WednesdayOct 10 at 6:48pm
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By surveying individuals, the allowance for trends, mental outlooks, and beliefs are able to be put into a quantitative format for analysis (Creswell, 2014). Participants can either be selected randomly or nonrandomly (Creswell, 2014). As a way to control the experiment randomization is chosen to assess whether the factors of the study influence the results or if other elements are the cause, while also allowing for an impartial selection of participation so the researcher can adequately represent the general population (Creswell, 2014). This form of sampling is referred to as a “true experiment” (Creswell, 2014).
Nonrandom sampling is referred to as a “quasi-experiment, ” and the participants are not randomly chosen and do not have the same probability of being selected as those in the random sampling (Black & Eldredge, 2002). This form of sampling is often viewed as bias and is not considered as suitable for most statistical methods due to the fact it does not account for the general population (Black & Eldredge, 2002). Nonrandom sampling is often chosen for convenience or by choice of the researcher and may be used to fulfill quotas or because of a referral (Black & Eldredge, 2002).
Samples are essential for research because it ensures all pertinent categories of persons are represented so the results will be accurate and impartial (Austin, 2014). Moreover, sampling provides a general inclusion of individuals, groups, and features are equally reviewed.
Pretesting, pilot testing or field-testing is necessary to demonstrate the “content validity of scores on an instrument” and to enhance inquires, design, and the ratio (Creswell, 2014). Additionally, pilot testing and field-testing guarantees that the data can be conducted without inconsistencies and pretesting makes sure there are no issues associated with the questions which could affect the results (Creswell, 2014).
Nonresponse bias occurs when contributors do not respond to the questionnaire (Creswell, 2014). When participants do not respond then, the collected data could potentially be affected as their replies may be considerably different from those who did respond (Creswell, 2014). If this occurs then the results of the study could be severely affected subsequently changing the final results (Creswell, 2014). To address these problems, researchers must contact the nonresponse participants to see if their answers coincide or substantially differ from the received data in a process referred to as a “respondent-nonrespondent check” (Creswell, 2014, “Data Analysis and Interpretation”).
Everyone in life understands that no person is created equal. Every person has a different weight, height, or age, as such, researchers must account for these disparities by measuring the data through a process called dispersion (SoSci, 1999). Dispersion allows for characteristics like that of location or shape to be considered (Creswell, 2014). Central tendency is a measurement which takes a complete collection of statistics with a single worth which signifies the middle or center of the distribution (ABS, 2013). Central tendencies are placed in three categories, mode, median, and mean (ABS, 2013). The mode is the most frequent value, the median is the central value, and the mean is the final value found in the study (ABS, 2013). In order to incorporate all the variables of a study, one must accurately account for the measures of dispersion, and central tendencies or the study will be lacking
When using previously used instruments, it is essential to get permission from the developer before using (Creswell, 2014). Additionally, the researcher must demonstrate “validity of scores” attained from previous use by announcing the prior endeavors of the original researcher (Creswell, 2014, “Instrumentation”). To establish the validity of previously used instruments the researcher must conclude whether the data was measured as envisioned if the scores are anticipated and if the data is measured comparable to a hypothetical idea serve the objective (Creswell, 2014). By following these steps, the researcher is able to identify the usefulness and reliability of the instrument in their study (Creswell, 2014).
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This discussion supports your understanding and application of the quantitative method and qualitative method of research approach. In your initial post, address the following:
Differentiate between randomized and nonrandomized approaches to sampling populations. Discuss the importance of the role of sampling when conducting research.
Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen (Creswell & Creswell, 2018). A sample chosen randomly is meant to be an unbiased representation of the total population. If for some reasons, the sample does not represent the population, the variation is called a sampling error. For these reasons and to minimize costs, researchers often turn to other sampling methods, known as nonrandom sampling. When using these alternative methods, researchers generally select elements for the sample based on hypotheses about the population of interest, known as selection criteria. For example, if someone selects their samples by stopping people on the street, attempting to stop an equal number of men and women (to coincide with the presumed gender distribution in the population) would be a criterion of nonrandom sampling.
Sampling is important in research to be able to produce accurate results. It is impractical and undesirable to study the whole population. If the sample is too small or excessively large, it may lead to incorrect findings. Sampling techniques may be used to find representative samples to avoid bias.
Snowball sampling is a form of sampling where participants recommend people they know as research participants. Explain one pro and one con to this approach.
One pro of snowball sampling is that it can be difficult to identifying units to include in your sample, perhaps because there is no obvious list of the population you are interested in. Since snowball sample does not select units for inclusion in the sample based on random selection, unlike probability sampling technique, it is impossible to determine the possible sampling error and make generalizations from the sample to the population.
Discuss the usefulness of pretesting, pilot testing, or field-testing a survey or interview questionnaire.
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