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Experimental (incorporating: Multivariate experimental comparison, Quantitative data collection, Intervention, Quasi-experimental, Cross-sectional) Quantitative data collection Experiments tend to focus on quantitative data, i.e. information in the form of numbers of some kind. It is perfectly possible for qualitative data to be gathered as well, i.e. information in verbal form, though usually in experiments this is seen as of secondary importance compared with quantitative data. Quantitative data do have some disadvantages in that much of the 'richness' of human psychological processes is necessarily lost in reducing it to numerical form. However, there are also many advantages to these forms of data (which is why they have played such a large role in psychological research). Firstly, it can be easier to compare quantitative data from different researchers who are using the same measures, compared with qualitative research, where data can not be 'standardised' in the same manner. Once data are in quantitative form, all sorts of mathematical and statistical manipulations can be employed, giving rise to possibilities that simply don't occur with qualitative research (which does, of course, has its own strengths, as described in the 'methods' section describing these forms of data). For example, average (or mean) scores can be worked out (and perhaps used to compare different groups of people, or between experimental conditions). Statistical tests can be used to calculate whether differences between particular scores are likely to reflect a genuine psychological phenomenon (or are just due to random fluctuations in the data). Other statistical techniques can be used to see if different aspects of data are associated with each other (or 'correlated'). The power of quantitative data can be very easily seen with sciences such as physics, which has given human beings great understanding and control over our natural world. Many psychologists over the past century or so have aspired to placing psychology on the same kind of footing. Experimental Method

Experiments have been the most commonly used psychological method over the last century of psychological research. It aims to discover if there are 'cause-effect relationships' between variables by changing one variable (the 'independent' variable), and measuring the results of this on another psychological factor (the 'dependent' variable). While doing this, the experimenter will attempt to control all other variables that may affect the results, so that whatever changes occur can be explained in terms of the effect of the independent variable. There is a clear distinction here from observational methods, in that this method is based on a deliberate intervention by the researcher. Experiments can be done in natural settings or in laboratories, though because of the need to control other factors (not easy to do 'in the field'), the majority of experimental research has been done in laboratory settings. ('Laboratory', for a psychologist, often just refers to a room with a computer.) There is something of a trade-off here for a psychologist – the more 'controlled' the setting is, the more certain the psychologist can be that the results are due only to changes in the independent variable and nothing else. However, the very measures taken to reach this careful control can result in an environment unlike 'everyday life'. There is then the question, 'will the results still apply outside the laboratory, in everyday life?' (which is, after all, the usual aim of psychological research). This has been a particularly strong issue for social psychology (e.g. when looking at how people in group come to make decisions), where the socio-cultural setting is clearly going to be a significant component. It is arguably less of a problem with cognitive psychology, where memory research, for example, has benefited from being able to isolate particular memory processes from their everyday contexts.

Within the general field of experimental research, there are a number of specialised methods, such as: Multivariate experimental comparison. Multivariate approaches are designed to assess the affects of multiple variables simultaneously, instead of just looking at a single variable. This is very important in dealing with complex psychological processes which often have more than one cause. Quasi-experimental, Experiments involve two or more experimental conditions. Ideally, researchers will have full control over who is allocated to the different conditions, e.g. by randomly allocating people to one or other condition. However, with quasi-experimental designs, this control is limited in some way. An example might be research into gender differences, – people are already either men or women, so obviously can't be randomly allocated to the different conditions. Another example might involve looking at people's psychological reactions to experiencing a rail crash (compared with a control group who weren't in the crash). Again, the participants aren't randomly allocated between the conditions. The possibility then arises that there is some other distinction between the groups apart from the chosen independent variable which is giving rise to any differences found in the results. In the second example above, perhaps train travellers in the rail crash are untypical in significant ways from the people in the control group (e.g. if it was a commuter train, there is likely to be an age bias, and possibly also class, gender etc.). One way to try and limit these effects is to use matching – the people in the control group may be chosen to match the people in the 'rail crash' group on age, class, gender and so on. The difficulty is that the researcher can never be certain to have matched all the variables that have an effect. However, for many phenomena (such as the examples above) this method may be the only way possible to study them experimentally. Cross-sectional Studies Cross-sectional research is the most commonly used survey research design. It can provide good descriptions of the characteristics of the groups on whom the research is done and the differences between them. With this method, groups of people are selected from different sections of society. For example, they can come from different age bands. The researchers then compare these different groups (at more or less the same moment in time), looking for developmental trends, or age-related changes.

This method is commonly used in developmental psychology, to provide data which can be used to examine developmental theories such as Piaget's theory of cognitive development, or Freud's theory of emotional and personality development. Researchers may also do cross-sectional studies with factors such as social class, gender, ethnicity or occupational group being the basis of the division. One potential disadvantage of cross-sectional studies is that researchers can't be sure two different groups are similar enough for direct comparison. This would mean that there may be other reasons they are different, apart from the one the researcher assumes accounts for the difference. The longitudinal method is designed to overcome this disadvantage.