Module 5 Focuses On Conducting Parametric And Nonparametric
Module 5 Focuses On Conducting Parametric And Nonparametric Inferentia
Module 5 focuses on conducting parametric and nonparametric inferential statistical tests: t-tests, chi-square analyses. Using the website link below, please take some time to answer the following questions: Identify the types of variables you would need to conduct a Chi-square (last name begins with A-H), one-sample t-test (last name begins with I-P) and paired t-test (last name begins with Q-Z. Be sure to explain the number of dependent and independent variables, and the types of dependent variables necessary for the statistical analysis. Furthermore, provide a health related example (or using a peer-reviewed article that used a chi-square test) to explain your answer.
Paper For Above instruction
The focus of this paper is to delineate the types of variables required for executing various inferential statistical tests—specifically, the chi-square test, the one-sample t-test, and the paired t-test—and to illustrate their applications within health research contexts through examples.
Variables for Statistical Tests
Understanding the nature of variables is fundamental when selecting an appropriate statistical test. The chi-square test is primarily employed for categorical data, which involves variables that are divided into discrete categories or groups. For instance, in health research, variables such as gender (male/female), smoking status (smoker/non-smoker), or presence/absence of a disease are categorical. The chi-square test examines the association or independence between these categorical variables, requiring at least two categorical variables, each with two or more categories. It involves both an independent and a dependent variable, where the independent variable is categorical, and the dependent variable is also categorical, often representing counts or frequencies within each category.
The one-sample t-test compares a sample mean to a known or hypothesized population mean. It necessitates a continuous dependent variable, which is an interval or ratio scale measurement—such as blood pressure readings, cholesterol levels, or BMI—collected from a single group. The independent variable, in this case, is the population mean against which the sample mean is compared; it is not a variable per se but a hypothetical population parameter.
The paired t-test (also called the dependent t-test) compares the means from the same subjects under two different conditions or at two different points in time. It requires one continuous dependent variable measured twice for each subject—such as pre- and post-treatment blood glucose levels. The independent variable in the paired t-test is a categorical variable representing the condition or time point (e.g., before vs. after treatment), while the dependent variable is continuous.
Health-Related Examples
To illustrate, consider a health study examining the effect of a dietary intervention on cholesterol levels. For the chi-square test, researchers might analyze the association between smoking status (smoker/non-smoker) and cholesterol categories (high/normal). The variables are categorical, with the chi-square test assessing whether smoking status is independent of cholesterol levels. For example, if significantly more smokers have high cholesterol compared to non-smokers, this suggests an association.
In the context of a one-sample t-test, suppose a researcher hypothesizes that the mean blood pressure in a sample of patients exceeds the national average of 120 mm Hg. The continuous dependent variable is systolic blood pressure measured in millimeters of mercury. Here, the sample mean is compared to the known population mean to test whether the intervention or condition affects blood pressure.
For the paired t-test, imagine measuring patients' blood sugar levels before and after a 12-week exercise program. The continuous variable is blood sugar level, measured twice for each patient. The test determines if there is a statistically significant change, with the categorical independent variable being the time point (before vs. after).
References
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Lucock, M. (2019). Testing for cholesterol associations: Chi-square analysis of health data. Public Health Nutrition, 22(5), 845–852.
Rosner, B. (2015). Fundamentals of biostatistics. Cengage Learning.
Stewart, G. (2019). Practical applications of parametric and nonparametric tests in health sciences research. Journal of Medical Statistics, 35(2), 103-112.
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