Identify The Types Of Variables You Would Need To Conduct

Identify the types of variables you would need to conduct

For the given statistical analyses—Chi-square test, one-sample t-test, and paired t-test—understanding the types of variables involved is essential. Each test requires specific data types and variable configurations to produce valid results. The assignment asks students to identify the variables necessary for each test, including the dependent and independent variables, and to provide health-related examples or peer-reviewed articles illustrating their use.

Paper For Above instruction

The Chi-square test, the one-sample t-test, and the paired t-test are fundamental statistical tools in healthcare research, each suited to different data types and research questions. Analyzing the variables involved in each test involves understanding the nature of dependent and independent variables and their measurement scales. Additionally, illustrating each with health-related examples enhances comprehension of their practical applications.

Chi-square Test

The Chi-square test is primarily used to examine the association between categorical variables. In the context of health research, it often assesses whether the distribution of categorical outcomes differs across groups or whether observed frequencies align with expected frequencies. The essential variables for this test include an independent variable, which is categorical, such as gender, smoking status, or treatment group, and a dependent variable that is also categorical, like disease presence or absence, or response categories.

For instance, a study investigating the association between smoking status (smoker vs. non-smoker) and the incidence of lung disease (present vs. absent) employs the Chi-square test. Here, both variables are categorical: smoking status is the independent variable, and lung disease status is the dependent variable, both measured at the nominal level. The data is organized into a contingency table, and the Chi-square test evaluates whether the observed frequencies differ significantly from what would be expected under independence.

One-sample t-test

The one-sample t-test compares the mean of a single quantitative variable to a known value or a hypothesized population mean. The variables involved include a continuous, interval-level dependent variable, such as blood pressure, cholesterol level, or Body Mass Index (BMI). The independent variable is essentially a hypothesized constant or a known standard against which the sample mean is compared. There is no actual independent variable in the typical sense, as this test assesses whether the sample mean differs significantly from a specific value.

An example would be a study measuring the mean systolic blood pressure of a sample of hypertensive patients to determine whether the average blood pressure exceeds the national guideline value (e.g., 130 mmHg). The dependent variable is systolic blood pressure, a continuous variable. The test evaluates if the sample mean differs significantly from the known or hypothesized population mean, which is the reference point in this case.

Paresd t-test

The paired t-test compares means from two related groups, commonly used when the same subjects are measured before and after an intervention, or under two different conditions. The variables include a continuous dependent variable, such as blood glucose levels or pain scores, measured at two time points or under two conditions. The independent variable is a within-subject factor, often "time" or "condition," which is categorical with two levels.

An example can be a clinical trial assessing the effectiveness of a new drug on reducing blood sugar levels. The same group of patients is measured twice: once before the treatment and once after. The dependent variable is blood glucose level, a continuous variable, measured at two related points. The analysis examines whether the mean difference between the two measurements is statistically significant.

Conclusion

Each statistical test has specific variable requirements. The Chi-square test necessitates categorical independent and dependent variables, suitable for examining distributions or associations. The one-sample t-test requires a single continuous dependent variable compared against a known value or standard. The paired t-test involves continuous dependent variables measured under two related conditions or time points. Recognizing these variable types and configurations allows researchers to select appropriate analyses aligned with their research questions and data structures.

References

• Cook, A., Netuveli, G., & Sheikh, A. (2004). Basic skills in statistics: A guide for healthcare professionals. London, GBR: Class Publishing.
• Norman, G. R., & Streiner, D. L. (2014). Biostatistics: The bare essentials (4th ed.). Shelton, CT: PMPH-USA Ltd.
• Lemeshow, S., & Hosmer, D. W. (1982). A review of goodness-of-fit statistics for logistic regression. The American Statistician, 36(3), 115-120.
• Friedman, L. M., Furberg, C. D., & DeMets, D. L. (2010). Fundamentals of clinical trials. Springer Science & Business Media.
• Keppel, G., & Wickens, T. D. (2004). Design and analysis: A researcher's handbook. Pearson Education.
• Schultz, K. A., & Liao, E. (2016). Statistical Methods in Health Research. American Journal of Epidemiology, 184(8), 662-668.
• Harrell, F. E. (2015). Regression Modeling Strategies. Springer.
• Kale, R. (2014). The essentials of biostatistics for medical and healthcare students. Journal of Medical Statistics, 30(2), 87-94.
• Fletcher, R. H., & Fletcher, S. W. (2013). Clinical Epidemiology: The Essentials. Wolters Kluwer Health.
• Bland, J. M., & Altman, D. G. (1995). Comparing proportions: The chi-squared test. BMJ, 310(6987), 41.