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Chapter 14

Chapter 14. Quantitative Data Analysis. Quantitative Analysis. The technique by which researchers convert data to a numerical form and subject it to statistical analysis. Quantification : The process by which data becomes numerical

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Chapter 14

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  1. Chapter 14 Quantitative Data Analysis

  2. Quantitative Analysis • The technique by which researchers convert data to a numerical form and subject it to statistical analysis. • Quantification: The process by which data becomes numerical • Example: A computer cannot read the answer “strongly agree” so the researches assigns the numerical value 1…and so forth.

  3. Quantification Continued • Most times quantifying is important is with ordinal-level data. Why? • Nominal-level data: The numbers you may assign have no particular value. Women=1 and Men=0 doesn’t mean anything statistically. • Interval-level data: The numbers are already assigned in the data. Annual income is $50,000, or your age is 27. You’re not going to assign other numbers.

  4. Ordinal Quantification • Making sure that you’re properly representing the data by giving it the correct numerical status. • We are winning the War in Iraq. • 1 = Strongly Disagree • 2 = Disagree • 3 = Agree • 4 = Strongly Disagree

  5. Fool-Proof…certainly not! • There can still be flaws. To use the previous example: • We are winning the War in Iraq. • 1 = Strongly Disagree • 2 = Disagree • 3 = Agree • 4 = Strongly Disagree • There may be a bigger difference between 1 and 2, than between 2 and 3, which would jeopardize our analysis of reality.

  6. Where do we find all this? • When downloading a dataset, there is often a codebook. The codebook will list all of the questions, all of the answers, and how each of the answers were coded into numerical form.

  7. Baby Steps • Univariate Analysis: The analysis of ONE variable for the purpose of description. • You are simply trying to “describe” that one variable • What is the average, the frequency, the distribution, and so on.

  8. Distribution (or Marginals) • A description of the number of times the various attributes of a variable are observed. • Religious Attendance • 57 never attend • 67 attend once/month • 83 attend once/week

  9. Central Tendency • Mean: The average, done by summing all values and dividing by the number of observations. • Median: Represents the value of the “middle” case in a rank-ordering. • Mode: Represents the most frequently observed value.

  10. Dispersion • The distribution of values around some central value. • Can be very important, particularly in the case of “outliers”. • Example: • 20 observations of $20,000/year (avg. $20,000) • 19 observations of $20,000/year AND 1 observation of $200,000/year (avg. $29,000)

  11. Dispersion • Range: The distance between the lowest and highest observed value. • Standard Deviation: More complex measure in which 68% of observations are 1 standard deviation and 95% of cases are within 2 standard deviations of the average. Lower std. deviation means the data is bunched. Higher means it is more dispersed.

  12. Collapsing Categories • Moving responses and observations into more general categories. • Done occasionally to counteract high standard deviation. • Can be very tricky and should always have justification (theoretical or statistical) • Example: Moving “very liberal”, “liberal” and “somewhat liberal” into the same category.

  13. Don’t Knows • In all surveys you have a group of people who reply “Don’t Know”. How do they fit into the statistical analysis? • When reporting percentages and marginal's you should report them, but it gets much more complicated in terms of average and standard deviation.

  14. Bivariate Analysis • Describe a case in terms of two variables simultaneously. • Example: • Gender • Attitudes toward equality for men and women

  15. Constructing Bivariate Tables • Divide cases into groups according to the attributes of the independent variable. • Describe each subgroup in terms of attributes of the dependent variable. • Read the table by comparing independent variable subgroups in terms of an attribute of the dependent variable.

  16. Multivariate Analysis • Analysis of more than two variables simultaneously.

  17. Multivariate Analysis • Analysis of more than two variables simultaneously. • Can be used to understand the relationship between two variables more fully.

  18. Ethics and Quantitative Data Analysis • There is always the danger of defining and measuring variables in ways that encourage one finding over another. • Quantitative analysts must guard against this. • The quantitative analyst has an obligation to report both formal hypotheses and informal expectations that didn’t pan out.

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