Understanding Probability and non-probability Methods of Sampling

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• The mainstream sampling methods are probability and non-probability, but each has its sub-categories.

• The main reason for using any or both of the two in research paper writing is achieving fair representation of larger population.

• Sampling data in research paper writing helps you

select just a few representatives of larger population on which to apply analyses, because it is difficult to apply analysis on all members of a population.

• The main difference between the two methods of sampling is in their basis: probability sampling techniques rely on statistical theory, while non-probability techniques rely on determination of degree to which the sample is representative to the whole population.

• Such determination in the latter makes the method subjective, while former is an objective method. In the following section of this paper, we discuss each of the methods of sampling.

• Probability Sampling Method

• Research paper writers use methods based on statistical theory to analyze factors and variables defining selected sample.

• Findings or results from analysis are then applied to the larger population. Conclusions made from sample investigations and applied to the total population are termed as inferences.

• The researcher makes estimates of data variables that define sample, in light of quantifiable levels of confidence.

• The researcher uses confidence intervals to define ranges within which estimates are given. Point estimates (+/-) are used to express data (that describes variables).

• In most cases, probability sampling is applied where quantitative collection methods are involved.

• Each of the study units must be fairly represented in the sample, via assigning equal probability.

• Probability is simply the chances of appearance of all members in the research sample.

• To achieve this, a sampling frame that is exhaustive, is needful. Such a frame is a list of all the potential units of the population in the research.

• It is important to consider challenges of probability sampling, such as impossibilities and costs of constructing a sampling frame. Where it is difficult to construct a frame, cluster sampling can be helpful.

• Non-probability sampling

• In almost all cases of research paper writing involving qualitative methods, non-probability sampling is used.

• Where statistical inference is non-desirable in quantitative methods of study, non-probability sampling is beneficial.

• In this method, there is no formula for calculating the sample size because conclusions made from analyzing variables that describe sample cannot be applied to larger population.

• There is no statistical theory supporting application of inferences to a larger population.

• Sample selection is influenced by desire of research writers to get conclusions about total populations.

• In addition, such a desire also influences the sample size. As is the case with probability sampling, the intention of the whole research is arriving at a representative sample from the population.

• In order to stratify the sample, it is important to consider all factors influencing variables. Every subgroup must have a representation in the larger population.

• Non-probability sampling includes purposive sampling, random sampling and opportunistic sampling.

• It is likely that research writers get better data from use of random than opportunistic sampling. The ability to draw conclusions increases with the sample size.

• As discussed earlier, the size of sample is limited because the research involves large amount of data and information.

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