By Norman Blaikie
What uncomplicated wisdom and abilities do amateur researchers in social technological know-how require? How can scholars be helped to over-come `symbol phobia' or `figure blindness'? This beneficiant and continually insightful e-book is designed for social researchers who want to know what tactics to take advantage of less than what conditions, in useful learn initiatives. It accomplishes this with out requiring an in-depth figuring out of statistical conception, but additionally avoids either trivializing strategies or resorting to `cookbook' suggestions. one of the key positive factors of the e-book are:- Accessibility- association of the vast, frequently bewildering array of tools of information research right into a coherent and basic scheme of class: forms of research and degrees of dimension- Demystification - the 1st bankruptcy unpacks regularly taken-for-granted strategies similar to `analysis', `data' and `quantitative'- place of tools in genuine learn problemsThe e-book is a victorious advent to the idea and perform of quantitative equipment. it's going to fast identify itself as crucial examining for college kids doing social learn during the social sciences.`With this booklet Norman Blaikie keeps his attractiveness because the best rapporteur and raconteur of social examine technique. With many different introductory texts, info research turns into simply an workout unto itself, and scholars (sometimes) learn how to plow through the motions with out quite understanding why. After operating with Blaikie's textual content, amateur researchers will be aware of why quantitative inquiry is necessary' - Ray Pawson, college of Leeds
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Extra info for Analyzing Quantitative Data: From Description to Explanation
Categorical data involve the use of numbers and not words, allowing for simple numerical calculations. According to the definitions being used here, categorical data are clearly quantitative. Transformations between Levels of Measurement It is possible to transform metric data into categorical data but, in general, not the reverse. g. g. ‘low’, ‘moderate’, ‘high’ and ‘very high’). Thus, interval-level data can be transformed into ordinal-level data. Something similar could be done with age (in years) by creating age categories that may not cover the same range, say, 20–24, 25–34, 35–54, 55+.
The core of all social research is the sequence that begins with the description of characteristics and patterns in social phenomena and is followed by an explanation of why they occur. Descriptions of what is happening lead to questions or puzzles about why it is happening, and this calls for an explanation or some kind of understanding. The two research questions stated in the previous subsection illustrate these two research objectives. To be able to explain why people differ in their level of environmentally responsible behaviour, we need to first describe the range in levels of this behaviour.
2. They differ in their degree of precision, ranging from the least precise (nominal) to the most precise (ratio). The different characteristics, and the range of precision, mean that different mathematical procedures are appropriate at each level. It is too soon to discuss these differences here; they will emerge throughout Chapters 3–6. However, a word of caution is appropriate. It is very easy to be seduced by the precision and sophistication of interval-level and ratio-level measurement, regardless of whether they are necessary or theoretically and philosophically appropriate.
Analyzing Quantitative Data: From Description to Explanation by Norman Blaikie