DATA ANALYSIS for Research Methods
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 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.
THE NEED OF DATA
Most research projects need data in order to answer a proposed research problem. The data that need to be acquired, and the sources of such data, must be identified as a matter of utmost importance. No amount or depth of subsequent data analysis can make up for an original lack of data quantity or quality. Research problems and objectives (or hypotheses) need to be very carefully constructed and clearly defined, as they dictate the data that need to be obtained and analyzed in order to successfully address the objectives themselves. In addition, the quantity of data, their qualities, and how they are sampled and measured, have implications for the choice and effectiveness of the data analysis techniques used in subsequent analysis. • Most research requires data and data analysis.
• Data acquisition is of utmost importance and considerable effort should be made to obtain or generate good data. • Good data are data whose characteristics enable the research objectives to be met. • Data of poor quality or undesirably low quantity will lead to unsatisfactory data analysis and vague results. • The characteristics of the data, particularly their type, quantity, and how they were sampled, constrain the choice of data analysis techniques able to be used on the data. • Data analysis can only be as good as the original data allow
Developing Conceptual Frameworks for DATA collection
Experience suggests that when developing the research questions it is very beneficial to also diagram the problem or topic. This is often called a conceptual framework. According to Miles and Huberman (1994), “A conceptual framework explains, either graphically or in narrative form [diagrams are much preferred], the main things to be studied - the key factors, constructs or variables...