Importance of Research Data
Importance of good quality data and
DATA ANALYSIS for Research Methods
INTRODUCTION
Conducting a survey is often a useful way of finding something out, especially when `human factors ' are under investigation. Although surveys often investigate subjective issues, a well-designed survey should produce quantitative, rather than qualitative, results. That is, the results should be expressed numerically, and be capable of rigorous 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
DATA ANALYSIS for Research Methods
INTRODUCTION
Conducting a survey is often a useful way of finding something out, especially when `human factors ' are under investigation. Although surveys often investigate subjective issues, a well-designed survey should produce quantitative, rather than qualitative, results. That is, the results should be expressed numerically, and be capable of rigorous 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
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