Aqa research methods 1

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research methods

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  • 1. The Research Process.The Experimental method
  • 2.  Statements made by psychologists about behaviour need to have evidence to back them up. Evidence has more credibility if it is collected using recognised and/or scientific methods. Common sense or intuition or “everyone knows that” is not sufficient.
  • 3.  The research process starts with an observation or a question about an aspect of behaviour. From these a theory is constructed which can be developed into an hypothesis. The hypothesis is then tested to find out if it is supported or refuted.
  • 4. DATA THEORY HYPOTHESIS REFINE RESEARCH TO TESTTHEORY HYPOTHESIS
  • 5.  Control group - a group used as a baseline measure against which performance of the experimental group is judged. Single blind trial – an experiment where either the people conducting the experiment or the participants do not know which treatment the participants receive. Double blind trial – an experiment where neither the people conducting the experiment nor the participants know which treatment the participants have received.
  • 6.  A true experiment has three key features:1. The researcher manipulates an independent variable (IV) in order to investigate whether there is a change in a second variable, known as the dependent variable (DV)2. All other variables, which might influence the results, are either controlled, held constant or eliminated. Unwanted variables are called extraneous or confounding variables3. Participants (Ps) are ideally allocated to the experimental conditions randomly
  • 7.  Laboratory experiment – conducted in a artificial environment in an attempt to control all relevant variables except the IV. Field experiment – behaviour is studied in a natural environment, but the IV is still manipulated and it’s effect on the DV measured. Quasi / natural experiment – the IV is not directly manipulated by the researcher but occurs naturally, i.e. gender, age, whether a country has capital punishment or not.
  • 8. Strengths Weaknesses•High levels of control limit the •The environment is artificialeffects of confounding variables and so the results may lack ecological validity•Replication is possible to checkthe study’s results •Participants’ behaviour may change if they know they are•It is possible to establish causal being studied – this is known asrelationships between variables. demand characteristics. •Ethical issues are sometimes raised as deception is used.
  • 9. Strengths Weaknesses•More natural environment raises •Lower levels of control make itecological validity more likely that extraneous variables will affect the results,•Demand characteristics are thus causal relationships areavoided as participants are more difficult to establish.usually unaware of the studytaking place. •Ethical issues can be raised as participants do not know they are being studied.
  • 10. Strengths Weaknesses•It is possible to use this method •There is no control overto study relationships that it extraneous variables so it is verywould be unethical to difficult to identify causalmanipulate, e.g. The effect of factors.capital punishment on seriouscrime.
  • 11. Experimental Design
  • 12.  When deciding on an appropriate design, the researcher must consider:  the precise nature of the experimental task  how to control the relevant variables  the availability of participants Independent groups design  different participants are used in each condition of the experiment Repeated measures design  the same participants are used in each condition of the experiment Matched pairs design  each participant in one group/condition is carefully matched on all the variables considered to be relevant to the investigation with a participant in another group/condition
  • 13. Design Strength WeaknessIndependent No order effects Participant variablesMeasures differ between conditions Need more participantsRepeated Measures Need fewer Order effects – participants practice or fatigue No effect of effects. participant variablesMatched pairs Limits some of the Matching participants effects of participant can be difficult & variables. time consuming
  • 14. HypothesesIndependent & dependent variables Operationalisation
  • 15.  An hypothesis is a statement that can be tested to see if it is true. it is a prediction.  There are two types: a) the experimental / alternate hypothesis b) the null hypothesis
  • 16.  Thisis a general prediction. It does not give enough detail on which to base an investigation. “Does eating cheese before going to bed cause nightmares?” (research question)“To investigate the effect of eating cheese on nightmares” (aim of the study)
  • 17.  This gives enough detail for the investigation to be carried out. The component parts of the hypothesis are operationalised – stated in terms which make it clear how they will be measured. “Eating 100g of cheddar cheese an hour before going to bed will increase the number of nightmares reported by participants by a significant amount”
  • 18. Two-tailed or non-directional One –tailed or directional This is used when it  This is used when cannot be certain what previous research the results will be. suggests what the A will be affected by B outcome will be.  Look for the use of A will be changed by B adverbs such as There will be a more, faster, higher, de difference between A crease, increase etc. and B  A will be ...... than B There will be a  There will be a positive relationship between A correlation between A and B and B.
