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.

To estimate the value of a population attribute.

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

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

The true value of some attribute of a population.

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

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

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

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.

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

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

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

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

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

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

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

Probability samples and non-probability samples.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Convenience and cost.

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

When dealing with low incidence or rare populations.

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

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.

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

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

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

Small sample sizes and low costs.

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

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

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

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

Purposive sampling, where units are selected deliberately for specific purpose