Examine a research article which incorporates a measure of central tendency and descriptive statistics. Breifly summarize and report the measure of central tendency and discuss whether the assumptions of the statistics were met and if the type of data (levels of measurement) was appropriate for the statistical test.
Descriptive statistics means a simple quantitative summary of a data set that has been collected. This statistics helps in the understanding of the experiments and data set specifically and tells us about the data that are to be needed for the setting the data in perspective. Statistics have the use of collection, analysis, interpretation, and presentation of the data, and is purely mathematical process (Fabian, 2010). The main use of the statistics is in the reduction of the huge amount of data. Statistics are divided into two parts that are descriptive and inferential. The main function of the descriptive statistics is to organise and describe the collected data in a specific study sample. It helps in the implementation of the correct data in the correct place by providing of the details as much as possible. Descriptive statistics have many uses in the daily life and are initially used in the measurements of the variance and the central tendency (Murray et al. 2014). There are many research questions that are asked to determine that if there is any difference between two groups on a variable or a group of variable. It is also checked whether the differences occurred just by chance or not. In the following essay the measurements are done for the development of the self help care for the heart patients.
The present article aims at identifying experienced patients of heart failure (HF) cases to develop strategies and treatments so that the self-care of HF patients improves and for development and providing recommendation for enhanced use of improved methods. A large evidence based study carried out to determine the viewpoint of various health professionals and nurses or caregivers and patients regarding self care of HF patients (Vazler, 2015). The article mainly focuses on examining experiences, behaviours and viewpoint of health professionals and patients and obtains various data on self-care to provide support to HF patients.
The measure of central tendency I aim to study in this article is mean. Mean as previously mentioned is the most common used measure of central tendency. Divided into several types weighted mean, arithmetic means, harmonic mean (HM) and geometric mean (GM). The reason for choosing mean in our study was that it provides a good representation of data as it uses every value in the data. Mean has the best ability to resist fluctuations between various samples for example we observe that samples drawn repeatedly from the same population gives similar means and thereby confirming mean ability to resist fluctuations. Other advantages of using mean is that it is a fast and easy way to calculate and available computer applications can be used to calculate it, no additional program is required. The calculation is easy and simple and does not require skilled statisticians to calculate it (Holst et al. 2000).
Mean also has a number of limitations. To calculate mean our given set out data should be numerical. Mean cannot be used for nominal data, for example, data on race, gender and appearances like the eye colour cannot be evaluated by mean. Outliers that are numbers that is much higher or much lower than the rest of the data set makes mean calculation sensitive. The assumptions for measures of Central Tendency are not applicable for all types of variable (Li & Shun, 2015). There are three types of variable, such as, nominal, ordinal, interval or ratio variables.
For the nominal variables, Mode is the most appropriate measure for central tendency. Median is not applicable in the case of nominal variables because there is no order in the nominal variable. Mean is not applied in the case of nominal variable, as nominal variable does not include any real numbers (DR. Anilkumar, 2013). The mean always needs mathematical accountings based on some numerical measures, i.e. real numbers which can be operated mathematically.
In the case of ordinal variables, Median and Mode are applicable. Mean may not be applicable. Ordinal numbers are not real numbers. So mathematical operations cannot be done in the case of ordinal numbers (Li & Shun, 2015). So mean is not applicable in the case of ordinal numbers. Ordinal numbers can give mode as the maximum frequency can be seen in the ordinal variable.
In the case of interval / ratio variable, the three measures of central tendency, are
median, mean, and mode are applicable. However, if there are boundaries in the data, measurement of mean is indistinct. Therefore, if there are boundaries in the data measurement of mean is a meager choice and median or the mode should be used (Murray et al. 2014).
Among 1421 papers identified, 37 met the condition for addition in this evaluation of patients’ self-care requirements. Incorporated studies concerned 1343 patients (565 male, 549 females 229 sex not accounted; mean age is 66.1 years and the range is 25-98 years), 63 healthcare providers and 75 caregivers. The greater part of studies were accomplished in USA (n = 24). In general, study feature was reasonable (n = 19) with ordinary study weak points being outward themes, over-reliance on expediency sampling and inadequate explanation of sample distinctiveness.
