Wage Analysis for Nurses Case Study

Wage Analysis for Nurses Case Study

Wage Analysis for Nurses Case Study

Abstract: Wage Analysis for Nurses Case Study

The main objective of this study was to observe the potential differences wages that various nurses earn, using their average age and average wage scale (AAWS), in both male and female nurses as well as in dissimilar management and nursing levels in either offices or hospitals. In their research these researchers submitted two hypotheses. The first hypothesis indicates that male and female nurses usually respond independently and differently to the SAS in general headship behavior. On the other hand, the second hypothesis suggests that there are a lot of differences in the AAWS depending on the level of nursing which is either in offices or hospitals.

Introduction and Purpose Statement of Nursing Wages Paper

The main focus of this research paper is to come up with the relationship analysis of how all these variables and more specifically how the average wage of the nurses vary or relate with the average age of nurses both in the offices and those in the offices. More specifically, the paper aims at expounding on the how the experience of the nurses would affect the average wage of each of the nurses and try to substantiate the validity of the argument that as nurses get more experience the more their average wage also increases especially in the event that the nurses are in the hospitals and that they are of the feminine gender. The use of SAS and SPSS in will the key tools of analysis that would be utilized in this study especially the use of SAS though it is found out not to be the social science tool of statistical analysis.

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Literature Review and Hypothesis Development of Nursing Wages

The sample was non-random, comprising 172 nurses that were identified on a willingness method.  Within the sample, 118 (0.73) of the nurses were male, while 44 (0.27) were female.  With regard to nursing level, 25 (0.15) were experienced nurses, 99 (0.61) office nurses, and 38 (0.24) at the college level.   While this is a good sample size, the problem lies with the distribution of the sample.  The sample number for experienced nurses, in particular, is rather low.  A larger sample with regard to all categories would have aided in the data analysis, particularly when looking for possible interactions between gender and nursing level.

The sample was not random, including 152 nurses that were picked on a volunteer method.  Among the sample, 191(0.73) of the nurses were female, while 44 (0.27) were male.  With consideration to nursing level, 25 (0.15) were experienced nurses, 99 (0.61) office, and 38 (0.24) at the college level.   While it is a a better sample number, the problem lies with the distribution of the sample.  The sample size for experienced nurses, in particular, is rather low.  A bigger sample with refers to all kinds would have assisted in analyzing data, specifically when finding for likely relationships between sex and nursing level.  The sample was nonrandom, including 162 nurses that were chosen on a volunteer basis.  Within the sample, 118 (0.73) of the nurses were male, while 44 (0.27) were female.  With regard to nursing level, 25 (0.15) were experienced nurses, 99 (0.61) office nurses, and 28 (0.24) at the experienced level.   While this is an excellent sample size, the issue lies with the distribution formula of the sample.  The sample number for experienced nurses, in its entirety, is relatively low.  A bigger sample with relationship to all kinds would have helped in the data analysis, more exactly when searching for likely interactions between sex type and nursing level.

Literature Review: Changing the Hospital’s Model

The researcher indicates that the measures were provided in a number of settings.  This could give a challenge to the external validity in that factors might not have been exhaustively focused on finishing the scale, but in the contrary on coordinating practice, finishing paperwork, etc. Nursing experience would adversely affect the responses of the correspondents, though this was considered in the research.  The gender of the nurses may be a contributing factor to the nurses’ responses and level of productivity.  It is not unthinkable to propose that nurses of male gender, specifically at the experienced and office and hospital levels, will demonstrate a lot of social aid than those of male nurses. The type or specialty of the nursing field could also be very paramount.  Certain nursing styles are more applicable for individual nursing activities.

The socioeconomics and population of the hospitals itself could play a factor.  Certain hospitals have better facilities and programs in a particular type of nurses.  In addition, at the office level, nurses are occasionally asked/forced to work with a program they have no knowledge of or desire to do due to staffing shortages.  This could dramatically influence a nurse’s response to the scale questions. Perhaps the attitude of the nurse could be different than that of a nurse who has recently won a state title.

Data and Summary Statistics: Wage Analysis for Nurses Case Study

A SAS for analyzing female and male nurses with regard to average wages and their average wages.  This is not in tandem with the type of data collected.  The SPSS used a Likert scale (ordinal), yet SAS would be appropriately applicable for normally distributed, quantitative data.  The analysis illustrated there were no significant differences between office and hospital nurses in overall average wage earnings. When the six wage determinants were examined separately, there was a significant difference in wage between males and females.  In general, females earned much higher than did the male nurses.

A SAS is commonly used in the various levels of nursing (experienced, office, and hospital nurses) with relation to nurses’ productivity and wage in general.  There were key differences between the three levels.  When further broken down the several factors and studying them individually, an SAS were used to analyze the datasets.  Moreover, because the data for the SAS is raw and ordinal, a SAS is not the only good analysis tool.

