sampling meathods in research facst

A sampling method where every member of the population has a calculable and non-zero probability of being included in the sample.

It requires listing every item in the population and constructing a large sampling frame, leading to extensive calculations and costs.

It helps arrive at conclusions or make predictions affecting the population as a whole.

By the way that sample observations are chosen, i.e., by the sampling method.

To improve the representativeness of the sample by sampling each stratum independently.

Convenience and cost.

Bias, as referrals may be similar to those initially sampled.

An estimate based on sample data of a population parameter.

By randomly generating numbers or picking them out of a box, ensuring each number has an equal chance of selection.

Skip interval (k) = Population size / Sample size.

The true value of some attribute of a population.

The selection of every Kth element from a sampling frame, where K is the skip interval.

Selecting every Kth item in a production line to ensure quality or detect defects.

When dealing with low incidence or rare populations.

Purposive sampling, where units are selected deliberately for a specific purpose.

When the target population size is small, homogeneous, and the sampling frame is clearly defined.

The selection of a unit from the population based on the judgment of an experienced researcher.

The researcher specifies the number of respondents (quota) to be drawn from each category in advance.

A sampling method that involves grouping the population into clusters and selecting a few clusters for study, which can be done in one or more stages.

A sampling process where each element in the target population has an equal chance of inclusion in the sample.

The sample is not representative of the entire population, leading to biased findings.

Selecting districts with voting patterns close to the overall state or country based on past trends.

By segmenting the population based on characteristics like gender and filling quotas for each category.

To estimate the value of a population attribute.

A company surveying its employees to gauge acceptance of a new potato chip flavor.

They select certain preferred cities that they consider representative of the total population of the country.

It can be subject to interviewer bias, affecting the representativeness of the sample.

Multistage sampling selects entire clusters, while stratified sampling selects elements from within strata.

It is free of classification error, requires minimum advance knowledge of the population, and eliminates human bias.

It ensures that each member of the population has an equal chance of being included, reducing bias.

Each population element has a known (non-zero) chance of being chosen for the sample.

It ensures that each element in the population is assigned to a specific group, improving the representativeness of the sample.

A sampling method that involves the selection of units based on factors other than random chance.

A sampling method where units are selected based on their availability and accessibility to the researcher.

Probability samples and non-probability samples.

To avoid the instance of a unit being sampled more than once.

They do not allow you to estimate the extent to which sample statistics are likely to differ from population parameters.

The sample may reflect superficial characteristics rather than the true diversity of the population.

A method where the population is segmented into groups, and a specified number of respondents is drawn from each group.

A sampling technique that involves the selection of additional respondents through referrals.

Small sample sizes and low costs.

A smaller sample size can be drawn from homogeneous strata, while a larger sample is needed from heterogeneous strata.

By collecting details of website visitors and asking them to refer other squash players.

The process of grouping members of the population into homogeneous groups before sampling.

In single-stage, all elements from selected clusters are studied, while in two-stage, a random selection of elements from those clusters is made.

Purposive sampling, where units are selected deliberately for specific purpose