2025 Prompts

 

 

 

Data Analysis and Hypothesis Development
Research Websites and Files
(1) Analyze the provided document `Abridged 2025.pdf` to define the variables in the table: `LOC_ENR` (likely enrollment), `TOTSITE` (potential funding/site metric), `School Type`, `ELA` score, `Math` score, and `Attendance` metric. Infer the meaning and scale of their values (e.g., proficiency rates vs. absenteeism rates).
(2) Extract the 'State Record' (page 24) and 'District Record' for Greenbrier County (page 5) from the document to establish baseline performance metrics for comparison.
(3) Formulate a set of hypotheses (approximately 3-4) by analyzing the relationship between `School Type` (Elementary, Middle, Secondary) and the performance outcomes (`ELA`, `Math`, `Attendance`), looking for trends as students progress.
(4) Formulate a set of hypotheses (approximately 3-4) by investigating the correlation between the `TOTSITE` metric and academic performance (`ELA`, `Math`) and the `Attendance` metric across various schools and districts.
(5) Formulate a set of hypotheses (approximately 2-3) by exploring the relationship between school size (`LOC_ENR`) and the `ELA`, `Math`, and `Attendance` metrics.
(6) Formulate a set of hypotheses (approximately 2-3) by analyzing the inter-correlation between the outcome metrics themselves, such as the relationship between `ELA` scores and `Math` scores, or the correlation between the `Attendance` metric and academic scores.
(7) Identify any significant performance gaps between `ELA` and `Math` scores and formulate a hypothesis on how this gap may change based on `School Type` or district.
(8) Formulate hypotheses based on significant performance variations observed between the 'District Record' data for Greenbrier County and other districts in the document (e.g., a notably high-performing or low-performing district).

 

Initial Data Analysis

I've begun by examining the provided document, which contains a large set of school performance data for West Virginia. I've successfully identified the key variables: 'LOC_ENR' is local enrollment (school size), 'ELA' and 'Math' are proficiency rates, and 'Attendance' appears to be a measure of absenteeism, where a lower number is better. I've also pulled baseline performance data for the entire state and for Greenbrier County specifically, which will be crucial for comparison.

Emerging Performance Trends

As I'm synthesizing the data, a few distinct patterns are already emerging. Most notably, there appears to be a sharp, consistent decline in Math proficiency as students move from elementary to middle and then to secondary school. This trend is visible in the statewide data and in the district-level data I've sampled. ELA proficiency seems more stable, but this creates a widening gap between language arts and math performance as students get older.

Exploring Financial and Size Metrics

I'm also looking at the 'TOTSITE' (a potential funding metric) and 'LOC_ENR' (school size) variables. My initial analysis suggests that the relationship between these factors and academic performance isn't straightforward. For instance, some schools with very high 'TOTSITE' values don't show top-tier performance, while some high-achieving schools have surprisingly low 'TOTSITE' values. Similarly, school size doesn't seem to have a simple linear correlation with success.

The Impact of Attendance

A very strong connection is emerging between the 'Attendance' metric and academic outcomes. Schools with low absenteeism (a low metric) are almost universally high-performing in both ELA and Math. Conversely, schools with high absenteeism (a high metric) consistently show some of the lowest proficiency rates. This suggests student attendance is a powerful factor in school performance.

District-Level Comparisons

By comparing district-level data, I can see significant variation. Districts like Putnam and Monongalia appear to be high-performing across all school types, while others like McDowell and Logan show much lower performance. My initial look at Greenbrier's data suggests it performs significantly below the state average, particularly at the middle school level.

Formulating Hypotheses

I have now gathered and analyzed enough data to move from exploration to formulation. The trends I've identified regarding the math proficiency decline, the impact of absenteeism, and the complex role of funding and school size are clear enough to form the basis of the 12 hypotheses. My next step is to formally synthesize these findings.