The required data will be made available via Google Drive. Each student has their own dataset which will not be exactly the same as anyone else’s.
The SPSS file (CMNR7009HealthdatasetX) contains data from 1000 individuals. All variables are fully explained in the “Variable View” window. Use these data to plan your descriptive and inferential analyses to address the assignment set out in the steps below. You will present the results in the form of a statistical report.
Get to know your data. Look at the variables and see what they are measuring and what types of data you have to analyse.
Plan and describe how you will summarise the socio-demographic and general health of the sample (descriptive statistics). Think about the following points: What summary statistics will you use for which kinds of data? What data will you put in tables and/or graphs? How will you assess the suitability of each of these methods? What assumptions are they based on? How will you treat each variable?
What are the sociodemographic, health and lifestyle characteristics of your sample participants? Describing your sample is the first part of your analysis and comes first in the report results. Think about why it is important to get a description of the sample before you present results from hypothesis testing.
Develop a series of hypotheses that can be tested using the different types of statistical tests below. What would be the hypotheses? What tests will you do and why? Explain why the statistical techniques that you will use are appropriate. (Hint: weeks 3-10). If you decide to create new variables, describe how you will do so and why you chose each method of doing so. (Hint: see information in weeks 1-4)
Select two categorical variables that are of interest to you and perform an appropriate univariate statistical test. Explain why the statistical test that you have used is appropriate, show the results and report your conclusion. Repeat this again using two new categorical variables or one new outcome (dependent) variable for the same potential explanatory (independent) variable.
Select a variable with two or three categories and investigate how the values of another continuous (scale) variable differ between categories. You may choose to create a new variable with two, three, or more categories from an existing continuous or categorical variable. (Example: blood pressure and gender or blood pressure and BMI categorised as normal, overweight, obese.) Repeat this again with another pair of variables that will lead to a nonparametric test if possible. (Hint: explore the data to look for skewed distributions of a continuous variable).
Perform a multiple linear regression analysis to find those independent variables (continuous and categorical) that are significantly related to systolic blood pressure at the 5% significance level. Use Enter method to add potential risk factors.