1. Create a Graph showing the practical approaches to address the social problem

1. Create a Graph showing the practical approaches to address the social problem according to interviewers
• Bar Graph: This is ideal for comparing the frequency or magnitude of different approaches suggested by interviewers. Each bar can represent a different approach, with the height of the bar indicating how often that approach was suggested or its perceived importance.
• Bubble Chart: For three-dimensional data (e.g., frequency of suggestion, perceived effectiveness, and ease of implementation), a bubble chart can be used. The X and Y axes represent two variables, while the size of the bubble represents the third dimension
2. Create a Graph showing barriers to address the social problem, barriers in applying the three current theories, barriers using
• Heat Map: If you have data that involves cross-tabulation (like different barriers experienced in different theories or research areas), a heat map can visually represent this data where the color intensity indicates the severity or frequency of each barrier within each category.
3. Create a Graph showing the possibilities of positive change using the proposed theories ad recommendations of participants.
• Radar Chart: If you want to compare multiple theories or recommendations across various attributes of positive change (like impact, feasibility, scalability), a radar chart can visually display this multi-dimensional data in a comparative manner.
• Stacked Bar Chart: If there are multiple components or dimensions of positive change (such as social impact, economic benefits, environmental improvements), a stacked bar chart can show how different theories or recommendations contribute to each aspect of change.
• Pie Chart: To illustrate the proportion of each theory or recommendation’s contribution to overall positive change, a pie chart can be effective. This works well when you want to emphasize the relative significance of each component in a holistic view.
4. Create a Graph showing the differences/comparisons of the urban and suburban schools and their service and educational offers according to comparison. Graph showing the historical difference between urban and suburban schools.
• Stacked Bar Chart: If you want to show multiple data points (like different types of services or educational programs) within each school type, a stacked bar chart can be useful. This allows for a comparison of the composition of services and offerings in urban versus suburban schools.
• Scatter Plot: If you’re looking to explore correlations between two variables (such as school funding versus student performance), scatter plots can be insightful. You can use different colors or symbols to distinguish between urban and suburban schools.
• Heat Map: For a more complex dataset, like comparing a wide range of services across many schools, a heat map can visually represent this data. Different colors can indicate levels of service offerings, with one axis representing urban schools and the other suburban schools.
5. Create a Data/graph showing the consensus of the interviewer’s answers to the research questions. (open ended ones) and another graph showing the explanations to the open-ended questions.
• Word Cloud: This is a popular choice for visualizing open-ended responses. Words that are mentioned more frequently in the responses are displayed larger in the cloud. This gives a quick visual impression of the most common themes or topics in the answers.
• Thematic Bar Chart: First, categorize the responses into themes or common topics. Then, use a bar chart to show the frequency of each theme. Each bar represents a different theme, and the length or height of the bar indicates how many times that theme appeared in the responses.
• Pie Chart: Similar to the thematic bar chart, after categorizing the responses, you can use a pie chart to show the proportion of each theme relative to the whole. This is useful for emphasizing the relative significance of each theme.
For the second part, visualizing explanations to open-ended questions:
• Flow Chart or Mind Map: These can be effective for showing the relationship between different parts of the explanations. For instance, a flow chart can illustrate how one idea leads to another, or a mind map can show the central idea and how other ideas branch out from it.
• Hierarchical Bar Chart: If the explanations can be broken down into a hierarchy of themes and sub-themes, a hierarchical bar chart can visually represent this structure, showing how different aspects of the explanations are related and their relative frequencies.
• Stacked Bar Chart: If you can categorize the explanations into several consistent themes, and each theme has sub-categories, a stacked bar chart can show this breakdown. Each bar represents a theme, and the segments in the bar represent the sub-categories.
• Sankey Diagram: This is useful for showing the flow of themes or categories within the explanations. It can illustrate how different aspects of the responses are interconnected and the relative weight of different paths or themes.
6. Create a Graph showing historical data of crime, poverty, etc. from the 1950s to present as it relates to Black folks.
• Timeline Chart: To highlight key events or policy changes that might have impacted crime or poverty levels, a timeline chart can be integrated with your data visualization. This provides context to the data and helps in understanding how external factors may have influenced trends.
• Heat Map: For complex datasets, such as comparing multiple metrics across several decades, a heat map can visually represent this data. Different colors can indicate levels of each metric, with one axis representing time and the other the metrics.
• Scatter Plot: If you’re exploring correlations between two variables over time (for example, the relationship between poverty levels and crime rates), scatter plots can be insightful. You can use different colors or shapes to represent different decades.
• Line Graph: This is ideal for showing changes and trends over a long period. You can plot years on the x-axis (from the 1950s to the present) and the metric (like crime rates or poverty levels) on the y-axis. If you’re comparing multiple metrics (e.g., crime and poverty), you can use different lines for each, possibly with different colors for clarity.
7. Create a Graph that shows the correlation between historical data and the interviewees’ testimonies/experience/knowledge
• Scatter Plot: This is ideal for showing correlations between two sets of data. On one axis, you can plot a historical metric (like crime rates, poverty levels, or educational attainment), and on the other axis, you can plot a quantifiable aspect of the interviewees’ testimonies (such as their perceived social mobility, satisfaction with education, etc.). Each point on the scatter plot represents an interviewee’s response in relation to the historical data.
• Dual-Axis Line Graph: This graph allows you to plot two different types of data that have different scales but are related. For example, you could have historical crime rates on one axis and a measure of interviewees’ sense of community safety on the other. The two lines will help visualize how these different datasets may correlate over time.
• Stacked Area Chart: This can be used to show how different themes or aspects from the interviews (like sentiments or topics) stack up against each other over a historical timeline. This visual representation can help in understanding the relative prominence of different experiences or knowledge areas over time.
• Correlogram: If you have multiple variables from both historical data and interviewee responses and you want to show the correlation between all these variables, a correlogram can be used. This shows the correlation coefficients between each pair of variables in a matrix format, often color-coded for ease of interpretation.