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Psych100B Day 1 Lecture Notes

by: rallen17 Notetaker

Psych100B Day 1 Lecture Notes Psych 100B

rallen17 Notetaker

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About this Document

These notes cover the housekeeping of the course and the basics of what we will learn about for the next few weeks. The main focus is on correlations.
Research Methods in Psychology
Darvick, E.R. Fourquet, N. Middlebrooks, C.D.
Class Notes
Correlations, Psychology, research
25 ?




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This 5 page Class Notes was uploaded by rallen17 Notetaker on Monday March 28, 2016. The Class Notes belongs to Psych 100B at University of California - Los Angeles taught by Darvick, E.R. Fourquet, N. Middlebrooks, C.D. in Spring 2016. Since its upload, it has received 30 views. For similar materials see Research Methods in Psychology in Psychlogy at University of California - Los Angeles.

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Date Created: 03/28/16
Housekeeping  11 or 12th edition textbook is fine  go to different lab if you miss your own lab, email TA beforehand, you can turn in  assignment ahead of time, there is an absence excuse form and it will be reviewed at the  end of the quarter to excuse missed points and missed quizzes.  material from quizzes come from lecture  final: practice exams on ccle, review session the week before  one extra credit through sona system Actual Content of Course  data: observations from experience (personal admission, physiological, etc) o pieces of information from your experience  theory/hypothesis o theory: body of statements that organization and explain the data that is observed o hypothesis: prediction for situations that we don't have data for o example: you have noticed that when you work in a group, people slack off  compared to when they work alone. So this is our theory (diffusion of  responsibility). You want to know if this is something really going on in the real  world. You want to collect some data. We test to see if this happens in an  emergency situation (or when people are in a group in a social situation). Data  now influences our theory. This is a circular process. Now collect more data  (circular)  we constantly collect data to modify our theory  we are constantly looking at data and trying to fit it with theory  another example: Mary isn't in class today and has missed many first days of school. This data can then inform our theory. So now we collect more data­ circular process.   We now have to translate our theory into measurable situation (we have to be able to  measure this behavior)  Data ­­­> Interpret ­­­> Theory/Hypothesis ­­­> measurable situation ­­­> repeat in circle  errors can be made in any step of this  research methods: allows us to decrease the amount of errors that we make  strict methodologies: allows to to be more precise  ideally: data is objective and controlled  errors: o example: errors in data: look at undergraduate opinion of presidential candidates.  get into groups of 5 and talk about political views. We conclude that male upper is more likely to be republican. Problems: our sample is not representative. Or  situation is out of control­ some people were scared to speak up. This study was  not controlled and not subjective. Therefore it is not informative­ error in data  collection o example: errors in interpretation. music improves mental performance. Listen  then perform spatial IQ test. They found that IQ was higher than when you  listened to Mozart. They concluded that classical music makes you smarter.  Problem: you can't make that conclusion from the data.   because there is so many potential for error, we never say that theory is proven, only  disproven or supported.  replication o literal replication: do the exact same study again o conceptual replication: we try to provide additional confirmation for the  hypothesis but in a different way  example: instead ­do surgeons take on more responsibility when in a  group  example: test other types of IQ. test ability to use may.   use different types subjects, different material, use different conditions,   psychology: the study of mental processes and behavior  belief vs scientific explanation o belief:   no evidence necessary.  maintained even when there is counter evidence  based on authority  example: when you go out without sweater, you catch a cold. Maintained  even with counter evidence.   example: when you get a cold chicken soup cures that. Comes from  tradition.   people don't stop to questions beliefs o scientific explanation:   opposite of belief  empirical evidence that's based on experiences  changed with counter evidence  empirical evidence: unbiased information  based on unbiased information  allows us to start with belief and then with scientific method we test it.  we must use experience and not faith to understand the world o example: mom said chocolate made brother act crazy. This is not empirical  evidence. this is biased. this is belief. o question: how to we use scientific method to get scientific explanation= this  course. There are many methodologies.....  Methodologies o Correlation:   an intuition that TV affects kid's behavior  commercials affect their buying behavior  we are interested whether violence on TV affects children's aggressive  behavior  example: something asks how much violent TV your child watches per  day  you get data in hours. we also ask them how aggressive you rate your  child. It looks like there is a relation. This is a correlation. We can predict  how aggressive they will be  correlation allows us to make predictions  two variables  correlation gives names for these two variables  1st: predictor variable  2nd: criterion variable.  example: predictor is TV violence. Criterion is aggressive behavior  predictor­ x variable  criterion­ y variable.   we operationally defined TV violence as number of hours watched  types of correlations  positive: as X increases, so does Y  example: shoe size and reading level  negative: as X increases, Y decreases  example: sleep and coffee  unrelated: variables are not related  example: GPA and commute.   correlation coefficient  two elements; sign and actual value  sign  positive sign: positive correlation  negative sign: negative correlation  actual value  strength  not affected by sign  the problem: there are many explanations of why these two variables are  related  one explanation; A causes B.  other explanation: B causes A  third: C causes A/B  example: ash trays in house and lung cancer­ issue with  correlation.   we will never know what the truth is with correlation  directionality problem  we can only show relationship, make prediction,   directionality problem (or Third variable problem): we can't make any  casual conclusions  example: more sex you have the younger you are. reality: the more sex  you have, the younger you look.   this doesn't mean correlations shouldn’t be used


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