Psychology Unit 2

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  • Created by: Ellen122
  • Created on: 18-03-21 19:58
Aim
What the researcher intends to study
Statement of what they are trying to investigate
'Aims' to work out if the hypothesis is true or not
An aim is a statement of what a researcher wants to find out
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Aim Example
Why are so few boys taking AS psychology at St. Cyres?
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Hypothesis
A prediction about what we expect to find out
A statement and should include the independent variable and dependent variables or the 2 or more variables being measured and the effect, difference or relationship that we expect to see from the finings and t
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What does a Hypothesis Always Consist of?
Both states of the independent variable
The dependent variable
The word 'significant/significantly'
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Example of a hypothesis
There are significantly fewer boys taking AS psychology than girls
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Null Hypothesis
Avoids bias
A statement that the independent variable has no significantt effect on the dependent variable
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Example of a Null Hypothesis
There is no significant difference in the number of boys and girls taking AS psychology
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Rejection of Null Hypothesis
If rejected we use an alternative/experimental hypothesis
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Directional Hypothesis
When previouos research or common sense allows us to make a predicttion
Contain more/less/higher/lower etc
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Example of a Directional Hypothesis
There are significantly more girls than boys taking AS psychology
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Non-directional Hypothesis
Statement that the independent variable will cause a significant difference to the dependent variable
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Non-directional Hypothesis
There is a significant difference in the number of boys and girls taking AS psychology
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Operationalised
The variables should be operationalised (easy to measure and define) to allow them to be tested
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Confounding Variables
A variable that is not the independent variable under study but which varies systemtically with the independent variable
Changes in the dependent variable may be due to the confounding variable rather than the independent variable
The outcome is then mean
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Extraneous Variables
Potential to become confounding variables if not controlled properly and will then influence the findings
Do not vary systematically with the independent variable and therefore do not act as an alternative independent variable
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Order Effects
Practice or fatigue
Repeated measures design
Arise from which order conditions are presented
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Participant Variables
Characteristics of individuals
Age, intelligence, etc
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Situational Variables
Factors in the environment
Noise, time of the day, behaviour of the investigator
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Investigator Effects
Things that affect participant performance
Direct and non-direct
Experimenter may act in a more positive way with experimental group
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Operationalisation of Variables
Once a psychologist decides on what they will investigate then they need to decide how to go about it
How will you measure the dependent variable in the study?
How will you manipulate the independent variable in the study?
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Repeated Measures
All participants take part in all conditions recieving the same independent variable
Compare each participant performance on both conditions
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Advantages of Repeated Measures
No individual difference between groups as participants in each condition are the same people there are no individual differences
Fewer participants
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Disadvantages of Repeated Measures
Order effects - tf they do a similar activity twice then they can get better the 2nd time due to practice - practice effect
If they do it again they may perform worse as they've grown tired or bored - fatigue effects
Demand characterisics - more likely to
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Overcoming Weaknesses Repeated Measures
Cover story about the purpose of the test to prevent them from guessing what it's about (single blind)
Use of conterbalancing ensures each condition is tested first or second in equal conditions
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Independent Measures
All participants only take part in one condition of the independent variable, such as group A does task with loud noise, group B does task with soft noise
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Independent Measures Advanatges
No order effect - only take part in one condition
No practice or fatigue effects
Some test for both groups
Deman charactersitics - less likely to guess the aim of the research
Avoids problems that occur with repeated measures design (needs more participan
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Independent Measures Disadvantages
Individual differences - groups might not be the same to begin with
Participant variables - no control different abilities characteristics of each participant
More participants required
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Overcoming Weaknesses Independent Measures
Random allocation of participants to condition
More participants spend more time and money
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Matched Pairs
2 different groups; each participant in group A paired with a particiapnt in group A. Pairing is done by key variables which are relevant. Put in separate groups.
