这是一篇来自英国的关于用统计模型回答社会研究问题的**作业代写**限时测试

**RESIT Assessment part **

Major clinical depression can be a severely debilitating illness, but even mild depressive episodes can severely affect quality of life. Roshanaei-Moghaddam *et al.* (2009) reviewed the relationship between physical activity and depression. There is certainly a negative correlation between physical activity and depression, such that lower levels of activity are associated with higher levels of depression and vice versa, but it is unclear as to the size of the causal relationship. For example, there may be confounding factors, such as attributes of individuals that make them both more likely to engage in physical activity and less likely to suffer depression.

In an effort to disentangle spurious from causal effects, you have decided to analyse data from the English Longitudinal study of Ageing (ELSA). This study surveyed a sample of English householders aged 50 or more, across multiple occasions, two years apart. The first occasion, or wave, was in 2002 and the second wave was in 2004.

**Figure 1:** Directed Acyclic Graph of the hypothesised causal relationships between lack of physical activity (notact1 & notact2) and depression (cesd1 & cesd2) across two waves of ELSA, and other potentially confounding variables.

**Data**

**Data**

The variables shown in Figure 1 were measured in ELSA. These variables are described in Table 1, below. These data are contained in the file “elsa12wide.csv”.

**Table 1: Variables from the DAG in Figure 1, as measured in the English Longitudinal Study of Ageing (ELSA), in the file “elsa12wide.csv”.**

Variable |
Description |
Coding |

0. id | Unique identifying label for each respondent | arbitrary numerical label |

1. age1 | Chronological age in wave 1 (2002) | Years since birth |

2. male1 | Biological sex in wave 1 | Male = 1, female = 0 |

3. illness1 | Limiting longstanding illness, self-declared in wave 1 | Has illness = 1, no illness = 0 |

4. cesd1 | Centre for Epidemiological Studies’ Depression (cesd) scale score, wave 1 | Score from 0 to 8 (higher score = more depressed) |

5. cesd2 | as above, in wave 2 (2004) | as above |

6. notact1 | Self-declared lack of moderate physical activity at least once a week, in wave 1 | 1 = no moderate physical activity at least once a week, 0 = at least some moderate physical activity |

7. notact2 | as above, in wave 2 | as above |

**Questions to answer**

**Questions to answer**

Use the R package and the data contained in “elsa12wide.csv” to carry out these tasks and answer these questions.

- Specify and fit a model to evaluate the total causal effect of notact1 -> cesd2, i.e. with cesd2 as the outcome and notact1 as the predictor. Based upon the DAG in Figure 1, include also in your model the
number of additional variables needed to block all backdoor paths between notact1 and cesd2.**minimum**- Explain the reasoning behind your choice of model specification, i.e. which variables did you choose to include as additional predictors in the model, if any. [20 marks]
- Discuss the substantive meaning of the b parameters for each predictor variable in the estimated model results. [10 marks]
- Discuss the effect size and statistical significance of the b parameters for each predictor variable in the estimated model results. [5 marks]
- Discuss the causal and statistical assumptions of your model, and how they affect what you can conclude about the relationship between physical activity and depression in the study population. [5 marks]

- Specify and fit a model to evaluate the total causal effect of cesd1 -> notact2, i.e. with notact2 as the outcome and cesd1 as the predictor. Based upon the DAG in Figure 1, include also in your model the
number of additional variables needed to block all backdoor paths between notact2 and cesd1.**minimum**- Explain the reasoning behind your choice of model specification, i.e. which variables did you choose to include as additional predictors in the model, if any. [20 marks]
- Discuss the substantive meaning of the b parameters for each predictor variable in the estimated model results. [10 marks]
- Discuss the effect size and statistical significance of the b parameters for each predictor variable in the estimated model results. [5 marks]
- Discuss the causal and statistical assumptions of your model, and how they affect what you can conclude about the relationship between physical activity and depression in the study population. [5 marks]

- Consider together the models from questions 1. and 2. Which is a more important cause, inactivity of depression, or depression of inactivity? Did you use the same predictor variables in each model or different ones, and why? When viewed together, what do the results from these models tell us about the relationship between physical activity and depression in the study population? [20 marks]

Copy/past the R script that you used to run the models for the questions above into an appendix of your submission. [You will not be graded on the correctness of this appendix. Marks will neither be awarded nor penalized for your R code. Rather it will be used to help in the understanding your models and results. Failure to include an R appendix will result in a penalty of 10 marks.]

**Guidance notes**

**Guidance notes**

- Your submission should answer each of the 3 questions above.
- You should use the models we discussed in class to answer the questions, i.e. a linear model (a.k.a. linear regression, multiple linear regression, general linear model) or a binary logistic model (a.k.a. logistic regression, generalized linear model). Different questions may require different models, so be sure to explain which model you chose and why.
- Good answers are those that clearly address all parts of the question.
- Use descriptive statistics and/or derived quantities (such as model-predicted values) to support and justify your answers, where this will help you to answer the question clearly.
- You may re-code, centre, and / or derive new variables if it will help you answer the questions. Be sure to clearly describe and justify any such manipulation of the data.
- Supporting and justifying your answers with references to books and articles in the course reading lists, and other scholarly material, is encouraged and will help contribute to a higher mark. Provide a full reference for any work that you do cite. Include no more than 4 citations in your answer.
- The word limit for your submission is 1,200 words. Note that this is a limit, not a target; you are permitted to use fewer than 1,200 words, but not more.
- The word limit includes all of the words in the main text of your answer, including headings, table, and figures, but does not include references or the R appendix. (You do not need to repeat the full text of the questions above in your answer)
- Submit your report via the Turnitin portal on Blackboard – follow the links to “submit your work here” in the “Assessment” section.

**Reference**

**Reference**

Roshanaei-Moghaddam, B., Katon, W.J., Russo, J. (2009). The longitudinal effects of depression on physical activity. General Hospital Psychiatry. Volume 31, Issue 4, pages 306-315. https://doi.org/10.1016/j.genhosppsych.2009.04.002.

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