From a personal perspective, a dictionary definition of any term presents its generalized meaning. In turn, an operationalized definition is the term’s thorough description that helps to perceive the characteristics and predictors of a particular phenomenon to differentiate it from similar ones. According to a dictionary definition, assault is a violent attack, either physical or verbal. As it was previously mentioned, this definition does not provide any specific information related to this term. In turn, an operationalized definition of assault presents it as an intentional act of a tortfeasor that causes a victim to fear imminent physical harm.
Based on this difference, it may be even possible to conclude that a dictionary and operationalized definitions provide different meanings of the same term. For instance, a dictionary defines assault as an attack, however, according to the law, assault does not presuppose an attack – it is an act that inflicts fear and there may be no contact between an offender and a victim. At the same time, the term assault may vary in dependence on jurisdiction and differ from similar terms, including battery. Taking into consideration that battery is a physical act that causes harm to a victim, this term is closer to the dictionary definition of assault than its operationalized definition.
Non-Probability Sampling Techniques
Probabilistic sampling techniques may be regarded as a highly efficient sampling method, especially for quantitative research, when samples are derived from a well-defined population. In addition, the probability of these samples being included in the population is known (Bachman & Schutt, 2020). At the same time, non-probability sampling techniques that presuppose the selection of samples that may not represent the target population seem unreliable. However, in particular situations, it is more feasible to be used for research in comparison with probability sampling. Thus, non-probability sampling techniques are used in situations when a population list is not available or the target population is too small for research. In addition, non-probability sampling is applied when a research question “does not concern a large population” (Bachman & Schutt, 2020, p. 130). Finally, non-probability sampling is applied to experimental studies – as previously mentioned, it is more suitable for qualitative studies that aim to develop the understanding of the under-researched population rather than to test a particular hypothesis related to a broad population.
At the same time, from a personal perspective, some limitations and conditions may impact the reliability and validity of non-probability sampling. There are four non-probability sampling methods: availability, quota, purposive, and snowball sampling. I think that quota and purposive sampling methods are more reliable as they presuppose the presence of the specific characteristics of samples “in proportion to their prevalence in the population” (Bachman & Schutt, 2020, p. 132). In addition, purposive sampling is ideal for the examination of a limited group, such as a community or an organization. In turn, availability and snowball sampling methods may provide less reliable results as they do not presuppose any criteria for samples to be included. That is why, there is a relatively small possibility that in the case of availability sampling, samples will represent the population and make generalizability available.
Concerning, snowball sampling, the results may be strongly affected by the subjectivity of samples even if this method is the only one available concerning hard-to-reach and secure populations.
Bachman, R. D., & Schutt, R. K. (2020). The practice of research in criminology and criminal justice (7th ed.). SAGE Publications.