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RESEARCH METHODS, SCENTIFIC PROCESSES, RESEARCH METHODS, DATA HANDLING AND…

- - - - Aggressive behaviour- vague Kicking biting and punching - operationalised
      - When variables and behaviours are clearly defined and explained in order to be replicated
- - - - Are methods of recording suitable/ functional
      - Are the behavioural categories appropriate
    - - Are any questions leading
      - Is interviewer appropriately trained
      - Is the structure appropriate for the topic
    - - Ethical issues
      - Order effects
      - Are the instructions clear
      - Is the amount of control appropriate
- - - - When research involves children or people considered to be unable or not competent to sign for
        themselves, a consent from a parent or guardian is required
      - What goes into a consent form
        
        Inform participants that their information will be kept confidential
        
        Inform participants that they are free to withdraw from participating at any point in the process, including afterwards, and their data will be removed
        
        Explain anything that may affect willingness to participate
        
        Leave space for a signature
        
        explain what participants are expected to do in the study
        
        Give the participant the opportunity to ask any questions
        
        the topic being investigated
      - A form which indicates that the participant has decided to take part in the research from their own free will. the form must be tailored to the individual experience, and so if you have different groups in different levels of the IV then a different form must be made to keep things specific.
    - - what goes into a debriefing form
        
        Remind participants of their right to withdraw still applying
        
        if needed, try to alleviate or lower the participants anxiety and guide them to potential sources of support
        
        Inform participants on what you expect to find
        
        Give the opportunity to ask any questions
        
        Tell the participant about the hypothesis which you were studying or testing
        
        Thank the participants for taking part
      - A form which participants sign after the experiment, checking that they're leaving how they arrived and are clear on the purpose of the experiment
- - - - Implications
        
        Bias- you would be restricted to certain people which do not represent all people in the target population
        
        Bad generalisability
      - Example- may be a friend or a person on the street
      - Participants are selected based on who is most easily available, easiest method to use
    - - Implications
        
        Should't be any bias involved as the researcher chooses blind/ isn't selective
        
        May not be generalisable to all as it may randomly exclude representation of certain groups
      - Example- Write down all participants names on identical pieces of paper and put them into a hat. Draw them blind until you reach the number of participants needed
      - All members of the target population have an equal chance of being selected
      - Researcher must have details of all of the target population
    - - Implications
        
        Volunteer bias- volunteers are more likely to have certain characteristics which set them apart from the general popualtion such as being more motivated and willing to help, having free time
        
        Means that results from volunteers may not accurately reflect the general or target popualtion so are not generalisable
      - Example- If you were interested in attachment to a PCG you might advertise in a nursery
      - Also referred to as a self selected sample
      - Researcher will place an advertisement for the research in a place which is likely to be seen by the ideal participant
    - - Implications
        
        Shouldn't be bias involved as it is a numerical method and objective
        
        May not be generalisable as it may still not represent all people
      - Example- Put all students in alphabetical order and choose every 10th one
      - Having access to the target population, you would put them in an order (usually alphabtical) and choose evey nth person
    - - Implications
        
        Most generalisable type of sampling as it represents every subgroup in the target population
        
        No researcher bias
      - Example- If the target population were nurses, and 70% of them were female, the sample would also have to be 70% female
      - A complex form of sampling where a researcher aims to reflect key characteristics of the strata in the target population
      - strata are smaller groups of the population organized based on the shared characteristics or attributes of the members in the group. The process of classifying the population into groups is called stratification
- - - - null hypothesis- states that the variables in a certain hypothesis have no relationship to one another
      - experimental hypothesis- states that the variables in a certain hypothesis have a relationship with one another
      - Alternative hypothesis- used with non experimental methods, such as observations or self report studies, no IV or DV
      - Directional hypothesis- predicts the direction of the results, that one level of the IV does more less ect things that the other one to the DV. usually used when there is previous research results to take into account
      - Non- directional hypothesis- less specific, states that there will be a difference in results but not in a particular direction. usually used when there's little existing research as results are hard to predict without it.
    - - You have to write it in the present tense
      - It has to be empirically testable
      - You have to write it in a declarative sentence
      - It has to contain all of the variables
      - It must contain three parts: the purpose statement, the problem statement, and the research question
      - needs to be written after the aim is decided, and after previous research has been reviewed
      - needs to be written clearly and fully operationalised
- - - - The variable that is measured (the research DEPENDS on it)
      - Example: researcher may measure reaction time in a drving simulator
    - - A variable other than the IV that might affect your DV (not ideal)
      - Reseacher tries hard to identify these and try and control or limit them, if one slips under the radar and affects the DV it becomes confounding
      - Examples: room temperature, time of day
    - - The variable that is manipulated by the researcher
      - Example: drinking 0.5 or 0.2 units of alcohol
      - The one that changes
    - - Type of extraneous variable that was not controlled and then affected the DV
      - Hard to establish cause and effect because the confounding variable could have caused certain results, not the IV. (not ideal)
      - Types of confounding/ extraneous variables
        
