Reference no: EM134003666

Data Literacy

The Cost of Caring

Section 1

Task: Build a simple regression of log wages on a caregiving measure, describe the raw pattern in the data, and be honest about what this model cannot establish.
What markers look for:

Descriptive statistics and a visualisation showing the wage distribution across carer groups - spread and shape, not just means.

A correctly specified SLR with the coefficient interpreted in percentage terms (if log wages is your outcome) and a confidence interval reported.

At least one named confounder with the direction of bias explained. This is the motivation for Section 2, not a disclaimer.
Your Milestone 2 hypothesis test (t-stat, p-value, CI) stated and connected to the SLR estimate. If the two differ, explain why.

Section 2 A More Defensible Model

Task: Build a controlled multivariate regression, handle non-linearity correctly, run diagnostics, and apply the Rubin Causal Model to assess what the coefficient actually means.
What markers look for:
Controls chosen for theoretical reasons, with a reference category identified for every dummy array. A justified decision about variables (like part-time status) that may themselves be caused by caring. No AI shortcuts — Get genuine assignment help from experienced, real tutors.
If a quadratic term is included: the marginal effect derived and reported at a meaningful value (e.g. 20 hours per week); the turning point identified and interpreted substantively - not just computed.
F-test reported and interpreted. Adjusted R2 used. Multicollinearity checked with VIF values or a qualitative discussion.
The Rubin Causal Model applied to your estimate: address SUTVA, Conditional Independence, and Overlap. Identify the single most serious threat and state what it means for your interpretation.
Your Milestone 2 minimum detectable effect (MDE) stated. Your estimate compared to the MDE - if close, flag this as a caveat.

Section 3 Who Bears the Burden?

Task: Investigate whether the wage penalty varies by gender and caring intensity, and reflect honestly on the ethics of reporting subgroup findings to a policy audience.
What markers look for:
An explicit, justified choice between interaction terms and stratified regressions. State the assumption each makes and why you chose the one you did.
If using an interaction term: interpret it as the additional effect for the interacted group. Compute and report the total effect for each group.
A clear verdict on whether the data support the advocacy group's claim. If heterogeneity is not significant, consider whether the sample was large enough to detect it.
An ethical reflection naming both directions of error: the cost of overstating the penalty and the cost of understating it, specific to this Senate context.
Your Milestone 2 gender wage test cited and compared to the interaction result - does the richer model confirm, sharpen, or contradict the simpler test?

Section 4 The Annual Income Picture

Task: Model annual income, connect the income penalty to your wage findings, and assess what the gap means for the adequacy of the CRC framework.
What markers look for:
A regression of annual income informed by your Section 2 specification, with the functional form justified.

An explicit comparison of the wage and income penalties. Carers face a double penalty
- a lower wage rate and fewer hours worked. Recognise and discuss both.
A specific assessment of the CRC framework grounded in your estimated figures, not in generalities.
Your Milestone 2 income decomposition figures stated and used as a baseline. Discrepancies between the decomposition and the regression explained.

Section 5 The Ministerial Brief

Task: Write a 500-600 word brief for the Minister that answers four specific questions, grounded in your analysis, honest about uncertainty, and ending with a concrete recommendation.
The four questions you must answer:
How large is the wage and income penalty for carers?
Who bears the penalty - does it fall equally across groups?
Is the finding causal, or associational?
Is the CRC framework adequate, and what should change?
What markers look for:
All four questions answered with specific figures from Sections 1-4 behind every claim.
Causal status characterised correctly using your Rubin assessment. Do not claim causation you have not established. Do not dismiss controlled evidence as "merely correlational."
Statistical significance distinguished from practical significance. The Minister needs to know if the effect is real and large - not the p-value.
Clear structure: context → key findings → what we can and cannot conclude →
recommendation. Within the 500-600 word limit.

How to report regression results
Markers expect results to be reported in one of two accepted formats depending on the model. Do not paste raw Excel or software output into your report.
Simple Linear Regression (SLR) - inline text
For a single-predictor model, report the key numbers inline within your prose. Include the coefficient, standard error in parentheses, and note significance.