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Quantitative Analysis (Babbie Chapter 14)

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)

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  1. Geography 237aResearch Methods Quantitative Analysis(Babbie Chapter 14) • Quantifying data • Univariate analysis • Bivariate analysis • Multivariate analysis Geog 237a

  2. Quantitative Analysis Quantitative Analysis • numerical representation of data • numerical analysis of data – e.g., using statistics Geog 237a

  3. 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

  4. Quantitifying Closed Ended Survey Data Words or Numbers? • with nominal data words can be used • numbers from counting frequencies of each response Geog 237a

  5. Quantitifying Open-Ended Survey Data Geog 237a

  6. Quantitifying Open-Ended Survey Data note codes are mutually exclusive: • academic vs non-academic • financial vs academic vs administrative vs facilities Geog 237a

  7. 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 Geog 237a

  8. Frequency Distributions Geog 237a

  9. Types of Analysis Univariate Analysis • central tendency (averages) • mean • mode • median Geog 237a

  10. Central Tendency Measures Mean • aka arithmetic mean • sum of observations/number of observations Mode • most frequent observation Median • middle value of ranked observations Geog 237a

  11. Central Tendency MeasuresExercise • Calculate the measures below for dataset 16 18 17 13 16 15 17 13 calculator Range Mean Mode Median Geog 237a

  12. 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

  13. 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 Geog 237a

  14. Central Tendency MeasuresExercise • Calculate the measures below for dataset2 (dataset sorted) 13 13 15 15 16 17 17 18 calculator Median Geog 237a

  15. 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 Geog 237a

  16. Central Tendency MeasuresExercise • Calculate the measures below for dataset3 below: Range Mean Mode Median Geog 237a

  17. Central Tendency MeasuresExercise Which measure of income is better (year 2000 common to both)? Geog 237a

  18. Dispersion Measures normal curve • relationship between mean, and sample scores • shows dispersion graphically • mean in middle (bisects) • skinny = low dispersion • wide = high dispersion Geog 237a

  19. 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 Geog 237a

  20. 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 Geog 237a

  21. 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 Geog 237a

  22. Bivariate Analysis Contingency Table • typical presentation format for presenting data about relationship between two variables • percentages are typically the cells Geog 237a

  23. Bivariate Analysis Example This is actually several bivariate tables • weight status X gender; weight status X age;… Geog 237a

  24. 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 Geog 237a

  25. 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

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