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Matched Pairs Advantages
No individual differences betweeen groups may hav ebeen matched to their conditions
No order effects on participants only take part in one condition so there is no practice or fatigue effect
Same test used
Demand characteristics are less of a problem
Cont
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Matched Pairs Disadavanatages
Matched pairs design can be time consuming and expensive
There still may be some individual differences between the groups
More participants are required
Matching is difficult
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Overcoming Weaknesses Matched Pairs
Restrict matching variables to make it easier
Conduct a pilot study to consider key variables
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Random Sample
Any member of the target population has an equal chance of being selected
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Advantages of Random Samples
Representative of all the members of target population have an equal chance of selection
Potentially unbiased because of equal selection
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Disadvantages of Random Sample
Very difficult to organise and usually results in small sample
More time and effort as you need to obtain list of all the member's etc.
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Opportunity Sample
Take a sample from people that you have the opportunity of studying, people who are easily available
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Advantages of Opportunity Sample
Easiest and most convenient methods
Quicker than other methods
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Disadvantages of Opportunity Sample
Unrepresentative because the sample is drawn from a small population - not everyone from the population has an equal chance of selection
Biased as sample drawn from part of the target population (e.g. asking people on a Monday morning)
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Self-selected (Volunteer)
An advert is placed for volunteers and so people volunteer themselves
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Advantages of Self-selected Sample
Access to a variety of participants
Easiest method as you just use the first participants you can find (quicker than other methods)
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Disadvantages of Self-selected Sample
Participants are likely to be more motivated and have more time on their hands
Biased on sample drawn from part of the target population (e.g. asking people on a Monday morning)
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Stratified/Quota
Getting a certain amount of people from each category. Selection from a strata is another opportunity method
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Advantages of Stratified/Quota Sample
Sample is spread evenly throughout the population avoiding bias
More representation of subgroups
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Disadvantages of Stratified/Quota Sample
Can be very time consuming as the categories have to be identified and calculated. If you don't have details of all the people in your target population you would struggle.
Although sample represent subgroups, each quota maybe biased
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Snowball Sample
Referrals from initial participants to generate additional participants
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Advantages of Snowball Sample
Enables a researcher to locate groups of people who are difficult to access, such as drug addicts
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Disadvantages of Snowball Sample
The sample is not likely to be a good cross-secction from the population becuase it is friends of friends
May be unrepresentative if the systematic basis does not allow for all subgroups within the population be captured
Not turly unbiased unless you sele
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Labratory Experiments
Conducted in a special environment where extraneous variables can be carefully controlled. To investigate causal relationships between IV and DV under controlled conditions. Participants are aware they are taking part in an experiment but may not know th
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Strengths of Labratory Experiments
Well controlled
Extraneous variables are minimised thus higher internal valdity
Easily replicated demonstrating external validity
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Weaknesses of Labratory Experiments
Artificial contrived situation
Participants may not behave naturally
Investigator effects and participant effects may reduce validity
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Field Experiments
An experiment conducted in a more natural or everyday environment i.e. in the field. To investigate causal relationships between an IV and DV in a more everyday surrounding. As with the lab experiment the IV is still deliberately manipulated. Participants
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Strengths of Field Experiments
Less artificial
Usually higher mundane realism
Higher ecological validity
Avoids participants effects (if participants aren't aware of the study) this may increase internal validity
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Weakneses of Field Experiments
Less control of extraneous variables, reducing internal validity
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Natural Experiments
Naturally occurring situations that cannot be manipulated
Investigate causal relationships between an IV and DV in situations where IV cannot be directly manipulated
Researcher has not manipulated the IV directly, but where the IV has varied as a consquen
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Natural Experiement Explained
Makes use of such 'naturally' occurring variables and observes their affect on the DV
Strictly speaking an experiment involves the deliberate manipulation of an IV
Causal conclusions should therefore not be drawn from a natural experiment
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Natural Experiment Strengths
Allows research where IV can't be manipulated for ethical or practical reasons
