Probability Sampling Methods for Social Science Research
In statistical analysis, sampling is the process of selecting a predetermined number of observations from a larger population. Since it is nearly difficult for a researcher to analyse the whole population under investigation, only a sample of the overall population is chosen to draw statistical conclusions and extrapolate population estimates. When compared to examining the entire population, sampling is thought to be a more time- and cost-effective method of data collection.
Types of sampling methods
Researchers in the social sciences frequently employ various sampling techniques so they do not have to study the complete population in order to gather useful information. The various sampling techniques can be broadly divided into approaches using probability sampling and those using non-probability sampling.
- Non-probability sampling: In case of non-probability sampling certain units of the population have no chance of being chosen for the sample. It is impossible to calculate the probability of selecting any unit of the population in this situation since the sample are chosen based on the researcher's convenience or judgement. Such sampling does not allow for the generalisation of statistics to the population from which the samples were drawn.
- Probability sampling: In case of probability sampling every unit in the population has a chance or likelihood (non-zero probability) of being chosen for the sample, and this chance may be precisely calculated. As long as the sampled units are weighted according to their likelihood of selection, the sample statistics that are thus generated, such as sample mean or sample standard deviation, are unbiased estimates of population parameters.
Different probability sampling methods used have two common characteristics which include:
1. Every unit in the population has a known non-zero probability of being sampled,
2. Random selection is included in the sampling process at some stage in all probability sampling methods.
Clearly, probability sampling is a better tool for social science research. This article will teach you about four different types of probability sampling methods:
Types of probability sampling:
1. Simple random sampling (SRS): In this method, the probability of selecting any potential subset of the population (or, more precisely, the sample frame) is distributed equally. Therefore, sample statistics are unbiased estimates of population parameters. Selecting responders from a sampling frame at random is known as simple random sampling. In order to select individuals from a population, it employs the "lottery method" or "random number tables." The inferences drawn from this method are the most representative of a population of all probability sampling procedures, despite being the simplest.
2. Systematic sampling: In systematic sampling, a researcher chooses an interval and a random starting point. After choosing a random unit, the researcher chooses the next Kth elements, where K is the formal name for the sampling ratio, which is defined as the ratio of the sampling frame size N to the desired sample size n (k = N / n). It is crucial that the starting point is randomly selected from among the first k elements on the list rather than being the first in the list automatically.
3. Stratified sampling: In stratified sampling, the sampling frame is split into homogeneous, non-overlapping subgroups (referred to as "strata"), i.e., into categories that are exhaustive and mutually exclusive based on some specific characteristics like gender, age, income, location, etc. A simple random sample is then taken from within each subgroup. When a population's traits are varied and researchers want to make sure that each characteristic is accurately represented in the sample, they rely on stratified sampling.
4. Cluster sampling: In statistical analysis, It might not be possible to undertake a simple random sampling of the complete population if they are spread out over a large geographic area. In this situation, it could make sense to divide the population into "clusters" (often along geographical boundaries), select a small number of these clusters at random, and measure all the units in each cluster. The variability of sample estimates in a cluster sample will, however, typically be higher than that of a simple random sample depending on between-cluster differences, and as a result, the results are less generalizable to the population than those obtained from simple random samples. For instance, to collect data from a vast country like India, we divide the country into different cities considered as clusters and conduct simple random sampling to select some of these clusters for the study.