Techniques Of Data Collection

The data obtained from a study may or may not be in numerical or quantitative form, that is, in the form of numbers. If they are not in numerical form, then we can still carry out qualitative analyses based on the experiences of the individual participants. If they are in numerical form, then we typically start by working out some descriptive statistics to summarise the pattern of findings. These descriptive statistics include measures of central tendency within a sample (e.g. mean) and measures of the spread of scores within a sample (e.g. range). Another useful way of summarising the findings is by means of graphs and figures. Several such ways of summarising the data are discussed later on in this chapter. In any study, two things might be true: (1) there is a difference (the experimental hypothesis), or (2) there is no difference (the null hypothesis). Various statistical tests have been devised to permit a decision between the experimental and null hypotheses on the basis of the data. Decision making based on a statistical test is open to error, in that we can never be sure whether we have made the correct decision. However, certain standard procedures are generally followed, and these are discussed in this chapter. Finally, there are important issues relating to the validity of the findings obtained from a study. One reason why the validity of the findings may be limited is that the study itself was not carried out in a properly controlled and scientific fashion. Another reason why the findings may be partially lacking in validity is that they cannot readily be applied to everyday life, a state of affairs that occurs most often with laboratory studies. Issues relating to these two kinds of validity are discussed towards the end of the chapter. QUALITATIVE ANALYSIS OF DATA There is an important distinction between quantitative research and qualitative research. In quantitative research, the information obtained from the participants is expressed in numerical form. Studies in which we record the number of items recalled, reaction times, or the number of aggressive acts are all examples of quantitative research. In qualitative research, on the other hand, the information obtained from participants is not expressed in numerical form. The emphasis is on the stated experiences of the participants and on the stated meanings they attach to themselves, to other people, and to their environment. Those carrying out qualitative research sometimes make use of direct quotations from their participants, arguing that such quotations are often very revealing. There has been rapid growth in the use of qualitative methods since the mid-1980s. This is due in part to increased dissatisfaction with the quantitative or scientific approach that has dominated psychology for the past 100 years. Coolican (1994) discussed a quotation from Reason and Rowan (1981), which expresses that dissatisfaction very clearly: There is too much measurement going on. Some things which are numerically precise are not true; and some things which are not numerical are true. Orthodox research produces results which are statistically significant but humanly insignificant; in human inquiry it is much better to be deeply interesting than accurately boring. Many experimental psychologists would regard this statement as being clearly an exaggeration. “Orthodox research” with its use of the experimental method has transformed our understanding of attention, perception, learning, memory, reasoning, and so on. However, qualitative research is of clear usefulness within some areas of social psychology, and it can shed much light on the motivations and values of individuals. As a result, investigators using interviews, case studies, or observations often make use of qualitative data, although they do not always do so. Investigators who collect qualitative data use several different kinds of analysis, and so only general indications of what can be done with such data will be presented here. However, there would be general agreement among such investigators with the following statement by Patton

Techniques Of Data Collection

Basic requirements for scientific data are that it should be reliable and impartial. In Sociology these conditions are hard to meet. Yet numerous methods are used to minimize errors in data. Some of the commonly used sources in collecting data are:

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