PSYC 1010 Lecture Notes - Lecture 3: Confounding, Dependent And Independent Variables, Central Tendency

• School: York University
• Department: Psychology
• Course: PSYC 1010
• Professor: Jill Bee Rich

Document Summary

3 groups studying for different times vs. test scores. If your data does not comply with your hypothesis, one should look at all the data and figure out if there is a reason for this or if the data is correct and your hypothesis is incorrect. In our example, we must also look at sleep, this is the confounding variable. Unfortunately, often the confounding variable is never found. If it is found, one can manipulate this variable as independent, and have a study with two independent variables. In the drinking example of last lecture, expectation is called a confounding variable. By randomly assigning, you"re assuming that all individual difference variables are evenly distributed among groups such that groups are essentially equal. Measures the degree of relationship between two variables. Naturally occurring variations on both variables are measured to see if they are related. The data is placed into a formula: the correlation coefficient is calculated.