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‎_quarto.yml‎

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text: "Week 13 - Moderation"
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- href: weeks/week-14.qmd
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text: "Week 14 - Model Building & Mediation"
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- href: weeks/week-15.qmd
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text: "Week 15 - Wrapping Up"
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- section: "Labs"
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contents:
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- href: labs/lab-1_data-workflow.qmd

‎index.qmd‎

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+--------------------------+----------+------------------------------------------------+---------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------+--------------------------------------------------------------------------+
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| [14](weeks/week-14.html) | 11-24 | Model Building + Mediation | [Chapter 15.8 - 15.10 - LSR](https://learningstatisticswithr.com/book/){target="_blank"} | [💻 Model Building](/slides/lec-14.html) | Continue Working on Final! |
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+--------------------------+----------+------------------------------------------------+---------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------+--------------------------------------------------------------------------+
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| 15 | 12-01 | Making R Work for You | [Chapter 17 & 18 - ST](https://statsthinking21.github.io/statsthinking21-core-site/){target="_blank"} | | |
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| [15](weeks/week-14.html) | 12-01 | Making R Work for You | [Chapter 17 & 18 - ST](https://statsthinking21.github.io/statsthinking21-core-site/){target="_blank"} | | Finish your Final Project |
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+--------------------------+----------+------------------------------------------------+---------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------+--------------------------------------------------------------------------+
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| 16 | 12-08 | Wrapping Up + Presentations | | | |
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+--------------------------+----------+------------------------------------------------+---------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------+--------------------------------------------------------------------------+

