1. Two types of empirical questions Descriptive –This kind of empirical question requires a researcher to describe some aspect of behavior –For example, a researcher might ask, What are people’s attitudes toward the homeless? Causal –This type of empirical question requires a researcher to determine what causes something to happen –For example, a researcher might ask, Does stress cause people to have road rage?

2. Our studies We want to know whether or not verbal estimate-based depth perception training benefits subsequent performance on verbal and active tasks? In other words, we want to know whether or not such training causes better performance later on

3. Question How do you determine that one thing caused something else to happen? For example, how could we determine that a new training simulation improved performance?

4. A simple logical method 1.Collect data about current behavior 2.Change the suspected cause 3.Do not change anything else 4.Collect data about subsequent behavior 5.Compare data collected before and after the change was made

5. Example

6. 1.Pre-test painting ability 2.Provide training via simulator 3.Do not change anything else 4.Post-test painting ability 5.Compare pre and post-test data

7. Complications The logical process outlined earlier is intuitive and straightforward When studying behavior, however, several issues could occur that would complicate the interpretation of the data

8. Potential Complications 1 & 2 Something other than the suspected cause changes –Something inside the participants changes This is known as a maturation problem –Something outside the participants changes This is known as a history problem

9. Maturation 1.Pre-test painting ability –The participant warmed up during the pre-test 2.Provide training via simulator 3.Do not change anything else 4.Post-test painting ability 5.Compare pre and post-test data –Is the difference due to training or warm-up?

10. History 1.Pre-test painting ability 2.Provide training via simulator –The simulator technician provides some advice 3.Do not change anything else 4.Post-test painting ability 5.Compare pre and post-test data –Is the difference due to training or advice?

11. Training, Maturation or History?

12. Potential Complication 3 The initial data collection may bias participants –This is known as a testing problem

13. Testing 1.Pre-test painting ability –Certain aspects of painting are assessed 2.Provide training via simulator –Participants work hard on aspects of painting that will be assessed 3.Do not change anything else 4.Post-test painting ability 5.Compare pre and post-test data –Is the difference due to training or bias?

14. Training or Testing?

15. Potential Complication 4 How one collects the Pre-Test data may differ from how the Post-Test data are collected –This is known as a instrumentation problem

16. Instrumentation 1.Pre-test painting ability –Test involves a door panel 2.Provide training via simulator 3.Do not change anything else 4.Post-test painting ability –Test involves a trunk lid 5.Compare pre and post-test data –Is the difference due to training or tasks?

17. Training or Instrumentation?

18. Potential Complication 5 Sometimes the Pre-Test scores are extreme, so it is likely that Post-Test scores will be different, no matter what –This is known as a regression problem

19. Regression 1.Pre-test painting ability –A number of participants score abnormally low 2.Provide training via simulator 3.Do not change anything else 4.Post-test painting ability –Those low scoring participants score more average, while others stay the same 5.Compare pre and post-test data –Is the difference due to training or abnormal scores?

20. Training or Regression?

21. Solution There is a simple way to capture these issues, if they occur –Include a control group –If Pre and Post-Test scores differ for both the experimental and control groups, then it is likely that the study was affected by one of these problems This is known as having a confound in a study

22. A more complex method 1. Collect data about current behavior 2a. Experimental group - Change the suspected cause 2b. Control group - Don’t change the suspected cause 3. Don’t change anything else 4. Collect data about subsequent behavior 5. Compare data collected before and after the change was made

23. Example 1.Pre-test painting ability 2.Provide training –Via simulator (Experimental group) –Via standard method (Control group) 3.Don’t change anything else 4.Post-test painting ability 5.Compare pre and post-test data

24. No Confounds

25. Confounds

27. Our studies Experimental group –Pre-Test, Verbal Training w/ Feedback, Post-Test Control group –Pre-Test, Verbal Training w/o Feedback, Post-Test

28. Potential Confounds Maturation History Testing Instrumentation Regression