alternate Hypothesis - Experiments
This says there WILL be a significant DIFFERENCE. Also known as an experimental hypothesis.
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Null Hypothesis - Experiments
This hypothesis says there WONT be a significant DIFFERENCE.
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One Tailed / Directional Hypothesis - Experiments
These state the direction of the experiment, e.g. Boys will perform significantly better than girls on the memory test out of 10.
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Two Tailed / Non Directional Hypothesis - Experiments
These dont state the direction of the experiment, e.g. there will be a significant difference between girls and boys performance on a memory test out of 10.
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Hypothesis - Correlations
These look for a relationship.
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Alternate Hypothesis - Correlations
This hypothesis says there WILL be a significant RELATIONSHIP. Also known as a correlational hypothesis.
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Null Hypothesis - Correlations
This hypothesis says there WONT be a significant RELATIONSHIP.
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One Tailed / Directional Hypothesis - Correlations
These state the direction of the correlation, e.g. there will be a positive correlation between happiness and chocolate.
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Two Tailed / Non Directional Hypothesis - Correlations
These dont state the direction of the correlation, e.g. there will be a significant relationship between chocolate and happiness.
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Structured Observation
The researcher has a behaviour checklist to tally so they have structure to the way they record data.
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Structured Observation - Advantages
Know what to focus on and what behaviours to look for.
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Structured Observation - Disadvantages
May be hard to find certain behaviours.
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Unstructured Observation
The researcher doesn't have a pre-determined behaviour checklist. They dont have structure as to how they record data.
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Unstructured Observation - Advantages
You can change your mind to fit the behaviours in the surrounding areas.
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Unstructured Observation - Disadvantages
Harder to record the data as it is collected in the moment.
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Naturalistic Observation
An observation carried out in a natural environment, where you'd expect that behaviour occur.
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Naturalistic Observation - Advantages
See realistic behaviours, it reduces the chance of demand characteristics, and is high in ecological validity.
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Naturalistic Observation - Disadvantages
Can be unethical, and there is generally less control.
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Controlled Observation
Usually conducted in a controlled setting. The researcher controls who they will observe and often the participants know they are being observed.
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Controlled Observation - Advantages
They have more control over the experiment.
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Controlled Observation - Disadvantages
The participants know they are being observed so they often display demand characteristics.
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Participant Observation
Where the observers are part of, or are pretending to be part of the participants that are being observed.
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Participant Observation - Advantages
It can give a more natural feel to the observation, and you gather primary data.
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Participant Observation - Disadvantages
Participants can often show observer bias or demand characteristics.
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Non-Participant Observation
The researcher does not participate in the behaviour/observation being held.
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Non-Participant Observation - Advantages
It is easier to observe others behaviour and there is less chance of observer bias.
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Non-Participant Observation - Disadvantages
You may miss certain behaviours.
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Overt
When the participants are aware that they are being observed.
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Overt - Advantages
You have the participants consent so it is more ethical.
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Overt - Disadvantages
The participants may express demand characteristics.
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Covert
When the participant doesn't know they are being observed.
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Covert - Advantages
It makes the observation more realisitic so there is a reduced chance of demand characteristics.
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Covert - Disadvantages
It can be unethical as the participant hasnt given formal consent, or may not be aware so they cannot withdraw either.
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Behavioural Categories
When there are categories of behaviours on a checklist to observe during an observation.
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Behavioural Categories - Advantages
You know what you are looking for (behaviours).
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Behavioural Categories - Disadvantages
It may limit you to only observe specific behaviours, which means you may miss other behaviours that help show the cause and effect of something.
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Coding Frames
When behavioural categories are coded and can be rated for severity, e.g. 1 = light punch, 5 = hard punch.
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Coding Frames - Advantages
It is easier to note the observations down, and give a bit more detail.
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Coding Frames - Disadvantages
It is harder for different observers to interpret, because someone may consider a punch light, is what someone may interpret as a hard punch.
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Observer Effects
When the presence of an observer in an overt observation changes the behaviour of the participants.
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Observer Effects - Disadvantages
It shows demand characteristics and means the observation is not measuriong what is intended.
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Time Sampling
The observer records what the participant is doing in a fixed time intervals e.g. every 5 seconds in 20 minutes.
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Time Sampling - Advantages
Having fixed intervals means it shows changes to behaviour through out a fixed period of time, and you are less likely to miss behaviours.
