Which one among the following relates to the probability-based sampling technique?
Stratified sampling
Understanding Probability-Based Sampling Techniques
Sampling is a fundamental process in research where a subset of individuals or units is selected from a larger population to estimate characteristics of the whole population. There are two main categories of sampling techniques: probability sampling and non-probability sampling.
Probability Sampling vs. Non-Probability Sampling
The key difference lies in whether every member of the population has a known, non-zero chance of being selected for the sample.
- Probability Sampling: In these techniques, every unit in the population has a defined probability of being included in the sample. This allows researchers to use statistical methods to make inferences about the population based on the sample data and to calculate the margin of error. Probability sampling is often used when the goal is to generalize findings to the entire population. Examples include Simple Random Sampling, Systematic Sampling, Stratified Sampling, and Cluster Sampling.
- Non-Probability Sampling: In these techniques, the selection of units is not based on random chance. The probability of selecting any particular unit is unknown. These methods are often used for exploratory research, qualitative studies, or when probability sampling is not feasible or practical. Findings from non-probability samples cannot typically be generalized statistically to the entire population. Examples include Convenience Sampling, Voluntary Response Sampling, Purposive (Judgement) Sampling, Quota Sampling, and Snowball Sampling.
Analyzing the Given Options
Let's examine each option provided and determine its category:
| Sampling Technique | Description | Category |
|---|---|---|
| Quota Sampling | Researchers select participants based on pre-set quotas, ensuring the sample represents the population according to certain characteristics (e.g., age, gender) in the same proportion as they exist in the population. However, within each quota, selection is non-random. | Non-Probability Sampling |
| Snow-ball Sampling | Participants are recruited and asked to refer other potential participants who meet the study criteria. This method is useful for reaching hard-to-reach or hidden populations. Selection is based on referrals, not random chance. | Non-Probability Sampling |
| Stratified Sampling | The population is divided into mutually exclusive subgroups or 'strata' based on relevant characteristics (e.g., income level, geographic region). Then, a random sample is selected from each stratum. This ensures representation from all key subgroups. | Probability Sampling |
| Judgement Sampling | The researcher selects participants based on their own expertise, knowledge, or judgement about who would be most representative or informative for the study's purpose. This relies on the researcher's discretion, not random selection. | Non-Probability Sampling |
Conclusion on Probability-Based Sampling
Based on the analysis, only Stratified sampling involves a process where elements are selected randomly from defined subgroups (strata) of the population, ensuring a known probability of selection for each member within their respective stratum. This makes it a probability-based sampling technique.
Revision Table: Key Sampling Methods
| Type | Method | Key Characteristic |
|---|---|---|
| Probability | Simple Random | Each element has an equal chance of selection. |
| Probability | Systematic | Every k-th element is selected after a random start. |
| Probability | Stratified | Random samples from pre-defined subgroups (strata). |
| Probability | Cluster | Random samples of naturally occurring groups (clusters). |
| Non-Probability | Convenience | Selecting easily accessible participants. |
| Non-Probability | Quota | Selecting to meet pre-set numbers for characteristics. |
| Non-Probability | Purposive (Judgement) | Researcher selects based on expertise. |
| Non-Probability | Snowball | Participants refer others. |
Additional Information on Stratified Sampling
Stratified sampling is particularly useful when the population is heterogeneous, meaning it consists of diverse subgroups that might behave differently. By dividing the population into homogeneous strata and sampling from each, researchers can ensure that these important subgroups are adequately represented in the final sample. This often leads to more precise estimates compared to a simple random sample of the same size, especially if the characteristic being studied varies significantly between strata.
There are two main types of stratified sampling:
- Proportionate Stratified Sampling: The sample size from each stratum is proportional to the stratum's size in the total population.
- Disproportionate Stratified Sampling: The sample size from each stratum is not proportional to its population size; researchers might oversample smaller strata to ensure sufficient data for analysis of those specific groups.
Important Questions from Sampling
The process of converting the analog sample into discrete form is called ______.
Respondents of a recent sample survey provided names of friends they thought would be likely users of a new product. These friends were contacted, completed a survey, and asked to supply names of other likely users. Which method of sampling has been used in this survey ?
In which process is the flat-top pulse amplitude modulated signal generated?
Consider a population of 3 units having values 2, 4 and 6. A simple random sample (without replacement) of 2 units is to be drawn from the population. Let M denote the sample mean of this sample. Then which of the following statements are true?
Let x(t) be a signal with Nyquist rate ω 0. Determine the Nyquist rate for y(t) = x(t)cos(ω 0t)
