Exercise 10: Description Of A Study Sample Statistical Techn ✓ Solved

Exercise 10description Of A Study Samplestatistical Technique In Revie

Most research reports describe the subjects or participants who comprise the study sample. This description of the sample is called the sample characteristics, which may be presented in a table and/or the narrative of the article. The sample characteristics are often presented for each of the groups in a study (i.e., intervention and control groups). Descriptive statistics are calculated to generate sample characteristics, and the type of statistic conducted depends on the level of measurement of the demographic variables included in a study (Grove, Burns, & Gray, 2013 ). For example, data collected on gender is nominal level and can be described using frequencies, percentages, and mode.

Measuring educational level usually produces ordinal data that can be described using frequencies, percentages, mode, median, and range. Obtaining each subject’s specific age is an example of ratio data that can be described using mean, range, and standard deviation. Interval and ratio data are analyzed with the same statistical techniques and are sometimes referred to as interval/ratio-level data in this text.

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The study conducted by Oh et al. (2014) provides a comprehensive example of how sample characteristics and statistical techniques are employed in health research. The sample consisted of 41 postmenopausal women aged between 51 and 83 years, with a mean age of 66.2 years (SD = 8.2). The demographic variables measured at the nominal level included variables such as gender, which was uniformly female in this study, and smoking status, which was indicated as "non-smoker" for all participants. Since these variables are categorical with distinct categories, they are appropriately described using frequencies and percentages. For instance, the study noted that all participants were non-smokers and most did not consume alcohol, which can be summarized using percentage data. Calculations involved tallying the number of participants in each category and calculating the percentage of the total sample that each category represented, thereby providing a clear demographic profile of the sample.

Regarding body mass index (BMI), the researchers calculated descriptive statistics to summarize the data. The mean BMI was 23.8 kg/m² with a standard deviation of 3.2, a suitable measure because BMI is a ratio-level variable. This allows for measures such as mean and standard deviation, which provide insights into the central tendency and variability of BMI within the sample. The appropriateness of these statistics is confirmed by the nature of the data; BMI is a continuous variable, and using measures of central tendency and dispersion is standard practice in such cases.

To determine whether the distribution of BMI scores was similar between the intervention and control groups, the authors likely examined the distribution shape and performed normality tests such as the Shapiro-Wilk test. Although the exact distribution details are not explicitly provided in the excerpt, the choice to use parametric statistics upon normal distribution assumptions suggests that the BMI scores were similarly distributed across groups. This is vital for the validity of inferential statistical tests that compare group means, such as t-tests, to determine if significant differences exist between the groups regarding BMI.

Indeed, the study reports no significant differences in baseline BMI between the intervention and control groups, indicating homogeneity at the start. Homogeneity or baseline similarity between groups is crucial as it ensures that any post-intervention differences can more confidently be attributed to the intervention rather than pre-existing disparities. This initial comparability strengthens the internal validity of the study and the conclusions about the intervention’s effectiveness.

Sample size is a key factor in statistical analysis. With N = 41, the number of participants who smoked can be calculated based on the report that "most participants did not consume alcohol," but specific smoking prevalence is not detailed. If, for example, 5 participants smoked, then the frequency would be 5, and the percentage would be (5/41) 100 ≈ 12%. Similarly, if 7 participants were non-drinkers, then the percentage would be (7/41) 100 ≈ 17%. These calculations involve dividing the count by the total sample size and multiplying by 100, then rounding to the nearest whole percent for clear reporting.

Bone mineral density (BMD) was measured via dual energy x-ray absorptiometry (DXA), which is considered a gold standard for assessing bone density. The quality of this measurement method is validated by the low error rate (

Differences in BMD between groups were assessed statistically, likely using t-tests or ANOVA for comparing means. The study’s results indicated significant improvements in the intervention group compared to the control group in terms of BMD, although exact statistical values are not provided in the excerpt. These statistical comparisons are crucial for establishing whether observed differences are statistically meaningful and attributable to the intervention.

The initial similarity in baseline characteristics, including demographics and BMD, indicates that the intervention and control groups were homogenous at study onset. This baseline equivalence is vital as it minimizes confounding variables and supports internal validity—any post-intervention differences can more confidently be linked to the intervention itself, rather than pre-existing differences.

The high adherence rate of 99.6% emphasizes the importance of participant compliance in intervention studies. High adherence ensures that the observed effects are truly reflective of the intervention’s impact, reducing bias and increasing the reliability and validity of the findings. Such adherence rates suggest effective follow-up and engagement strategies by the researchers, which is essential for the success of clinical trials.

Overall, the study’s sample was adequately described, including key demographic variables, previous health status, and baseline BMD scores. This comprehensive description allows for a clear understanding of the sample’s characteristics and supports the external validity, or generalizability, of the study results to similar populations.

References

• Grove, S. K., Burns, N., & Gray, J. R. (2013). The Practice of Nursing Research: Appraisal, Conduct, and Utilization. Elsevier Health Sciences.
• Oh, E. G., Yoo, J. Y., Lee, J. E., Hyun, S. S., Ko, I. S., & Chu, S. H. (2014). Effects of a three-month therapeutic lifestyle modification program to improve bone health in postmenopausal Korean women in a rural community: A randomized controlled trial. Research in Nursing & Health, 37(4), 292–301.
• Gray, J. R., Grove, S. K., & Sutherland, S. (2017). Understanding Nursing Research: Building an Evidence-Based Practice. Wolters Kluwer.
• Polit, D. F., & Beck, C. T. (2017). Nursing Research: Generating and Assessing Evidence for Nursing Practice. Wolters Kluwer.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3–4), 591–611.
• Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling. Guilford Publications.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
• Holt, R. J., & Craig, S. (2017). Essentials of Statistical Methods for Food and Nutrition Research. CRC Press.