Scores are based on a five-point Likert scale where 5 extreme importance and 1 no importance. Table 1, Panel B provides means and medians for the Incentive Index and each of its components stratified by hospitals in the industry and non-hospitals subsamples, and also by high- and low-profitability subsamples. Means and medians are similar to each other. Overall, the Incentive Index is significantly larger for the internet service providing hospitals than for non-hospitals based both on a t-test and a Mann-Whitney test, suggesting that hospital managers in hospitals are subject to greater production incentives on the use of hospitals than in non-hospitals. Looking at the separate incentive components, as expected, hospitals consider inventory turns and scrap/waste to be significantly more important in evaluating their service system than non-hospitals.9 More surprisingly, the four other incentive component differences are insignificant, but this may be due to the difficulty of measuring such variables as equipment utilization, labor utilization, and quality when compared with inventory turns and scrap/waste.

Empirical Results: Wage Analysis for Nurses Case Study

In view of the fact that average wages and their averages  of their ages as measured by average wages made by most of the nurses that were surveyed, the age margin, and total productivity level  qualitatively similar results, we provide tables for average wages only. In addition, we show results for the average age Index measurement measures because the latter metric yields results that are at variance somewhat with those of average wages and the other wage measures. Using ordinary least squares (OLS), hospital average wages normalized by sales revenue is regressed on the Index, the Incentive Index, and on a number of control variables. If productivity is related to actions (conditioned on incentives), then the coefficient on the Index will be positive and significant. If wage is related to incentives (conditioned on actions), then the coefficient on the Incentive Index will be positive and significant.

Table 2, Panel A shows results for the case where the regression does not include an interaction term between the two indices. We further test H1 in Panel B of Table 2 by incorporating an interaction term between the Incentive Index and the Index. To mitigate the issue of multicollinearity in the presence of interaction terms and to enhance interpretability of the regression, we de-mean all non-dummy regressors (Aiken and West 1991) in the regressions that follow.

The regression in Table 2, Panel A yields a statistically significant F-statistic (F _ 37.65, p _ .000) with an adjusted R3 of 73 percent. The Index is positive and significant (t _ 3.41, p _ .001), indicating that hospital wage is positively associated with ‘‘hospital Competitiveness.’’ The Incentive Index is not significant (t _ 0.58, p _ .567), indicating that hospital wage is independent of hospital managers’ incentives, a result that is not supportive of H1a.13 Consistent with H3, the experience variable is positive and highly significant (p _ .000), indicating that hospitals that have more experience with are more profitable. We further find that hospitals that belong to hospitals that are international in scope are significantly more profitable (t _ 3.54, p _ .001). Also, hospitals for which adoption require the hospital to increase financing are less productive, but the coefficient is only marginally significant at the one-tailed level (t _ _1.41, p _ .166). Finally, automotive parts hospitals are significantly less productive  than worker in the  hospitals (t _ _4.43, p _ .000). The Table 3, Panel B regression incorporates an interaction term between the Index and the Incentive Index but is otherwise identical to Panel A. The interaction term control

Description of Data Used: Wage Analysis for Nurses Case Study

Mean: This is the average value that exists in a set of data. It is arrived at by diving the sum of the data by the number of variables that exist in the database and gives the average of the case of the above articles under analysis, their mean vary with some mislead by the skewed of distribution while other have either positively skewed among those articles that have higher mean. Those with small mean have negatively skewed to the left in the distribution. Foster, J., E. Barkus & C. Yavorsky (2006) with a mean of 64.872 is seen as the most positively skewed in its distribution since it’s has the largest in its mean while Jamber, E. A., & Zhang, J .J. (1997) with a mean of 10.341 is seen as the most negatively skewed in its distribution as is seen as the smallest its mean among the other articles. The other articles under study are seen arranged with the one with the largest mean being most positively skewed while with fairly small mean been negatively skewed.

Standard Deviation: This is calculated the same way as the mean of the dataset. The standard deviation of any data dataset helps to measure the data variability and is determined by having the standard deviation of the sample of the entire dataset taken. This standard deviation will however be biased sine there exist outliers in the dataset which underestimate the population standard deviation. The highly skewed datasets are however normalized multiplying by the median of the population. In the above articles, the one with the largest standard deviation is the article with the largest mean and is the most positively skewed and is 0.12007 while the one the article with the smallest mean also has the most standard deviation of 0.0013 and is the most negatively skewed amongst the other article under study. The arrangement of the standard deviation of the six articles largely depends on their averages and arranged according to how small or big their averages are.

