Transcript Document
Student information pack: Validity Some key points which you may find helpful. High External Validity – Think about studies which have: High ecological validity – field studies Large sample sizes Low External Validity – Think about studies which have: Laboratory settings Small sample sizes High Internal Validity – Describe two studies : Single Blind Technique Double Blind Technique Standardised procedure Matched pairs design instead of independent groups Counterbalancing when using repeated measures Low Internal Validity – Describe two studies: Confounding variables Demand characteristics Experimenter bias Repeated measures Independent group Experimental Methods Validity If an experimenter fails to control extraneous variables then changes in the DV may not be due to changes in the IV – which means the findings would lack internal validity If the operationalisation of the variables does not measure what it intended to measure the findings would lack internal validity Experiments that take place under highly controlled artificial conditions ( in a lab for example) are often low in ecological validity. Experiments conducted a long time ago may lack in external validity as so it is hard to generalise the findings to today’s society THREATS TO INTERNAL VALIDITY Experimental Methods Confounding variables Demand characteristics Experimenter bias Repeated measures Independent groups Correlations Experimental Methods IMPROVING INTERNAL VALIDITY Reduce Demand Characteristics by using a Single Blind Technique Reduce Investigator effects by using a Double Blind Technique Reduce confounding variables through a standardised procedure Reduce participant variables by using a matched pairs design Reduce order effects through counterbalancing Observation Validity Problem? observer bias – that means whether your observations is influenced by your expectations or prior knowledge. i.e. If you think football fans tend to be quite aggressive this may lead you to ‘see’ more aggression than an observer who is more objective . Your ‘bias’ reduces the objectivity and validity of observations Improving validity : Observer bias is reduced by keeping the observers ‘naive’ about the aims of the research in order to prevent their expectations biasing their observations Self-Report Validity Face validity can also be used to demonstrate validity – the items on a questionnaire/interview/test should look like they are measuring what you intend to measure To improve validity the test should be revised by changing some of the questions or removing some to see if this improves the correlation with an existing measure. Correlation Validity Cannot conclude cause and effect as no manipulation of variables. You do not know if a caused b, b caused a or c caused both! EXTRANEOUS VARIABLE HOW DOES IT AFFECT VALIDITY? HOW CAN IT BE OVERCOME? Situational variables (anything to do with the environment of the experiment): time of day, temperature, noise levels etc Something about the situation of the experiment could act as an EV if it has an effect on the DV. For example, poor lighting could affect participants performance on a memory test Situational variables can be overcome by the use of standardised procedures which ensure that all participants are tested under the same conditions. Participants variables (anything to do with differences in the participants): age, gender, intelligence, skill, past experience, motivation, education etc. It may be that the differences between the participants cause the change in the DV. For example, one group may perform better on a memory test than another because they are younger, or more motivated. Participant variables can be completely removed by using a repeated measures design (the same participants are used in each condition). Matched pairs (participants in each group are matched) could also be used. Experimenter bias: this refers to how the behaviour and language of the experimenter may influence the behaviour of the participants. The way in which an experimenter asks a question might act as a cue for the participant. Leading questions from the experimenter may consciously or unconsciously alter how the participant responds. For example, the experimenter may provide verbal or non verbal encouragement when the participant behaves in a way which supports the hypothesis. Investigator effects can be overcome by using a double blind technique. This is when the person who carries out the research is not the person who designed it. Demand characteristics: There could be something about the experimental situation or the behaviour of the experimenter (see investigator effects) which communicates to the participant what is “demanded” of them. The structure of the experiment could lead the participant to guess the aim of the study. For example, participants may perform a memory test, be made to exercise, and then given another memory test. This may lead the participants to guess that the study is about the effect of exercise on memory, which may cause them to change their behaviour When designing a study, it is important to try and create a situation where the participants will not be able to guess what the aim of the study is. Participant effects: participants are aware that they are in an experiment, and so may behave unnaturally. They may be overly helpful and want to please the experimenter. This leads to artificial behaviour. Alternatively, they may decide to go against the experimenter’s aims and deliberately act in a way which spoils the experiment. Again, by designing a study so that the participants cannot guess the aims, participant effects can be reduced.