Convenience Sampling
Convenience sampling is understood as a method of sampling units on the basis of easy availability. Its objective is to evaluate the features of a population using comparatively small sample taken from the population.
Convenience sampling is the method of sampling in which units are preferred on the basis of easy availability. Sampling process aims at determining the characteristics of a population using a relatively small sample taken from the population. In regards to data collection, sampling means computing some items in a group procedure with the intention to symbolize them. Whether sampling the data of a whole population or a moving process, it is required to have a valid sample.
For giving meaningful results, a sample must be representative of the population. Undetected bias will influence explanation and conclusion about the process and the problem. These are some of the examples of sampling bias:
- Convenience Sampling: Convenience sampling is a type of sampling method where units are selected based on easy availability.
- Random Sampling: Random sampling is a sampling method which involves taking items for the sample ensuring that any unit in the population has an equal chance of being selected. This assures that the sample is not biased by the method of collection. Bias is used by selecting items that are easy to collect, or accessible.
- Stratified Sampling: It is another sampling method and it involves splitting of the population into more than one category that share same features. The items are assembled at random from each specific category, in relation to the proportion to the size of the category and also to the relative to the population. It is also the source of getting more consistent results than pure arbitrary sampling for the cause as it assures that the categories available are justly represented.
- Systematic Sampling: Unlike random sampling, systematic sampling involves taking a sample from the available data in a set pattern. They undergo a systematic telephone survey wherein they call every hundredth caller who is listed in the phone book. This process of systematic sampling is often convenient and economical, but carries the risk that there will be an unsuspected systematic pattern in the data.
The quality professionals are solely responsible for assuring that samples are random, unbiased and representative of the population.
In a fair business environment, it is possible to do either kind of sampling – population sampling or process sampling. When a data is drawn from a group of people or items that are really present there, this can be considered as a population sample. However, in order to know the changes over time so as to make out the degree and type of variation involved in the process, a process sample is required.
For developing an effective sampling plan, it is required to have a thorough knowledge about the data that are to be gathered. Some important terms in sampling are:
- Sampling Event: This is the act of collecting items from the process or population that is to be measured.
- Subgroup: Subgroup is the amount of units collected for measurement at each sampling events.
- Sampling Frequency: Sample frequency illustrates the number of times per day or per week a sample is taken.
To measure process capability on six sigma scale, following are some of the phases of measurement:
- Types and Intentions of Measure,
- Building Blocks of Lean and Six Sigma,
- Data Collection Methods,
- Sampling Techniques,
- Six Sigma Calculation Methods
Data collection process is an essential part in sampling method. The data implementation plan should include the “sample size” and the data collection should be stopped on reaching the appropriate sample size. In the whole data collection, it is essential to monitor both the procedures and devices used to collect data. The measurement and sampling method are important at the time of data collection process. However, the team of data collection should go through the process under proper investigation.
Six sigma performance measures are often rooted in defects as produced by the process. These are some of the advantages of measuring defects:
- Simplicity,
- Consistency, and
- Comparability