Identify and discuss 2 types sampling designs, their benefits and limitations
In essence, non-probability sampling and probability sampling are the only two types of sampling designs.
A researcher selects a small proportion of the units to represent the whole. The selection is deliberate without any defined procedure. For example, if the objective of the researcher is to study the economic conditions of a particular population residing in a defined area. The researcher deliberately chooses a few towns or cities to represent the entire area under study (Levy & Lemeshow, 2008). Based on the objectives and specificity of the study, the following are some of the sampling techniques under the non-probability sampling.
Purposive or Judgmental Sample
In purposive or judgmental sampling, the researcher selects the sample based on the familiarity of the population and the objectives of the study. The researcher prefers using a purposive sample because the sample under interview fits a specific objective or description (Levy & Lemeshow, 2008).
a) Quota Sample
In quota sampling, the researcher selects units into a sample based on pre-specified characteristics. This ascertains that the total sample selected shares similar distribution of characteristics with the whole population under study (Thompson, 2002). For instance, a researcher conducting a national quota sample might want to know the proportions that represent the male and female. As well, the researcher might want to know the proportions that represent, gender, age, race or ethnicity and so forth. The researcher then collects a sample with similar proportions as the national population (Schaeffer, Mendenhall & Ott, 2006).
B. Probability sampling:
Probability sampling also share others names, such as random sampling or chance sampling. In the sampling procedure, the researcher gives each unit in the population an equal probability of selection for the sample. Every selection is independent of each other. Probability measures the accuracy of the obtained results (Thompson, 2002). This means that the researcher is capable of measuring the errors of estimation from a random sample. This fact makes probability-sampling design preferred than probability sampling design.
Probability sampling maintains the principle of Statistical Regularity, which confirms that if the selection of the sample is random, the sample bears the components and features as the whole. Therefore, probability sampling remains the best methodology to choose a representative sample (Schaeffer, Mendenhall & Ott, 2006).
b) Simple Random Sample
This is an essential sampling technique applied in statistical and computational methods. In the process of collecting a simple random sample, each proportion of the sample has a number. The researcher generates a set of random numbers and the units assigned those numbers are part of the sample. For example, suppose the researcher selects a population with 200 and decides to use a random sample of 40 people. he/she assigns them numbers 1 to 200, and then generates a list of 40 random numbers (Thompson, 2002). The persons assigned those numbers are the ones used in the sample.
c) Systematic Sample
In a systematic sample, the researcher lists down the elements of the population, and then selects every nth element to include in the sample. For example, in a population, a study that consists of 50 units, and the researcher wants 25 units, the researcher will list down the units and then choose every fifth unit to include in the sample. In avoiding any business, the researcher must choose the first unit at random (Thompson, 2002).
Benefits of probability sampling
I. The sampling requires simple information as opposed to non-probability sampling.
II. The selection mode is dependent and any unit selected has a probability of being used in the research study.
Limitations of probability sampling
I. It requires a sampling frame of elements, and it may be difficult to obtain suitable sampling frame. It is impractical to construct one that is appropriate.
II. Contain larger sampling errors that may obstruct accurate results.
Benefit of non-probability sampling
I. The statistical procedures used in analyzing and computing data are easier
II. The procedure does not require any advanced auxiliary information in computing data
Limitations of non-probability sampling
I. The results obtained may contain larger errors because a small amount of sample represents the whole
II. In a widely dispersed population, the costs incurred in data collection are higher than in probability sampling.