Data Advocacy

Organizing data is a critical precursor to effective data analysis. Since many schools and districts already use Google for Education, tools like Google Drive and Google Sheets can make excellent resources for organizing and storing data – assuming you are using a district account and have first checked your student data privacy policy. (Note: The process described below would also work with Microsoft Sharepoint or OneDrive and Excel.) These best practices will help you gather all of your data files and then organize your data into a database:

• Create a folder that is accessible to the members of your research team. Ensure that only the people who need access to the data have permission to do so.
• Store a copy of all raw data files you export from surveys, assessment platforms, or other data systems (such as your student information system or human resources database). If you make a mistake during your analysis, you can still refer back to the original data source.
• Maintain a shared document to keep track of all procedures you use to clean and analyze your data. For example, make a note of any items that you rename or data points that you decide to remove.
• Import all data into a Google Sheets workbook and organize it around a common identifier such as a student or teacher ID number.
• Once you have a single dataset for analysis, ensure it is set to ‘View Only.’ Encourage your research team to copy that dataset before running any analyses to prevent accidental edits.

In addition to Google Sheets or Microsoft Excel, research teams may choose to use other statistical programs such as Tableau, R, or SPSS to analyze data. Regardless of the tool you use, make sure to save all files or outputs in a shared folder.

Leveraging data to support data advocacy begins with an understanding of how to analyze that data. As you prepare for analysis, it is important to remember that strong data analysis will enable stakeholders to make decisions, glean insights, and communicate findings more confidently. While it can be overwhelming and tempting to only look for patterns that validate existing perceptions, strong data analysis begins from a place of inquiry.

The three questions below will help you think about data analysis to support effective advocacy.

• For whom are you advocating? By identifying your target populations or subpopulations (e.g., racial groups, gender, students receiving special education services, grade level), you will know how you want to organize your data and make comparisons. For example, you might want to look at differences in reading progress based on whether students are classified as learning English or how progress varies by school and grade.

The following strategy explores ways that families and community members can access data for their students.

The following strategy provides ways to identify target populations or subpopulations by creating subgroups.

• What trends or patterns do you want to understand? With quantitative data, the goal is to understand specific trends or patterns in the numbers. This could include the level of progress over time, the number of incidents per year, the differences in graduation rate, or the level of agreement associated with a survey. You might calculate and examine one of the following:
  
  Frequency: The rate at which something occurs, reported as either a count or a percentage, and usually displayed as a bar chart or histogram.
  
  Average: A measure of central tendency can be calculated as the arithmetic mean, the median (the middle number in the distribution of numbers), or the mode (the most frequently reported number). Particularly if you are examining a large volume of data, you should report the average number to understand general approximations. Data illustrating averages can be displayed as a bar chart or histogram.
  
  Variation: This displays how the data is spread out (i.e., the range between the minimum and maximum values and the standard deviation around the mean). Variation can illustrate whether something is an isolated trend, an outlier, or representative of the broader population.

• What stories can support your observations and provide rich descriptions of the context in which the data was collected? Although most people associate data with numbers, qualitative stories offer rich descriptions of the people behind the statistics. In addition, qualitative data – whether collected through observations, interviews, focus groups, or document reviews – can be triangulated with quantitative analysis to increase the credibility of findings (and vice versa). Often, advocacy begins with a story and then shifts to an analysis of numeric data. It is important not to lose nuance in the quest for the objective.

The following strategy shares a process for gathering rich descriptions through a focus group.

How data is analyzed will depend on WHO you are as a stakeholder, WHAT data is available, and HOW you uncover key trends to support advocacy messages. The example shared below illustrates strategies for analyzing disciplinary data. Analysis at the level presented in the model might be restricted to district or school leaders who have full access to the data. However, other stakeholders (e.g., teachers, parents, students, community advocates) can advocate for analysis using the strategies below to gain a better understanding of students’ learning experiences.

The following strategies explore methods for analyzing data for advocacy.

How can you best communicate your observations? Spreadsheet programs such as Microsoft Excel and Google Sheets make it easy to quickly build charts and figures. However, different types of data visualizations communicate other trends. While a line chart quickly illustrates change over time, a histogram or bar chart represents the frequency with which a particular value occurs. Beyond charts and figures, tables can communicate multifaceted information such as descriptive statistics (e.g., minimum, maximum, mean, standard deviation) or comparisons between a targeted subpopulation and the broader population.

For example, imagine that the student population is, for the most part, evenly divided based on race, but you wanted to illustrate racial discrepancies across different types of academic courses such as AP, honors, and remedial. The table below (using “dummy data”) illustrates, Asian and White students are overrepresented in AP and honors courses while Black and Latino/a students are overrepresented in remedial courses, signaling an equity issue that should be addressed by stakeholders advocating for students.

Table 1: Students Enrolled in AP, Honors, and Remedial Courses

Source: XYZ High School Student Records Database

You are now ready to complete Activity 3: Putting Data into Action - HOW to Work with Data to Support Advocacy in the Data Advocacy Reflection and Planning Workbook. Once you complete this activity in the workbook, you can move forward to Addressing Immediate Needs in Education Through Data Advocacy.