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Time Sampling - Disadvantages
You may miss some behaviours during the periods of time it is being recorded.
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Event Sampling
Researcher recording behaviour everytime its happens.
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Event Sampling - Advantages
It is easy to record, and you may get more data than in time sampling.
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Event Sampling - Disadvantages
You may miss a behaviour if lots of different behaviours occur simultaneously.
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Inter-rater Reliability - Internal
When 2 or more observers observe the same behaviours at the same time using the same checklists. They come together at the end of the observation and correlate their results to see how reliable they are.
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Inter-rater Reliability - Advantages
Means the data is more reliable.
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Inter-rater Reliability - Disadvantages
If the data isnt (70%) sufficiently similar then they may have recorded the data wrong and its time consuming.
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Observer Bias
When the observer interprets the behaviour or the data how they want to.
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Observer Bias - Advantages
Relative to the object of the observation.
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Observer Bias - Disadvantages
It is bias, so can make the data unreliable.
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Self-Reports
Main 2 types are questionnaires and interviews.
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Questionnaire
Set of questions for the purpose of a study or survey.
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Questionnaire - Advantages
They are practical, and can gather large amounts of data in a short period of time and its cheap.
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Questionnaire - Disadvantages
They can lack validity due to social desirability bias and response bias, also not everyone will respond.
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Structured Interview
Interview with pre-determined questions, e.g. a formal job interview.
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Structured Interview - Advantages
It allows the interviewer to ask the questions they need to, they can find specific behaviours and have clear direction.
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Structured Interview - Disadvantages
It is time consuming, you may experience demand characteristics and you may not ask other questions if you are so focused on the set questions.
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Unstructured Interview
An interview without pre-determined questions.
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Unstructured Interview - Advantages
More flexible as questions can be changed and adapted to fit the respondents answer, you collect qualitative data and it has increased validity.
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Unstructured Interview - Disadvantages
You may get confused as to what questions have or havent been asked and there is no direction so you may miss questions.
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Semi-Structured Interview
An interview with pre-determined questions but is allowed to go off topic if neccessary.
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Semi-Structured Interview - Advantages
You can gather a large amount of data and it is flexible, so it is reliable.
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Semi-Structured Interview - Disadvantages
Participants may express demand characteristics and flexibility may lessen the reliability.
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Open Questions
A question with no restricted response.
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Open Questions - Advantages
It allows for more detail to be given.
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Open Questions - Disadvantages
It gives qualitative data which is hard to analyse.
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Closed Questions
A question with a restricted response, e.g. yes/no questions.
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Close Questions - Advantages
It gives quantitative data which is easy to analyse.
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Closed Questions - Disadvantages
It doesnt allow for clarification or detail and it doesnt show the reason for answers (no cause and effect).
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Rating Scale
Where you can rate your response e.g. 1-10 (where 1 = not stressed and 10 = stressed).
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Rating Scale - Advantages
They are easy to read and give quantitative data, so easy to analyse.
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Rating Scale - Disadvantages
They show no reason for answers or give no detail, also open to demand characteristics, response bias and social desirability bias.
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Semantic Differential Rating Scale
A rating scale which uses opposing adjectives e.g. happy / sad.
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Semantic Differential Rating Scale - Advantages
They are easy to read and give quantitative data, so easy to analyse.
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Semantic Differential Rating Scale - Disadvantages
They show no reason for answers or give no detail, also open to demand characteristics, response bias and social desirability bias.
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Likert Scale
A question that uses agree and disagree, neutral point is 'neither agree or disagree'.
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Likert Scale - Advantages
They are easy to read and give quantitative data, so easy to analyse.
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Likert Scale - Disadvantages
They show no reason for answers or give no detail, also open to demand characteristics, response bias and social desirability bias.
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Response Bias
When participants opt for the middle number on a scale to not show extreme values.
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Response Bias - Disadvantage
It reduces the validity, as the researcher isnt measuring accurate responses.
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Social Desirability Bias
When participants change their behaviour to be seen as favourable by others.
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Social Desirability Bias - Disadvantage
Reduces validity as the researcher isnt measuring accurate responses just how people want to be seen.
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Demand Characteristics
When participants change their behaviour to suit the needs of the study.
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Demand Characteristics - Disadvantage
Reduces validity as it isnt measuring accurate responses.
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Positive Correlation
When the value of both co-variables increase together.
