This document provides you with information about the requirements for assessment. Detailed instructions and resources are included to help you to complete and submit the task. The Criterion-Referenced Assessment (CRA) Rubric that markers use to grade the assessment task is included and should be used as a guide when working on the assessment task.
Assessment Overview
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Assessment name: |
Problem Solving Task |
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Description: |
As part of this assignment, you will be required to: 1) Analyze a nutrition dataset and answer a series of knowledge‐based questions; and 2) Use the same nutrition dataset to follow the scientific method and write a report on your process of developing a research question and hypothesis, testing your hypothesis, as well as presenting and discussing the importance/relevance of your findings. The dataset to be used for this assignment task is available in the Assessment 3 folder of the Blackboard site. The data is from a cross‐ sectional study of Australian adults. A separate document describing all variables in the dataset is available in the Assessment 3 folder. |
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Length: |
Part A: no specific word limit, but make sure to just present the answers to the specific analytical tasks. Part B: 2,000 words maximum (strict limit) Note: the word count includes in‐text referencing and excludes tables, figures, and the references list. Please do not use Appendices. |
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Weighting to overall: |
55% |
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Individual or Group: |
Individual |
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How will I be assessed: |
Part A is worth 20% (of the 55% for this assignment). You will be allocated marks out of 50 for correct responses. Your score will be converted to a percentage. Part B is worth 35% (of the 55% for this assignment). Refer to Qualitative rubric (7 – 1) (below) for the criteria used to assess Part B. |
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Your overall percentage and grade for this assignment will be determined by adding together your percentages obtained in Part A and B and applying the corresponding weighting for each part. |
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Learning outcomes measured: |
1. Apply the fundamental principles of nutritional epidemiology in an ethical and culturally competent manner, including study design, diet and nutrition data collection methods, and assessment of nutritional status in population groups. 2. Describe strengths and limitations of nutrition-related data collections, including bias, confounding, and generalisability of research findings. 3. Analyse data using appropriate statistical methods and report the findings for communication to others. |
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Moderation: |
This assessment item will be moderated before marks are released. |
Assessment Details
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What you need to do: |
To complete this assignment, you need to undertake the following tasks: Part A: Analysis of a nutrition dataset (20% ‐ marked out of 50) § Use Jamovi software to analyse the nutrition dataset provided and respond to a series of (knowledge‐based) activities and questions which are designed to assess your knowledge and application of statistical principles we have leant during semester. The tasks completed in the statistics workshops in Weeks 5‐7 will be of particular help in addressing these questions. § It is expected that you will provide screenshots of computed variables, transformed variables, descriptive statistics, tables, graphs and output from statistical analysis in Jamovi where it is appropriate to do so.
§ Descriptive statistics (6 marks) § Calculate BMI for each of the participants and create this as a continuous BMI variable. Briefly describe your process for creating this BMI variable. Then use suitable statistical indicators and a graph to summary the distribution of this variable. § Transform BMI into a categorical variable (based on the WHO categories for underweight, normal weight, overweight and obese). Briefly describe your process for transforming this variable. Include a graph that summarizes the new categorical BMI variable. § Create a table to summarize the frequency distribution for the categorical BMI variable. Please report on the proportion (%) of individuals in each category of BMI. § Looking at your newly created continuous BMI variable, do you think this variable has a Normal distribution? Please explain your answer. Describe the process you used to determine this. § What graph would you use to visually represent the relationship between the continuous BMI and daily energy intake (kjouls_day) variables? Create this graph and briefly interpret it. § Create a box‐and‐whisker plot to visually represent the relationship between BMI as a continuous variable and sex. Briefly interpret the graph.
§ Correlation (10 marks) § In general, what does it mean if two variables are positively correlated? What does it mean if they are negatively correlated? § Pick 4 continuous variables from the dataset and review the correlation matrix. Are there any pairs of |
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variables that are significantly correlated and if so, which ones? Which null‐hypothesis and alternative hypothesis are being tested? Please describe the process you used to determine which variables are correlated, including a justification for your choice of a suitable type of correlation coefficient.
§ T‐test (10 marks) § What is the purpose of a t‐test? What types of variables are used in a t‐test? § Run the most appropriate t‐test to see if there is a significant difference in height between men and women. What is the null hypothesis? What is the alternative hypothesis for this test? Please describe the steps involved in comparing the groups, including your evaluation of suitability of the t‐test (whether statistical assumptions were met). § Linear Regression (12 marks)
§ Choose 2 continuous variables from the dataset and carry out a linear regression analysis. Which variable is your dependent variable and which variable is your independent variable? § What is the p‐value (level of significance) and r‐ squared value for this linear regression analysis? How do you interpret these? (P‐value and r‐ squared) § Please interpret the beta‐coefficient/s in the linear regression model. § Adjusted Linear Regression (12 marks)
§ Building on the above linear regression analysis, choose another variable from the dataset that you would like to investigate because of its potential to be a confounding variable of the association you assessed. Briefly explain why you think this variable may be a confounder with respect to the variables you selected for the linear regression. § Carry out a multiple linear regression by including your potential confounding variable. Based on your analysis, do you think your additional variable is indeed a confounding variable? Why or why not? What process did you use to determine this? |
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