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reserach methods in psychology - sampling, self report and types of…
reserach methods in psychology - sampling, self report and types of experiment
types of experiment
lab - where the IV is manipulated in controlled conditions and the effect on DV is recorded. good because all variables are controlled- scientific. it is easily replicable to check results to improve reliability. bad because there is a likely chance of demand characteristics because of the artificial situation- easy to guess the purpose. bad because there is low external validity/ ecological validity because results cannot be adapted to real life settings. experimenter bias- their expectations can affect results.
field experiment- the IV is still manipulated but in a real life setting. good because less likely to be subject to demand characteristics because it is dine in a real life setting. it also has high ecological validity. BUT bad because there is less control over variables- cannot control all variables if it is in real life. cannot be easily replicated to check reliability of results. ethics- when pps not aware they are in a an experiment- lack of informed consent. there also may be sample bias because the pps are not randomly allocated to groups.
Natural experiments- where the IV is not manipulated and varies naturally. good because no demand characteristics and high ecological validity but bad because no control so cant replicate it.
Quasi Experiment- where the IV occurs naturally- the researcher cannot manipulate it because it is something already set- like gender, having OCD etc. good because high ecological validity but bad because lack of control and replicability/
self- report techniques
Questionnaires- a set of written questions given to pps so they can record their responses. closed questionnaires- questions that only produce an answer that is yes or no, or a fixed response like 'usually, always, sometimes, never'. good because they are easy to quantify but bad because they restrict pps answers. Open Questions- allows pps to answer how they want to. good because allow freedom of expression and greater depth but bad because they are difficult to analyse.
Evaluation of Questionnaires- good because can get big samples, they are quick to do, there is a lack of investigator effects, they produce both qualitative and quantitative analysis- closed- quantitative and open- qualitative. As questionnaires use standardised questions (for everyone) they can be easily replicated. BUT pps may misinterpret questions- like there maybe problems with technical terms, emotive language or leading questions. biased samples- certain types of people will answer them- only those willing to spend time answering questions. there may be low response rates questionnaires are not suitable for sensitive issues. social desirability- may lie in order to give answers expected of them.
Designing Questionnaires: You need an exact aim, as it makes it easier to write the questions, so they can address that aim. Questionnaires should be short and to the point . You can also use example of previous questionnaires that have been successful to base your design on. Questions should be easy to understand, unambiguous and concise. A pilot study should be done to get detailed and honest feedback on your questionnaire design. Some questionnaires use measurement scales which involve statemnts on wich pps rate levels of agreement or disagreement- e.g doing exercise is good for you. 1- strongly agree. 2- agree. 3- unsure. 4. disagree. 5. strongly disagree.
Interviews - involve researchers asking face to face questions to participants. 3 types of interviews- 1. structured- involves identical closed questions being read to pps, with interviewer writing down answers. interviewers dont need much training, as they are easy to conduct. 2. Unstructured- involves an informal discussion on a particular topic. interviewers can explore interesting answers by asking follow up questions. more training is needed for this. 3. semi structured- involves combining structured and unstructured questions, producing quantitative and qualitative data.
Evaluation of Interviews: Complicated/ sensitive issues can be dealt with face to face and can make pps feel relaxed- especially in unstructured interviews. also any misunderstandingsof questions can be dealt with therfe and then. Semi- structured interviews produce qualitative and quantitative data- which may complement eachother. whilst structured interviews produce quantitative data which can be easily analysed The more standardised and structured the interbiew is, the easier it is to replicate. BUT- interviewer effects- where interviewers may consciously or unconsciously bias answers like by appearance- women may be uncomfortable talking about sex to a male interviwer. interviews are also subject to demand characteristics and social desirability bias. ethical issues- pps may not know true purpose of study and may reveal more than they wish. also interviews are not for people who have trouble talking about their feelinbs and opinions etc.
Who Would Make The Most Appropriate Interviewer: the sex and age of interviewer- may affect pp answers when about a sexual/ sensitive topic. Ethnicity- interviewers may have difficulty interviewing ppl from a different ethnic group to themselves. personal characteristics-and adopted role- Interviewers can adopt different roles within an interview setting, and use of formal language, accent and appearance can also affect how someone comes across to the interviewee.
Difficult and emotional questions- best left to the ned of the interview and the person is more relaxed. whereas the first questions are better for gaining factual info.
Sampling
Random Sampling: where evryone in the target population has a chance of being in the study. names / number are drawn out of a hat or randomly generated. good- unbiased selection + can generalise the results as it is representative. BUT bad- may not be representative because an unbiased selection doesnt guarantee an unbiased sample- all females may be selected. also, it is impractical because the people selected may not be available or want to take part and it may be hard to get details of the target population.
Opportunity Sampling- where pps are asked to take part because they are there. e.g when people approach you on the street. its good because it is easy to do- you just use people who are already available. with natural experiments, it usually has to be used as researchers dont have control over who is studied. Its bad because t may not be representative of everyone because if it was done in the street on a weekday, not everyone would be around then. also it may be similar to a volunteer sample because you are only getting the types of people willing tp take up their time to do this and they could decline aswell.
volunteer/ self selected sample- where pps will volunteer to participate in the study due to replying to an advert or something. It is good because it is easy to do- just put up a poster and wait for people to reply. also because they are willing to do it- the pss will take it seriously and wont sabotage it. BUT bad because there might be a small sample because the researcher may not get many replies , also it wouldn't be representative of everyone because they are people who are willing to give up their time to do this. there may also be demand characteristics because they want to try and please the researcher.
systematic sampling- involves taking every nth person from a list to create a sample. to work out what 'n' is you have to get the size of the target population and then divide it by the number of pps in the sample you want. e.g say if the target population Is 1000 and you want a sample of 20, you would do 1000/ 20=50. so would be very 50th person. good because its unbiased- making it more representative. it is also generalisable as it is representative of the population. BUT it is bad because of periodic traits- which are hidden set traits of a population- so if in a two- every 50th building was a flat with young ppl in- then this would not be representative. also, an unbiased selection doesnt guarantee an unbiased sample- e.g all females could be selected- which is not representative.
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OBSERVATIONAL DESIGN
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Behavioural categories- researchers use grid/ coding sheet to record behaviour- behavioural categories should reflect what is being studied-r when you code behaviours in the different categories- like sex- M (male) or observed behaviour= T (talking).
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INTER- OBSERVER RELIABILITY= a way of reducing observer bias - it is where observers consistently code behaviour in the same way