# Probability And Nonprobability Sampling In Research

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Probability sampling such as simple random sampling (SRS), guarantees that all scientific components have an equal chance of being included in the sample (Monette et al., 2011, p.139). For example, a popular way of selecting an individual randomly would be putting names in a hat then having someone pull a name from the hat. When this occurs, each person has an equal probability of inclusion. In contrast, nonprobability sampling is not a method where random selection processes occur. Instead, subjects are usually selected on the basis of accessibility or by the researcherâ€™s personal judgment. One method that utilizes a nonprobability approach to research is the snowball sampling method. In this method, a minimal amount of test subjects recruit others thus causing the sampling group to grow. As the sample continues to grow, there is eventually enough data collected for exploration (Monette et al., 2011,

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Nominal measures use categories such as gender, faith, and culture as a way to label variables and any numbers utilized in this scale have no numeric value. Ordinal measures are utilized to put variables in a specific order. Any numbers utilized in this scale are used only as a means of ranking measures. For example, this measure can be utilized to measure how one feels on a scale of one to ten. Interval scales have similar characteristics as ordinal scales in that they both utilize variables in a specific order. However, unlike ordinal measures, the scales of measurement are used to measure the distance between values. Lastly, ratio scales are the maximum level of measurement because it offers information about order and value between units. In addition, ratio scales have an absolute zero which means it can be used to add, subtract, multiply and divide data (Monette et al., 2011, p.109-113). Furthermore, it can be used to make data