The following notes are made from Ch11, Analysing Quantitative Data, Tim Philips, in Social Research Methods: an Australian perspective. (Ed) Maggie Walter. Oxford UP, South Melbourne, 2006 (2008).
This is a very useful chapter if you need help in establishing a way of talking about the datasets. It uses examples that are almost exactly the same as our assignment.
Primary data collection: 3 very important things.
1 Time: Spend a sustained period of time designing the survey.
2 Resources: Attain a large research grant to pay for survey administration.
3 Specialist knowledge: Get advice from different experts about the best way to undertake particular steps.
Secondary data collection
When designing your own research instrument for high quality data collection is not feasible, undertaking secondary data analysis is an alternative. this generally involves the reanalysis or additional analysis of publicly available survey data., eg within a social science data archive, like ASSDA (see links at bottom of page). Supporting such an archive contributes to the shared scientific method standards of professionalism, transparency and integrity.
Looking at a dataset from the Australian National Survey of Social attitudes 2003,the textbook introduces a research problem for a mini-anlalysis: whether the difference in among Australians in feeling about globalisation is connected with divergences in patterns of mass media usage.
I put this in bold because I had to return to it, and re-read their background briefing. The textbook reports positive perspectives in the mass media about globalisation. It then produced evidence from a 2001 study which indicated variables such as education level, internet usage and overseas travel as factors towards Australian peoples’ cool feelings about globalisation. At this stage, the textbook example suggested bringing mass media consumption into the equation, which had been left out in the 2001 study. And finally, they chose a set of particular variables in order to create a null hypothesis for the cross tab analysis.
[the rest of this post is really boring - i'm just reiterating key concepts and recording some detail about the examples used, but it may not make much sense without the textbook]
1) frequency tables – good for initial feel of where responses are located across response categories. eg, around 20% or 1 in 5 responses relied on abc/sbs, radio and newspapers for their most news and information, but 37% relied on commercial tv channels.
Using frequency tables to calculate Summary statistics:
2) measures of central tendency -
“Individual variable data is measured at different levels, building in complexity from nominal data, through to ordinal data and interval data to ratio data.” (p292)
Mean: the average of the scores. Used when variables are measured at interval or ratio level
Median: the middle in a set of ranked scores. Used for ordinal level variables.
Mode: the most frequent score in a set of scores Used for nominal level variables
3) measures of dispersion -
Standard deviation – a statistic that shows the spread of scores or variance around the mean.
Variance – a measure of the spread of scores.
Illustrating central tendency: Median response for “Large international companies are doing more and more damage to local business in Australia” was Agree.
Median response for “International organisations are taking away too much power form the Aust. govt” was “Neither agree nor disagree”.
Illustrating dispersion: Among the questions included one about total number of years of education. Although the mean for men was 13.28, slightly higher than women (12.96), the standard deviation of men was more widely spread out at 4.10 than women at 3.86.
Cross-tabular analysis
So far, we’ve looked at a subset of questions gauging pessimism towards globalisation, and (b) a single question tapping preferred source of information. But we want to be able to bring these questions in isolations together so we can bear upon the question of key concern: is media usage connected with disengagement form globalisation, and if so what is the link.
1. return to the key question of concern, ie, what is going to be your independent variable that you’ll check against a range of demographic variables? For this example:
Independent variable: A7 Which of the following sources of information would you say you rely on MOST for your news and information?
Response choices: Commercial TV, ABC/SBS, Radio, Newspapers, Internet, Talkback radio, News magazines, Friends and family.
Cross tab analysis provides us with a frequency distribution within the categories of the independent variable.
End summary:
Quantitative data analysis is at its strongest when:
• looking at complex relationships (cross-tab analysis)
• making inferences from samples to populations (chi-square test)
• examining grand claims within social theory and specifying the conditions under which they hold up
• adjudicating between competing theories. NB: It should be made clear though, that while in some cases quantitative analysis may function as an ”objective”‘ perspective to view potentially competing theories, it may not be appropriate. For example, a cost-benefit analysis of Catholicism vs paganism may produce some interesting data, but is it useful?
Quantitative data analysis is at it’s weakest when:
• variables are poor measures of concepts
• the status of vairables in causal chains is determined arbitrarily
• statistical methods are used that place too many demands on the data
• it is done in the absence of theory.
Final notes:
• High quality quantitative data analysis always takes place through a process of iteration. This is where the researcher must be creative and dynamic.
• There is never simply a ‘right way’ to analyse a quantitative dataset to derive an answer to a research question’ (Becker 1986)
• The research must figure out a persuasive and compelling analytic strategy. Try unleashing your ’sociological imagination’ (Mills 2000)
• A lot of it is about balancing convention and innovation. [sounds like design theory!] Perhaps established ways of doing things are appropriate. However, sometimes conventional approaches engender a sense of doubt within you (Bauman & May 2001), so you might be better off demonstrating your own version. In this way, imagination and intuition come to the fore as vital qualities for ’steering’ your data. Giddens (1990)
Useful links
Australian Social Science Data Archive (ASSDA) http://assda.anu.edu.au/analysis.html
Nestar Light http://assda224-100.anu.edu.au/nesstarlight/index.jsp
References
Becker, H. (1986) Writing for Social Scientists. Chicago: University of Chicago Press.
Bauman, Z. & May, T. (2001) Thinking Sociologically. 2nd edition. Oxford: Blackwell.
Giddens, A. (1990) The Consequences of Modernity. Stanford, CA: Stanford University Press.
Mills, C.W. (2000) The Sociological Imagination. 40th Anniversary Edition. Oxford: Oxford University Press.
Philips, T. (2008) ‘Ch11 Analysing Quantitative Data’. in Social Research Methods: an Australian perspective. (Ed) Maggie Walter. South Melbourne: Oxford UP.