Work sampling is the statistical technique used for determining the proportion of time spent by workers in various defined categories of activity (e.g. setting up a machine, assembling two parts, idle…etc.). Other names used for it are ‘activity sampling’, ‘occurrence sampling’, and ‘ratio delay study’.

Advantages of Work Sampling: (1) It is a cheaper technique than time study and production study. (2) Only one analyst can perform work sampling study of many activities. (3) Observers does not require much training. (4) It is more useful in non-repetitive and indirect jobs.

Work sampling is lower cost because it uses random samples instead of continuous observations. Work Sampling, in general, does not require a trained time-study analyst to take the observations. Also, stopwatches or other timing devices are not required.

L.H.S. Tippet

The most common confidence level is 95%. The area under the curve at 2 sigma or two standard deviations is 95.45% which is rounded off gives 95% This indicates that the probability is 95% of the time the random, observations will be true or represents the fact and 5% of the time false or will not.

Where was work sampling first used? The British textile industry 2. What are advantages of work sampling over stopwatch time study?

Some of the steps involved in activity sampling technique are,

1. Define the manufacturing task for which the the standard time is to be determined.
2. Define the task elements.
3. Design the study.
4. Identify the observers who will do the study.
5. Start the study.
6. Make random visits and collect the observations.

The Work Sampling System is an early childhood assessment tool that allows teachers to evaluate the skills of children age 3 through third grade. Students demonstrate what they know through a series of evaluations, which allows their teachers to make informed decisions about how to guide instruction.

1. What does stratification mean in random sampling? In random sampling, stratification means the researcher creates layers in the random or systematic selection process. The technical term for discussing the magnitude of sampling errors is precision.

These include simple random samples, systematic samples, stratified samples, and cluster samples. Simple random samples. are the most basic type of probability sample, but their use is not particularly common. Part of the reason for this may be the work involved in generating a simple random sample.

Stratification is defined as the act of sorting data, people, and objects into distinct groups or layers. It is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together, the meaning of the data can be difficult to see.

The purposive sampling technique is a type of non-probability sampling that is most effective when one needs to study a certain cultural domain with knowledgeable experts within. Purposive sampling may also be used with both qualitative and quantitative re- search techniques.

Probability sampling

Researchers use purposive sampling when they want to access a particular subset of people, as all participants of a study are selected because they fit a particular profile.

Purposive sampling is intentional selection of informants based on their ability to elucidate a specific theme, concept, or phenomenon.

Purposive sampling is sometimes called a judgmental sample, which is a bit of a misnomer; there’s no intended bias in purposive sampling. However, due to a lack of random sampling, purposive sampling is sometimes open to selection bias and error. Readers of your study may doubt if the sample was representative.

Definition. • Sample: a portion of the entire group (called a population) • Sampling procedure: choosing part of a population to use to test hypotheses about the entire population. Used to choose the number of participants, interviews, or work samples to use in the assessment process.

Just as with purposeful (or purposive) qualitative sampling, theoretical sampling involves selecting participants based on specific characteristics. The difference between the two lies in the stage at which participants are selected. This is where theoretical and purposeful sampling diverge.

Disadvantages of Purposive Sampling (Judgment Sampling)

• Vulnerability to errors in judgment by researcher.
• Low level of reliability and high levels of bias.
• Inability to generalize research findings.

Theory-based sampling involves selecting cases according to the extent to which they represent a particular theoretical construct. Purposive sampling is used as the population of the particular theoretical construct is difficult to determine.

Unlike the various sampling techniques that can be used under probability sampling (e.g., simple random sampling, stratified random sampling, etc.), the goal of purposive sampling is not to randomly select units from a population to create a sample with the intention of making generalisations (i.e., statistical …

Advantages of non-probability sampling Getting responses using non-probability sampling is faster and more cost-effective than probability sampling because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.

There are five types of non-probability sampling technique that you may use when doing a dissertation at the undergraduate and master’s level: quota sampling, convenience sampling, purposive sampling, self-selection sampling and snowball sampling.

Advantages and disadvantages A major advantage with non-probability sampling is that—compared to probability sampling—it’s very cost- and time-effective. It’s also easy to use and can also be used when it’s impossible to conduct probability sampling (e.g. when you have a very small population to work with).

The primary disadvantage of nonprobability sampling is the lack of generalizability. Samples that are more representative of a target population are more generalizable to the target population. Thus, the claims or findings of the study are more likely to also be found in the larger target population.

In the most basic form of probability sampling (i.e., a simple random sample), every member of the population has an equal chance of being selected into the study. Non-probability sampling, on the other hand, does not involve “random” processes for selecting participants.

Non-probability sampling often results in biased samples because some members of the population are more likely to be included than others. Example of sampling bias in a convenience sample You want to study the popularity of plant-based foods amongst undergraduate students at your university.

How to get quota sampling right

1. Divide the sample population into subgroups. These should be mutually exclusive.
2. Figure out the weightages of subgroups. The weightage is how much of your sample a given subgroup will be.
3. Select an appropriate sample size.
4. Survey while adhering to the subgroup population proportions.

The main difference between stratified sampling and quota sampling is that stratified sampling would select the students using a probability sampling method such as simple random sampling or systematic sampling. The main argument against quota sampling is that it does not meet the basic requirement of randomness.