Midterm Study Guide
Midterm Study Guide 400
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This 6 page Study Guide was uploaded by Dora Notetaker on Sunday October 18, 2015. The Study Guide belongs to 400 at University of Alabama at Birmingham taught by John E McNulty in Summer 2015. Since its upload, it has received 53 views. For similar materials see Research in Political Science in Public Relations at University of Alabama at Birmingham.
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Date Created: 10/18/15
PSC 4002D Research in Political Science Dr McNulty Midterm Study Guide Theory Building 0 Why something happens the way it does 0 Theory Something Y happens because something else X happens exists How do we test theories 0 Observations 0 Experiments 0 Formal Theory Hypotheses 0 These are propositions things that have to be true if the theory is true Concept Measure Accuracy Precision 0 Concept An idea concrete or abstract which is important to the researcher 0 Measure A means of measuring the concept represented in a variable 0 Accuracy How well accurately a measure can be calculated 0 Precision The degree to which a measure reflects the concept Concepts are things that are important to the researcher o It is important to match concept to measurement Relationship between concepts and measurement 0 When we reduce concepts to something we can measure they become variables Variables 0 These are symbols that represent a value that can vary 0 They are usually letters 0 English language letters 0 Greek letters 0 Symbols figures shapes 0 Conventions o Yy Dependent variable 0 X x Independent variable 0 M m or Beta beta Slope coefficient I Coefficient a value that represents the relationship between variables 0 B b or Alpha alpha Yintercept I The value of y when xO Correlation c As the value of one variable changes the value of another changes too y Cl 3X s o This equation is used to find graph lines o g is epsilon and it stands for error 0 y alpha beta x epsilon Curves o A normal curve is an average around a distribution Standard Deviation o It is the estimated difference between the mean and the part of the curve where it starts to slope down o It is represented with 5 Mean 0 Represented with the Greek Mu M 0 Middle of your graph Logistic curve 0 This is just a curve that represents a logarithmic relationship not a linear one o Graphed with an S curve Some date will have ceilings or floors 0 Which are places where there is just no more data Accuracy and Precision 0 These are the 2 types of measurement error Accuracy 0 How well a measurement reflects a concept 0 So image a target with many spots hitting the center and some are off 0 The ones hitting the center are more accurate Precision o The degree to which your measure reflects the concept 0 So when you are not measuring what you need to you are imprecise 0 Imagine a target but all the shots are clustered outside of it Levels of Measurement Categorical 0 Deal with things that aren t numeric 0 Types 0 Nominal I Things you can t put in order I Citizenship race gender etc o Ordinal I You can put them in order I Education approval of the president etc Numeric 0 Measured in numbers 0 Example income age height cholesterol 0 Types 0 Interval I Means they don t have a true 0 point 0 Ratio I Ratio I Means they have a true 0 point 0 People will use numbers to stand for categorical data but that doesn t make them numeric and you mustn t treat them as such Criteria for Causality These are conditions that must be met for a causal claim to be empirically valid Types 0 1 Correlation I But remember CORRELATION ALONE DOES NOT IMPLY CAUSATION I There can be a great number of reasons that 2 variables can covary I Ex There is a correlation between education and income 0 2 Time Order Clock I Refers to the direction of causal order I Ex If someone says high income causes you to be welleducated they would be wrong since most of the time you get an education first and then go on to maximize your income later in life 0 3 Causal Mechanism I A logical explanation why the causality is happening I It answers WHY the correlation is happening I The process by which a change in 1 variable causes another to change I Ex The correlation is happening because people with higher degrees of education will get better paying jobs and therefore more income 0 4 Controlling of Confounds I Confounds are outlying variables that are causing the independent and dependent variables to change I Ex lf good education does not cause higher incomes than what does Maybe natural talent better work ethic more experience etc I You need 2 things for a confounding variable 0 1 You need Z to cause Y Z gtY o 2 You need X to correlate with Z XZ Measures of Central Tendency 0 Mode 0 The most frequently occurring value 0 Nominal Ordinal Scale 0 Median o The middle value when data are sorted in order 0 Ordinal Scale 0 Mean Average 0 The sum of the values divided by the number of cases 0 Scale a ratio when describing a level of measurement Example Ranking things from excellent to poor Excellent Very Good Good Fair Poor 8 15 17 8 2 o In this case your median would be Good o If your values are numbers and if you have an even number of values your median will be the average between the 2 middle figures 0 So if you have 50 cases like above you can average 2527 to get a median Mode 0 You mode would be Good because that s what you have the most of Measures of spread o What is spread o The dispersion of data 0 How far data ranges from the central tendency How is spread determinedl displayed 1 Visually a graphs 2 Numerically a Nominal i Think of it as buckets of numbers b Ordinal i Buckets put in order ii Percentiles tenths Deciles hundredths Quintiles fifths Quartiles fourths c Scale i Standard Deviation Range 0 The variation between the maximum and minimum values Quartiles o A way of splitting up data 0 Example When measuring income there may be great variation between the richest and the poorest Very wealthy Middle class Very poor o The gray area is called the interquartile range Standard Deviation o A number used to identify the variation or error in a data set Steps to Calculating Standard Deviation and Variance 1 Calculate the mean 2 For each case subtract the mean from the value of the case to get the mean deviation 3 Square each value that you get mean deviations a Why do we square it Because it gets rid of any negative numbers and it puts extra weight on the bigger numbers which allows us to set apart outliers 4 Calculate the sum of squared mean deviations 5 Divide that sum by number of cases N minus 1 a Divide by N1 for a sample b Divide by N when looking at a whole population in your data c When in doubt divide by N1 d The value you get is called a variance 6 Take the square root of the variance a The value you get in the standard deviation Sample data set Ages of presidents Carter 91 G H Bush 91 Bill Clinton 69 GW Bush 69 Obama 54 Central tendencies 0 Mode 69 93 list both if there is more than one 0 Min 54 0 Max 91 0 Median 69 if you have more than 1 value that are the same number count all of them 0 Mean 9191696954 3745 748 rounded 75 Range o maxmin 91 54 37 Quartiles 0 Zero quartile 54 same as min 0 1st quartile 69 0 2nd quartile 69 same as median 0 3rd quartile 91 0 4th quartile 91 same as max 0 If you have multiple values in one quartile you have to average them Inter quartile range 0 Always subtract the third quartile from the first quartile o 91 69 22 Calculating Standard Deviation 0 Mean 75 1 Mean values a 91 75 16 b 91 75 16 c 69 75 6 d 69 75 6 e 54 75 21 2 Square these values a 16 256 b 16 256 c 6 36 d 6 36 e 21 441 3 Add them up a 2562563636441 1025 4 Divide by n1 a 10254 256 b So 256 is the variance 5 Take square root a 256 16 So 16 is the standard deviation Mean 75 Variance 256 Standard Deviation 16 000 When looking at data set about income religion race party affiliation and vote in 2012 0 Examples of using the 4 C s about the data 0 Examples for correlation 0 Household income affects who you vote for 0 You religion affects your vote 0 Examples for time order 0 You have an income before you go vote 0 You have a religion before you go vote 0 Examples for causal mechanism 0 People voted for a certain candidate because they agreed with his religion 0 People voted for a candidate because their race matches the voter s race 0 Examples for confounding variables 0 People could be voting for a candidate because they like their name not because of income race etc
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