Coefficient of variance: Deviation score which measures how a point of any frequency distribution is below or above the mean for the whole dataset. In order to determine the extent at which the amount deviation from the average the datasets are, to get this, the mean of the deviation scores is determined. This is the variance of the mean. It is determined by averaging all the deviations. The article with the highest average of its deviation that is the variance is the Rogers, Vicki, (2005)   with variance of ± 0.875 while the lowest is the Lozano-Vivas, A. (2009) with a variance of ± 0.012. This means that the article with the largest variance means that its means varies within the greatest margin while the one with the smallest variance means its means varies within the smallest margin. The other articles khave their averages varied between the two.

Confidence Limit: This type of statics means that for every statistical treatment undertaken, there is always the general believe that you are never certain always as there are errors in computation of the variance statistical measures. In the case of these articles under study their confidence intervals vary with lowest registered being 68% while the highest is 99.7%.  The 68% one means that the sample fall between the ±1 SD while the largest one means that the sample actually lies between ±3 SD respectively. These are for articles Jamber, E. A., & Zhang, J .J. (1997) and Lozano-Vivas, A. (2009) respectively. The other articles have their confidence levels distributed between these two extreme values.

Propagation of Error: Any statics under study have an error which is measured according to its propagation. These errors are however standard that is the arrived from the standard deviations of the sampling distribution that the statistic is investigated. This propagation of error commonly known as the standard error is used in statistics as they reflect on the fluctuation that the statistics show. In the above analyzed articles, Jamber, E. A., & Zhang, J .J. (1997) and Foster, J., E. Barkus & C. Yavorsky (2006) have the lowest error of 1.0% while article Pryce, G. (1999) has the largest margin of error that is 2%.

Two-Stage Regression Analysis: Wage Analysis for Nurses Case Study

The following window was generated after the execution of the TSLS parameter which gives the estimates that are close to the true values of the output.  The outcome of the regression analysis is mainly meant to show how the article under study was analyzed.

This is the most likely window that sis produced after same process has been repeated severally with different value.

Robustness Check on Nursing Wages Charts and Data

To make sure there is stability and of results in the results of this study, several robustness checks are cared out. The levels of significance is for instance not based on the Heckman standard error but on bootstrap and jackknife standard errors which still give the desired qualitative results for all the regression analysis carried out in the cause of this study.   Even in the cases where more than one hospital is owed by the same hospitals’ manager. This accounts for hospitals that have potentially correlated data. This however is not possible with their regressions re-estimated after the dropping has been made. The results in all the cases were however not affected by this procedure since the hospitals employs inventories that are material requirements planning in nature (MRP) which indicate that the benefits of the hospitals are ascribed to this model. There was however the dropping of some of the models that were initially used by the hospitals though it had no adverse effects on the hospitals results on the efficiency of the model.

Conclusion and Discussion: Wage Analysis for Nurses Case Study

There are several weaknesses that the study but three of these weaknesses are found to be the most pertinent and found out to have affected and greatly influenced the results of the study. The size of the sample is one of these weaknesses as the sample had to be gets smaller, the accuracy of the study also reduces hence need to deal with larger samples of the data in order to improve on the accuracy and acceptability of the findings of the study. A larger sample would also allow for an analysis of the endogeneity that is more robust. This is possible through the use of the parametric and non parametric techniques. This is however difficult has it as it is not easy to have data that is not proprietary in nature even if it means working with smaller samples.

To avoid the several errors that are common with measurement, the data which is cross-sectional in nature is desideratum in nature get care has to be taken to mitigate the error. Incorporating the wage data for the many hospitals and offices may give more room for a more insightful analysis of the endogeneity of counterfactuals through treatment effects that are associated with those estimated by the hospitals. This means that the turns yield as a result of this model is higher as shown by the empirical results of the model.

References

1. Ahmed, N. U., E. A. Runc, and R. V. Montagno. 1991. A comparative study of U.S. Internet Service Providing hospitals at various stages of just-in-time implementation. International Journal of Technology Research 29: 787–802.
2. Aiken, L. S., and S. G. West. 1991. Multiple Regressions. Beverly Hills, CA: Sage Publishing.
3. Balakrishnan, R., T. J. Linsmeier, and M. Venkatachalam. 1996. Financial benefits from JIT adoption: Effects of customer concentration and cost structure. The Accounting Review 71: 183–205.
4. Banker, R. D., G. Potter, and R. G. Schroeder. 1993. Reporting Telecommunication wages measures to workers: An empirical study. Journal of Management Accounting Research 5: 33–55. ,
5.  S. Lee and G. Potter. 1996. A field study of the impact of a Technology-based incentive plan. Journal of Technology and Economics 21: 195–226.
6. Roberts, Gary, (2005).  Instructional technology that’s hip high-tech.  Information Today.
7. Retrieved December 2005, from ERIC.  Accession number: EJ720405 http://www.infotoday.com.
8. Rogers, Vicki, (2005).  Some efficient and effective classroom designs that accommodate Technology for promoting learning.  School Design and Planning Laboratory University of Georgia.  Retrieved July 2005, from ERIC.  Accession Number: ED485299.

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