Literature Evaluation Paper

Section I: A Review of the Data Analysis

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Introduction

Medical knowledge is increasingly dependent of theoretical studies and the findings are normally presented as well as synthesized with statistical methods. Therefore, it is paramount that a physician gets acquainted with the most widely used statistical tests, since this is the most appropriate way for him or her to assess the statistical methods used in scientific journals and interpret the results correctly. There are several ways of analyzing data in qualitative researches. The statistical method of data analysis used is based on numerous considerations. Since the process of data gathering and synthesis is in agreement with new analytic procedures informing the procedure of extra data collection, it is vital to understand that qualitative data analysis processes are not completely separable from the actual data. This paper, therefore, seeks to analyze three empirical studies and will focus on these statistical methods: nonparametric tests; t-tests; and correlations.

Nonparametric Test Paper

Research problem

Trauma is amongst the most prevalent causative agents of mortality for individuals aged from 1 to 45. Obesity is a national catastrophe, which impacts all areas of health care, trauma included. Obesity is a critical health issue owing to its close correlation with major medical diseases in addition to heightened probability of unclear conditions. The probable number of obese trauma patients characterized by complications and critical injury increases with the proportional increase in the number of obese adults. The study sought to assess whether trauma patients with dissimilar body mass indexes varied in the utility of resources calculated as a complex outcome variable.

   Data collection and source

Reliability is the measure of consistence of stable results. Trauma registry was utilized for a surveying research of adult in-patients in a Midwestern level 1 trauma center. Participants were categorized into three groups: non-obese, obese, and morbidly obese. Subsequently, the correlation between patient/injury features as well as hospital resource usage was determined with the help of three canonical correlation analyses.

 Variables

Categorical variable

Race

Sex

BMI

Ordinal variable

Mechanism of injury

Discharge destination

Mortality

          Sample size estimation

A large sample size was used in this study. It comprised 69 percent of the US population and 64 percent of patients treated in the facility. The size of the sample used in the study, largely helped in determining the accuracy of approximates of study population characteristics. Being a clinical trial, power analysis was conducted to aid in choosing an appropriate sample size, which was indicative of US trends in morbidly obese, obese, and overweight patients. It was specifically important in testing the null hypothesis, that a vivid depiction of the methodology as well as rationale for selecting the sample size was obtained.

          Appropriateness of statistic.

In this exploratory research, canonical correlation analysts were used to determine the connection between patient/injury features in addition to usage of resources across BMI groupings. The assumptions were not met in that from the study; it emerged that injury factors were responsible for the main difference in hospital resource utilization in the trauma patient. The level of measurement was appropriate since canonical correlation analysis is instrumental when the surveying dimensions symbolizing the mixture of variables are not known. It was wise for the researchers to choose non-parametric tests because the assumptions important for the parametric method did not hold. The study average inter-item correlation to increase the reliability of the study. This is a subgroup of internal consistency reliability, which is gotten by taking all the items on an experiment, which investigate the same construct.

   Data display.

Raw data, means, medians, and other statistics for experimental units were presented in clearly labelled tables. Raw data offered the clearest and easiest summary of the study. Moreover, it offered a straightforward access to the information for any interested reader to go through. In particular, the paper had tables with data in parentheses with additional explanation.

Correlation Paper

 Research problem.

The study seeks to answer the following research problem:

The self-perceived health situation of the adult population in Haiti. It also seeks to determine the self-perceived health literacy needs of the adult population in Haiti. Further, it assesses the disparities in self-perceived health situation in urban and rural Haitian adults. Lastly, it seeks to determine whether the Community Based Participatory Research strategy is successful in obtaining health information in rural and urban Haitian population.

  Data collection and source.

The study employed the use of surveys to gather demographic data, and it was effective in acquiring the individual importance of numerous health education topics. Native Haitians were put through the training on how to make use of the survey instrument as well as carried out a short interview after acquiring the consent of subjects in urban and rural settings.

 Variables.