Allows psychologists to study 'real' problems such as the effects of disaster on health (mundane realism and ecological validity increased)
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Natural Experiment Weaknesses
Cannot demonstrate causal relationships as the IV has no been directly manipulated
Less control of extraneous variables therefore threat to internal validity
Can only be used where conditions vary naturally
Participants may be aware of being studied reduc
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Quasi Experiments
Research designs that are 'almost' experiments but lack one or more features of a true experiment
Includes natural experiements and difference studies
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Non-participant Observations
Someone who is not taking part in the activity are being observed
Record the DV
Form the whole of a research project
Behavioural checklists are used to structure observations
Naturalistic or controlled
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Non-participant Observations Advantages
Observations record what people actually do rather than what they want
Actions recorded rather than what they say
Makes them more valid
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Non-participant Observations Disdvantages
Observer bias
May lack reliability
Differences between observers
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Content Analysis
Behaviour is observed indirectly in written or verbal material such as the media
May involve qualitive analysis to produce categories or quantitive to count frequence
Qualitive data examples and quotes can be colled by observer
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Content Analysis Advantages
High ecological validity based on things people actually do
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Content Analysis Disadvantages
Observer bias can be a problem
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Structured Interviews
Face-to-face 'real time' interaction involving fixed questions with a section of answers in the same ordder used to investigate what people think and feel
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Structured Interviews Advantages
Can easily be repeated
Requires less skill than unstructured interviews
Easier to analyse as answers are given to the same set of questions
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Structure Interviews Disadvantages
Interveiwer's expectations may influence the answers given - interviewer bias
People don't actually know what they think
May want to presen themselves in a good light and therefore give socially desireable answers - social desirability bias
Gain limited a
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Semi-structured Interviews
Combination of pre-determined questions and some questions developed in response to answers given to have more detailed answers
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Semi-structured Interviews Advantages
Generally more detailed information can be explained than structured one
Can access information that may not be revealed by predetermined questions
Respond to unexpected answers to gain information to increase validity
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Semi-structured Interviews Disadvantages
More affected by interviewer bias than structured ones
Even more affected by leading questions than questionnaires
Requires well trained interviewers which make it more expensive to produce reliable interviews
Answers form unstructured interviews are less
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Structured Questionnaires
Data is collected through use of written questions which may be open or closed questions used to investigate what people think and feel
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Structured Questionnaires Advantages
Can easily be repeated
Easier to analyse as answers are given to the same set of questions
Open questions rich in data providing detail and good insight into what people think and feel (qualitative data)
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Structured Questionnaire Disadvantages
Self reporting method leads to issues with honesty (social desirability bias)
Open questions provide qualitive data which is difficult to analyse closed questions provide quantitive date which is easier to anlse but answers are limited and may not represe
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Correlational Studies
2 variables - determine if there is a positive or negative relationship between them
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Correlational Studies Advantages
Useful for making predictions about effect of one variable on another
Can show strength of relationship between two variables (co-variables) can be used when it would be unethical or impracticable to manipulate variables and can make use of existing data
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Correlational Studies Disadvantages
Only show that a relationship exists between 2 variables
Cannot determine a cause and effect relationship
There may be other confounding variabls that affect the results
Misinterpret correlations and assum causal relationship has been demonstrated
Lead to
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Case Study
Researcher collects data through interviews, observations, psychological tests or examination of documents and records
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Case Study Advantages
Offers rich in-depth data (qualitive)
High levels of ecological validity
Can be used to investigate instances of human behaviour and experiences that are rare or cases which could not possibly be created in labs
Complex interaction of many factor can be s
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Case Studies Disadvantages
Results cannot be generalised
Researchers own subjective feelings may inflyence the study
Lacks objectivity
Theoretical bias may lead them to interpret the data less objectively
Take a long time for the collection of data and analysis, findings are not qu
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Self Report
Participants are asked to 'report' their own attitudes abilities and/or feelings
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Self Report Advantages
Provides interesting information but may not be accurate
