Research methods: Data analysis

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

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