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Week 7 Research Methods

by: Lauren Jones

Week 7 Research Methods Pols 201

Lauren Jones
GPA 3.88

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This covered what happened this past week! My review will be up tomorrow!
Research Methods in Political Science
Adam Eckerd
Class Notes
research methods, Pols 201
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This 3 page Class Notes was uploaded by Lauren Jones on Sunday March 6, 2016. The Class Notes belongs to Pols 201 at University of Tennessee - Knoxville taught by Adam Eckerd in Winter 2016. Since its upload, it has received 30 views. For similar materials see Research Methods in Political Science in Political Science at University of Tennessee - Knoxville.

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Date Created: 03/06/16
Evidence, data, and measurement 3.1  Coining words o Has to be widely applicable  Gentrification o Just an example of weird definitions  Last Week’s examples o Education leads to wealth  Argument made  To measure education o At a census tract level  Binary o Median  One person, household, what is? o Don’t work, don’t have outcome, asked at the individual level o Wealth isn’t the same as income  Income is low but wealthy  Abstraction o Conceptualization  From an idea/thing/ experience o Operationalization  From a concept a tangible definition o Measurement  From a definition to indicator o WE have to agree about what that word means  Abstractor to indicator, we do lose information  Indicator o Numbers of years in school  Precision and Bias o Maybe theoretically you can measure something perfectly  Probably could measure wealth well for population o What things are worth  Never get an actual depiction o Summary calculation loses further precision  Anyway measured, can be all over the map o Because of bias and lack of precision  Reliability o Repeatability o  Validity o Neither over or understating the true value of the indicator  Bias  Selection bias Information bias, observation bias  Exclusion bias  Recall bias  Purposeful vs. accidental exclusion  Inability to accurately recall what happens.  People are terrible at remembering  Indicator= concept+ error  Validity o Has to be a valid representation  Convergent validity  Is it actually a meaure of the thing we are trying to measure  Discriminant validity  Types of indicators o Observed variables o Perceptual variables  Translate indicator back to concept o Indicator can take on meaning itself and become divorced from the concept  Three modes of indicators o Instrumental, conceptual, symbolic  Look to previous studies at what other people do it o Come up with a new way to measure things instead of symbolic measure  Find something that what you are trying to measure  Don’t look for just a number o Look at critically, is this measuring the things they are trying to measure  Considerations o By whom or how data collected  Conceiving a measure of divorce  Discuss why this isn’t a good measure  If you come up with a measure, and still say that is a bad measure  Northeastern University o SAT scores, o AMA, medical doctor Chapter 14 Measurement Principles  14.1 Measurement scales o Ca be binary, presence or absence o Binary  Quantifies object as present or absent o Nominal  Objects into different categories o Ordinal  Categories organized logically in the greater less than way o Interval  Temperature, opinion on a 1-10 scale  Differ by equal amounts o Ratio  Weight, ordered categories  14.2 Testing Hypothesis o Research can’t prove correct, but prove wrong  Operational hypothesis  Defined measurement characteristics  Rejection of null gives difference by hypothesis and the theory  If null is supported, difference predicted by hypothesis didn’t occur o Operational hypothesis narrows the measurement possibilities down  14.3 Probability o Null Hypotheses are statistically  95% is the acceptable likelihood  .05 level of confidence is confidence expressed in rejecting the null hypothecs  Type one errors o False positive results  Incorrect rejection of null  Type two o False negatives  Incorrect acceptance of null  14.4 Randomness o Mathematical principle  Random Events  Cannot predict each outcome  Probability reflects statistical testing, highly calculated gambles  Random sampling error could be reasoning 


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