Research methods: Data analysis
Qualitative analysis of data
Recording experiences and meanings
Distinctions between quantitative and qualitative studies Reason and Rowan’s views Reicher and Potter’s St Paul’s riot study McAdams’ definition of psychobiography Weiskrantz’s study of DB Jourard’s cross-cultural studies Cumberbatch’s TV advertising study A bulimia sufferer’s diary
Interpretations of interviews, case studies, and observations Some of the problems involved in drawing conclusions from non-experimental studies.
Studying the messages contained in media and communications.
Quantitative analysis: Descriptive statistics
What to do with all those numbers and percentages at the end of the study.
Measures of central tendency: Mean, median, and mode Levels of measurement Measures of dispersion: range, interquartile range, variation ratio, standard deviation Frequency polygon, histogram, and bar chart Types of data: nominal, ordinal, interval, ratio Statistical significance Tests of difference: Mann-Whitney U test, sign test, Wilcoxon test Scattergraphs, Spearman’s rho Test of association: chi-squared Questions to test experimental validity Varied definitions of ecological validity
Data presentation and statistical tests
When to use a chart or a graph. Which statistical test to choose and why.
Issues of experimental and ecological validity
Does your study test what you say it does? Has it any relevance to real life?
Writing up a practical
Presenting your results.
Style and approach Headings to use
Note: Cross references to “PIP” refer to the printed version of Psychology: An International Perspective by Michael W. Eysenck.
© 2004 Psychology Press Ltd
2 Research methods 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.
How would you define “validity”? How does it differ from “reliability”?
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...
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