Categorical variable

Age

Children

Ordinal variable

Health-related status

Years of education

There are four measurement scales: ordinal, nominal, interval and ratio. They are just simple ways of categorizing different types of variables. All these scales are mutually exclusive and they do not have any numerical significance. Ordinal scales helps in showing the significance of the order of the values, where the differences between each of them is not known. Ordinal scales typically measures non-numeric concepts such as happiness, satisfaction, discomfort and other. Nominal scales will be used to label variables that have no quantitative value. It will be used to label a series of values. On the other hand, Ratio scales are nirvana when measuring scales because they show the order, they show the exact value between the units, and they have an absolute zero that permits a wide range of both inferential and descriptive statistics to be applied. For instance, variables such as height and weight will be used. Ratios will aid use of additions, subtractions multiplication, division, central tendency and measures of dispersion.

Sample size estimation

In the current research, subjects (n = 340) were drawn from residents of either Gressier, Leogane, or Carrefour, Haiti. It only involved both male and female adults who were ready to fill in the survey. Being a clinical journal, a pre-study power analysis was conducted to arrive at 340 experimental units. Moreover, the sample size was appropriate for the statistical test based on the fact that it produced a power value of over 80%.

 Appropriateness of statistic.

Simple descriptive statistics were carried out on demographics, health education, and health-related status. One-way ANOVAs and Independent t-test were carried out between variables to determine the disparities, which existed in the sample. The statistical method used was appropriate in that intended to determine whether disparities exist within the sample. The assumptions of the study were met in that health-related status largely differed by urban versus rural community type. The level of measurement was appropriate for this study in that it determined successfully the level of difference in health-related status. Those in urban areas (SD = 1.10, M= 2.64) perceived their well-being as largely better compared to those in rural settings (SD = 1.14, M = 2.26), p = .004, t (324) = 2.93. The quality and the validity of the study was introduced by the use of community based participatory research.

 Data display

The data were displayed in elaborate tables that were labeled correctly. This was appropriate in that it aided in presenting detailed results as well as sophisticated trends, patterns, and relationships concisely and clearly; minimizing the length of the report in addition to enhancing readers comprehension of the research findings.

T test Paper

 Research problem.

The study sought to confirm the impact of the primary percutaneous coronary intervention (PPCI) using <60 min door-to-balloon time on ST-segment elevation myocardial infarction (STEMI) patients prognoses. PPCI is the best-suited intervention for STEMI patients. Moreover, for transfer patients, PPCI ought to be carried out not more than 90 minutes after arriving at a health facility. The door-to-balloon time is highly connected to the probability of survival. Moreover, the door-to-balloon time is the acknowledged indicator of care quality. Since recent studies indicate that greatly reduced door-to-balloon time does not necessarily translate into enhanced mortality rate of STEMI patients undergoing PPCI, there is a need to know whether reducing the door-to-balloon timeframe is important.

          Data collection and source.

The patient clinical and demographic data was gathered from the ED administrative database. The research was carried out in a three thousand tertiary medical referral hospital situated in Kaohstung, Southern Taiwan. More than 130, 000 patients are admitted to the emergency department each year. Over 150 STEMI patients have attended to annually, and almost all of them acquired PPCI as reperfusion intervention. STEMI patients who acquired PPCI from 1st January 2011 to 31st December 2014 were involved in the research. Moreover, patients aged above 18 who reached the emergency department not more than 12 hours following the onset of the symptoms and satisfied the diagnostic criteria of severe STEMI evaluated via electrocardiogram in addition to coronary artery disease verified by PPCI met the inclusion criteria. The exclusion criteria involved patients with greater than 90 min door-to-balloon time together with those with extended cardiopulmonary resuscitation in the emergency department owing to their potential poor outcomes. Also meeting the exclusion criteria were patients from other hospitals.

 Variables

Categorical variable

Age

Male

Body mass index

Ordinal variable

Mean artery pressure

Diabetes

Hypertension

Hyperlipidemia

Previous myocardial infarction

Interval variables

Smoking

History of PCI

Sample Size Estimation

A single-center research with a comparatively smaller sample size was used in this study. This is a clear indication that power analysis was not conducted. The sample size was not appropriate for the statistical test. The smaller sample size or under powering rendered the study statistically inconclusive and made the entire protocol a catastrophe. This is among the main limitations that are present in this study.

