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1.A elements of researching behaviour, 1 APPROACHES TO RESEARCHING…
1.A elements of researching behaviour
Hypotheses and Variables
Hypotheses
Is a single testable statement
Two types
Alternative Hypothesis (H1)
Predicts an effect
Also called research hypotheses
Null Hypothesis (H0)
Predicts no effect
Either one tailed or two tailed
One tailed hypothesis
Predicts an effect in one direction
Two tailed hypothesis
Predicts an effect in either direction
Rejecting hypotheses
Through significance testing
You look at the data, and see if the is actually a difference between data set, if there is a difference, you can reject the null hypothesis which would claim there is no difference, therefore accepting the Alternative hypothesis
Through hypothesis testing
The two hypotheses are compared to each other using the collected data, and based on error rates, one is rejected and the other supported
"an educated guess"
Variables
Any quantity or quality that differs across a population
Quantitative Variables
Any variable that can be easily measured (usually numerically)
E.G Height, Weight, Reaction Time, Speed
Categorical Variables
Variables which could be used to apply a categories onto individuals
E.G Gender, Age, Nationality
Reification
This is where you take an abstract concept, or something that isn't real and make it real through research
should be careful of doing this, largely avoid
E.g "Verbal memory = a variable that can be measured with a list of words" but verbal memory isnt a real or concrete thing
Operationalised Variables
This is where you take a variable that isnt easily measured and give it an "Operational Definition", That is, how you will measure it in this study
E.G > Variable: Depression -------------> Example operational definitions: Depressive symptoms (Quantitative), score on depression scale (Quantitative), receiving therapy or not (Categorical)
Independant and Dependant variables
The independant variable is what is controlled and changed by the researchers
The dependant variable is what changes as a result of the independant variable changing, this is what is measured and studied by researchers
Sampling and Research designs
Sampling
Sampling techniques
Non-probability
Convenience sampling
This is taking people that are nearby and want to participate, based on the convenience for the researcher
Snowball sampling
This is where the first participants (chosen through purposive samples) help recruit more participants for the researcher based on their connections and contacts
Purposive sampling
Choosing people because they have the desired characteristics, qualities, etc
Often faces criticism for selection bias
Volunteer sampling
Samples based on who volunteers
Probability
Simple random sampling
Gives every person an equal chance of selection, can only be done through statistically random methods to select, e.g random number generator
Organise a sampling frame and then just choose using a statistically random method
Only really possible when every member of target population is available
Stratified sampling
Subgroups of the target population are made and then simple random sampling takes place within each subgroup
Provides a more representative sample as efforts are made to make sure that there is a variety of people within the sample
Systematic sampling
Choosing every
K
th person in the sampling frame
Often one of the easiest probability sampling methods to do, and can draw a truly representative sample, however it can do the opposite, if e.g every person selected has same characteristics
Follows the idea that researchers follow this path
Target population = who the results can be generalised to
Sampling frame = who the researchers have access to
Sample = who are the people selected
Research designs
Independant measures
Each participant group is only exposed to one condition of independant variable
This limits many potential biases as they might not be able to figure out the hypothesis and therefore change their behaviour
However it can lead to participant variables if the characteristics of this group are different to that of another group, therefore altering the data
This can be limited through the use of random assignment, where participants are randomly assigned to groups, avoiding potential researcher bias
Repeated measures
All participants are exposed to all conditions of the independant variable
This is useful as it completely negates participant variables, e.g gender, age, health, etc, meaning you can see the exact difference between the independant variables as its the same people participating
Can result in order/carryover effects whereby the participation in one condition of the experiment affects participant behaviour in later conditions
e.g practice --> if the participant has gotten good at a task due to performing it earlier
e.g fatigue/boredom --> if the participant is tired and/or bored, meaning they wont do as well
e.g context --> testing during the first condition may affect how participants interpret later conditions
one way to counteract this is through counterbalancing
this is where one group will do the experiment in order AB and the other will do it in BA
this doesnt get rid of the effects, but cancels them out as the experiment was done in different orders, meaning the data average should be the same as if there were no effects
Matched pairs
A balance between Independant and Dependant
Participants are matched into pairs based on similar characteristics and them one is assigned to one group whilst the other is assigned to the other group and the rest is carried out as an independant measures study
This is meant to keep the two groups as similar as possible so that participant variables would have no effect/there would be no participant variables
Standardisation and Control
Standardisation
This is where efforts are taken to remove or prevent extraneous variables by making the process the same for every participant in every test
However this can lead to criticism, as it increases the artificiality of the study, meaning it cant be applied to real life
Standardised procedure
This is making variables the same, or standard, for all participants
E.g Standardised.........
Environment
Making sure that the location is the same each time, as factors such as time of day, temperature, etc can affect results
Instructions
The instructions that all participants receive are the same, usually accomplished through a script that is read aloud, with answers for any potential questions
materials and tests
Research materials and tests they take should of course be the same for all, except for the variables researchers want to change
Procedure
The procedure for how the test is run, how researchers interact, etc should be the same for all
Done to limit extraneous variables, which can become confounding variables
A confounding variable is one that may be an explanation for the research results, separate to what you are investigating
Control methods
Blinds
Hiding variables under study from participants, or researchers, or both
Single Blind Method
Leaves the participant unaware of whether they are in an experimental group or a control group
Double Blind Method
Having both participants and researchers unaware of which groups are experimental and which are control groups
Usually done with one researcher changing the independant variable with no knowledge of results, and another researcher measuring results, not knowing which groups are experimental and which are control
Ethical considerations
Informed consent
Physical/psychological harm, stress or discomfort
Right to withdrawal
Confidentiality and Anonymity
Deception
Debriefing
1 APPROACHES TO RESEARCHING BEHAVIOUR
1.B + 1.C Research Methods
Quantitative research methods
Goal is to classify, count and compare
Focuses on numbers, logic and objectivity
Objectivity means that the researchers bias and perceptions are removed so that any researcher can reach the same conclusions
Most follow these characteristics
Begins with a clear hypothesis that can be objectively measured or answered
Specifically designed to reduce extraneous variables and make sure that the data is replicable
Meant to be representative of a large population
Data collected using structured research instruments which allow for numerical comparisons
Data is usually collected in numerical or statistical form, which can be later presented in graphs and tables
Often considered reliable, as results are almost always replicable
Strengths
Allows for greater objectivity
Allows for greater accuracy
Large amounts of data can be summarised and compared to other research
Standardisation allows for later replication
Higher reliability and validity
Larger sample sizes allow for high generalisability
Weaknesses
Rigid methodology means that insight into more vague concepts like emotion is hard
Often can lead to high artificiality, and therefore low ecological validity
Arguably represents human psychology too superficially
Easily impacted by things like research design or how variables get operationalised
Seeks to isolate variables and therefore establish cause-and-effect relationships
Experiment Types (IV = Independant Variable)
Simple experiment
This is the most basic form of experiment, just two conditions for the independant variable (control and manipulated IV)
Field experiments
Usual manipulation of IV but is conducted in the "field", meaning it is in real life scenarios, allowing for less control over extraneous variables
Quasi-Experiments
No random assignment, the groups are formed based on pre-existing characteristics which act as the IV. E.g how will a group of X age vs Y age perform, or X culture vs Y culture
Natural experiments
Pre-existing IV's which are manipulated naturally, the researchers just observe, and therefore assignment of participants is not possible to experimental groups
Qualitative research methods
1.D Analysing Data
1.E Evaluating Research