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Self Report Disadvantages
We are not always the best witness of ourselves becuase we forget what we did etc.
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Inter-rater Reliability
Number agreed observations
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Total number observations
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Mean
Add all the numbers up then divide by the number of numbers
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Mean Advantages
Sensitive measure
Takes into account all of the values of the raw data
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Mean Disadvantages
Can be skewed by unusally high or low data
Not used appropriately cause silly decimal places
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Median
Put all the numbers in ascending order and working into the middle
If there is no middle work our the mean of the middle number
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Median Advantages
Not affected by extreme scores
Useful when considering ordinal data
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Median Disadvantages
Only takes into account the positions of scores and not their values
Not appropriate for a small sample size
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Mode
What number/ thing appears the most
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Mode Advantages
Not affected by extreme scores
Only measure of central tendency that can be used for nominal data
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Mode Disadvantages
Tells us no information about the data not in the modal class
Not appropriate to use if there is more than one mode in a data cell.
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Qualitative Data
Describes qualities or characteristics
Collected using questionnaires, interviews, or observation, and frequently appears in narrative form
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Qualitative Data Strengths
Complement and refine quantitative data
Provide more detailed information to explain complex issues
Mutliple methods for gathering data on sensitive subjects
Data collection is usually cost efficient
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Qualitative Data Weaknesses
Findings usually cannot be generalised to the study population or community
More difficult to analyse; don't fit neatly in standard categories
Data collection is usually time consuming
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Quantitative Data
The value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it
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Quantitive Data Strengths
Findings can be generalised if selection process is well-designed and sample is representative of study population
Relatively easy to analyse
Data can be very consistent, precise and reliable
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Quantitive Data Limitations
Related secondary data is sometimes not available or accessing available data is difficult/impossible
Difficult to understand context of a phenomenon
Data may not be robust enough to explain complex issues
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Primary Source and Research
Immediate, first-hand accounts of a topic from people who had direct connection with
Methodology used by researchers to collect data directly
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Secondary Source and Research
Created by someone who did not experience it first-hand or participate in the events or conditions being researched
The use of already existing data
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Nominal Data
When the data is in separate categories like male and female
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Ordinal Data
Ordered, but the intervals are not equal, for example if people are lined up according to their height, the differences between adjacent people will not be the same
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Interval and Ratio Data
Use of units measurement with equal intervals
The difference between 1cm and 2cm is the same as the difference between 36c and 37cm
More precise measures than ordinal data
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Ratio Data
Has a true zero like height
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Interval Data
Does not have a true zero for example a satisfaction score in a survey
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Measures of Dispersal
Describe the spread of data
Easiest measure of dispersal to calculate is the range - difference between the highest and lowest value
Extreme values outside of most of the values can make it worthless as a measure of dispersal
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Standard Deviation
The spread of data around the mean
One standard deviation either side of the mean will include 67% of the values
More difficult to calculate than the range, still relevant measure of dispearsal of data sets with extreme values
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Standard Deviation Equation
S =---------
\/ E(X-X)2
-----------
N
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Internal Reliability
Concerns whether the questions used are consistent with each other, do they all measure the same thing?
The Split half method is a useful method to test this
Responses to questions on the 2 halves of a test are compared; if the questions are measuring the
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External Reliability
Measure of consistency over different occasions
Test-retest method compares the same people doing the same test twice
A high correlation between the scores than there is good external reliability
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Inter-interviewer Reliability
When 2 interviewers get the same response
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Other cards in this set

Card 2

Front

Aim Example

Back

Why are so few boys taking AS psychology at St. Cyres?

Card 3

Front

Hypothesis

Back

Preview of the front of card 3

Card 4

Front

What does a Hypothesis Always Consist of?

Back

Preview of the front of card 4

Card 5

Front

Example of a hypothesis

Back

Preview of the front of card 5
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