  

Appropriateness of statistic. 

For continuous variables, the collected data were summed up both as the mean as well as standard deviation and later analyzed with the help of Students t-test. Further, categories were summed up as percentages and figures, and the relationships between the outcome groups were determined using the chi-square test. Binary logistic regression models evaluated the impact of less than 60 min door-to-balloon time on known patient outcomes to cater for the probable confounding factors in the multivariate analyses. The effects were then estimated in regards to altered odds ratios and the equivalent 95% confidence interval. The results were only regarded as being statistically important if a p-value < 0.05 was acquired using two-tailed Students -test. The two-tailed Students -test was appropriate for this type of data because it helped parallel the means of the disparities between unmatched or two paired samples. It involved dependent data because there was a 1:1 corresponding agreement in each sample. In this study, the disparity between any set of pairs is measured, and the average of the disparity of the pairs is evaluated for statistical importance. Here, the assumption was that the paired disparities are independent observations. The reliability of the study was obtained by seeking the approval of the retrospective study from the Chang Gung Medical Foundation Institutional Review Board.

 Data display.

The data were displayed in elaborate tables that were labeled correctly. The correct labelling of tables was significant in helping achieve proper presentation of detailed results, patterns, and relationships concisely and clearly. Moreover, it helped in summarizing the report in addition to enhancing readers comprehension of the research findings.

Section II: Data Analysis Evaluation

Unarguably, reading the data analysis section is a hard and the most sophisticated of all stages of analyzing a qualitative project, and one that is given the least focus by nurses. Nurses will often skim through the data analysis for obvious reasons. For trainee nurse researchers, most of the data collection strategies, as well as data presentation strategies used qualitative projects, are familiar and comfortable. Nurses have always focused their clinical practice in elucidating as much as possible regarding the individuals they work with. In most cases, nurses may ignore the data analysis section because the data is not represented in elaborate tables, diagrams, or graphs that may make it easier to understand. If data is not presented in an easy and logical manner, it is boring for the nurse going through the data analysis section.

For readers of both quantitative and qualitative types of research, the language of analysis could also be the other reason why nurses avoid reading the data analysis section. If the language used is confusing, it becomes difficult for one to decipher what the researchers did at this stage and to comprehend how their results evolved out of the information that was gathered or constructed (Thorne, 2000).

Moreover, in illustrating their processes, some researchers use language, which accentuates this magic and mystery. For instance, they may argue that their theoretical categories arose from the data (one may wonder whether the data was left overnight and the researcher awoke to get that, the data had organized itself into a coherent new structure (Kaplan, 2012). In short, the language used in the data analysis section of the qualitative research is what makes nurses avoid going through the section.

Why understanding data analysis section is important for advanced nurses

Advanced nurses who not only have knowledge of descriptive methods but also with Fishers exact test, Pearsons chi-square test, as well as Students t test have the advantage of analyzing a huge sample of clinical research journals. Clinical journals normally compare the efficiency of a new intervention in an experimental unit with that of established intervention. Aside from a mere description, they would also want to find out whether the observed disparities between the experimental units are really present or random. The difference could be a result of chance variability in a parameter like the efficacy of the intervention in the experimental group. To this end, it is important that nurses get acquainted themselves with statistical methods of data analysis so that they can avoid skimming through the data analysis section.

Conclusion

       In conclusion, the essay sought to analyze three types of statistics and focused on nonparametric tests; t tests; and correlations. Three qualitative articles were used in this research. Each of these articles represented a certain statistical method. The statistical method of data analysis used is informed by various considerations. Since data gathering, as well as analysis processes, is somehow in line with new analytic procedures informing the procedure of extra data collection in addition to new data directing the analytic process, it is vital to understand that qualitative data analysis processes are not completely separable from the actual data.

References

Kaplan. (2012). Reading and Critiquing a Research Article. American Nurse Today,

2012.

Thorne, S. (2000). Data analysis in qualitative research. BMJ Journal, 1-1.