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Quantitative approaches

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Quantitative approaches J.Maclean SHCS Quantitative research Deals with numeric measurement (ie. quantities) Aims to test hypotheses, identify numerical difference ... – PowerPoint PPT presentation

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Title: 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 interventions 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 populations
    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 malefemale
  • 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?
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