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Geography 237a Research Methods. Quantitative Analysis (Babbie Chapter 14). Quantifying data Univariate analysis Bivariate analysis Multivariate analysis. Quantitative Analysis. Quantitative Analysis numerical representation of data numerical analysis of data – e.g., using statistics.

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quantitative analysis babbie chapter 14

Geography 237aResearch Methods

Quantitative Analysis(Babbie Chapter 14)
  • Quantifying data
  • Univariate analysis
  • Bivariate analysis
  • Multivariate analysis

Geog 237a

quantitative analysis
Quantitative Analysis

Quantitative Analysis

  • numerical representation of data
  • numerical analysis of data – e.g., using statistics

Geog 237a

quantitifying survey data
Quantitifying Survey Data

Closed-ended Responses

  • nominal/ordinal – categories pre-assigned numbers (but do not have to)
  • categories counted
  • interval/ratio ready to analyze

Open-ended Responses

  • responses categorized then counted
  • mutually exclusive categories

Geog 237a

quantitifying closed ended survey data
Quantitifying Closed Ended Survey Data

Words or Numbers?

  • with nominal data words can be used
  • numbers from counting frequencies of each response

Geog 237a

quantitifying open ended survey data6
Quantitifying Open-Ended Survey Data

note codes are mutually exclusive:

  • academic vs non-academic
  • financial vs academic vs administrative vs facilities

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types of analysis
Types of Analysis

Univariate Analysis

  • variable at a time
  • typical first-step to more sophisticated analysis
  • frequency distribution
    • number or percent of each category of a variable
    • specific time period
    • percent used for comparisons
    • raw numbers to know sample size
  • min, max, range
    • how widely the data vary
    • range = max – min

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types of analysis9
Types of Analysis

Univariate Analysis

  • central tendency (averages)
    • mean
    • mode
    • median

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central tendency measures
Central Tendency Measures

Mean

  • aka arithmetic mean
  • sum of observations/number of observations

Mode

  • most frequent observation

Median

  • middle value of ranked observations

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central tendency measures exercise
Central Tendency MeasuresExercise
  • Calculate the measures below for dataset

16

18

17

13

16

15

17

13

calculator

Range

Mean

Mode

Median

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central tendency measures exercise12
Central Tendency MeasuresExercise
  • Calculate the measures below for dataset1 (dataset sorted)

13

13

15

16

16

17

17

18

calculator

Range

Mean

Mode

Median

Geog 237a

central tendency measures exercise13
Central Tendency MeasuresExercise
  • Calculate the measures below for dataset1 (dataset sorted)

13

13

15

16

16

17

17

18

calculator

Range - 5

Mean - 13.6

Mode – 13, 16, 17

Median - 16

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central tendency measures exercise14
Central Tendency MeasuresExercise
  • Calculate the measures below for dataset2 (dataset sorted)

13

13

15

15

16

17

17

18

calculator

Median

Geog 237a

central tendency measures exercise15
Central Tendency MeasuresExercise
  • Calculate the measures below for dataset (dataset sorted)

13

13

15

15

16

17

17

18

calculator

Median – 15.5

  • two middle values/2
  • i.e., values in 4th and 5th “slots”/2
  • (15+16)/2

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central tendency measures exercise16
Central Tendency MeasuresExercise
  • Calculate the measures below for dataset3 below:

Range

Mean

Mode

Median

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central tendency measures exercise17
Central Tendency MeasuresExercise

Which measure of income is better (year 2000 common to both)?

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dispersion measures
Dispersion Measures

normal curve

  • relationship between mean, and sample scores
  • shows dispersion graphically
  • mean in middle (bisects)
  • skinny = low dispersion
  • wide = high dispersion

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dispersion measures19
Dispersion Measures

spread around the mean

  • variance – too abstract, a step towards standard deviation
  • standard deviation (from mean) – more intuitive

http://www.sysurvey.com/tips/statistics/variance.htm

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dispersion measures20
Dispersion Measures

standard deviation

  • average “distance” between mean and each value in dataset
  • translates variance into same “scale” as mean and all the values
  • high values are generally bad

http://www.sysurvey.com/tips/statistics/standardd.htm

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bivariate analysis
Bivariate Analysis
  • move beyond mere description
  • towards explanation

dependant variable

  • the variable whose result you want to predict
  • e.g., voted “Liberal” last federal election

independent variable

  • predictor variable
  • e.g., age , gender, income

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bivariate analysis22
Bivariate Analysis

Contingency Table

  • typical presentation format for presenting data about relationship between two variables
  • percentages are typically the cells

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bivariate analysis example
Bivariate Analysis Example

This is actually several bivariate tables

  • weight status X gender; weight status X age;…

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multivariate analysis
Multivariate Analysis
  • well beyond mere description
  • more rigourous explanation
  • statistical control (hold all other independent variables constant while test effect of one independent variable at a time)
  • e.g., voted liberal example above:

test effect age, controlling for gender and income

test effect gender, controlling for age and income

test effect income, controlling for age and gender

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multivariate analysis example
Multivariate Analysis Example

Residents were more likely to agree they would vote in favour of a by-law banning pesticides (except in cases of severe infestation) if they:

lived in Halifax

agreed they wished their neighbour(s) would stop using chemical pesticides

disagreed a yard should be weed free

agreed pesticides, even when used properly, still pose health risks to adults

Geog 237a