Quantitative as well as qualitative research performed to obtain data regarding self-care from perspectives of healthcare givers and patients. We studied aspects of disease management by qualitative meta-synthesis approach (Murray et al. 2014). We used 11 databases for comprehensive, systematic and detailed research. We used research papers published from 1995 to 2012 in the study, which included about 37 studies from Ovid EMBASE, Ovid AARP Ageline, CSA Sociological Abstracts Ovid MEDLINE, EBSCO Academic Search Complete, EBSCO Soc INDEX, EBSCO CINAHL, Scopus and Proquest Dissertations and Theses, Ovid Psyc INFO ISI Web of Science (Lee & Priebe,2012). We studied 63 health care professionals, 75 caregivers and 1343 patients HF self-care cases. Only those studies were included in the study, which provided primary data containing population specific data partly or wholly of mixed methods designs on samples above the age of 18. We excluded the papers published before 1995 and those not published in English. We used a quality appraisal strategy and based on ten questions research was performed. These questions validated by CASP (Critical Appraisal Skills Programme) to maintain the high quality of research. We calculated the mean age of patients to be 66.1 years, and the range of age was 25-98 years (DR. Anilkumar, 2013). We obtained most of the study cases about 24 in USA. We performed a moderate level of study about 19 cases and main weaknesses in studies were found in over-reliance on convenience sampling, superficial themes and insufficient description of sample characteristics. For all the cases, we examined data, which did not support higher-order or lower-order themes and according to that, further analysis or redefinition of themes was performed. We enlisted the study on 37 papers in a table. Method of study used was face-to-face interview, structured, semi-structured interview and unstructured interview. We used theoretical, convenience and purposive mode of sampling strategy. For each paper number of patients, health professional and caregivers enlisted and mean age for each was calculated and mentioned. The measures of central tendency that has been evaluated in the given journal article is regarding the measurement of central tendency of mean. The article reflects improving support of the failure of heart of the various patients in a given segment of a country. The organization needs to implement several tools and techniques to measure the improvements of the patients to maximize supporting options to nullify heart failure. The analysis of data is done through the help of quantitative data analysis with a sample size of 1343 patients and 63 health care professionals. The approach, which is selected here, is described design of research analysis (Li & Shun, 2015). The evaluation of data reflects that form of self-efficacy assists the health care patients to enable themselves to minimize the effect of heart attack. In addition to this, the measurement of central tendency has helped the researcher of the given journal article to attain the respective research hypothesis. However, the level of central tendency interprets that there are several limitations in the process of measurement of mean. The scope and quality of the data cannot be measured through the analysis of mean. In addition to this, there are no open-ended questions in the given research article. This does not emphasize the effect of analysis of the respective data analysis.
This measure of central tendency was appropriate as it provided a single value for a large set of values. From the research, we could convey the experience of HF patient’s symptoms, which is mostly ambiguous due to the limited knowledge of HF patients about self-care. From the interview sessions, we were enlightened with their personal experiences, needed to improve self-care strategies.
The results of this research indicate that even after recalling self-care advice from health professionals by patients they were unable to use and integrate this knowledge into their daily life. Clinical indicators in the study were ignored and rather the patients’ feelings and attempts to manage HF was evaluated. Results from the study indicate that patients and caregivers used their experiences for self-care (Murray et al. 2014).
The quality and scope of the included study constrained the findings of this meta-synthesis. A major drawback, which had an impact on the depth of our analysis, was that our studies related to superficial themes. The data represented was inappropriate as in this study older patients were under-represented; theory was under-utilized for understanding the influence of age and social determinants and lack of age based analysis and the link between patients and self-care failed to address. Problematic areas included lack of HF knowledge and self-care.
Conclusion
From the patients’ reported accounts, scientific statements and professional recommendations on HF self-care lacked evidence based approaches. Inspite of the large-scale research carried out the relation between patients’ knowledge and self-care remains unclear. Inspite of all the drawbacks, some strength were also analysed in the study. This study provides approaches persuasive and new and provides empirical evidence for non-pharmacological management of HF patients. By the use of experience, feedback and coaching HF self-care is learnable and can be optimized with time. For better study of HF self-care, we can use algorithms or decision aids with caregivers and patients when available to help them in important decision making in self-care. From the studies, it was found that sophisticated knowledge of HF and misconception of knowledge does not facilitate better self-care.
Research evidence shows that for an effective HF self-care in future, interventions should be conducted on the context of patients and should not be considered as a completely knowledge based issue.
References
Cowie, M., Anker, S., Cleland, J., Felker, G., Filippatos, G., Jaarsma, T., Jourdain, P., Knight, E., Massie, B., Ponikowski, P. & López-Sendón, J. (2014). Improving care for patients with acute heart failure: before, during and after hospitalization. ESC Heart Failure, 1(2), pp.110-145.
DR. P. Anilkumar, D. (2013). Refining Measure of Central Tendency and Dispersion. IOSR Journal of Mathematics, 6(1), pp.1-4.
Fabian, Z. (2010). New Measures of Central Tendency and Variability of Continuous Distributions. Communications in Statistics – Theory and Methods, 37(2), pp.159-174.
Holst, M., Iwarson, A. and Cline, C. (2000). Characteristics of patients referred to a nurse-led outpatient clinic for heart failure: A potential for improving care. European Journal of Heart Failure, 2, pp.60-60.
Improving Self-management and Reducing Hospital Readmission in Heart Failure Patients. (2009). Clinical Nurse Specialist, 23(4), pp.222-223.
Lee, D. & Priebe, C. (2000). Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator. Computational Statistics, 15(2), pp.169-181.
Li, C. & Shun, S. (2015). Understanding self care coping styles in patients with chronic heart failure: A systematic review. European Journal of Cardiovascular Nursing.
Murray, C., McDonald, C. & Atkin, H. (2014). The communication experiences of patients with palliative care needs: A systematic review and meta-synthesis of qualitative findings. Palliative and Supportive Care, 13(02), pp.369-383.
Vazler, I., Sabo, K., & Scitovski, R. (2012). Weighted median of the data in solving least absolute deviations problems. Communications in Statistics-Theory and Methods, 41(8), 1455-1465.
Spaling, M., Currie, K., Strachan, P., Harkness, K. & Clark, A. (2015). Improving support for heart failure patients: a systematic review to understand patients’ perspectives on self-care. J Adv Nurs, p.n/a-n/a.
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