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Social Statistics

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Death penalty data from Florida, 1976-77 (Radelet, 1981, American Sociological Review) ... Association is found easily. Causation is rarely found ' ... – PowerPoint PPT presentation

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Title: Social Statistics


1
Social Statistics Instructor Tamás
Rudas Office 7.92 Office hours Tuesdays
1pm-2pm, always by appointment E-mail
dekan_at_tatk.elte.hu Website http//statisztika.tat
k.elte.hu/tanszeki_honlap/Rudas_Tamas.htm Class
meets Thursdays, 10 1130
2
  • Text notes to be posted on the website
  • Frankfort-Nachmias, C., Leon-Guerrero, A. Social
    Statistics for a Diverse Society. Pine
    Forge Press. Copies are available int he library.
  • Aims To provide the students with an
    introduction to using data and statistical
    methods for the description of the society.
    Develop basic understanding of the main sources
    of data, of methods of interpretation of data and
    of elements of statistical inference.

3
Class procedures Class attendance will be
recorded Every student has to make two
20-minutes presentations Topics for
presentations Topic 1 Data and data
sources Hungary, economic data Hungary,
demographic data Hungary, regional data European
Union, economic data European Union, demographic
data European Union, regional data The World,
economic data The World, demographic data The
World, regional data
4
Topic 2 Use and misuse of data and
statistics Find, present and criticize the use
of data or statistics in the media Grading
Each presentation 30 Final test (last
class) 40
5
Bonus example Death penalty data from Florida,
1976-77 (Radelet, 1981, American Sociological
Review)
How many white/black defendants? How many
death/other penalties? Who has a higher chance
to receive the death penalty, a white or a black
defendant? Has this anything to do with racial
discrimination?
6
More complete form of the data
How are the two tables related? Does the race
of the victim has any influence ont he chance of
receiving the death penalty?
7
Some basic concepts Data Statistics Official
statistics Census Surveys Designed
experiments Observational studies
8
Data(plural)Usually numerical
informationNumbers are not dataOne needs to
know the method of collection
9
StatisticsThe science of collection, analysis
and interpretation of dataOfficial statistics
mathematical (inferential) statisticsAdministrat
ion scienceCentral role in governance
feedbackNew role evidence based medicine,
governance, etcNarrow meaning data summary
(e.g., average)
10
Official statisticsThe word statistics comes
form the word stateCollect, arrange (in
tables, lists, etc) knowledge about the
population, the economy, etc.Hungarian Central
Statistical Office Census Bureau, Bureau of
Labor StatisticsNatonal Statistics
11
CensusComplete enumeration of the
populationUsually every 10th yearLegal
requirementPolitical decision about the
questionsExtensive and expensiveMicrocensusC
ensus undercountPost-enumeration survey
12
SurveysA small fraction of the population is
interviewedSample and populationGeneralization
to the entire populationSample selection,
scientific methodsIntensive and cost effecftive
13
Tuition fees and incomesAbout 45 of university
students in Hungary pay tuition feesThe others
are in state-supported spacesCurrent
legislation makes these to pay about HUF 105 000
per year from next year onTuition fees are
about 250 000 400000 a yearHow much is
this?Average monthly gross income about 220
000Average monthly net income about 130
000The tuition is 2 3 months average net
income
14
Who pays tuition?Little is known but it is
likely that children of families in less
advantageous positions otherwise these children
would do better and would obtain the cheaper
placesA central issue involving lots of
statistics is inequality within the
societySocial stratification crystallization
of inequalitiesSocial mobility moving form one
stratum to anotherIntra- or intergenerational
mobilityThe school system does not necessarily
reduces social ineaqualitiesSome researches
claim it may even enlarge the differences
15
Do most of the people earn above or below
average?Income distribution
16
Most people earn below averageMedian is the
value that half the peole earn less than this,
half the people more than thisTypical incomes
over lifetimeVariations depending on
educational levelDifference if the graph is
made for an individual or for from cross
sectional data
17
(No Transcript)
18
Comparison of incomes for high school and
university graduatesUniversity graduate
starts to earn laterhigher income (the
advantage is the highest among the OECD
countries)earns longersmaller decrease before
retirement age(Cross sectional data. Individual
data are different)
19
Comparison of data sourcesReliability and
validityParticulars of data collection about
human populationsThe respondents
attitudeSensitive questions
20
Data and causalityDecison makingIntervention
researchCause effect relationships
Association and causation
21
Data and inference Is smoking bad for your
health?How do you know?Medical
evidence?Statistical evidence?What is
statistical evidence? Data or their
interpretation?What is scientific evidence? How
is it based on data?Will giving up smoking make
you healthier?Will you live longer if you give
up smoking?
22
Association is found easilyCausation is rarely
foundhappens after is different from happens
because ofAssociation is not
causation.Having long hair is associated with
having babies.Having long hair does not cause
having babies
23
How does one see association from
data?
24
Those having long vs short hair may also differ
in other aspectsOne cannot conclude that the
difference in the the chances of having children
is caused by differences in hair length.Perhaps
some of the other differences may be more
relevantConstruct a sex x hair x baby table
25
  • Men
    Women All
  • Long hair 0 50 350
    150 350 200
  • Short hair 0 400 150
    50 150 450
  • baby no baby baby
    no baby baby no baby