‎labs/final-proj.qmd‎

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## Part 3 (Due 12/8 - In Class)​
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The final part of the project consists of a brief presentation to the
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class and a written report of your data analysis plan and results.
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class and a written report of your data analysis plan and results. This
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should be completely reproducible and be a [zipped
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folder](https://letmegooglethat.com/?q=how+to+zip+a+folder){target="_blank"}
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with all relevant materials.
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### **Presentation**​
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### **Presentation (5 mins & 1-2 mins of Q & A)**​
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Your presentation should be a brief overview of your project. It should
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be well-organized and engaging. This can take the form of a slideshow (5
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- **Discussion**: Interpret your results and discuss their
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implications.
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Do not put walls of text on your slides. Your slides/materials should
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focus on the **visualizations** of your data (e.g., scatterplots with
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regression lines, interaction plots). You should narrate the methods and
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interpretation, rather than reading them off the slide.
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### **Written Report**
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::: callout-important
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- ​**Conclusion**: Briefly summarize your main findings and their
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significance.
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In the **Methods** or **Results** section, provide information on how
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you checked the assumptions for your regression (e.g., linearity,
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homoscedasticity, normality of residuals) and what the results of those
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checks were. If assumptions were violated, explain how you addressed
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them. (use `check_model()` from easystats)
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**APA Style:** "APA Formatting" for results is strict.
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- Do not copy-paste raw software output (like `summary()`) directly
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into the text.
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- You must generate a clean Table (using packages like `sjPlot`,
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`apaTables`, `stargazer`, or `gt`) that reports b, SE, t, p, and R2.
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Your final submission should be a single folder that is 100%
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reproducible, meaning that all of your data and code should be included
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and organized in a way that someone else could easily re-run your
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analyses.
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### Helpful Tips
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- **The "Run-All" Test:** Before zipping your folder, test your code
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on a fresh R session (close everything and re-open the project). If
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your code references a file path like
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`C:/Users/YourName/Downloads/Project`, it **will not work** on
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anyone else's computer.
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- *Tip:* Use **relative paths** (e.g.,
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`import(here(("data", myDataFile.csv")`) and a project based
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workflow
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- **Folder Structure:** Here is a suggested file structure:
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- `/Data` (Raw data files)
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- `/Scripts` (Analysis code, e.g., .R, .Rmd, .qmd)
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- `/Output` (The final report in PDF or Word)
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- **Code Quality:** Annotate your code. Comments should explain *why*
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a step is being taken (e.g.,
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`# Log transforming income to correct for skewness`) rather than
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just *what* the code is doing.
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- **Page Count Clarification:** The 5-7 page requirement refers to
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**prose text**. Large figures, massive tables, and bibliography do
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not count toward the minimum.
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## Rubric
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**Total Points: 200**
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#### Reproducibility & Code Quality (40 Points)
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*Focus: Can the instructor download the folder and run the analysis
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without editing a single line of code?*
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| | | | |
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|----|----|----|----|
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| **Criteria** | **Proficient (34–40 pts)** | **Competent (26–33 pts)** | **Needs Improvement (0–25 pts)** |
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| **Functionality & Organization** | Code runs immediately upon download without error on a fresh environment. Uses relative paths or `here()` correctly. | Code runs but requires minor troubleshooting (e.g., installing a missing package, fixing a hard-coded path like `C:/Users/...`). | Code breaks significantly, references local hard drives, or necessary files are missing from the folder. |
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#### Data Preparation (20 Points)
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*Focus: Did you prepare the data correctly before analyzing?*
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| | | | |
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|----|----|----|----|
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| **Criteria** | **Proficient (18–20 pts)** | **Competent (14–17 pts)** | **Needs Improvement (0–13 pts)** |
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| **Cleaning & Sampling** | Random subset (N=400) created correctly (if applicable). Cleaning steps (handling NAs, recoding variables) are justified and executed efficiently. | Subset created but method is unclear/not reproducible. Minor errors in data cleaning (e.g., missed an obvious outlier). | No subsetting performed on large data. Data is "dirty" (e.g., typos in factor levels) affecting the analysis results. |
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#### Statistical Rigor (50 Points)
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*Focus: Does the logic follow and build a cohesive statistical
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argument?*
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| | | | |
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|----|----|----|----|
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| **Criteria** | **Proficient (44–50 pts)** | **Competent (34–43 pts)** | **Needs Improvement (0–33 pts)** |
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| **Analysis Selection** | Chosen analyses (must include regression) are perfectly aligned with the research question and data type. | Analyses are generally appropriate, but a better model existed (e.g., used standard linear regression on a binary outcome). | Analysis does not address the hypothesis or is statistically invalid for the data type. |
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| **Assumption Checks** | All relevant assumptions (normality, linearity, homoscedasticity, etc.) are explicitly tested, reported, and handled if violated. | Assumptions are mentioned but not thoroughly tested, or violations are noted but ignored in the final model. | Assumptions are completely ignored or misunderstood. |
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| **Interpretation** | Interpretation of coefficients, p-values, confidence intervals, and effect sizes is accurate and nuanced. | Interpretation is generally correct but relies too heavily on "significance" (p<.05) rather than effect size or practical significance. | Fundamental misunderstanding of statistical output (e.g., interpreting p>.05 as "proof" of no effect). |
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#### Presentation (30 Points)
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*Focus: Can you synthesize the project in a concise way to an audience?*
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| | | | |
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|----|----|----|----|
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| **Criteria** | **Proficient (26–30 pts)** | **Competent (20–25 pts)** | **Needs Improvement (0–19 pts)** |
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| **Communication** | Engaging, clear, and well-paced (within 7 min). Speaker demonstrates mastery of the material during Q&A. | Clear but relies heavily on reading notes. Slightly over or under the time limit. | Reading directly from slides (wall of text). Unable to answer basic questions about their own study. |
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| **Visual Aids** | Slides are visual-heavy and support the narrative. Graphs are clean, large, and legible. | Slides are text-heavy. Graphs are present but small, pixelated, or unformatted default outputs. | Slides are disorganized, confusing, or missing. |
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#### Written Report Content (60 Points)
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*Focus: Can you communicate the findings in a written format?*
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| | | | |
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|----|----|----|----|
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| Criteria | Proficient (52–60 pts) | Competent (40–51 pts) | Needs Improvement (0–39 pts) |
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| **Introduction & Logic** | Clear narrative arc from problem identification to hypothesis. Citations (3+) are highly relevant and integrated well. | Hypothesis is stated, but the background justification is weak, disjointed, or citations are loosely related. | Hypothesis is missing or unclear. Introduction is disorganized or lacks citations. |
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| **Formatting (APA)** | Tables and Figures are perfect APA style. **No raw code output** (e.g., R console text) in the document. Prose is professional and academic. | Minor APA errors in tables/figures. Some raw software output included in the text. | Major formatting errors. Figures are unreadable, missing labels, or screen-capped from software. |
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| **Discussion** | Deeply contextualizes results within the field. Discusses limitations honestly and provides insightful future directions. | Summarizes results well but lacks depth in "implications." Limitations are generic (e.g., "sample size could be bigger"). | Discussion just repeats the results section in words. No limitations mentioned. |

‎weeks/week-15.qmd‎

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---
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title: "Week 15 - Having R Work for You: wRapping up"
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editor: visual
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---
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We are almost at the end of the course! This week, we will be focusing on some random things related to R that could make things easier. Plus, there will be time for working on your final project, helping your peers and "office hours".
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## Data for Today
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We may have a demo of a workflow. In that case, we will be able to pick some data from this website below. I think the DASS (Depression, Anxiety and Stress Scale) dataset would be a good candidate.
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[Open Source Psychometrics Project](https://openpsychometrics.org/_rawdata/){target="_parent"}
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## Rando CalRissian
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[papaja: Reproducible APA Manuscripts in R](https://frederikaust.com/papaja_man/){target="_blank"}
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[WTF-R: What They Forgot to teach about R](https://rstats.wtf/){target="_blank"}
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[Definitive R Markdown Guide](https://bookdown.org/yihui/rmarkdown/){target="_blank"}
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[Shiny Apps](https://shiny.posit.co/){target="_blank"}
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[Big Book of R](https://www.bigbookofr.com/){target="_blank"}
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[Convenience Functions for Psych](https://rempsyc.remi-theriault.com/index.html){target="_blank"}
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## In-Class Activity
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📎Final Statistical Consulting
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## For Next Time
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Present your final project!
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Turn in the final paper
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<br><br>
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::: {style="font-size: 0.875em;"}
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Back to [course schedule](/ "Course schedule") ⏎
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:::

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