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# HLSC647 Quantitative Research Methods

## Question:

In systematic sampling, we select every kth (or decided number of participants) member of apopulation from a list.

In cluster sampling, the sampling units are groups rather than individuals. For example if we want to compare all Australian schools of Exercise Science, the units to be sampled are “Schools”.

Non-probability sampling is any sampling method where some elements of the population have no chance of selection (these are sometimes referred to as 'out of coverage'), or where the probability (or chance) of selection cannot be accurately determined. The selection of elements is non-random. For example, convenience or opportunity sampling) is a type of non-probability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a population is selected because it is readily available and convenient. Another example of nonprobability sampling is snowball sampling, where existing study subjects are used to recruit more subjects into the sample. In such non-probability sampling, the results may not be representative of the population.

Determining the sample size (will be briefed later in the course)

Sampling and data collecting (will be briefed later in thecourse)

Consistency of the association: Consistency exists if the association has been  repeatedly reported by different researchers, different and independent research projects,and in different times.

Specificity: If for example an observed association is limited to certain persons, or certain types of diseases, then we may draw conclusions about possible causation. For example, AIDS is caused by being infected with HIV retrovirus, and the infection occurs in specific situations and contacts.

Temporality: Before we assume any possible causation, we must ask about temporal relationship of the cause and effect. For something to be a cause of another, it mustprecede it. For example, in order smoking-associated lung cancer to happen, the person first needs to be exposed to smoking. Cancer follows the exposure to smoking.

Biological gradient: Another important feature of causation is the presence of a doseresponse effect between the exposure cause and effect. In his paper, Hill gives the example of death rate from cancer rising linearly with the number of cigarettes smoked daily.

Plausibility: Causation is supported if a plausible biological explanation exists between the exposure cause and the outcome effect. For example, infection with HIV does indeed lead to AIDS and this is supported by a biological explanation.

Coherence: The findings should be logical and sound and should not contradict generally well known facts about the association being investigated.

Experiment: the strongest support for the causation hypothesis can be revealed through an experimental study design, for example an RCT.

Analogy: Often association can be suspected if another agent is likely to cause the outcome. For example, we know that rubella virus is teratogenic (if a pregnant woman is infected with the virus in a certain trimester). Since this virus is likely to cause malformations to an embryo or foetus, we are ready to consider possible similar teratogenic effects of another agent.

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[Accessed 28 May 2023].

My Assignment Help. 'HLSC647 Quantitative Research Methods' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/hlsc647-quantitative-research-methods/evidence-based-child-health.html> accessed 28 May 2023.

My Assignment Help. HLSC647 Quantitative Research Methods [Internet]. My Assignment Help. 2021 [cited 28 May 2023]. Available from: https://myassignmenthelp.com/free-samples/hlsc647-quantitative-research-methods/evidence-based-child-health.html.

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