Random Sampling Methods & Statistical Investigation Stages
Methods of Random Sampling & Stages of Statistical Investigation
Definitions
Sampling: The process of selecting a representative subset of individuals (a sample) from a larger set (a population) to accurately estimate the characteristics of the whole population.
Random Sampling: A sampling method where each member of the population has an equal and independent chance of being selected, ensuring a representative sample.
Random sampling allows for incomplete investigations, eliminating the need to study the entire population.
Basic Types of Random Sampling
Simple Random Sampling
Individuals are chosen randomly using a table of random numbers, giving each individual or subset an equal probability of selection.
Advantages:
- Minimizes bias and simplifies analysis.
- Provides a good indicator of population variance, making it easy to estimate accuracy.
Disadvantages:
- Vulnerable to error if the sample doesn’t reflect the population’s makeup.
- Can be cumbersome and tedious.
Systematic Sampling
Individuals are selected at regular intervals from an ordered list.
Disadvantages:
- Vulnerable to periodicities in the list.
- Difficult to quantify accuracy.
Stratified Sampling
The population is divided into distinct categories (strata), and a random sample is drawn from each stratum.
Advantages:
- Allows for inferences about specific subgroups.
- Provides more efficient statistical estimates.
- Data may be more readily available for individual strata.
- Different sampling approaches can be used for different strata.
Disadvantages:
- Requires selecting relevant stratification variables, which can be challenging.
- Not useful when there are no homogeneous subgroups.
- Can be expensive.
Cluster Sampling
The population is divided into clusters, and a random sample of these clusters is selected. All observations within the selected clusters are included in the sample.
Cluster vs. Stratified Sampling:
- Cluster sampling treats the cluster as the sampling unit, while stratified sampling analyzes elements within strata.
- Cluster sampling aims to reduce costs by increasing efficiency, while stratified sampling aims to increase precision.
Non-Random Sampling
Examples include judgmental sampling, paired sampling, and matching sampling.
Stages of a Statistical Investigation
- Design the experiment and prepare a plan.
- State the hypothesis.
- Define the population, sampling method, data collection criteria, statistical unit, and variables.
- Review relevant literature.
- Choose data collection criteria and statistical methods.
- Collect data through surveys, records, questionnaires, or examinations.
- Control and ensure data completeness.
- Process data using statistical calculations and evaluations (e.g., mean, standard deviation, error estimation).
- Draw conclusions and present results in collaboration with medical professionals.