Talent-Oriented Intervention for Learning Disability

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Table of Contents

Sample/Setting
Sampling Strategy
Research Design
Reference List

Sample/Setting

In order to identify the effects that a talent-oriented intervention will have on PLD, the patients will be selected from a local facility. To determine the sample size of the target population, the following formula was used:

Formula 1.

as the most common formula used for one-sample t-test procedures in the nursing research environment. The sample size of the population, therefore, is 102. The sampling interval, in its turn, can be calculated according to the following formula: K = N/n, where N is the total size of the population, and n is the sample size. Given the fact that 200 participants will take part in the process, the sampling interval of the research will equal K = 200/102 = 1.96.

Learning disability and age (18-30 years) will be the primary inclusion criteria. In other words, the people with the identified condition and within the required age group will be qualified. Comorbid conditions, in their turn, are the primary exclusion criteria.

Sampling Strategy

Since the target population will not be stratified into groups and all members will participate in the same experiment, it will be reasonable to use the Simple Random Sampling (SRS) strategy as the tool for retrieving samples from the target population. By definition, SRS implies that each of the observations carried out in the context of a particular experiment has an equal probability of being selected as a sample (). While being rather standard and applied often to a variety of researches, SRS can be characterized as lacking accuracy (Hans-Vaughn & Lomax, 2013).

Nevertheless, SRS permits for retrieving rather objective results because of the equal opportunities for each observation mentioned above. Therefore, SRS will serve as the tool for generalizing the research outcomes so that the intervention strategy that can be applicable to a variety of scenarios involving PLD could be created. Therefore, SRS should be viewed as the sampling strategy for the research.

Research Design

Type

To make sure that the framework for addressing the needs of PLD is created and all research questions are answered, one will have to use a mixed research type. Specifically, the quantitative approach (case study) will be used to carry out the experiment and identify the quantitative changes that will occur to the participants. As a result, the efficacy of the identified tools (i.e., the crossing networks and the promotion of a talent-based approach in therapy) can be assessed with the following implications for a follow-up research and the design of the nursing framework.

The qualitative approach, in its turn, will require that the interviews among the nurses should be conducted. As a result, the key effects of the new strategy can be located, and the possible issues can be identified successfully. More importantly, the characteristics mentioned above will be arranged in the framework that will help understand how to reinforce the positive effects of the talent-based model and inhibit the development of the negative ones (Sigelman & Ryder, 2014).

Description

As stressed above, the study will require carrying out observations of the online interactions between the patients and the members of the community involved in the experiment. The conversations will be supervised by the nursing staff, who will also provide assistance to the patients when needed. The PLD members will use the online software that will help them communicate successfully.

Rationale

The reasons for choosing the specified research design concern primarily the fact that the framework suggested for testing is very new. Therefore, quantitative results are not enough to understand whether it works properly, as well as locate possible issues and be able to address them in a timely fashion. Therefore, a mixed research is required.

Reference List

Hans-Vaughn, D. L., & Lomax, R. G. (2013). An introduction to statistical concepts (3rd ed.). New York, NY: Routledge.

Randolph, K. A., & Myers, L. L. (2013). Basic statistics in multivariate analysis. Oxford: OUP.

Sigelman, C. K., & Ryder, E. A. (2014). Life-span human development. Stamford, CT: Cengage Learning.

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