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Negative Correlation
As the value of one variable increases, the value of the other variable decreases.
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No Correlation
When there is no relationship between the variables.
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Correlations - Advantages
You can see the direction/relationship between 2 variables. It can be the only ethical way to do research because they often use data that has already been conducted.
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Correlations - Disadvantages
You cannot assume cause and effect and the data is limited, so we dont always know the details behind the given data as it is quantitative.
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Correlation Co-efficient
Calculates the strength and direction of the data, e.g. strength = weak/moderate/strong, direction = positive/negative.
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Correlation Co-efficient - Scale
-1.0 = perfect/strong negative, -0.8 = moderate negative, -0.1 to -0.5 = weak negative, 0 = no correlation, 0.1 to 0.5 = weak positive, 0.8 = moderate positive, 1 = perfect/strong positive.
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Quantitative Data
Data in the form of numbers, e.g. % or frequencies.
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Quantitative Data - Advantages
Easy to analyse, e.g. in graphs, percentages, mean etc.
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Quantitative Data - Disadvantages
Lacks depth and detail.
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Qualitative Data
Data in the form of words.
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Qualitative Data - Advantages
It gives depth, detail and insight.
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Qualitative Data -Disadvantages
It is hard to analyse and difficult to replicate.
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Primary Data
When the researcher collects data themselves, e.g. a interview or questionnaire.
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Primary Data - Advantages
More reliable and the data will fit the needs of the experiment.
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Primary Data - Disadvantages
It can be biased, and can take a long time to gather.
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Secondary Data
When the researcher uses data collected by another person or source, e.g. a newpaper or online articles.
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Secondary Data - Advantages
It saves time and money, and it is less biased.
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Secondary Data - Disadvantages
The data may not fit the needs of the experiment.
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Nominal Data
Data in categories of behaviour, e.g. 3 groups of people of tall, average and short height.
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Ordinal Data
Data that is ordered, e.g. grades from best to worst. Rating scales are also considered ordinal.
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Interval Data
Data that uses scales that increase in standardised intervals, e.g. height in cm, temperature, scores on a test (that havent been ordered).
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Validity
Whether the study is measuring what it intends to.
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Internal Validity
Whether the is measuring what it intends to within the study itself.
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Face Validity - Internal
Accurate on the surface
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Ecological Validity - External
How real to life a study is.
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Descriptive Statistics
Describing the data.
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Mean
Arithmetic average, calculated by adding all the scores together in each condition then dividing by the number of scores.
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Mean - Advantages
Takes all scores into account.
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Mean - Disadvantages
There can be extreme values which may inflate or deflate the average.
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Median
Middle number, calculated by finding the midpoint in a numerically ordered list.
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Median - Advantages
It can be used if there is an extreme value to make the data more representative.
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Median - Disadvantages
Not all of the scores are taken into account in the final value.
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Mode
Most common, it is calculated by finding the most common value in a set of values.
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Mode - Advantages
It is useful when there are categories of data e.g. most common behaviour.
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Mode - Disadvantages
Doesnt take any other scores into account.
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Bar Graph
The height of each bar represents the frequency of each category and space should be left between bars.
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Bar Graph - When to use it
When you have data that isnt continuous most likely use this when you have categories of behaviour; particularly for observations.
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Histogram
Like a bar chart, axis must start at 0, no spaces between bars.
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Histogram - When to use it
When the data is continuous, e.g. ages, weight, height, time.
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Scattergraph
Used to represent relationships between data and plots are not joined.
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Scattergraph - When to use it
Only if you’ve conducted a correlation.
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Pie Chart
Alternative to a bar chart, you make each pie slice a category of behaviour.
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Pie Chart - When to use it
When there is non-continuous data. You divide each frequency by the total frequencies and multiply by 360 degrees.
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Line Graph
Alternative to a histogram but a dot represents each bar and a line connects each dot.
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Line Graph - When to use it
Used to show how something changes over time, and can be useful to compare 2 or more conditions.
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Standard Deviation
Tells us how spread out the data is from the mean, e.g. a high SD = data is very spread out from the mean, and low SD = data is close to the mean.
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Distribution Curves
Graphs which are plotted to represent the mean and the spread of data (SD) and tell us mathematically where most of the results sit.
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Positive Skew
Data is more spread out towards the left, which means not many scores are near the top, e.g. salaries.