        Investigator effects
        
        When a researcher consciously or unconscously exerts an influence on the results of research
        
        Types
        
        The physical prescence of a researcher, perhaps in an interview, can effect responses due to conscious or unconscious bias of the participant, as there is somebody to judge
        
        Expectation effects: when a researcher is devoted to a certain outcome, it may skew thier interpretation of events to suit thier hypothesis (chldren play fighting or violent behaviour?)
        
        All investigator effects reduce internal validity :cry:
        
        How to reduce- double blind technique ensures that the researcher cannot be biased as they lack information
        
        Demand characteristics
        
        Change in behaviour can be conscious or unconcious
        
        Unnatural behaviour is a confounding variable, leads to inaccurate results and and reduced internal validity
        
        When pps use cues in an experiment to try and work out how the researcher wants them to behave, causes them to behave in a way they believe desirable in the research
        
        How to reduce
        
        Use independent groups so they they aren't exposed to multiple levels of the IV and are able to see the difference, and guess what is being researched
        
        Single blind so that they do not know what level of the IV they are exposed to (double blind also helps)
- - - - Aspects of the environment are controlled in an attempt to give all participants the same experience, often done in a lab
      - Strength- Controls for extraneous variables- Procedure is standardised allowing replicability
      - Weakness- Artificial environment may cause unnatural behaviour
    - - Takes place in the natural real world environment where the participants would already be such as school work or in public
      - Strength- Finding have higher ecological validity as its done in a real life environment- High realism so more natural behaviour
      - Weakness- lack of control so extraneous varibales may be affecting behaviour
  - - - Weakness- Researcher may lose objectivity as they become closer with pps (Zimbardo conformed to his role instead of remaining researcher only)
      - Strength- The researcher collects firsthand infromation and knowledge- Is able to build rapport with pps leading to more information gained and potentially more honesty and naturalistic behaviour
      - Researcher becomes inolved and is directly amongst the group of participants, often doing the experiment with them or the same thing as them
    - - Strength- Researchers maintain objectivity and focus
      - Weakness- Lack of relationship or rapport with participants (this is not essential though so)
      - The researcher is kept seperate from the participants and does not get involved with them
  - - - Participants are not aware of the observation and cannot see the researcher
      - Strength- No risk of changing behaviour due to observation, no demand characteristics or social desirability bias
      - Weakness- Participants do not give informed consent which is an ethical problem
    - - Participants can see the researcher and know they're being observed for research
      - Strength- Follows ethical guidelines of informed consent, people know they're being watched
      - Weakness- Risk of demand characteristics or showing social desirability bias
- - - - Post
      - Telephone
      - Internet
    - - Closed
        
        When the researcher determines a range of possible answers and allows the respondent to select one
        
        Best when straightforward factual information is required
        
        How often do you excercise, daily, weekly, monthly, never
        
        Produces quantitative data
      - Open
        
        Produce qualitative data
        
        Available answers are not restircted
        
        Peoples answers will largely vary making it harder to analyse
      - Leading
        
        Questions where choice of wording encourages a certain reply
        
        SHOULD BE AVOIDED
    - - +
      - _
- - - - Low reliability as results aren't replicable due to how unique case studies are
      - Results may be subjective as the subject and researcher will form a elationship over the time of the case study, it is also usually one or a small amount of researchers so there is room for investigator effects and researcher bias
    - - Allows study of conditions which aren't ethical to manipulate (seperation of families or accidents leading to brain damage)
      - Large amounts of detailed information- valuable insights into complex phenomena
      - High degree of realism in results
- - - - Evaluation
        
        Strength- unaffected by extreme scores
        
        Weakness- only takes into account 1 or 2 scores, not representative
      - When there are two scores in the middle add them up and divide by 2
      - Middle score after all data is ordered
    - - Evaluation
        
        Strength- unaffected by extreme scores
        
        Weakness- doesn't take all scores into account
      - The most frequently occurring score
    - - Evaluation
        
