Research Design

In this study, the meta-analytic design was employed in the early phase, followed by a quasi-experimental technique in a later phase. Regardless of the precincts associated with quantitative meta-analysis, more than a few conclusions were drawn, which assisted in drawing formula for getting sample size. A quasi-experimental design was executed using an online questionnaire at the pre-intervention baseline. Participants answered questions premeditated to elicit rejoinders that elucidate their knowledge of the subject matter. The questions checked the level of Training of participants, the impact of Training and development on the junior staff and senior management staff of Isalu Hospitals, and gave answers on know-how Isalu hospital’s junior staff and the senior management staff contributed to the attainment of sustainable competitive advantage. Analysis of the Outcomes was done to examine if all the factors are deemed useful in the know of the knowledge and frequency of to know how Isalu Hospitals junior staff and senior management staff were contributing to the attainment of sustainable competitive advantage. Age, gender, and position of staff in the Management of Isalu Hospital were considered.

 

 

 

 

Threats to Validity of the Research Design

External and internal validity are ideas that reflect if the results of a study are meaningful and trustworthy. Whereas internal validity focuses on how effectively a study is carried out (its structure), external validity gives focus to how usable the findings are in the actual world.

During my research, I identified a number of Factors that posed Threat Internal Validity. These included;

Instrumentation: The impact of the online questionnaire utilized in a study would affect the response of the participants. Whereas it appears to be strange, it’s probable that participants would be primed into a study in particular ways and measures used, which would make some them show a reaction in a manner that differs from what they would have reacted.

Statistical regression: It is possible that several participants would respond in a given direction just as a result of passing the time and not as a result of any particular intervention.

Attrition: Some participants individuals could stop studying, which therefore means that the outcomes would be centered on a subjective sample group of persons who chose to continue.

Diffusion: This means the treatment in studying spreading from the participating group to control the groups by ways such as interactions as well as conversations with each other as well as observation. This may culminate in another issue, such as resentful demoralization. Here, the control group fails to take part in activities as they direct hate on another group.

Factors That posed Threats to External Validity

External validity will always be endangered if the study fails to put into account the interactions of variables in reality.  Some of the dangers include but not limited to;

Situational factors: For instance, as the time during the day, noise, researcher behavior, location, as well as the number of measures deployed might have an effect on the generalization of the findings.

Pre/post-test effects: It means that before the test and after the test situation, which is coherent with the effect observed in the study in given ways, for example, the cause-and-effect relationship vanishing despite lack of those additional tests.

Sample features: Pointed out to the case whereby particular characteristics of a given sample was the cause of the effect (or maybe partly responsible), resulting in the insufficient generalization of the findings.

Selection bias that included the issue of variations among individuals in a study group that may result from the independent variable (for instance, readiness self-motivation to participate in the study, specific groups of participants ending up with a more likelihood of resulting to an online study.

To enhance the study’s external validity of a study and avoid threats, these points were taken into account;

Exclusion and inclusion criteria well utilized in order to guarantee a categorically defined population in study research.

Psychological realism: The aim of the study was well explained to ensure that, participants behaved the way they would in real life

Replication: Meta-analysis was used to regulate the effect of the variable that is independent.

Field experiments would as well be deployed whereby you carry out a study without utilizing thee laboratory hence doing it outside in a natural environment.

Reprocessing or calibration: This was deployed as a form of regulation of problems related to external validity.

The Figure of the Design

Study Site

The study was conducted at Isalu Hospitals Limited. Isalu Hospitals Limited was chosen based on its ability to provide high quality medical and related services to clients using the best existing professionals, empowered with a suitable level of technology and progressive Training. It was noted that Isalu hospital had grown in an enormous way over the years and developed a good reputation and goodwill in the society. The Hospitals also enjoyed enormous goodwill and occupied a desirable position as a strong and competitive leader in the market within the healthcare industry since it offered. Due to the different services offered in the hospital, it became the ideal hospital for the study.

Sample Size Consideration

The Cochran formula (51) was applied to determine the minimal sample size.

Equation one: Cochran formula for sample size calculation.

 

n= Z² * p (1-p)

d²

 

 

 

Where: Z is level of significance (1.96) at an α-value of 0.05 for a two-tail hypothesis.

P was the prevalence of MRPs

d was precision of estimate around MRPs (5% or 0.05).

N was the sample size.

 

Sampling Method and Sample Size

A convenient sampling method was employed. Every staff who met the inclusion criteria outlined in the eligibility checklist was included in the study until the desired sample size was met. Convenient sampling was to be adopted in the research in order to eliminate voluntary response bias as well as to eliminate under coverage bias. The research population was made up of the entire 220 staff (170 junior staff and 50 senior management staff) employed by the organization while the sample size was made up of 22 senior management staff and 75 junior staff. Both the senior and junior management staff were considered for the study because they were both responsible for the daily running of activities of the organization, and they all interacted with customers at one level or the other. The senior management staff comprised of heads of departments and strategic business unit managers who oversaw the various departments of the hospital while junior staff comprised the nurses, assistant nurses, pharmacy technicians, records personnel and other operational staff.

Eligibility Criteria

Staff was included if they had worked in the hospital for at least five years. Staff who had worked for less than five years were excluded.

Data Analysis Approach

Analysis of data would be carried out in descriptive statistics with a five (5) point Likert scale. The advantage of Likert scales is that they produce a cumulative response process, i.e., they make it possible to sum up, the scores and produce a cumulative score. Likert scales enhance speed, confidentiality, and collection of honest feedback. However, it is possible for the respondents to give false information in an attempt to maintain privacy. Lack of understanding of the question asked may also make respondents give wrong feedback. Another major shortcoming of Likert scales is difficulty in achieving the internal consistency of the scale, which can affect result interpretation. Also, intervals between the different points on the scale may not be a true representation of the changes in individual attitudes.

In view of the hypotheses stated above, nominal and ordinal data would be collected for the research because they were both classified as categorical data, i.e., non-quantitative and could be represented by string variables. Nominal data do not have quantitative values and are used for the ‘naming’ of variables, as the name implies. Ordinal data, on the other hand, have their variables arranged in an ordinal form but are not measured mathematically. They are in the form of labels used form representing opinions. Similarly, a nonparametric test would be conducted since the hypothesis was aimed at determining how senior management and junior staff contributed to organizational performance as well as the impact of Training and development on the organization’s competitive advantage. A nonparametric test was considered because the analysis does not require data to fit a normal distribution. Nonparametric tests rely mostly on ranking and orders in which data are sorted.