Data Collection Techniques

1. Data Collection Techniques

2. Biblical Reference But where can wisdom be found? Where does understanding dwell? Job 28:12

3. Physics Labs • The purpose of performing physics labs is not only to reinforce the material you learned in the classroom, but also to enhance your understanding of how scientific knowledge actually progresses. • Labs also give you hands-on experience with making measurements and interpreting data

4. Data Collection • Each lab will involve either quantitative or qualitative data. • Quantitative Data Sources: • Gauges • Micrometer, Meter Stick, Thermometer, etc. • Sensors • Force, Motion, Light, etc. • Meters • Voltmeter, Ammeter, etc. • Qualitative Data Sources: • Type of Condiment, Color, Type of Mirror, etc.

5. Data Integrity • It is important to record accurate and consistent data. • However, there will be a learning curve with some of the equipment, and some of the equipment may be less precise and/or accurate than others. • In addition, there may be some environmental sources of error.

6. Handling Sources of Error • Relative Discrepancy or Experimental Error is one method to document experimental error. • Most labs will include questions about your relative discrepancy. • All reported results should include the experimental error. Relative Discrepancy = |Error| x 100 Accepted Value Example: 0.56 m ± 0.005 m

7. Graphing Data • During most labs, you will only change one factor at a time. • A variable is any factor that might affect the behavior of an experimental setup. • The independent variable is a factor that is changed or manipulated in an experiment. (variable you control) • The dependent variable is a factor that depends upon the independent variable. Example – The temperature of a balloon is changed to observe the affect upon the volume.

8. Graphing Data Line of best fit is a better method of predicting the trend of the graph versus one point. Is the graph missing anything? Regression Equation

9. Direct vs. Inverse Relationships Direct- This is where the two variables do the same thing. That is, if one increases so does the other and vice versa. Inverse- This is where the two variables do the opposite. If one variable increases, the other variable decreases.

10. Correlation • Part of the data analysis process is to create a regression equation to find a relationship between the independent and dependent variables. • Correlation is a term used to describe how well your data fits the proposed regression equation. • Correlation (represented by the letter “r”) varies from -1 (inverse relationship) to +1 (direct relationship). • The closer to ±1, the better the fit.

11. Various Correlation Values y y y y x x x x r = 0.91 r = 0.88 Strong negative correlation Strong positive correlation r = 0.42 r = 0.07 Weak positive correlation Nonlinear Correlation

12. Common Lab Mistakes • Mistakes are different from experimental errors. • The following mistakes can be avoided if care and caution are taken: • Misreading scales (using equipment incorrectly) • Arithmetic and algebraic errors • Incorrectly transferring raw data to the data tables • Using the wrong theory and/or equations

13. A Few Last Words… • You will only get out of the labs what you put into them. • The lab equipment is sensitive and very expensive. • Lab etiquette entails cleaning up after yourselves and returning all equipment to its correct location. • Have fun!