Assessment Template

Suggested Report Structure — This template follows the requested sections. Each section contains guidance text and an empty R code cell for your analysis. Download it from hereand work on it with your own Rstudio.

Setup

Load commonly used libraries for statistical analysis and data manipulation.

# Core tidyverse
library(tidyverse)    # includes ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats

# Tables & reporting
library(knitr)
library(kableExtra)

# Stats helpers
library(broom)

## These need to be installed on your PC.

Once all the chunks execute correctly (no errors when running all the code, Ctrl +Alt + R ) you can render the qmd file. Follow the instructions here to render the document to a PDF. Remove this message and any irrelevant text from the final submission.

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Introduction

Aim. State clearly what you want to investigate (e.g., association between X and Y).
Relevance. Explain why the topic matters (theory, policy, practice).
Gap & Research Question (RQ). Identify what is missing in the current knowledge and pose 1 RQ (avoid yes/no questions; ask how, to what extent, which factors).
Structure. Briefly preview how the rest of the article is organized.

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Literature Review

What we know. Summarise key findings from prior studies relevant to your RQ.
Predictor rationale. Justify the inclusion of variables (theory-driven, prior empirical evidence).
Remaining gap. Specify precisely the gap this report addresses (e.g., population, geography, time period, variable, method).
Note: The goal is a clean, sensible analysis grounded in existing ideas—not necessarily novel theory.

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Methodology

Data. Briefly describe the dataset (who collected it, when, sample size, key variables).
Transformations. Describe any recoding/aggregation (e.g., bin income into bands; reduce age groups from 11 to 3). Provide justification.
Techniques. Outline and justify the statistical methods used (e.g., GLM/LM, logistic regression, mixed models, matching).

Important: do not include statistics here, or graphs, or tables.

Results and Discussion

# summary variables, distribution, plots, etc

Present the variables Start with descriptive statistics for your variables and distribution. If relevant, include discuss one-to-one associations (briefly, e.g. correlation plots). You can provide correlation plots and/or boxplots but do not overload the report with graphs.***

# run the model, show tables summarising the Regression model.

Results + interpretation. Present estimates and interpret them in plain language, tying back to the RQ. The Regression model should the core of this section!! Link to literature. Compare/contrast with prior findings. Selective visuals. Use clear charts/tables to highlight key results only (avoid clutter). ————————————————————————

Conclusion

Summary. Recap the main findings vis‑à‑vis the RQs (do not introduce new results).
Limitations. Offer a brief, honest self‑critique (data, measurement, design, external validity).
Implications. Indicate what the findings suggest for practice, policy, or future research.

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