Sampling Design Techniques Quiz

Although easy to utilize, convenience sampling poses a threat to the reliability of the results due to sampling bias. This may be an option as a last resort when researchers face problems in data gathering which can not be addressed.

Snowball sampling has been utilized in this scenario. Snowball sampling is a non-probability sampling technique where initial participants are selected based on the researcher's judgment or convenience, and then they refer other potential participants who meet the criteria for the study. In this case, Marice determines her respondents by asking people who would be most suited for her study, and through this process, she is referred from one respondent to another. This method is often used when the target population is difficult to access or identify, and it allows for the recruitment of participants through social networks and referrals.

Systematic random sampling is a method where elements are selected from an ordered population at regular intervals, known as the sampling interval. The formula k = N / n is used to calculate this interval, where:

k is the sampling interval,

N is the total population size,

n is the desired sample size.

By dividing the total population (N) by the desired sample size (n), the researcher determines how frequently (every k-th element) to select an individual from the list. For example, if there are 1,000 individuals in a population (N) and a researcher wants a sample of 100 (n), then the sampling interval (k) would be 10. This means every 10th person from a randomly ordered list would be selected for the sample, ensuring a systematic but still random selection of participants across the entire population. This technique helps in achieving a representative sample while maintaining simplicity and cost-efficiency in data collection.

Quota sampling is a type of non-probability sampling where the required sample and sample per stratum are determined and complied. It involves dividing the population into different strata based on certain characteristics, such as age, gender, or occupation, and then selecting individuals from each stratum until the quota for that stratum is met. However, unlike other sampling methods, quota sampling lacks randomization in the selection of respondents. This means that the sample may not be truly representative of the population, as the selection of individuals is based on convenience or judgment rather than random chance.

Stratified random sampling comes in handy when researchers prefer to consider the strata for the population. This ensures that each stratum has adequate respondents either of equal number to the other strata or having it in proportion to its respective population stratum.

The main difference between probability and non-probability sampling lies in the selection method. Probability sampling uses random selection, which gives every element in the population a known and equal chance of being included in the sample. This allows for the results to be generalizable to the population. On the other hand, non-probability sampling does not involve random selection, and the elements are selected based on the researcher's judgment, convenience, or other criteria. This makes non-probability sampling less generalizable but useful in exploratory research where probability sampling is not feasible.

Lucas chose his respondents based on their ability to provide him with the needed data for his study, indicating that he purposefully selected individuals who he believed would have relevant information. This type of sampling is known as judgment sampling, where the researcher uses their own judgment to select participants who they believe will be most informative for the study.

When using equal allocation in stratified random sampling, the sample size is the same for each stratum regardless of the size of the subpopulation. In this scenario, Michael wants an equal number of samples from each of the four subpopulations. Given the total population of 352, dividing it by 4 strata results in 88 samples per stratum. This approach ensures that each stratum is equally represented, which is useful in certain studies where equal representation is more important than proportional representation.

To determine the sample size for each stratum:

Calculate the proportion of each stratum:

Adolescents: 163 out of 514

Young Adults: 201 out of 514

Late Adults: 150 out of 514

Determine the overall desired sample size:

For this example, let's assume the desired sample size is 225 (this could be calculated using a formula like Yamane's formula, but for simplicity, we are using 225).

Calculate the sample size for each stratum:

Adolescents: (163 / 514) * 225 = 71

Young Adults: (201 / 514) * 225 = 88

Late Adults: (150 / 514) * 225 = 66

These calculations result in the sample sizes being proportional to the population sizes: 71 Adolescents, 88 Young Adults, and 66 Late Adults. This ensures that the sample reflects the population distribution accurately, giving us the correct allocation.

Cluster sampling is a type of probability sampling where the researcher randomly selects groups, or clusters, from a larger population. Each selected cluster represents a subset of the population, and all individuals within the selected clusters are considered to be part of the study. This method is useful when the population is large and dispersed, as it allows for a more efficient and cost-effective way of gathering data. By selecting clusters instead of individual participants, the researcher can reduce the time and resources required for data collection.