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Lecture

3N06 January 9, 2014.doc

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Department
Political Science
Course
POLSCI 3N06
Professor
Todd Alway
Semester
Winter

Description
Political Science 3N06 Semester II 2014 Lecture 2a: Frequency distributions: Tables and Graphs - Identify and understand the kind of variable involved; Mechanism where by you can transform the variable and what is associated with it. - FREQUENCY DISTRIBUTION - Some basic math to get things started - A) Proportion and Percentage o Comparisons between different sized groups possible o Controlling for sample size - B) Ratios and Rates Ratio = f1 / f2 f1 - number of cases in first category f2 - number of cases in second category - Compare the size of one group against the size of another group - Provides meaningful information in certain context, o E.G 2006 survey, it asked question about federal sponsorship scandal; o f1 = 3351 o f2 = 630 o f1 / f2 - In the case of ratios, we are interested in the relative size of two categories with respect to one another - How many men are in this class compared to women? - How many are satisfied with the performance of the Conservatives compared to how many who are dissatisfied? - A Rate (think unemployment rate, birth rate, murder rate) is calculated by dividing the number of actual occurrences of an event (over a given period of time), by the number of possible occurrences - The answer is often multiplied by some unit of 10 to shift the decimal point, and make the number easier to interpret o MURDER RATES o 2010 = 554 people were murdered o How many people were murdered compared to how many could have been murdered? o 554 / 34,349,200 = 0.0000161:1  1.61 / 100,000 (allows us to compare two differently sized populations) - Rates are useful for making comparisons between two groups of different size **** Frequency Distributions - The first step in moving from a huge collection of raw data into a summary statistic is to construct a frequency distribution table 1 - Taking raw data and breaking it down into the first form of order; - The type of frequency distribution will depend upon the level of measurement of VARIABLE (Nominal, Ordinal… etc.) o (Note that some of the charts below differ somewhat from the Charts in the Healey and Prus, Second edition): o Sex = Nominal (for nominal level variables it does not matter) o Marital Status = Nominal o Satisfaction with Services = Ordinal (have to list in order) o Age = Interval-ration level - From this - To this: - A) For Nominal Variables (does not matter if it’s not listed in order) - B) For Ordinal Variables (have to be listed in order) - The listing of the categories of the variable should reflect their rank – from low to high or from high to low 2 - C) For Interval-Ratio Variables - Follow the same pattern as is the case for ORDINAL (ranking, etc.) real complication when it comes down to doing frequency tables, many interval ratio levels have LARGE ranges. o Have to group together different attributes to simplify it. o 0-10,000 Group A o 10,001 - 20,000 Group B… etc. - The listing of the categories of the variable should also reflect the rank – from low to high or from high to low - The real complication here is that interval-ratio level variables (like income) often have a large range of possible scores - Like Income – from 0 to 2,000,000,000, and everything in between - This can make a
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