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jan 17 2012- psych 215.docx

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PSYC 215
Michael Sullivan

Tuesday, January 17th, 2012 Story: about divulging his sins during confession and feeling so good about it after. Usually we feel better when we finally disclose something negative about ourselves (disclosure). Studies have shown that disclosing about the self is therapeutic. Another study showed that people who had secrets (6 or more) had 3 times more colds that those who do not have secrets. Disclosure Experiment His experiments; the first was in CEGEP. He read about an experiment that interested him; when you interact with someone, it is a level of intimacy (different levels depending on the conversation). The theory suggests that people will match the level of intimacy, otherwise you will have trouble finishing the conversation. His experiment: told people at a train station that he was studying hand writing. He gave samples with varying intimacy, and he predicted that people would match the intimacy. The effects of disclsure… He didn’t give up! (Paper we read). Pain Catastrophizing Scale measures the alarmist reaction that some people have to pain symptoms. People who had a high PCS score experience pain more severely than those with lower scores. They gave catastrophizers the opportunity to disclose their fears. Catastrophizing, Disclosure and Pain The lab has advantages (complete control over the environment and the painful stimulus), but it is not the real world. You are told ahead of time that it is completely safe, there will be no long term harm done to you, etc. In this experiment, though, they did it in a real-world setting (dental clinic). Participants were able to write in a little book about their fears and concerns about going to the dentist. This was their disclosure opportunity. In this experiment, there was a TWO WAY FACTORIAL. The control group described the path they want to take to get to school while the other group is writing about worries. The other variables are high and low catastrophizing. Therefore the two variables are level of catastrophizing, and the disclosure manipulation. In this study, the dependent variable was people’s pain intensity rating. In studies, you are looking at the effect of one variable on another. Whatever comes after the word “of” in “the effects of…” will be the independent variable, “on” will be the dependent variable. We can see that catastrophizers are feeling more pain than non-catastrophizers. But if you give them the chance to disclose, catastrophizers have a 30% reduction in pain. However, there is no benefit for non-catastrophizers; there is actually a little bit of an increase in pain. MIDTERM: identify the independent and the dependent variables in the study. In experiments, you need to use statistics that will assist in determining if the changes being seen are actually meaningful changes. Here we use something like variance. If you find that overall disclosure reduces pain, statistically that means that there is a “main effect” for the disclosure manipulation. Here, the effects of being a catastrophizers or disclosure depends on the conditions (they are valid only for some levels of another variable). This is an “interaction”. This study indicates an INTERACTION between catastrophizing and disclosure. Types of Variables Disclosure and catastrophizing were both independent variables, but not the same kind. Disclosure was manipulated, but catastrophizing was measured in others. Therefore catastrophizing is a subject variable. Experimental design: an experiment where you have manipulated all of the factors that you are interested in. But in the situation of Dr. Sullivan’s experiment, it is a quasi-experimental design (when you manipulate one variable and not the other). Eg: sex cannot be manipulated. Statistical Significance In statistics, when we are, for example, looking at the difference between two means, you address the meaningfulness of the difference between two averages by asking: “how likely is it that this occurred by chance?”. It is not meaningful if this is a high probability. T test = test that compares two different means = t(30), for example t (30) = 5.3, p
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