Question

What ethical issues are relevant to this study? Describe the sampling plan. Analyze its strengths and weaknesses. Describe the research design. Analyze its strengths and weaknesses.   

Answer 1

INTRODUCTION

important ethical issues include voluntary participation and informed consent, anonymity and confidentiality, and accountability in terms of the accuracy of analysis and reporting. ... This paper focuses on sampling as a nexus of ethical dilemmas experienced by social workers and other applied empirical researchers.

1} Maximizing External Validity through Unbiased Sampling

The ultimate goal of sample design is to select a set of elements from a population in such a way that descriptions of those elements accurately portray characteristics of the population (i.e., parameters) from which they were selected. Another important goal of sample design is to yield maximum precision (i.e., minimum variance) per unit cost. The sampling process begins with the identification of an appropriate population to study to answer researchable questions, and includes (1) the formulation of a sampling strategy, and

(2) determination of sample size and composition to maximize the external validity.

2.) What Random Sampling Does and Does Not Do

The term random sample, also called probability sample, is used to describe a sample that is free from systematic error. A sample is unbiased, then, if every element in a population has an equal chance of being selected. According to classical statistical sampling theory, if random selection from a known population is performed, characteristics of the sample can be inferred and tend to mirror corresponding characteristics of the population. If random sampling is not performed, there is no theoretical basis for statistical inference. Only information about a sample can be described. However, although random sampling for representativeness minimizes systematic error, sampling biases still can occur for the following reasons: 1. A complete randomization process is usually not implemented (Cook, 1993). Frequently, only units are randomized, which is only one of three different areas that define an event. The other two areas that definean event are place or setting, and time (Cook & Campbell, 1979); 2. Random sampling does not minimize all error in a research design. There are other types of bias in the sample that may contribute to error, such as non-sampling bias (e.g., measurement error (Henry, 1990); 3. The sample may not be representative of the population because it is too small, and therefore, likely to be too homogeneous; 4. The representativenes of the sample may be impacted by attrition and refusal of the participants to take part in a study; 5. Random sampling permits causal generalization to a target population but not across multiple populations (Cook & Campbell, 1979). The latter is important for establishing an abstract principle of causality and is best done through multiple replications across units, setting, and

Suppose you have to run a survey about the coffee drinking habits of high school students of USA. The population of the students is about 4 million. You can not even imagine running the survey by asking each and every student to get the relevant data because of requirement of huge amount of time, money and other resources. The cost of the survey in this case would be too monumental to justify the effort. To solve these types of problem, sampling can be used.

Definition Of Sampling

Application of certain queries to less than 100% of the population(group of all items that we are trying to observe and analyze) is known as Sampling. In simple terms, sampling is the process of selection of limited number of elements from large group of elements (population) so that, the characteristics of the samples taken is identical to that of the population. In above examples, suppose you choose 1000 students among 4 millions students. then:

• 4 millions students is population
• 1000 is the size of sample

Sampling is a great tool if you have to deal with a huge volume of data and you have limited resources. When you have large population of the data, then it can also be the only option you have.

Although you do not subject all the data to your queries, the chance that you get the desired results is almost similar to that when you do thorough checking. Provided that your choice for the sampling techniques must be appropriate.

strengths:

Sampling have various benefits to us. Some of the advantages are listed below:

• Sampling saves time to a great extent by reducing the volume of data. You do not go through each of the individual items.
• Sampling Avoids monotony in works. You do not have to repeat the query again and again to all the individual data.
• When you have limited time, survey without using sampling becomes impossible. It allows us to get near-accurate results in much lesser time
• When you use proper methods, you are likely to achieve higher level of accuracy by using sampling than without using sampling in some cases due to reduction in monotony, data handling issues etc.
• By using sampling, you can get detailed information on the data even by employing small amount of resources.

weakness of Sampling:

Every coin has two sides. Sampling also have some demerits. Some of the disadvantages are:

• Since choice of sampling method is a judgmental task, there exist chances of biasness as per the mindset of the person who chooses it.
• Improper selection of sampling techniques may cause the whole process to defunct.
• Selection of proper size of samples is a difficult job.
• Sampling may exclude some data that might not be homogenous to the data that are taken. This affects the level of accuracy in the results.

Research design

A research is valid when a conclusion is accurate or true and research design is the conceptual blueprint within which researches conducted. A scholar for his research, prepare an action plan, it constitutes the outline of collection, measurement and analysis of data.

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data.

Research design

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of intervention strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new intervention strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

Strengths

1. This is a collaborative and adaptive research design that lends itself to use in work or community situations.
2. Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
3. When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
4. Action research studies often have direct and obvious relevance to improving practice and advocating for change.
5. There are no hidden controls or preemption of direction by the researcher.

Weaknesses:

1. t is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
2. Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
3. Personal over-involvement of the researcher may bias research results.
4. The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
5. Advocating for change usually requires buy-in from study participants.

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