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IB Math SL Internal Assessment: Directions

A introductory guide to the IA

Directions

Length: 12-20 pages - no word count; logic, precision and clarity count more than length. 

Method: 

  1. An introduction that states your rationale/purpose for the topic
  2. A logically developed exploration that is easy to follow by your peers, including:
    1. Cite any references or direct quotes using MLA format
    2. State all definitions and explanations of concepts
    3. Use proper notation, applicable graphs, and mathematical computations
    4. Personal engagement
  3. A conclusion that ties up the major ideas, including whether the results are reasonable. What went wrong? Why? Think TOK
  4. Use the rubric to guide you

Introduction

  1. Can an examiner clearly identify your topic?

  2. How did you collect your data? What questions did you ask? Did you do a survey? Who answered it? Did you give it personally or over social media?

  3. Do you have well defined processes and well defined parameters?

    1. Step by step explanations.

    2. What are your variables?

    3. What do you need to find to complete your process?

    4. What else do you need to know?

  4. All processes should be in the order that you will complete them. Correlation must be completed before Linear Regression, so it should be described in the introduction before linear regression. 

  5. Make sure your pages are numbered and that your work is double spaced. IB prefers Arial font.

Data Analysis

1. If creating a graph, make sure everything is labeled

2.  Show ALL work, every step

3. Include all equations - use the format from class, do not Google the equations

4. After each process, give a brief explanation to the results of the process and how they connect to your topic

5. Create your model, then use technology to compare

6. Analyze for extrapolating and interpolating data

 

Data

1. Data should be collected through survey, observation, or research

2. Data should be relevant

3. Data should be sufficient in both quantity and quality: 30-50 for a good model

4. Data should be organized in a form that is appropriate for analysis

5. If you are using a survey, you must include the survey - it can be in the paper or added as an appendix

6. Data taken from another source must be cited and must be raw, unanalyzed data; do not use percentages for Chi Squared

Conclusion

1. Create a meaningful conclusion, bringing back together all of your processes and summarizing the results

2. Explain how each process connects to bring you to the conclusion of either proving or disproving your hypothesis

3. Indicate validity - did the processes you used properly help you get to the conclusion you wanted to achieve? Is there any other process that could have been added or anything you would have done differently?

4. Discuss reliability of your model if using statistics

5. Think of real world applications, do outside factors affect the outcome?

6. Could this project lead you into more research or analysis? How?

7. BE REFLECTIVE!