Statistics VS Medical Statistics
Statistics are quantities or sets of quantities which one can calculate from observed data. Thus, unless they are ratios, statistics should be reported in units. Medical statistics is a subdiscipline of statistics. Medical statistics can assist researchers in answering healthcare-related challenging questions.
“It is the science of summarizing, collecting, presenting and interpreting data in medical practice, and using them to estimate the magnitude of associations and test hypotheses. It has a central role in medical investigations. It not only provides a way of organizing information on a wider and more formal basis than relying on the exchange of anecdotes and personal experience, but also takes into account the intrinsic variation inherent in most biological processes.”
Kirkwood, 2003.
Population VS Sample
In relation to statistics, the term population refers to a well defined group of subjects that a researcher chooses to investigate about a particular issue. The size of such a population may be known or unknown, but when the study population is too big to be investigated fully, sampling becomes needed.
A sample is a feasible number of subjects chosen to represent a population, thus, the sample involved in the study needs to be as representative as possible to the target population. This can be achieved by:
- selecting an adequate sampling population
- using randomly selected participants rather than convenience sampling
Simple Random Sampling
Simple random sampling is a sampling method in which all members of a population have an equal chance of being chosen to participate in the study sample.
Stratified Random Sampling
In stratified random sampling, the population is stratified into defining blocks eg. gender and age.
Weighted Sampling
In weighted random sampling the subjects are weighted and the probability of each item to be selected is determined by its relative weight. This allows the sample to be more representative of the population.
Cluster Sampling
In cluster sampling, random groups of individuals are recruited for the study sample.
Convenience Sampling a.k.a. Opportunity Sampling
In this type of sampling, no consideration is taken with regards to representation. Thus, all members of a population that a researcher can access have the opportunity to be recruited.
Snowball Sampling
When recruiting members into a sample population becomes difficult, researchers revert to snowball sampling, where recruits are asked to suggest friends who may be willing to participate in the study.
Sampling Used in Qualitative Studies
Sampling used in qualitative studies is usually either purposeful sampling or theoretical sampling:
- PURPOSEFUL SAMPLING – the researcher seeks individuals who can provide the required data
- THEORETICAL SAMPLING – the researcher uses a sampling method which, although similar to purposeful sampling, also includes changing and/or adapting the participants’ selection throughout the study based on results obtained from previous participants
NOTE: sample size does not matter in qualitative studies, since the aim is to acquire in-depth understanding of a phenomena.
Data Collection Variables in Medical Statistics
Variables are characteristics, numbers, or quantities which can be measured or counted. Some examples of variables include age, sex, blood pressure results, oxygen saturation levels etc.
Categorical Variables a.k.a. Qualitative Variables
Data collection in qualitative studies typically takes place during in-depth interviews such as one-to-one interviews or focus group interviews, and in some cases, non-structured observation may also be involved.
Categorical variables give qualitative information about the subject being investigated. Thus, possible responses in this variable are not numerical in nature, but instead are different categories related to the subject.
Categorical variables can also be divided into two:
- Nominal Variable – a variable with a number of categories eg. occupation
- Binary Variable – a variable with only two possible responses eg. yes or no
Continuous variables a.k.a. Quantitative Variables
Continuous variables give quantitative information about the subject in question. Thus, continuous variable responses can be any quantities within a set interval of values. Some examples would be age and BMI.
Data collection in quantitative studies may include:
- readily available data such as data related to hospital activity, registers, prevalence and determinants
- self-administered questionnaires which may include numerical scales
- structured interviews through phone, electronic media, or face to face interviews, all of which allow an element of explanation and feedback between the researcher and the participant
- structured observation which typically happen during observation schedules within a particular setting
Ordinal Variables a.k.a. Discrete Variables
Ordinal variables give limited quantitative information because responses achieved are numerically related to each other, yet have to be one within a limited number of values.
Data Analysis
Descriptive Statistics
Descriptive statistics feature a summary of data in a clear, concise and easy-to-understand way, usually through a numerical approach.
Inferential Statistics
Inferential statistics are statistics which, after being calculated from a sample, inferences are made on the original population using the same statistics.
Reference
Kirkwood, Betty R. (2003). essential medical statistics. Blackwell Science, Inc., 350 Main Street, Malden, Massachusetts 02148–5020, USA: Blackwell. ISBN978-0-86542-871-3.
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Claire is keen about public education on health-related subjects as well as holistic patient-centered care. She is also passionate about spreading awareness on the negative effects that domestic abuse leaves on its victims’ mental, emotional, social and physical wellbeing.
Claire aspires to continue studying following completion of her Nursing Degree, because she truly believes in lifelong education.
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