In your peer responses, offer suggestions and other thoughts for your colleagues to consider. 1, Responds to this post in half a page use different reference to response please Jacque What does the term Descriptive Statistics mean? Data that is summarized or broken down, and turned into more meaningful measures (Salkind & Frey, 2022). Variables are quantitative descriptions, such as central tendencies (Salkind & Frey, 2022). This includes the mean, mode, and median, for example. 2. Identify which demographic and outcome data points in the case study a mean, range, and standard deviation will be calculated for. Men and women fell within the mean age of 42 years old. However, there was a great deal of variability in the ages. The youngest participant was 19 years old and the oldest was 82 years old. When calculating the standard deviation for pre- vs post-test questions, preQ5 and postQ5 had a lower standard deviation. This indicates question number five, on the self-efficacy questionnaire, had less of a spread within the scores. Participants were more consistent in how they answered Q5 before and after the educational session. Q5 was closest to the average score when compared to Q4. This infers that the educational sessions have helped participants increase self-efficacy within Q5. On the other hand, Q4 had a standard deviation greater than Q5. PreQ4 SD , while postQ4 SD , hypothetically the clinician can gather that the educational session was less impactful on the participant’s self-efficacy on the topic. 3. Identify which demographic and outcome data points in the case study frequencies and percentages will be calculated for. The gender percentages revealed that there were more female participants, compared to males, 56% and 44% respectively. These statistics are appropriate data points because it helps to identify which gender had more involvement in the study. It also makes the project manager ask questions: Why are more women involved than men? Are there cultural influences? Are women more involved because they do most of the cooking or management of medications for the household? Evaluating this further helps the clinician to better understand cultural differences, in order to improve cultural competence among clinicians. The largest ethnic group within this case study were Hispanic or Latino; there was no participation from Asians or Pacific Islanders. This can infer a couple of things: One ethnic group wished to be more involved in the study over the other. One ethnic group had better hypertensive control than the other, so they did not participate. This type of statistical analysis is appropriate because it helps clinicians to better prepare educational sessions for a particular ethnic group. Lifestyle changes are often easier for clients to follow when their cultural preferences are incorporated and respected. According to Mora and Golden (2017), it is crucial to identify and consider cultural preferences before teaching lifestyle modifications. 4. Discuss one method the task force could use to analyze the collected outcome data (. a group mean comparison of pre and post protocol values, a percent change, a t-test, etc). Include a rationale of why you chose this method. Also discuss any potential limitations of using this approach for data analysis. (Hint: you will be using this in your NUR 704 data evaluation plan or your related MSN courses). The task force could gain more insight from a t-test. The t-test for dependent means measures two conditions within the same participant group. The conditions in this study are a pretest prior to an educational session and a post-test afterwards. In results with little change, there would not be enough information to reject the null hypothesis (Salkind & Frey, 2022). A limitation with t-test is it usually works best with larger sample sizes; while this sample size consisted of only 25 participants (Salkind & Frey, 2022). The task force may conclude that a small difference found from a t-test is due to a sampling error or too much variability within the groups. Reference: Mora, N. & Golden, Sherita (2017). Understanding cultural influences on dietary habits in asian, middle eastern, and latino patients with type 2 diabetes: A review of current literature and future directions. Curr Diab Rep, 17(126), 1-12. 0952-6 Salkind, . & Frey, B. (2022). Statistics for people who (think they) hate statistics. Using Microsoft Excel (5th ed.). Sage Publication. In your peer responses, offer suggestions and other thoughts for your colleagues to consider. 2, Responds to this post in half a page use different reference to response please Folashade Descriptive Statistics Descriptive statistics refer to data analysis that defines or summarizes specific data in a meaningful way to create a specific pattern from the same data (Lind, Marchal & Wathen, 2019). Descriptive statistics organize data in such a way that the pattern makes it easy to understand. As a result, its significance is in simplifying data presentation for easy visualization. It does not provide a conclusion based on the data analyzed, but rather defines the data in a simple and understandable manner. Descriptive statistics typically include a sample description of the data, which is divided into two categories: central tendency measures and variability measurements (Kaur et al., 2018). 