Introduction to quantitative and qualitative research

Editor's Notes

• #8: The Role of the Researcher The researcher can be either involved, or external, detached. These two positions, again, tend to link to the ontology and epistemology, with the positivist approach leading to a detached view, and the social constructionists tending towards the researcher being part of the world and therefore influencing, and being influenced by, events. Choices and Trade-Offs The choice of any particular research design, from ontology, through epistemology to methodology and then methods and techniques, involves trade-offs. All of the main research traditions have strengths and weaknesses. The most important aspect of designing your research is what you want to find out. Whatever methods you use, together with their underpinning philosophy, must answer your chosen research questions. Find more at: http://www.skillsyouneed.com/learn/research-methods.html#ixzz40KrH4t80
• #12: Popper uses falsification as a criterion of demarcation to draw a sharp line between those theories that are scientific and those that are unscientific. The classical view of the philosophy of science is that it is the goal of science to prove hypotheses like "All swans are white" or to induce them from observational data. Popper argued that this would require the inference of a general rule from a number of individual cases, which is inadmissible in deductive logic.[2]:4 However, if one finds one single swan that is not white, deductive logic admits the conclusion that the statement that all swans are white is false. Falsificationism thus strives for questioning, for falsification, of hypotheses instead of proving them. For a statement to be questioned using observation, it needs to be at least theoretically possible that it can come in conflict with observation. A key observation of falsificiationism is thus that a criterion of demarcation is needed to distinguish those statements that can come in conflict with observation and those that cannot (Chorlton, 2012). Popper chose falsifiability as the name of this criterion.
• #13: Have I made any Type I (false positive – i.e. rejecting null hypothesis incorrectly) or Type II errors (false negative – i.e. rejecting alternate hypothesis incorrectly)?
• #14: When most people say average, they are talking about the mean. It has the advantage that it uses all the data values obtained and can be used for further statistical analysis. However, it can be skewed by ‘outliers’, values which are atypically large or small. As a result, researchers sometimes use the median instead. This is the mid-point of all the data. The median is not skewed by extreme values, but it is harder to use for further statistical analysis. The mode is the most common value in a data set. It cannot be used for further statistical analysis. The values of mean, median and mode are not the same, which is why it is really important to be clear which ‘average’ you are talking about. The range is the difference between the largest and smallest values. Researchers often quote the interquartile range, which is the range of the middle half of the data, from 25%, the lower quartile, up to 75%, the upper quartile, of the values (the median is the 50% value). To find the quartiles, use the same procedure as for the median, but take the quarter- and three-quarter-point instead of the mid-point. The standard deviation measures the average spread around the mean, and therefore gives a sense of the ‘typical’ distance from the mean. The variance is the square of the standard deviation. They are calculated by: calculating the difference of each value from the mean; squaring each one (to eliminate any difference between those above and below the mean); summing the squared differences; dividing by the number of items minus one. This gives the variance. To calculate the standard deviation, take the square root of the variance. Find more at: http://www.skillsyouneed.com/num/simple-statistical-analysis.html#ixzz40Puk0Bhw
• #15: If you get it wrong you risk using an incorrect statistical procedure or you may use a less powerful procedure. Non-paramteric statistical procedures are less powerful because they use less information in their calulation. For example, a parametric correlation uses information about the mean and deviation from the mean while a non-parametric correlation will use only the ordinal position of pairs of scores. The basic distinction for paramteric versus non-parametric is: If your measurement scale is nominal or ordinal then you use non-parametric statistics If you are using interval or ratio scales you use parametric statistics. There are other considerations which have to be taken into account: You have to look at the distribution of your data. If your data is supposed to take parametric stats you should check that the distributions are approximately normal.The best way to do this is to check the skew and Kurtosis measures from the frequency output from SPSS. For a relatively normal distribution: skew ~= 1.0kurtosis~=1.0 If a distribution deviates markedly from normality then you take the risk that the statistic will be inaccurate. The safest thing to do is to use an equivalent non-parametric statistic. www.csse.monash.edu.au/~smarkham/resources/scaling.htm - Nominal, ordinal etc.
• #17: Significance level of a statistical hypothesis test is a fixed probability of wrongly rejecting the null hypothesis H0, if it is in fact true. The probability value (p-value) of a statistical hypothesis test is the probability of getting a value of the test statistic as extreme as or more extreme than that observed by chance alone, if the null hypothesis H0, is true. Small p-values suggest that the null hypothesis is unlikely to be true. The smaller it is, the more convincing is the rejection of the null hypothesis. It indicates the strength of evidence for say, rejecting the null hypothesis H0, rather than simply concluding "Reject H0' or "Do not reject H0". The power of a statistical hypothesis test measures the test's ability to reject the null hypothesis when it is actually false - that is, to make a correct decision. Ability to detect a difference.
