Probability Sampling Practice Quiz

Probability sampling ensures that every member of the population has a known and non-zero chance of being selected, which is fundamental to making valid generalizations. This method minimizes bias by relying on random selection procedures.

A sampling frame is a comprehensive list of all individuals or elements in the target population. It is critical because it provides the basis for employing random selection methods in probability sampling.

Simple Random Sampling is a probability sampling method where every individual has an equal chance of being selected. The other options rely on non-random or subjective criteria, making them non-probability sampling methods.

Random selection is crucial because it minimizes selection bias and ensures that every member of the population has an equal opportunity to be included in the sample. This randomness supports the representativeness of the data and helps in making valid inferences.

Increasing the sample size generally decreases the sampling error, leading to more reliable and precise estimates of the population parameters. A larger sample tends to better represent the diversity of the population, thereby reducing variability.

A sampling frame provides the complete list of the population from which the sample is drawn. This is essential for ensuring that every member has a known and equal chance of being selected, making the sampling process transparent and less biased.

Stratified sampling involves dividing the population into homogeneous subgroups, or strata, and then randomly selecting samples from each subgroup. This approach ensures that specific segments of the population are adequately represented in the sample.

Cluster sampling involves dividing the population into natural groups or clusters, and then randomly selecting entire clusters for the study. This method is particularly useful when the population is large and dispersed, even though it can sometimes increase the sampling error.

Systematic sampling requires choosing a random starting point in a list and then selecting every kth element thereafter. This method provides a structured way of sampling and is an efficient alternative to simple random sampling as long as the list does not have a systematic pattern.

When 20 individuals are selected out of a population of 100 using simple random sampling, the chance of a specific individual being selected is 20/100, which is 20%. This basic probability calculation is central to understanding random sampling.

The advantage of probability sampling is that it provides a framework to calculate sampling errors and supports generalizing the study results to the broader population. Using known selection probabilities makes the estimation of inaccuracies in the sample more manageable.

A larger sample size results in lower sampling error, as it tends to more accurately represent the population. This improvement in precision is one of the main benefits of increasing the sample size in probability sampling.

Even when a sample is selected randomly, non-response or low response rates can introduce bias if the characteristics of non-respondents systematically differ from those of respondents. This can compromise the representativeness of the final sample.

Cluster sampling is often more efficient in situations where the population is widespread geographically, as it allows the researcher to survey entire groups or clusters at a lower operational cost. This method is especially useful when the population is homogeneous within clusters.

Random digit dialing is a commonly used probability sampling technique in telephone surveys where phone numbers are generated at random. This method ensures that every potential number has an equal chance of being dialed, contributing to the representativeness of the survey sample.

For proportional stratified sampling, the number drawn from a stratum is proportional to its size. Since the middle stratum represents 200 out of 600 individuals, multiplying (200/600) by the total sample size of 60 gives 20 individuals.

Cluster sampling can lead to higher sampling error because individuals within the same cluster tend to be more similar to each other, a phenomenon known as intra-cluster correlation. This reduces the effective sample size and increases variability in the estimates.

If the list from which the sample is selected has an underlying ordering that coincides with the sampling interval, it can introduce periodic bias. This bias may lead to over- or under-representation of certain segments within the population.

A 70% response rate means that 30% of selected individuals did not participate, and if these non-respondents differ significantly from respondents, the sample may become biased. Non-response bias can jeopardize the representativeness of the sample and lead to inaccurate conclusions.

When selection probabilities vary among the elements of a population, researchers calculate sampling weights to adjust the analysis. This weighting compensates for the unequal probabilities and helps ensure that the results are representative of the entire population.