OTHY305 Wk 1.

OTHY305 Wk 1.

1. Categorical (Qualitative)

   • Involves the classification of fate into categories or characteristics which have no order.
   • E.g colour of care, colour of eyes, mode of transport to university, quality of services, types of hand-splints.

2. Numerical (Quantitative)

   • Involves counting numbers.
     • Discrete: whole numbers. e.g number of children in therapy group. think: cannot have a half.
     • Continuous: Part number (decimals fraction). E.g hours of practice of therapy.

3. Population -Sample.

   • Population: includes everyone in the entire population.
   • Sample: is like a window to the population and takes a selection of participants that reflect the entire population.

4. Sampling methods

   • Probabilistic sampling
     • Simple random sampling : everyone has an equal chance of bing selected. E.g. use a table of random numbers or computers generated random numbers.
     • Systematic random sampling: take every 10th person from a sample, but remember to start at a random place and not the beginning.
     • Stratified random sampling: divide the population into state tan take a simple random sample from each sup-group.
   • Non-probalistic sampling
     • Convenience (opportunity) sample: the sample is drawn from a population that is accessible e.g. sample of participants from local paediatric hospital currently undergoing OT.
     • Snowballing sampling: existing study subjects are used to recruit more, e.g through social media.

5. Variables: a characteristic of a population that can take different values. An example of population is "Year 2016 ACU OT students. Variables for this population can include age, gender, weight, height and hobbies etc.

   • Indépendant Variable: does not depend on the value of another. presumed to cause changes in another variable. E.g in randomised control trail of the GM's participants were classified into three groups: cardiac, surgical and control.
   • Dependant variable: does depend on the value of another, changes because of another variable an effect or outcome variable.
   • Confounding variable: alternative explanation for the cause of the outcome. The confounder is related to both independent and dependant variables and is the reason to cause 'confusion'

6. Descriptive Vs Inferential statistics.

   • Descriptive: are used to provide a summary or description of what was observed in a given variable and usually what your first read in a paper.
   • Inferential: the word comes from 'infer' meaning to conclude, and uses patterns in the sample data to draw inferences about the population being represented.

7. Quantitative research: methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques.

   • Strengths: -Provides quantitative numerical data. - large numbers of people can be investigated. - data collection using some quantitative methods is relatively quick. - data analysis is relatively less time consuming.