Age related differences in working hours

By how much do self-reported work hours differ between male and female full-time workers on average in Sydney after correcting for age? (You should address this question using linear regression and include associated descriptive analyses.)

• You should only use the variables ‘work’, ‘sex’ and ‘age’.
• Correcting for ‘age’ is just including ‘age’ in the regression model.  When age is in the model all other variables are corrected for it.
• Documenting your analysis plan is recommended but not required. (If your analysis report is complete then your plan must have been complete also.)

*working hours for male and female

*frequency of male and female

401077 Introduction to Biostatistics Assignment 3

Please answer each question in the template document provided and submit via Turnitin on or before the due date. The marks allocated to each question are shown in the assignment. A total of 40 marks are available and this assignment is worth 40% of your overall grade.

Question 1 (18 marks)

Read the paper Weston, G., Zilanawala, A., Webb, E., Carvalho, L. A. & McMunn, A. (2019). Long work hours, weekend working and depressive symptoms in men and women: findings from a UK population-based study. J Epidemiology Community Health, 0, 1-10.

Critically appraise of the statistical material in this paper against items 10, 12-17 of the STROBE checklist. Present your review as a 400-500 word (approx.) report.

Note:

- Only review the provided paper Weston et al, 2019. Do not read any other papers.

- Restrict your review to how well Western et al have documented their statistical methods – that is, items 10, 12-17 of STROBE only. You may not have to address every item; just describe the major strengths and weaknesses of the authors’ descriptions of their statistical methods and results.

- For each important STROBE item:

o state whether you believe the STROBE item is met or not,

o support your judgment with proof or examples from the paper, and

o describe why this inclusion or exclusion is important / how it will impact on the reader’s understanding and decision making.

- The 400-500 words is a guideline not a rule. There are no penalties for exceeding this guideline.

- There are no marks for adding a reference list. Referencing is optional.

Question 2 (22 marks)

Using R Commander and the data set from the sample of full-time workers in Sydney assigned to you address the following research questions:

a) By how much do self-reported work hours differ between male and female full-time workers on average in Sydney after correcting for age? (You should address this question using linear regression and include associated descriptive analyses.)

b) Using the model in a), predict the number of self-reported work hours for 25-year-old male workers. Repeat for 25-year-old female workers.

Note: To answer this question, you need to use R Commander and the data set assigned to you for assignments. This data set contains the (fictitious) data from a random sample of full-time workers in Sydney, Australia and is the same data set as that you have previously used in Assignments 1 and 2. See ‘Description of your data set.docx’ for the descriptions of the variables.

Note: This assignment is assessing your skills, not the skills of the computer. You will need to include graphs from R Commander into your assignment but all other R Commander output will attract 0 marks and is discouraged. It is your task to identify the relevant results in the R Commander output and write these up in your assignment.

Also note:

- You should only use the variables ‘work’, ‘sex’ and ‘age’.

- Correcting for ‘age’ is just including ‘age’ in the regression model. When age is in the model all other variables are corrected for it.

- Documenting your analysis plan is recommended but not required. (If your analysis report is complete then your plan must have been complete also.)

- Do report the results of your descriptive analyses

o Well labelled graphs can be copied from R Commander

o Summary statistics and tables should be manually typed

o Summarise the main findings of your descriptive analyses in words and describe how these findings inform your expectations and interpretation of the more complex models.

- Do report the results of your statistical inference and/or regression models

o Any fitted regression models should be manually typed and described in the text.

o Any hypothesis tests should contain all relevant information (use the 5 step method to be sure)

o Any confidence intervals should be manually typed and described in the text.

o Summarise the main findings of your regression model and statistical inference in one or two paragraphs.

- Do remember to answer the research questions

o Write a final paragraph which summarises the key findings of your analysis and your answers to the research question.

- Do check the Learning Guide for the marking criteria

- Do write your answers yourself and keep them private.