  • 19.  This assumes that there will be no effect in the population from which the samples are drawn. A will not be affected by B A will not be changed by B There will be no difference between A and B There will be no relationship between A and B “Eating 100g of cheddar cheese an hour before going to bed will have no significant effect on the number of nightmares reported by participants”
  • 20.  Remember “hypotheses don’t have to be true, they just have to be possible”
  • 21.  Thismeans ensuring that variables are in a form that can be measured. Itcan be easy to do – e.g. % or seconds or number of correct answers Itcan be very difficult – how can we measure helpfulness or aggression or happiness?
  • 22. “Drug A affects memory”compared with? measured how?how?administered how? when?
  • 23. A variable is anything in an experiment which can come in different forms or in different values. There are four to remember: a) Independent variables b) Dependent variables c) Extraneous variables d) Confounding variables
  • 24.  AnIndependent Variable (IV) is one which is manipulated (changed) to do the experiment.A Dependent Variable (DV) is one which is measured to obtain results.
  • 25.  Extraneous variables (EV) are anything (other than the IV) which could affect the results (DV). As far as possible these are controlled for fair testing. Confounding Variables are extraneous variables which cannot be controlled for various reasons: a) it may not be possible to control them b) it may be unethical to control them c) it may not be known precisely what they are.
  • 26.  Participant variables Individual differences – the unique characteristics of participants can act as an extraneous variable. This can be addressed by using a repeated measures or matched pairs design. If you have to use independent measures then randomly allocating participants to conditions goes some way towards balancing out individual differences.
  • 27.  Participant variables. Demand characteristics – aspects of the research situation can act as signals to participants about how they should behave. Depending on the participants motivation, this can have different effects, they can either behave naturally, try to co-operate, behave negatively (screw you effect) or try to present themselves in a positive light (social desirability effects). Strategies to address participant reactivity include a single blind technique, the use of placebos and the use of standardised instructions & procedures.
  • 28.  Research effects. Participants react differently to researchers because of biosocial characteristics (i.e.age, gender, ethnicity) and / or psychosocial characteristics (i.e interpersonal skills). This can act as a source of bias. Researchers can also pay more attention to things which fulfil rather than contradict their expectations; this is known as investigator effects and, in itself, can act as an extraneous variable. One technique that can be used to address this is the use of a double blind procedure – where neither the researcher nor the participants know the research aim and / or which condition they have been allocated to.
  • 29.  Situational variables Laboratory settings give researchers high levels of control but can also introduce other extraneous variables e.g. Low ecological validity - participants do not behave naturally in artificial settings. However, in field settings there are many potential extraneous variables that could influence results and cause the wrong conclusions to be drawn about the variables under investigation.
  • 30. Sampling
  • 31.  One of the main aims of a research study is to be able to generalise from small samples. Thereare three technical terms to think about: a) population b) target population c) sample
  • 32. Definition ExampleAll the possible members of Single mothersa group from which thesample will be taken
  • 33. Definition ExampleThe part of the population Single mothers with onefrom which the sample is child, who live inselected Cheltenham
  • 34. Definition ExampleThe group selected from 20 single mothers withthe target population for one child in Cheltenhamexperiment or study who answered the advertisement
  • 35. Samples need to be as representative aspossible so that we can use the findings fromthem to generalise to the population withoutbeing biased.There are many ways of sampling, including:a) random samplingb) opportunity samplingc) self-selected / volunteer samplingd) stratified sampling
  • 36.  Everymember of the target population has an equal chance of being selected. Methods: a) names in a hat b) random number tables c) computer generated random numbers
  • 37. Strengths LimitationsProvides the best chance of May be impractical,a mathematically unbiased particularly with largerepresentative sample. samples. May get a skewed sample that is unhelpful for the investigation, even though it is mathematically unbiased.E.g. the national lottery is random but can still producea sequence of 10,11,12,13,14.
  • 38.  Selecting participants that are available at the time and fit the criteria you are looking for. Methods: a) asking students in the common room b) asking family or friends or people at work c) asking people in the street.