        Strength- most sensitive and representative as it takes all scores into account
        
        Weakness- can be distorted by extreme scores and become unrepresentative
      - The average
      - Add up all of the scores and divide the sum by the number of scores
  - - - Evaluation
        
        Strength- quick to calculate
        
        Weakness
        
        Doesn't give any idea of the distributions of values around the centre
        
        Seriously affected by extremely low or high scores as these are the only ones taken into account
      - Subtract the lowest score from the highest score and add one to allow for any rounding up or down that may have occurred in the data
      - The difference between the highest and lowest scores
    - - Evaluation
        
        Strength- takes into account all scores so is the most powerful measure of dispersion
        
        Weakness
        
        Because it is calculated around the mean it can be affected by extreme scores like the mean can
        
        More difficult to calculate than the range
      - Types
        
        Large SD- much variation in results, less consistency, more individual differences, more varied response to the IV
        
        Small SD- data is closely clustered around the mean, results are consistent, all participants responded similrly to the IV
      - A measure of the spread of data away from its mean
  - - - A statistic which has a value between +1 and -1
      - Meanings
        
        Positive
        
        r = +1 is a perfect positive correlation
        
        r = + 0.2 is a very weak positive
        
        r = + 0.9 is a very strong positive
        
        Anything above 0 is a positive correlation
        
        Negative
        
        Anything below 0 is a negative correlation
        
        r = -0.2 is a weak negative
        
        r = -0.9 is a strong negative
        
        r = -1 is a perfect negative correlation
        
        0
        
        Anything measures close to 0 shows no correlation
        
        Includes scores from -0.09 to +0.09
        
        both r= -0.02 and r= -0.06 show no correlation
      - expressed as r = + or - anything ranging from -1 to +1
    - - +
        
        Correlational analysis an indicate the strength and direction of a relationship which helps provide a pecise quantitative measure and inform the researcher
        
        Correlational analysis allows researchers to investigate things which couldn't be manipulated such as time spent away from parents affecting aggression, analysing existing data is more ethical than creating it
      - _
        
        Only establish the existence of a relationship, not cause and effect, this means we have to be wary when drawing conclusions from correlations
        
        Non linear relationships can't be analysed this way
  - - - To find the amount of pps who achieved a score of interest out of all pps do pps of interest/ total pps X 100
      - If 7 out of 18 students got a A grade you'd do (7/18) X100 to get 38.88, round this up to 39 and put the unit, 39%
    - - Do the increase/ original value
      - X by 100
      - Do new value - old value to get the increase
    - - Do decrease/ original value
      - X by 100
      - Do original value - new value to get the decrease
- - - - Presentation and display of quantitative data
        
        Histograms
        
        Frequency on y axis
        
        make use of raw interva/ ratio and ordinal data
        
        Shows the distribution of scores that are measured along a continuous scale
        
        Scattergrams
        
        Used for plotting correlations
        
        Shows a clear image of the correlation found whether it is weak strong pos or neg
        
        Bar charts
        
        Height of the bars indicates frequency
        
        Used with nominal data
        
        A series of vertical bars of equal width apart (GAPS INBETWEEN BARS)
        
        Tables
        
        Needs proper title and names for each row and column
        
        Should include a description of the data underneath
        
        Shows descriptive statistics
- - - - High scorers have pulled the mean towards the middle but the mode and median are unaffected by high scores
      - long tail is on the right side of the peak
    - - Lower scorers have pulled the mean to the left, median and mode unaffected
      - long tail is on the left side of the peak
- - - - What is the experimental design? (independent groups repeated measures matched pairs)
      - What type of data do i have? (nominal ordinal interval/ ratio)
      - Looking for a difference or a correlation/ relationship?
- - - - Written as P (equal to or less than)
      - Tells us there is a 5% probability that results occurred by chance
      - Tells us there is a 95% chance that results occured because of the IV
      - Regarded as the minimum level of significance in psychology as it is inbetween 1 and 10, one is too strict and the other not strict enough
    - - Written as P (equal to or less than)
      - Tells us there is a 10% probability that results occurred by chance
      - Tells us there is a 90% chance that results occured because of the IV
      - Causes type 1 errors (false positive) to be more likely as this significance level is not strict enough (optimistci)
    - - Tells us there is a 1% probability that results occurred by chance
      - Tells us there is a 99% chance that results occured because of the IV
      - Written as P (equal to or less than) 0.01
      - Causes type 2 errors (false negative) to be more liekly as it is too strict (pessimistic)