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Negative Skew
Data is more spread out towards the right, which means not many scores are near the bottom, e.g. if most of students did well on a test and few did badly.
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Normal Distribution/Bell Curve
When the mean, median and mode and in line with one another, e.g. weight and height, IQ score.
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Inferential Statistics
Used to fraw conclusions and make predictions (inferences) based on the descriptions of the data.
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Probability
This is the likelihood of an event or a pattern of numbers occurs due to chance.
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P Value - One Mainly Used
p≤ 0.05, meaning the results are 95% not up to chance.
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Type 1 Error
False Positive - if you accept the alternate when you should have accepted the null.
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Type 2 Error
False negative, these can occur when you accept the null when you should have accepted the alternate.
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Statistical Tests
Chi square test, Binomial sign test, Mann-Whitney U test, Wilcoxon Signed Ranks test, Spearmans Rho correlation co-efficient.
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Parametric - When would you use it?
When you have interval data, when you dont have extreme scores (normal distribution) and when the standard deviations are not significantly different between conditions.
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Non-Parametric Tests
Chi square test, Binomial sign test, Mann-Whitney U test, Wilcoxon Signed Ranks test, Spearmans Rho correlation co-efficient.
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Chi Square Test - More Than
When you have nominal data and the design is independent measures. You have to calculate degrees of freedom = (number of rows-1) x (number of columns-1).
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Binomial Sign Test - Less Than
When you have nominal data and repeated measures design.
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Mann-Whitney U Test - Less Than
When you have ordinal data and independent measures design.
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Wilcoxon Signed Ranks Test - Less Than
When you have ordinal data and repeated measures design.
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Spearmans Rho Correlation Co-efficient - More Than
When you have ordinal data and are testing for a correlation.
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Psychology as a Science - Induction
Data comes first and is often from observing behaviour. Looks for patterns then formulate hypotheses to explore to develop conclusions and theories.
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Psychology as a Science - Deduction
Idea comes first, then is tested (general idea to specific researching).
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Psychology as a Science - Objectivity
Sciences should not be biased (subjective). Personal ideas should not influence results.
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Objectivity - How it makes Psychology more Scientific
Based on facts/quantitative data; rather than the opinions, or thoughts.
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Psychology as a Science - Replicability
If a study has lots of control and can be tested again.
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Replicability - How it makes Psychology more Scientific
Another researcher may obtain similar results that support/dont support previous findings.
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Psychology as a Science - Falsification
For something to be considered scientific, hypotheses must be able to be proven false.
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Falsification - How it makes Psychology more Scientific
New research may find new evidence that counter acts previous hypotheses.
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Psychology as a Science - The Study of Cause and Effect
The aim of research is to demonstrate a cause by manipulating an IV to observe the effect on the DV.
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The Study of Cause and Effect - How it makes Psychology more Scientific
Controlling variables means we can say that the change in the IV has had an effect on the DV.
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Psychology as a Science - Hypothesis Testing
Sciences should be able to predict behaviour, so must be operationalised.
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Hypothesis Testing - How it makes Psychology more Scientific
Hypotheses can be proven wrong (null) so this goes with falsifiability.
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Psychology as a Science - Manipulation of Variables
The aim of research is to demonstrate a cause by manipulating an IV to observe the effect on the DV.
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Manipulation of Variables - How it makes Psychology more Scientific
Controlling variables means we can say that the change in the IV has had an effect on the DV.
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Psychology as a Science - Control and Standardisation
Having controls and keeping things the same (standardisation).
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Controls and Standardisation - How it makes Psychology more Scientific
This increases the replicability another researcher may/may not find the same.
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Psychology as a Science - Quantitative Data
Recording data numerically makes the data more factual.
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Quantitative Data - How it makes Psychology more Scientific
Makes the results more objective (no bias).
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Other cards in this set
Card 2
Front
Deception, Consent, Confidentiality, Observation, Withdrawal, Protection, Advice, Debriefing.
Back
DCCOWPAD
Card 3
Front
Consent, Confidentiality, Withdrawal. Must gain permission from participants and allow them to withdraw. Must respect personal details and data by keeping it confidential.
Back
Card 4
Front
Advice. Psychologists must work within their own capabilities, and not giving advice if no qualified to do so.
Back
Card 5
Front
Protection, Debrief. Psychologist have a responsibility to protect participants and ensure they're fully debriefed.
Back
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