  • 39. Strengths LimitationsQuick and convenient Biased on the part of the researcher who will beEconomical selective in the choice of participants.Frequently used Therefore it is likely to be unrepresentative which reduces the generalisability of the findings.E.g. asking fellow students in the common room toanswer a questionnaire about Saturday jobs.
  • 40.  Sample selected on the basis of the participants’ own action at arriving at the sampling point. There are two types of self-selected sample: a) volunteers b) people in a particular place being asked/tested about that place.
  • 41. Strengths LimitationsConvenient Biased on the part of the participant – volunteers areUsing volunteers can make different from non-it easier to ensure ethical volunteers, and peoplepractice. choosing a particular place e.g. a gym, may beNot biased by the different from those whoresearcher don’t, or who use it at a different time of day.Well motivated participants This reduces generalisability. e.g. responding to an advertisement, being in the common room when a study of usage of the common room is being undertaken.
  • 42.  Sample organised so that particular groups are selected in proportion to their size in the target population. Membership of each sub- group is selected randomly. Method: a) Count how many there are in each sub-group (e.g. year groups in school) and the overall total. b) Use e.g. 10% of each sub-group for your sample – picked randomly from the sub-group list.
  • 43. Strengths LimitationsReasonably simple to do Can be time consumingAn effort has been made to Need access to the wholemake it as representative target populationas possibleThe element of randomsampling increases thegeneralisabilitye.g. Using 20% each of first choice subject maths,psychology, French and drama students in the sixth formto complete a questionnaire on the usefulness ofGeneral Studies.
  • 44.  What factors influence the number of participants used? a) availability b) topic being researched c) expense Ifthe sample is too small it may be biased and therefore unrepresentative. Ifthe sample is too large it may smooth out interesting variations.
  • 45. Types of studies Types of data
  • 46.  There are five more useful methodological terms to learn. Cross-Sectional Studies Longitudinal Studies Snapshot Studies Qualitative Data Quantitative Data
  • 47. Participants of different ages or ethnic groups ornationality are studied at one point in time and comparedStrengths LimitationsImmediate results so Findings may quicklyconvenient become out of dateNo subject attrition Participant variables may distort findingsQuick and cheap Large sample needed Cohort effect may bias data
  • 48. The same group of participants are investigatedseveral times over a long period of time.Strengths LimitationsThe same participants are Participant attritionused which minimisesparticipant variables Difficult to generalise as only one groupGives the opportunity tocollect a lot of qualitative Difficult to replicateas well as quantitativedata. ExpensiveCan assess development May have to use many different researchers – inter-rater reliability can be an issue
  • 49. A study conducted at one point in timeStrengths LimitationsQuick to obtain results Cohort effects may reduce validityProvides a one-off view ofthe immediate situation Participant variables may distort findings Findings may quickly become out of date
  • 50. Data in numerical form, the results of measurementStrengths LimitationsCan be analysed using May be reductionist and/ordescriptive and inferential have low ecological validitystatistics Numbers can beInferential tests give the emotionally “cold”probability of the resultsoccurring by chance and May not tell you why/howthus give the confidence the findings have occurred.limits on the data andwhether the null hypothesisis accepted or rejectedHigh reliability, objectivedata
  • 51. Data that is not in numerical form.Strengths LimitationsMay give information on Hard to analyse –how/why the findings particularly statisticallyoccurred May lack objectivity if theInformation is rich and researchers are toodetailed involved with their participantsMay have high ecologicalvalidity Low reliability
  • 52. Studies using correlational analysis
  • 53.  Correlation is a technique for analysing data rather than a research method. It usually involves collecting data by some other means, i.e. observation / survey. The data are paired scores and the researcher generally looks for linear relationships between them. Such relationships can be illustrated visually in the form of a scattergram and as a statistic called the correlation coefficient (+1 to -1) The correlation coefficient indicates both the direction and the strength of the relationship( the number indicates the strength while the sign indicates the direction)
  • 54.  There are 3 types of Correlation Coefficients correlation: +1.0 Perfect Positive Positive Correlation +0.8 Strong positive Negative Correlation +0.2 Weak positive Zero Correlation 0 Zero correlation Most correlations fall -0.2 Weak negative somewhere between -0.8 Strong negative these. For example: -1.0 Perfect Negative
  • 55.  Strengths  Weaknesses Correlational analysis is  It is not possible to infer useful for revealing cause & effect as there is patterns in data where no manipulation of the IV manipulation would be  Even when relationships are unethical (e.g. In found it is not always attachment studies) possible to understand why Looking at scattergram the relationship has illustrations of data can occurred, other address some the (unidentified) variables may weaknesses identified such be responsible for the as when outliers are pattern observed. present or when the  Data can be skewed by relationship is not linear. outliers  Curvilinear relationships can be missed
  • 56. Observational techniques
  • 57.  In observation research, behaviour is observed and recorded and there is usually no deliberate manipulation of variables. Observational research can differ in several important ways, depending on:  the setting of the study, e.g. naturalistic or laboratory- based  the role of the researcher, e.g. participant or non- participant  the amount of structure imposed, e.g. use of a coding system to record instances of behaviour.
