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The Class: Quantitative Analysis of Historical Data

The Class: Quantitative Analysis of Historical Data. 1. Quantitative methods and statistics 2. Historical methods 3. Urban History and Milwaukee History as examples 4. Outline of the Class: http://www.uwm.edu/~margo/595/595syl2007.htm. Uses of Quantitative History.

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The Class: Quantitative Analysis of Historical Data

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  1. The Class: Quantitative Analysis of Historical Data • 1. Quantitative methods and statistics • 2. Historical methods • 3. Urban History and Milwaukee History as examples • 4. Outline of the Class: http://www.uwm.edu/~margo/595/595syl2007.htm

  2. Uses of Quantitative History • Allows the study of the history of ordinary people who don’t leave archival records, aren’t famous or powerful • Thus history using averages and patterning rather than study of individual events. • We will ask how people lived: • 1. What kinds of jobs did they have? • 2. What were their houses and neighborhoods like? • 3. What was the ethnic composition of the city?

  3. Uses of Quantitative History, cont. • We can also study trends – in population, in economic change, social attitudes, political activities….. • We can draw graphs and create visual displays of information….

  4. Timeline • 1456: Gutenberg Bible (Invention of movable type) • 1492 Columbus’ “Discovery” of the New World • ca 1500 Renaissance • 1517 Protestant Reformation • 1607: Founding of Virginia (Jamestown Colony) • 1620-30: Founding of Plymouth Colony and Massachusetts Bay

  5. Timeline: Growth of Human Population

  6. Timeline • 1456: Gutenberg Bible (Invention of movable type) • 1492 Columbus’ “Discovery” of the New World • ca 1500 Renaissance • 1517 Protestant Reformation • 1607: Founding of Virginia (Jamestown Colony) • 1620-30: Founding of Plymouth Colony and Massachusetts Bay

  7. Growth of the U.S Population compared to the UK and France

  8. Growth in the Size of the U.S. House of Representatives

  9. Admitting States to the Union

  10. Poverty Trends in the U.S.

  11. Terms for Today, 1 • Matrix, Database: A Table of Data • Codebook: A Guide to the Matrix or Database • Case: Unit of analysis (Row) • Variable: Information collected on each unit (Column) • Value, Code: Cell

  12. A Database or Matrix Case YRBUILT PERSON AGE OCC$ NATIV$ • 1 988 2 39 profcl amer • 2 893 6 39 skille amer • 3 892 6 47 skille germ • 4 . 5 45 skillp germ • 5 . 1 72 unskil germ • 6 . 4 31 skillp amer • 7 . 6 48 skillp germ • 8 . 3 34 unskil amer • 9 . 3 62 germ • 10 889 6 46 skille germ

  13. A Database or Matrix Case YRBUILT PERSON AGE OCC$ NATIV$ • 1 988 2 39 profcl amer • 2 893 6 39 skille amer • 3 892 6 47 skille germ • 4 . 5 45 skillp germ • 5 . 1 72 unskil germ • 6 . 4 31 skillp amer • 7 . 6 48 skillp germ • 8 . 3 34 unskil amer • 9 . 3 62 germ • 10 889 6 46 skille germ

  14. A Database or Matrix Case YRBUILT PERSON AGE OCC$ NATIV$ • 1 988 2 39 profcl amer • 2 893 6 39 skille amer • 3 892 6 47 skille germ • 4 . 5 45 skillp germ • 5 . 1 72 unskil germ • 6 . 4 31 skillp amer • 7 . 6 48 skillp germ • 8 . 3 34 unskil amer • 9 . 3 62 germ • 10 889 6 46 skille germ

  15. A Database or Matrix Case YRBUILT PERSON AGE OCC$ NATIV$ • 1 988 2 39 profcl amer • 2 893 6 39 skille amer • 3 892 6 47 skille germ • 4 . 5 45 skillp germ • 5 . 1 72 unskil germ • 6 . 4 31 skillp amer • 7 . 6 48 skillp germ • 8 . 3 34 unskil amer • 9 . 3 62 germ • 10 889 6 46 skille germ

  16. A Database or Matrix Case YRBUILT PERSON AGE OCC$ NATIV$ • 1 988 2 39 profcl amer • 2 893 6 39 skille amer • 3 892 6 47 skille germ • 4 . 5 45 skillp germ • 5 . 1 72 unskil germ • 6 . 4 31 skillp amer • 7 . 6 48 skillp germ • 8 . 3 34 unskil amer • 9 . 3 62 germ • 10 889 6 46 skille germ

  17. Terms for Today, 1 • Matrix, Database: A Table of Data • Codebook: A Guide to the Matrix or Database • Case: Unit of analysis (Row) • Variable: Information collected on each unit (Column) • Value, Code: Cell • Case YRBUILT PERSON AGE OCC$ NATIV • 1 988 2 39 profcl amer • 2 893 6 39 skille amer • 3 892 6 47 skille germ • 4 . 5 45 skillp germ • 5 . 1 72 unskil germ • 6 . 4 31 skillp amer • 7 . 6 48 skillp germ • 8 . 3 34 unskil amer • 9 . 3 62 germ • 10 889 6 46 skille germ

  18. Terms, 2: Types of Data • Microlevel: data collected on the characteristics of individual cases, people, houses, events, that is, discrete units. For example an individual, with characteristic information on sex, age, state of residence, etc. • Aggregate: Tabular data representing counts of units falling into particular categories, e.g., populations of states. The state is the unit of analysis; the variables are the name of the state and the population of the state.

  19. A Database or Matrix: Microlevel Case YRBUILT PERSON AGE OCC$ NATIV$ • 1 988 2 39 profcl amer • 2 893 6 39 skille amer • 3 892 6 47 skille germ • 4 . 5 45 skillp germ • 5 . 1 72 unskil germ • 6 . 4 31 skillp amer • 7 . 6 48 skillp germ • 8 . 3 34 unskil amer • 9 . 3 62 germ • 10 889 6 46 skille germ

  20. U.S. Population Data: Aggregate

  21. Terms 3: Sources of Data • Survey: collected specifically for the research purpose, e.g., Current Population Survey, General Social Survey, census. • Administrative record: records of immigrant arrivals by port; tax filings; vital registration records; case files of judicial proceedings, health records.

  22. Terms 4: Characterizing Datasets • Population or Sample • Population: all possible units in the universe • Sample: a selection of possible units • Probability or Random Sample: sample selected using probability methods • Parameters and Statistics • Parameters: characteristics of populations • Statistics: characteristics of samples • Cross Section or Time Series • Cross Section: Set of measurements at one point in time • Time Series: Set of measurements on the same phenomenon at different points in time.

  23. Levels of Measurement or Types of Variables • Nominal:  categories are exclusive but bear no other relationship to each other. Ex: (1) Jews; (2) Protestants; (3) Catholics; (4) Muslims. 2P does not equal M.       • Ordinal:   Ordinal: categories are continuous and in ascending sequence but distance between two categories is not equal. Ex: ordinal scales of wealth. wealthiest, second wealthiest . . .n wealthiest. The wealthiest does not necessarily have 3 times the wealth of the 3rd wealthiest.      • Interval: True numbers are potentially continuous and in  ascending sequence and the distance between any two units of value is equal. Ex: family size. 1,2,3,4, etc.; income.1000,2000,3000. • Ratio: interval variable with a zero point.

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