• #19: If the null hypothesis is true, you expect F to have a value close to 1.0 most of the time. A large F ratio means that the variation among group means is more than you'd expect to see by chance. You'll see a large F ratio both when the null hypothesis is wrong (the data are not sampled from populations with the same mean) and when random sampling happened to end up with large values in some groups and small values in others. The P value is determined from the F ratio and the two values for degrees of freedom shown in the ANOVA table.  Degrees of freedom - http://ron.dotsch.org/degrees-of-freedom/ to describe the number of values in the final calculation of a statistic that are free to vary. partial eta squared = measure of effect size. .02 ~ small .13 ~ medium .26 ~ large
• #23: owever, there are some pitfalls to qualitative research, such as: If respondents do not see a value for them in the research, they may provide inaccurate or false information. They may also say what they think the researcher wishes to hear. Qualitative researchers therefore need to take the time to build relationships with their research subjects and always be aware of this potential. Although ethics are an issue for any type of research, there may be particular difficulties with qualitative research because the researcher may be party to confidential information. It is important always to bear in mind that you must do no harm to your research subjects. It is generally harder for qualitative researchers to remain apart from their work. By the nature of their study, they are involved with people. It is therefore helpful to develop habits of reflecting on your part in the work and how this may affect the research. See our page on Reflective Practice for more. Find more at: http://www.skillsyouneed.com/learn/quantitative-and-qualitative.html#ixzz40SxYY76I
• #25: 1. Content Analysis Here, you start with some ideas about hypotheses or themes that might emerge, and look for them in the data that you have collected. You might, for example, use a colour-coding or numbering system to identify text about the different themes, grouping together ideas and gathering evidence about views on each theme. 2. Grounded Analysis This is similar to content analysis, in that it uses similar techniques for coding. However, in grounded analysis, you do not start from a defined point. Instead, you allow the data to ‘speak for itself’, with themes emerging from the discussions and conversations. In practice, this may be much harder to achieve because it requires you to put aside what you have read and simply concentrate on the data. Some people, such as Myers-Briggs 'P' types, may find this form of analysis much easier to achieve than others. 3. Social Network Analysis This form of analysis examines the links between individuals as a way of understanding what motivates behaviour. It has been used, for example, as a way of understanding why some people are more successful at work than others, and why some children were more likely to run away from home. This type of analysis may be most useful in combination with other methods, for example after some kind of content or grounded analysis to identify common themes about relationships. It’s often helpful to use a visual approach to this kind of analysis to generate a network diagram showing the relationships between members of a network. 4. Discourse Analysis This approach not only analyses conversation, but also takes into account the social context in which the conversation occurs, including previous conversations, power relationships and the concept of individual identity. It may also include analysis of written sources, such as emails or letters, and body language to give a rich source of data surrounding the actual words used. 5. Narrative Analysis This looks at the way in which stories are told within an organisation or society to try to understand more about the way in which people think and are organised within groups. There are four main types of narrative: bureaucratic, which is highly structured and logical, and often about imposing control; quest, where the ambition is to have the most compelling story and lead others to success; chaos, where the story is lived, rather than told; and postmodern, which is rather like chaos narratives, in that it is lived, but where the ‘narrator’ is aware of the story and what they are trying to achieve. 6. Conversation Analysis This is largely used in ethnographic research. It assumes that conversations are all governed by rules and patterns which remain the same whoever is talking. It also assumes that what is said can only be understood by looking at what went before and after. Conversation analysis requires a detailed examination of the data, including exactly which words are used, in what order, whether speakers overlap their speech, and where the emphasis is placed. There are therefore detailed conventions used in transcribing for conversation analysis.
• #30: http://www.crec.co.uk/docs/Trustworthypaper.pdf Lincoln and Guba's Evaluative Criteria Lincoln and Guba posit that trustworthiness of a research study is important to evaluating its worth.  Trustworthiness involves establishing: Credibility - confidence in the 'truth' of the findings Transferability - showing that the findings have applicablity in other contexts Dependability - showing that the findings are consistent and could be repeated Confirmability - a degree of neutraility or the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest. http://www.qualres.org/HomeLinc-3684.html