26
What kind of data?quantitative
qualitativediscrete continuousLevels of
measurement1. Nominal or categoricalcategories
are different no specification of the
differencedisjoint and exhaustive
categoriesusually few categoriesgender, hair
colour
27
2. Ordinal scaleordering among the
categories(larger, more beautiful,
)educational level3. Interval
scaledifference between categoriesno
zero-point on the scale??
28
4. Ratio scalealso ratios are
meaningfulzero-point is impliedincomeLeve
ls of measurementobjective or subjectiveit
may depend on the use for proxy variables
29
Data summaries aka descriptive statistics
  • Mode typical observation most frequent
    category or value
  • Median middle observation only if ranking is
    meaningful
  • Mean average observation interval or ratio
    scales
  • Parameters of location

30
  • The mean may not be typical
  • 3,3,3,3,3, 11,11,11,11,11 Mean 7
  • 2,2,2,2,2, 92 Mean 17
  • 2,2,2,2,2,212 Mean 37
  • The median is less sensitive
  • 2,2,2,2,2,4,10,50,100 Mode 2, Median 3
  • 2,2,2,2,2,4,10,500,1000 Mode 2, Median3

31
  • The mean does not exist for nominal variables
  • Faculties of Elte Number of students who
    applied
  • Science 2500
  • Arts 7000
  • Law 2500
  • Social Science 2000
  • Informatics 2200
  • Special education 1500
  • Teacher training 2000
  • What is the variable? What is its level of
    meaasurement? What are the observations?
  • Where did the average student apply?

32
  • Data sets with the same location may be very
    different
  • 1,1,1,1, 19,19,19,19
  • 9,9,9,9,11,11,11,11
  • The observations differ
  • Variation / dispersion
  • Measures of variation

33
  • Number of different categories or values
  • Range interval scale (largest minus smallest)
  • Mean absolute deviation ratio scale
  • 2, 3, 4, 5, 11
  • Mean 5
  • Absolute deviations 3, 2, 1, 0, 6
  • Mean absolute deviation 12/52.4

34
  • Standard deviation (SD)
  • Variance square of SD
  • SD square root of variance
  • Variance mean squared deviation from the average
  • 2, 3, 4, 5, 11
  • Mean 5
  • Squared deviations 9, 4, 1, 0, 36
  • Variance 50/510

35
About the final test
  • In class, about 100 minutes
  • Closed book, also no personal notes
  • No calculators are needed
  • Topics
  • Sources of data (census, survey)
  • Quality of data (validity, reliability, interview
    situation)
  • Inference based on data (association, causation)
  • Basic numerical facts about Hungary

36
Possible types of questions
  • Definitions of basic concepts
  • Assessment of data quality
  • Interpretation and evaluation of analyses based
    on data
  • Logical relationships between basic economic and
    demographic statistics

37
Grading
  • Your final grade will be calculated form the two
    presentations (30 each) and the final test (40)
  • Grades will be available from the 2nd week of
    January by sending an e-mail to me.
  • Those unhappy with their grades will be offered
    an oral examination on January 24. This exam will
    override your test grade but not the presentation
    grades. If you wish to take the exam, you have to
    sign up using ETR
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