Quantitative research traditions

1. Quantitative research traditions How many versus how well

2. Aim To understand the role of quantitative research

3. Objectives • To understand the principle of research methods adopting a quantitative approach, basically involving experimental design • To interpret the role of quantitative research • To comment on the strengths and weaknesses of quantitative research

4. What is research? (perhaps) consisting of The generation ofinformationandunderstanding New conceptsNew modelsNew theories as opposed to So we need evidence unsupported opinion as opposed to anecdote validity Determinants are reliability generalisability

5. Quantitative research traditions • Focus predominantly (though not exclusively) on experimental design. A good example might be a survey questionnaire • Experimental design has long been perceived as the gold standard for medically-orientated research

6. Why and when do we prefer to talk in quantitative terms? • We like to quantify “things” because we have an awareness of scales • There’s an element of “value” attached to a quantity • When we’re “being professional” • When it’s necessary to get a message across

7. What is quantitative research? Burns & Grove (1987) “... a formal, objective, systematic process in which numerical data are utilized to obtain information about the world" and "a research method which is used to describe and test relationships and to examine cause-and-effect relationships".

8. Elements of quantitative research • Tests and experiments under controlled conditions • Cause and effect relationships • Gathering numerical data objectively • Results lend themselves to statistical analyses • Evaluation of results confirm or refute the original hypothesis

9. Elements of quantitative research can be described as positivist paradigms… • Quantitative data • Statistical analysis within definitive concepts (logical mathematics) • Atomistic (focusing on component parts) • Studying discrete relationships • Being of low complexity??? • Potentially seeking to explain laws • Requiring control subjects/sets • Ultimately, we should know what we don’t know!!

10. Samples for study Your “sample” is the group of cases (people, organisations, etc.) that you study in your research • It should be either comprehensive i.e. everyone or a selection that is truly randomised, inclusive and controlled • It should not be a limited study of 12!!!

11. In relation to our MMR theme… Studies by Gillberg & Heijbel (1998); Peltola et al (1998); Taylor et al (1999); Honda (2005) quantified the number of doses of MMR given over a period of time in a specific country or area and also quantified the number of cases of autism diagnosed in the population given the MMR vaccine. Then, using statistical testing to determine whether there was any evidence of an association between these variables (MMR and Autism), they tried to ascertain the strength of the association: this is quantitative research

12. Using Wakefield’s work on the MMR debate as our metaphor… • The information and understanding generated was highly questionable • His opinions have been heavily criticised • His claims were based on a study of 12 children • A Scandinavian study of 300,000 came to the opposite conclusion • Any comments about generalisability?

13. Validity of data generation • Do your data relate to the concepts you think they do? • What steps were taken to tackle these issues? Convince the reader that you’ve thought about this and confront the issue • Are your data appropriate? • Are the cases under study able to generate the data required for the study? • Could there be extrinsic factors influencing your results?

14. Validity • Are hidden factors at play? • You think you’re looking at the effects of X but other factors you’re unaware of are really what are affecting the situation • Your explanation applies to much of your data even though you sought negative instances/ alternatives

15. Reliability • Could another researcher repeat this work, using the same data, and end with the same result? • Have you consistently used standardised protocols and techniques?

16. Generalisability • Could your findings be applied to the wider population? • If not, why not?

17. Strengths of quantitative research • You can manipulate your numbers to create visual images e.g. graphs • Concepts can be “measured” and directly compared to previous/subsequent work • There may be direct correlation between cause and effect – this is the ideal • It may be possible to generalise towards external validity i.e. predict • Breadth of coverage of big population

18. Weaknesses of quantitative research • The whole may not be equal to the sum of the parts • Lack of depth i.e. looking at just one part of the whole • Defining everything, in terms of numbers, is risky when dealing with humans especially • Ultimately, everything is qualitative!

19. Prime reference Chapter 5 of… Hek G, Judd M and Moule P (2002)  Making sense of research: an introduction for health and social care practitioners (2nd edition) London: Continuum

20. Objectives • To understand the principle of research methods adopting a quantitative approach • To interpret the role of quantitative research • To comment on the strengths and weaknesses of quantitative research