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# MSU COM 225 - COM 225 Notes

Type: Lecture Note
Pages: 2
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6 pages

6 pages

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1/22/20 Experiments: One of the biggest dangers in experiments is the threat if an experimental confound - confound occurs when a researcher accidentally manipulates a variable other than his/her dependent variable - RQ: Does how hungry people have an effect on how much they smile at others? - Hypothesis: hunger causes less smile - One of the variables comes first (hunger in this case) - Researcher manipulates hunger to see how much it changes the amount of smiling - Confederate: a person who is in on the experiment We have recorded our findings in Qualtrics - What can we observe? - Frequency of positive vs. negative conversations What did we do to measure according to our hypothesis - The positivity of a person's emotions conversations is positively related to the nonverbal intimacy displayed in that conversation - 2 variables - The positivity of a person's emotional conversation (emotionally) - The nonverbal intimacy displayed in a person's conversation (nonverbal intimacy) - The more joyful the conversation, the less sad it should be Reliability - We measure reliability when we use multiple items to measure the same construct - When measures of multiple items are consistent with each other, we say the measure has reliability - Some items can just be bad and not correlate What else do we need to do to answer questions with our data? - Use a measure of central tendency - Mean, median, or mode - We do this to summarize emotionally and nonverbal intimacy How do we draw conclusions about our data? - Test of mean differences - Looks to see if two groups have statistically different scores than one another - Most common in experiments and tests of causal hypotheses - Ex: we can see if dyads you said were “mostly negatively” interacting were less nonverbally intimate than those who were “mostly positive” - Anything that has a p value of .05 (5%) is a statistically significant difference - Test of correlations - Looks to see if two variables are related more strongly than we would expect by chance - Most common for surveys and correlational by chance- Most common for surveys and correlational hypothesis - This is the test we would use to draw a conclusion about our hypothesis about our data

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