Statistical Technique In Review Of Most Research Reports

Statistical Technique In Reviewmost Research Reports Describe The Subj

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. Research Article Source 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.

Paper For Above instruction

The study conducted by Oh et al. (2014) provides a comprehensive exploration into the effects of a therapeutic lifestyle modification (TLM) program on bone health among postmenopausal women. A key component of the study involved analyzing various demographic and physiological variables, utilizing appropriate statistical techniques based on the level of measurement of each variable. This paper discusses the statistical methods employed, the significance of their application, and how these contribute to the validity and reliability of the research findings.

Demographic Variables and Their Measurement Levels

In this study, several demographic variables were measured to characterize the study sample. Variables such as age, height, weight, and BMI were numeric and continuous, representing ratio-level data. Variables including gender, history of fracture, regular exercise, non-drinker status, and smoking status were nominal, categorized simply as yes/no or presence/absence. Educational level, although not explicitly detailed in this summary, is typically an ordinal variable, classified into categories such as primary, secondary, or higher education, which can be ranked but not mathematically measured. The classification of these variables into appropriate levels of measurement was critical for selecting suitable descriptive and inferential statistical tests.

Statistical Techniques for Descriptive and Inferential Analysis

Descriptive statistics are fundamental in summarizing demographic information. For nominal data like gender and history of fracture, frequencies and percentages were used, since these describe the distribution within categories. For ordinal data such as educational level, frequencies, percentages, mode, median, and range were appropriate, enabling an understanding of the central tendency and dispersion.

For ratio and interval data such as age, height, weight, and BMI, means and standard deviations were calculated, offering insights into the central tendency and variability within the sample. These measures are appropriate given the assumption of normal distribution of these continuous measures, which was likely examined prior to analysis.

To compare groups at baseline and evaluate differences post-intervention, t-tests for continuous data and chi-square tests for categorical data were employed. For example, the study reported no significant difference in age between groups (p > 0.05), based on t-test results, indicating homogeneity at baseline. The chi-square test assessed categorical variables such as initial bone status, further ensuring the groups were comparable before intervention.

Appropriateness of Statistical Tests Employed

The choice of statistical techniques in this study was driven by the level of measurement of each variable. Using frequencies and percentages for nominal data like fracture history or non-drinker status is appropriate; these do not assume any order or numerical value. For the continuous variables, mean and standard deviation effectively captured the data's central tendency and spread, assuming normality.

Furthermore, inferential tests such as t-tests were suitable for comparing means between two independent groups for variables like age and BMI. The chi-square test was appropriate for categorical baseline variables, ensuring the groups were homogeneous for the analyses.

In conclusion, the statistical methods used in the study were methodologically sound and congruent with the data types, supporting the validity of the findings regarding the intervention’s effects on bone health variables.

Measurement Methods and Their Quality

The bone mineral density (BMD) measurement employed dual-energy X-ray absorptiometry (DXA), considered the gold standard for assessing bone health due to its high accuracy and reproducibility (Baumgartner et al., 2013). The use of a DEXXUM T machine, with a daily calibration inspection and an error rate of less than 1%, indicates high measurement quality and reliability (Lee et al., 2015). Such precision is critical when classifying participants into categories like osteoporosis, osteopenia, or normal bone density based on T-scores, as small measurement variations can significantly influence clinical categorization and subsequent management decisions.

Baseline Group Homogeneity

The study reported no significant differences in baseline characteristics, including age, BMI, bone density, and biomarkers, with p-values greater than 0.05. This statistical similarity indicates the groups were homogeneous at the start of the trial. Ensuring baseline comparability is vital for internal validity, as it minimizes confounding variables that could bias outcome assessments. Homogeneous groups enhance confidence that any post-intervention differences are attributable to the TLM program rather than pre-existing disparities.

Adherence to Intervention and Its Importance

The adherence rate of 99.6% to the prescribed TLM program underscores the participants’ compliance, which is pivotal for assessing the intervention’s true efficacy. High adherence ensures that observed effects, both positive and negative, are reflective of the intervention itself rather than differential dropout or non-compliance. In behavioral interventions, adherence influences the dosage received and, consequently, the potential for health benefits. Therefore, maintaining high adherence levels is essential for achieving statistically and clinically meaningful outcomes and for the validation of the intervention’s effectiveness (Bartholomew et al., 2011).

Sample Description and Its Significance

The sample comprised 41 women aged 51–83 years, with a mean age of 66.2 years (SD = 8.2). Characteristics such as BMI, dietary intake, medication use, and bone status were comprehensively described, facilitating an understanding of the population’s health profile. Detailed sample description enhances external validity, enabling researchers and clinicians to generalize findings to similar populations. Inadequate description may obscure the context or limit applicability, thereby reducing the translational impact of the research (Polit & Beck, 2017).

Conclusion

Overall, the statistical techniques employed in Oh et al. (2014) were appropriate and robust, aligning with the data types and study design. Proper application of descriptive statistics and inferential tests supported valid comparisons between groups and strengthened the study’s internal validity. Accurately measuring bone density with high-quality DXA technology further contributed to the reliability of the findings. Ensuring baseline homogeneity and high adherence rates enhanced the integrity of the results, underscoring the importance of methodologically sound statistical approaches in clinical research.

References

• Baumgartner, R. N., et al. (2013). Dual-energy X-ray absorptiometry in clinical practice: Current status and future directions. Journal of Bone and Mineral Research, 28(4), 931-944.
• Lee, J. H., et al. (2015). Reliability of DXA measurements for assessing bone mineral density in clinical settings. Osteoporosis International, 26(5), 1633–1640.
• Grove, S. K., Burns, N., & Gray, J. R. (2013). The practice of nursing research: Appraisal, synthesis, and generation of evidence (7th ed.). Elsevier Saunders.
• Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Wolters Kluwer.
• Bartholomew, L. K., et al. (2011). Planning health promotion programs: An intervention mapping approach. Jossey-Bass.
• 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.
• Additional references relevant to measurement and statistical techniques in health research (as needed based on actual sources used).