Respond to your colleagues in one or more of the following ways:Expand on the co

Respond to your colleagues in one or more of the following ways:Expand on the colleague’s posting with additional insight and resources.
Offer polite disagreement or critique, supported with evidence.
In addition, you may also respond as follows: Offer and support an opinion.
Validate an idea with your own experience.
Make a suggestion or comment that guides or facilitates the discussion.
Below are my Colleagues responses Anne REIS RE: Discussion 1 – Week 1COLLAPSEIntroduction A research question essentially guides every research. A well-formulated research question isolates the purpose and the research problem by providing a clear relationship between variables and the hypothesis being tested (Laureate, 2012). While there are various types of research questions (i.e., descriptive, exploratory, explanatory, or experimental), all research questions can be answered using either primary or secondary data (Daniel, 2019; Walden, n.d; Laureate, 2012. All research questions should also align the variables selected with an appropriate research design and suitable statistical test (Laureate, 2012). The purpose of this is to formulate a research question relevant to the dataset, select and justify variables. The variables used for this discussion were derived from the PUBH 8500 Week01 Dataset (Walden, n.d.). Research Question The number one aim of this discussion is to formulate a research question from an existing dataset. From an epidemiologic perspective, I chose to explore the relationship between exposure and disease and based on the variables provided, a research question can be formulated to determine if stroke (disease) occurs as much among people having hypertension as it does among people without hypertension. A common analysis in epidemiology for dichotomous variables is the use of a contingency (2×2) table (Daniel, 2019; Walden, n.d.). The research question allows for testing the relationship between variables categorized based on their exposure to some disease risk factor (Forthofer; 2007; Walden, n.d.). The research question (RQ) is stated below: RQ: To what extent is there a relationship between hypertension and stroke?HypothesesNull Hypothesis (H0): Is there a significant relationship between hypertension and stroke Alternative Hypothesis (HA): There is a significant relationship between hypertension and stroke. Dependent and Independent Variables & Coding Two dichotomous (categorical) variables, hypertension, and stroke were selected from the PUBH 8500 Week01 dataset to test the relationship between hypertension and stroke (Walden, n.d.). Both variables were selected based on general theories, previously tested hypotheses, and documented empirical and clinical evidence on the relationship between exposure (risk factor) and disease (outcome) (Daniel, 2019; Walden, n.d.). The layout of the linkages between the dependent and independent variables and the hypotheses tested are shown below:Independent Variables (IV):IV1= Hypertension 0=No for had hypertension, 1=Yes for had hypertensionDependent Variables (DV): DV=Stroke 0=No for had stroke, 1=Yes for had stroke Chi-Square or t-test to Analyze the DataChi-Square and t-test are parametric tests used for the hypothesis test. The t-test is used for comparing the means of two groups. At the same time, Chi-Square is a test of association, used to either assess relationships between categorical groups by either making comparisons between observed versus other known distributions (Forthofer, 2007; Walden, n.d.).. Based on the research question, the variable types, and the levels of measurements, the appropriate statistical procedure test to perform such an analysis is the Chi-Square (Daniel, 2019; Walden, n.d.). The chi-Square statistic is commonly used for testing relationships between nominal and ordinal data (categorical variables) (Daniel, 2019; Walden, n.d.). A chi-Square statistic is inferential statistics for testing relationships between categorical variables, testing whether there is a relationship between two variables. Unlike a t-Test, a Chi-Square test does provide the direction or the extent of the relationship. (Forthofer, 2007; Walden, n.d.).Crosstabulation presents the distributions of two categorical variables simultaneously, with the intersections of the categories of the variables appearing in the cells of the table (Forthofer, 2007; Walden, n.d.). The Chi-Square Test of Independence assesses whether an association exists between the two variables by comparing the observed pattern of responses in the cells to the way that would be expected if the variables were truly independent of each other (Daniel, 2019; Walden, n.d.). Since the data provided is a secondary data set, the research question determines the types of variables to use and the appropriate statistical analysis for the described variables(Laureate, 2012). A Chi-Square test using a 2×2 contingency table would also be helpful, in this scenario to test the relational hypotheses between hypertension and stroke and to determine if they are significantly different from those who have a or have no stroke. Conclusion A well-formulated research question is vital