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[SOAN 3120] - Final Exam Guide - Comprehensive Notes for the exam (38 pages long!)


Department
Sociology and Anthropology
Course Code
SOAN 3120
Professor
Andrew Hathaway
Study Guide
Final

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UofG
SOAN 3120
FINAL EXAM
STUDY GUIDE

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Wednesday, September 14
Displaying Distributions
Lying with Statistics
- It is possible to lie with statistics
oWe expect them to come from experts who make them for specific purposes
oPeople that don’t have the knowledge may not understand data properly
oGiven the raw data and ability to examine them graphically, careful analysis makes deception
more difficult
If you know statistics it makes it less likely to be duped
- Always be critical when evaluating a study
oAlways potential for falsification of data, omissions, and selective presentation of data
oThere is an importance of replication
Without it, we cannot verify earlier results from earlier studies
- Having the ability to plot data by way of visual representation can serve to forewarn certain problems in
that data
Basic Definitions
- Data sets made up of cases
oCan refer to people, groups of people, countries, corporations, etc.
- Cases made up of variables
oSomething that can take on different values for different individuals
oCan vary by certain characteristics (age, gender, SES)
-Categorical variables differentiated by name only
oEx. Gender, occupation, age groups
-Quantitative variables take on numerical values of a given unit of measure
oEx. Age (years), weight (lbs), height (cm)
- The distribution of a variable tells the range and frequency of values it includes
oHighest value and lowest value within the distribution, and how often it takes on those values
Looking at Data
- Usually organized as tables in which rows represent units of observation and columns represent variables
oRows ex. Individual, countries
oColumns ex. Age, gender, income
Two Types of Categorical Data
-Nominal variables have no intrinsic order
oEx. Regions
-Ordinal variables have a natural order
oEx. Survey attitude statements (Strongly Agree, Somewhat Disagree)
Types of Quantitative Data
-Counts are non-negative integers (whole numbers)
oEx. Population
-Amounts are also non-negative, but need not be integers (also called ratio variables, since meaningful
ratios of two values be formed)
oEx. Ratios that can be compared across cases (8x…5x…)
-Relative frequencies (proportions, percents, and rates) have both minimum and maximum values
oEx. Infant Mortality Rate: 1000 x # of kids that died in the first year of birth / # of live births
-Interval scales, since zero point is arbitrary (e.g., zero Celsius), can compare ratios of difference, or
intervals, but cannot form meaningful ratios
oCannot say 20 degrees is 2x hotter than 10 degrees Celsius because the zero point is arbitrary
(does not necessarily correspond with zero heat)
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- Methods of analysis often depend on the nature of the variables
Categorical Data
- Little more than simple counts and percentages over the categories
- Bar-graphs or pie-charts are rarely worth the effort, and the latter presumes exhaustive categories
oMeaning you need to know what the whole of the pie is in order to talk about the pieces of that
pie, all the data needs to be there
Quantitative Data
- Since few individuals have exactly the same value of a quantitative variable, bar graphs divide its range
into equal class intervals
- A histogram shows the count or percentage falling into each interval (horizontal axis gives the range of
the variable, vertical axis gives the count or percent
oStem and leaf plot is a quick way to make a histogram if you have only a little bit of data
Far left represents the stems, the right side represents the leaves
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