Quantitative approaches

1. Quantitative approaches J.Maclean SHCS

2. Quantitative research… • Deals with numeric measurement (ie. quantities) • Aims to test hypotheses, identify numerical difference between groups • Often on a larger scale than qualitative research • Often aims to “infer” ie to project findings from a sample on to a population

3. Quantitative research ‘a formal, objective, systematic process in which numerical data are used to obtain information about the world. This research method is used to describe variables, examine relationships among variables and determine cause-and-effect relationships among variables’. Burns & Grove (1995)

4. Design The research design provides the plan for answering research problems. The design becomes ‘the vehicle for hypothesis testing or answering research questions. The design involves a plan, structure and strategy’. LoBiondo-Wood & Harber (1998)

5. Design Design may be broadly divided into Experimental designs • true experimental • quasiexperimental Nonexperimental designs • Survey studies, observational studies • Studies of relationship / association

6. Experimental design • An intervention is manipulated by the investigator, under defined and controlled conditions. • The intervention’s effect is then assessed (effect of the independent on the dependent variable) • Comparison is made between the group exposed to the intervention, and a control group which has not been exposed.

7. Experimental design • Randomisation of sample is required – that is assignment of participants to experimental or control group using a randomising procedure.

8. Experimental design Involves use of pre- and post-test measurement. “Cause and effect” relationship may be tested Independent variable .. …has a presumed effect on the dependent variable • manipulated by the researcher • demonstrates cause

9. Experimental design Dependent variable the consequence or presumed effect that varies with the change in the independent variable • is not manipulated • is observed/measured and assumed to vary with changes in the independent variable • demonstrates effect

10. Experimental design • May be known in health research as “randomised controlled trial” • RCT – quantitative, comparative, controlled experiment, where investigators seek to measure and compare outcomes of 2 or more clinical interventions.

11. RCTs • Participants allocated at random to receive one of interventions • Control may be standard practice, a placebo, no intervention. • Eg. Study of new NSAI drug: Patients with arthritis randomly allocated to conventional drug, or new drug Effect quantified.

12. Quasi-experimental design • Used when full experimental control is not possible • Either control is not possible or randomisation is lacking • Does involve the use of an experimental group • Used to determine cause and effect, but confidence in the results is weakened

13. Non-experimental design Survey • Alternative approach if experiment inappropriate • Types of variables of interest can be opinions, attitudes, facts • Data collection from field of interest by methods such as questionnaire, interview.

14. Survey • Prospective/retrospective/longitudinal • an economical method to obtain a large amount of data • If sample is representative, data can give a reasonably accurate picture of the population • Information obtained may be superficial • Large scale and longitudinal studies may be logistically hard, and expensive

15. Design Non-experimental – association/correlation • Correlational design is used to examine the relationship between two or more variables • No attempts made to determine causation • Researchers investigate the strength of relationship between the variables

16. Prediction • May want to see if any variable has power of prediction • Following correlation study, select variables for a regression study • An independent variable is regressed on a dependent variable • Indicates whether one can “predict” another

17. Samples The sample relates to the population, which may be too large to study in its entirety. • Population – aggregate of people/objects which are the focus of interest, for example

18. Sampling • In practice we cannot study the entire population so get our information from a selection of units from the population • Sampling is the process of identifying a suitable sample in order to determine characteristics of whole population • Statistical inference is the process by which informed estimates of the population’s characteristics are made.

19. Sampling • Aim is to draw a representative sample so we can make generalisations, inferences about the target population • A “miniature” version of the overall population • An unrepresentative sample reduces the validity of the study

20. Sampling techniques • Random sampling – each unit of the population has a calculable chance of being selected • Non-random sampling – units of the population are selected on the basis of some kind of judgment

21. Random sampling • Unrestricted – numbers/ allocated to entire population; random numbers generated to select. • Each unit returned to population before each draw – so a number can be selected several times • Equal chance of selection

22. Random sampling • Simple – numbering as in “unrestricted”, but selected units are not returned to population before next draw • Equal chance of selection

23. Systematic and stratified random sampling • Systematic – choose start point at random then select every nth unit • NB is list truly random – eg not in alphabetical, chronological order? • Stratified – population subdivided into “strata” eg male:female • Select randomly from each but in proportions found in population

24. Cluster sampling Deals with dispersed population • Initially area to be covered is grouped together into clusters. • Clusters are then randomly sampled:

25. Non-random sampling • Convenience sampling – selection of units of population which are accessible/close at hand/available at a given time period. • Quota sampling – like stratified sampling but with no random element • Purposive sampling – selects sample which fulfils certain criteria but is not randomised

26. Sample size • Optimum sample size is that which allows correct inferences to be drawn with regard to population. • Larger the sample, closer to measuring population • BUT may be impractical, costly, unethical • Statistical techniques will give indications of accuracy

27. Sampling error • This is probability that any one sample from a target population, is not fully representative of that population. • It shows the potential mismatch between characteristics of sample versus the population - an inherent uncertainty in any sample

28. Statistical power • Measures likelihood of a study to produce a statistically significant difference between groups/subjects • If power is “low” then results may not give a true picture • Power calculation helps avoid a “Type II” error

29. Analysing quantitative data • Numeric data • Statistical approaches • Descriptive and often inferential statistics • Parametric or non-parametric approach?