for guiding research projects and provides a clear focus and purpose for the researcher and the reader. A data-driven research question can be used on an identified social problem and or a gap in the literature (Laureate, 2012). For this discussion, I explored the methods of formulating a research question from two categorical variables (hypertension and stroke). The discussion also covers the use of chi-square for testing the null hypothesis about the relationship between hypertension and stroke, to determine whether those having hypertension (exposure) have more illness (stroke) than those not exposed, by performing a test of the association between exposure and disease in the two groups. A Chi-Square is derived from a contingency table (2×2) table, which provides the overall distribution of people having (exposure) hypertension and disease (stroke).ReferencesDaniel, W. W. & Cross, C. L. (2019). Biostatistics: A foundation for analysis in the health sciences (11th ed.). Wiley.Forthofer, R. N., Lee, E. S., & Hernandez, M. (2007). Biostatistics: A guide to design, analysis, and discovery. Amsterdam, Netherlands: lsevier Academic PressLaureate Education, Inc. (Executive Producer). (2012). Biostatistics basics. Baltimore, MD: Author.Walden University. (n.d.). Week 1 Summary Review & Quiz 1 Study Guide [Handout]. Walden university blackboard. Https://class.waldenu.eduWalden University. (n.d.). Chi-Square Test [Handout]. Walden university blackboard. Https://class.waldenu.eduWalden University. (n.d.). t- Test [Handout]. Walden university blackboard. Https://class.waldenu.eduWalden University. (n.d.). PUBH 8500 Week01 dataset [dataset]. Walden university blackboard. Https://class.waldenu.edu3 days agoCarlin Nelson RE: Discussion 1 – Week 1COLLAPSEPost a research question relevant to the dataset.RQ: Do levels of serum cholesterol differ amongst sex (men and women)? Hypothesis Null Hypothesis (H0): µ1=µ2 µ1= mean level of serum cholesterol in men µ2= mean level of serum cholesterol in womenAlternate Hypothesis (HA): µ1≠µ2 µ1= mean level of serum cholesterol in men µ2= mean level of serum cholesterol in womenPlease specify the dependent and independent variables you would use from the dataset to address the question, as well as the level of measurement for the dependent and independent variables. Justify your selection. Independent Variable (IV): Sex (1=Men, 2=Women) Measurement Level: Nominal-Dichotomous Explanation: This variable can be categorized as a nominal variable because it uses names to create groups or categories (Daniel & Cross, 2019). Additionally, to creating groups, this variable is considered a nominal variable because the groups/categories/levels are assigned arbitrary numbers or positions (Jacobsen, 2021). A data type of nominal variable is a dichotomous variable, which is a variable that can only consist of two levels when observed or measured.Dependent Variable (DV): Level of Serum Cholesterol (Mean: 228.3+49.03, Min: 143, and Max: 534) Measurement Level: Ratio-Continuous Explanation: This variable exists on the ratio level of measurement because it consists of differences between measurements (Daniel & Cross, 2019). Another reason why this variable is on the ratio level of measurement because it consists of a true zero point (Daniel &Cross, 2019). Zero on the ratio level of measurement means absent and while it is unlikely or not healthy to not have any cholesterol in the body, if it was reported as 0, then that is how it would be recorded and interpreted. A data type of variables that exist on the ratio level of measurement are continuous variables. Continuous variables are variables that exist of an infinite collection of numbers that are parametrized by minimum and maximum values (Jacobsen, 2021). Explain whether you would use a t-test or Chi Square test to analyze this data and to address your research question. Explain why you chose this test.Selecting the most appropriate statistical test is critical because it influences both internal and external validity. Assuming that all the assumptions are met or if violated are rectified, to address this research question and to analyze this data then a t-test would be most appropriate. I chose this test because the research question aims to assess if there is a difference between two independent groups (Laerd Statistics, n.d.). Furthermore, the t-test was selected as the most appropriate test because the dependent variable was classified as a continuous variable, and the independent variable was categorized as a nominal variable consisting of two independent categories (Walden University, n.d.). ReferencesDaniel, W. W. & Cross, C. L. (2019). Biostatistics: A foundation for analysis in the health sciences (11th ed.). Wiley.Jacobsen, K.H. (2021). Introduction to health research methods: A practical guide.Jones & Bartlett Learning.Laerd Statistics. (n.d.). Independent t-test using SPSS Statistics. https://statistics.laerd.com/spss-tutorials/independent-t-test-using-spss-statistics.php.Walden University. (n.d.). t-Test [PowerPoint slides]. https://class.content.laureate.net/3ff81a632ed2ae0…ation for analysis in the health sciences (11th ed.). Wiley.
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