  • 58.  Naturalistic – involves the recording of spontaneously occurring behaviour in the participant’s own natural environment Controlled – involves the recording of spontaneously occurring behaviour but under conditions contrived by the researcher (i.e.Bandura)
  • 59.  Strength – high ecological validity, especially if the observer is hidden. Strength – can be more ethical than manipulating behaviour as in field experiments Weakness - Cannot infer cause & effect as there is no control over confounding variables Weakness – Replication is difficult as there are so many uncontrolled variables. Weakness – some studies raise ethical issues like invasion of privacy.
  • 60.  Strength – More control over the environment usually leads to more accurate observations Strength – This type of observation is more replicable as the environment is more controlled Weakness - If the subject is aware they are being observed you can get participant reactivity Weakness - Lower ecological validity than naturalistic observations and a higher probability of demand characteristics.
  • 61.  Structured observation – usually involves the use of a pre-determined system for assessing behaviour, e.g. recording behaviour using a pre-determined time schedule and/or using a behaviour checklist. Unstructured observation – this involves trying to record as much behaviour as possible, e.g. using video recording.
  • 62.  Strengths – checks can be made on inter- observer reliability using pilot studies. Strengths - Data is usually easier to analyse using quantitative methods Weakness – behaviour that occurs outside of the pre-determined time schedule can be missed. Weakness – behaviours may occur that do not fit into pre-determined categories and are therefore not recorded.
  • 63.  Strength– This is the most useful method when the behaviour being observed may be unpredictable Weakness – Results in large amounts of data that can be difficult to analyse. Weakness – behaviours that are most prominent are easily noticed whereas more important behaviours may be less visible or obvious to the observer.
  • 64.  The researcher can either become a member of the group being studied or observe from the outside. Strengths of participant observation – Very high ecological validity, especially if the researcher is undisclosed Extremely detailed data can be gained using this method Weakness of participant observation - Difficult for the researcher to remain objective and impartial Ethical problems arise when using an undisclosed researcher
  • 65.  Time sampling – Behaviour is recorded at discrete time intervals. i.e. every 30 seconds Advantages – Reduces the amount of time spent in sampling (easier to manage), which may improve accuracy Disadvantage – Behaviours may be missed if they occur between the discrete time intervals.
  • 66.  Event sampling – Key behavioural events are recorded every time they occur. Advantages – Reduces the chances that behaviours of interest will be missed Disadvantages – Other behaviours that are important but were not anticipated may be missed because they are not on the behaviour checklist.
  • 67.  Behaviour checklists / frequency grids – nominal data is scored in a tally chart for a range of behaviours, this produces quantitative data that is more easily analysed.1. Each category must be clearly defined so that it is understood in the same way by each observer.2. Each category should be mutually exclusive3. There should be enough categories to code all observed behaviours4. The system should be easy to use This will raise inter-observer reliability – the degree to which observers record the same things when doing the same observations. A pilot study should be under taken to test all aspects of the procedure, location and recording materials to ensure that they are suitable.
  • 68.  Weaknesses of coding schemes for behavioural categories:1. Observers can be affected by personal bias when recording behaviours.2. Participant reactivity can occur if participants know they are being observed.3. Coding schemes categorise human behaviour into a series of categories, thus can be considered reductionist.