Quantitative Research

Quantitative research is used to answer questions that have numerical value answers. Quantitative research is also used to establish cause and effect relationships  between variables.

Quantitative research designs

  • Randomised controlled trial – considered to be the best design to establish cause and effect relationships. Key features of a RCT include a treatment arm/group and a control arm/group.
  • Quasi-experimental – similar to RCTs with no randomisation.
  • Cohort studies – follow a predetermined sample group to measure the incidence of outcomes. The purpose of cohort studies is to link an exposure to an outcome. Purely observational with no intervention from the researcher.
  • Case control studies – the retrospective form of a cohort study. Individuals with the desired outcome are chosen, with the researcher attempting to discover the exposure that the outcome can be attributed to. Highly prone to recall bias.
  • Cross sectional studies – used to determine the prevalence of an outcome within a specific group. Often conducted using surveys, cross sectional studies are common in healthcare due to being cheap and easy to conduct.

Types of data collection within quantitative research

  • Biophysical
  • Pre-existing data
  • Observation of behaviour
  • Self-reporting

Strengths and limitations of quantitative research 

Strengths:

  • Data can be interpreted using statistical analysis
  • Can establish cause and effect relationships
  • Computer software available to analyse data – saves time and helps to minimise risk of human error
  • Easy to replicate and generalise

Limitations:

  • Do not reflect real life due to the high control applied.
  • Reductionist – simplifying complex situations into simpler versions
  • All confounding variables cannot be controlled
  • Lacks breadth within data

Terminology associated with quantitative research

  • Internal validity – whether the results are based on the intervention or an unknown variable.
  • External validity/Generalisability – how well what is being measured can be generalised to the wider population.
  • Confidence interval – usually expressed as a percentage. Represents how certain the researchers can be that the mean for the entire population would fall within the identified range.
  • Hypothesis – a theory or idea that needs to be tested.
  • P value – a measure of the strength of evidence against the null hypothesis. a small p value < 0.05 indicates evidence against the null hypothesis, this is then rejected and an alternative hypothesis developed.
  • Independent variable – the variable manipulated by the researcher to measure its effect on the dependent variable.
  • Dependent variable – what the researcher is interested in measuring in the study.
  • Confounding variable – an outside influence that can affect the results of a study.

 

Love,

T x

 

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