2. Demographic Data Points – Mean, Range, and Standard Deviation Central tendency and spread measurements are known as mean, range, and standard deviation. They take on a quasi-numeric value and can be divided into more manageable fractional or nominal increments (SAGE Publications, 2015). In the case study, age as a data point is a significant direct consequence for describing a group with a chronic condition such as uncontrolled hypertension. The mean, on the other hand, has the excellent analytical quality and is frequently used for quantitative modifications and analysis. As a result, in the case study calculation, the average/arithmetic mean of the sample age can aid in determining the average age of most patients with uncontrolled hypertension. One of the benefits of using an arithmetic mean is that it is a commonly used metric because it is easily estimated and interpreted. The disadvantages, on the other hand, are that it has no effect on the mean if a data component is omitted and is heavily influenced by abnormal sequence variables (Brinks et al., 2013). The range of a distribution is the difference between its maximum and minimum score. What is required is a distinction between the highest value of data collection and the lowest number of data. The disadvantage of a range is that it is a reductive metric of data dispersion because it is extremely susceptible to anomalies. As a result, the usefulness of a true range of data is restricted in specific respects to statistics because a single data point can have a significant impact on the significance of the range (de Sousa Rodrigues et al., 2017). The standard deviation (SD) describes the variability in a set of values. It quantifies the sample’s deviation from the mean and has a favorable square root of the difference. Blood Pressure Variability (BPV) may need to be understood by uncontrolled hypertension researchers within 24 hours or weeks in the example study. As a result, the standard deviation (SD) of blood pressure measurements is the most used index. This has significant implications for intervention, where the primary goal has previously been to lower mean blood pressure levels. In the case study, gender can be used as a data point to investigate the variation in blood pressure variability measures between men and women for a successful diagnosis (Sykes et al., 2016). 3. The outcome data points will be the effect of increased self-efficacy. Gender, ethnicity, and use of the internet for health information will be used as demographic data points for computing frequencies and percentages. This is since percentages and frequencies are appropriate for describing nominal and ordinal variables (Lind et al., 2019). Gender is a nominal variable in this case because it can be assigned labels M (for males) and F (for females) with no meaningful order. The use of the internet for health information is nominal because it can be assigned labels N (No) for no internet use and Y (Yes) for internet use. Ethnicity is an ordinal variable because researchers can assign each ethnic group a unique letter and arrange the groups in a meaningful order, such as from largest to smallest. In the case study, calculating the incidence and prevalence rates of hypertension, as well as gender, ethnicity, and race, aids in obtaining trends of the standardized aggregate rate of incidence/prevalence that can be visually displayed (Noordzij et al., 2010). One of the most used measurements is the percentage. They are useful for investigating deviations from the initial value in addition to being particularly effective in evaluations. The percentage’s proportional significance is the hundredth fraction of each measure. Age, gender, and ethnicity can be used to calculate age-, ethnicity-, and sex-standardized incidence and prevalence rates of hypertension based on the case study. The overall standardized incidence/prevalence rate trends can then be calculated and presented as percentages (Lynn, 2020). 4. Mean comparison of the pre and post protocol values is an appropriate method. The task force would use this approach to determine whether the use of self-efficacy is effective in the management of chronic diseases. It will do so by comparing the means of the protocols before and after they are implemented. Researchers will determine whether there is a significant difference between the two means by performing a two-tailed t-test at a certain level of significance (such as 95%) and thus whether self-efficacy is effective in the management of chronic diseases. This method is appropriate because it accurately and effectively compares the outcomes of each group using the 20% value of mean total efficacy (pre and post protocol populations). One of the limitations of this approach is that data is only obtained from a subset of the entire population. The method also uses an ordinal or continuous scale and is applicable to normally distributed data. The method is also appropriate if two populations have the same variance. References Brinks, R., Landwehr, S., & Waldeyer, R. (2013). Age of onset in chronic diseases: new method and application to dementia in Germany. Population Health Metrics, 11(1), 1-5. de Sousa Rodrigues, C. F., de Lima, F. J. C., & Barbosa, F. T. (2017). Importance of using basic statistics adequately in clinical research. Brazilian Journal of Anesthesiology (English Edition), 67(6), 619-625. Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 4(1), 60-63. Lind, D. A., Marchal, W. G., & Wathen, S. A. (2019). Basic statistics for business and economics (9th ed.). New York: McGraw-Hill Education. Lynn, E. (2020). How to Calculate the Percentage of Something from a Data Set. Sciencing. Noordzij, M., Dekker, F. W., Zoccali, C., & Jager, K. J. (2010). Measures of disease frequency: prevalence and incidence. Nephron Clinical Practice, 115(1), c17-c20. SAGE. (2015). Learn about dispersion of a continuous variable in SPSS with data from the world databank (2012). SAGE Publications – SAGE Research Methods Datasets. databank-2012 Sykes, L. M., Gani, F., & Vally, Z. (2016). Statistical terms Part 1: The meaning of the MEAN, and other statistical terms commonly used in medical research. South African Dental Journal, 71(6), 274-278. ISSN 0375-1562
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