Overview of the Analysis Process

The analysis of qualitative data involves several key steps:

• Data Preparation: Transcription and organization of interview and open-ended survey responses.
• Coding: Identification of key themes and sub-themes.
• Categorization: Grouping codes into broader categories related to the student needs.
• Quantification: Assigning numerical values to categories and developing an index based on the intensity and urgency of needs.
• Index Construction: Combining the categorized data into a final Student Need Index (SNI).

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2. Key Steps in the Data Analysis Framework

Step 1: Data Preparation

• Transcription: Interviews and focus group recordings will be transcribed verbatim. This will include all verbal responses, including pauses, emphasis, and interruptions.
• Data Cleaning: Open-ended survey responses will be reviewed to correct any spelling or formatting errors, and irrelevant or incomplete responses will be removed.

Step 2: Coding the Data

Coding is the process of labeling and categorizing responses into themes. This is crucial for identifying patterns and converting qualitative data into quantifiable information.

• Initial Open Coding: Begin by reading through the transcripts and survey responses to identify initial codes. Codes are short phrases or words that represent the essence of the response. These might include terms like “academic support,” “stress management,” “mental health,” or “technology access.”
• Codebook Development: A codebook will be created to ensure consistency in coding. The codebook will define each code, describe when it should be used, and provide examples of responses that align with each code. Example of codebook entries:
  • Code: Academic Support
    Definition: Any mention of support related to academic challenges, such as tutoring, mentoring, or faculty help.
    Example: “I need more access to tutors for my subjects.”
  • Code: Mental Health Support
    Definition: Any mention of mental or emotional well-being support, including counseling services or stress management.
    Example: “I often feel overwhelmed with coursework and could use more mental health resources.”
• Axial Coding: Once initial codes are applied, group related codes together under broader themes (axial codes). For instance, codes like “academic support,” “tutoring,” and “study resources” could be grouped under the axial theme “Academic Needs.”
• Selective Coding: The final step in coding involves refining the axial codes to develop core categories that align directly with the SNI categories (e.g., academic, technological, social, emotional, accessibility).

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3. Categorization of Themes

After coding the data, the next step is to categorize the codes into broader themes. These categories will map directly to the components of the Student Need Index.

Sample Categories:

1. Academic Needs
   • Themes: Academic support, tutoring, course materials, faculty engagement.
   • Example Codes: “Tutoring,” “Study Resources,” “Faculty Help.”
2. Technological Needs
   • Themes: Access to technology, online learning platforms, digital tools.
   • Example Codes: “Laptop access,” “Internet connectivity,” “Online resources.”
3. Social and Emotional Support
   • Themes: Peer relationships, mental health resources, social isolation.
   • Example Codes: “Counseling,” “Peer Support,” “Stress Management.”
4. Accessibility Needs
   • Themes: Physical accessibility, accommodations for disabilities, digital accessibility.
   • Example Codes: “Wheelchair access,” “Assistive technologies,” “Accessible materials.”
5. General Feedback and Suggestions
   • Themes: Broad suggestions, specific barriers or challenges that fall outside predefined categories.
   • Example Codes: “Financial aid,” “Health care,” “Campus facilities.”

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4. Quantification of Qualitative Data

To transform qualitative data into a measurable format, the following steps will be taken:

Step 1: Frequency Counting

• Counting Occurrences: For each theme or sub-theme, count how frequently each code appears across responses. This provides a quantitative measure of how common a particular need is among students. Example:
  • “Technology Access” might appear in 60% of the interview responses and 45% of the survey responses. This indicates a high prevalence of technological needs among the student population.

Step 2: Intensity Scoring

• Intensity Rating: Each theme will be rated based on the intensity or urgency of the need. Intensity could be measured through:
  • Frequency: How often the theme appears in the data.
  • Verbal Emphasis: How strongly the issue is emphasized in the responses (e.g., “urgent,” “critical”).
  • Severity: The extent to which students describe the issue as a barrier to success (e.g., “Without access to textbooks, I cannot pass my courses”).
  A simple scale (1-5) can be used to rate intensity, where:
  • 1 = Low Intensity (e.g., “It would be nice to have more tutors.”)
  • 5 = High Intensity (e.g., “I cannot keep up with my studies because I don’t have access to reliable internet.”)

Step 3: Weighting the Themes

• Assign Weights: Some categories of needs may be more critical than others in shaping the academic success of students. These categories can be weighted based on their urgency or impact. For instance, academic support might have a higher weight than access to social activities because it directly affects academic outcomes.
• Example Weights:
  • Academic Needs: 40% of total index
  • Technological Needs: 25% of total index
  • Social and Emotional Support: 20% of total index
  • Accessibility Needs: 15% of total index

Step 4: Index Construction

• Building the SNI: The Student Need Index (SNI) will be constructed by combining the frequency, intensity, and weight of each category. Each category will be scored, and the final index will be calculated by aggregating these scores. Example Formula for SNI Calculation: SNI=(Academic Needs Score×0.40)+(Technological Needs Score×0.25)+(Social and Emotional Support Score×0.20)+(Accessibility Needs Score×0.15)\text{SNI} = (\text{Academic Needs Score} \times 0.40) + (\text{Technological Needs Score} \times 0.25) + (\text{Social and Emotional Support Score} \times 0.20) + (\text{Accessibility Needs Score} \times 0.15)SNI=(Academic Needs Score×0.40)+(Technological Needs Score×0.25)+(Social and Emotional Support Score×0.20)+(Accessibility Needs Score×0.15) The final SNI will produce a score that reflects the overall level of student need. A higher score indicates greater educational needs, which will help SayPro prioritize areas for improvement.

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5. Validation of the Student Need Index (SNI)

To ensure the validity and reliability of the Student Need Index, the following steps will be undertaken:

• Pilot Testing: Conduct a pilot study with a smaller group of students to test the framework and adjust the scoring method if needed.
• Cross-validation: Compare the SNI scores with other data sources (e.g., institutional performance data, student satisfaction surveys) to verify consistency.
• Expert Review: Have educational experts review the framework and index to ensure it accurately reflects real-world student needs.