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Quantitative Data Analysis: Exploring Phenomena Through Numerical Manipulation

Dive into the world of quantitative analysis, where observations are represented and explained numerically. Understand the significance of codebooks, frequency distributions, averages, modes, medians, dispersions, and more in uncovering patterns within data. Learn about variables and relationships through univariate, bivariate, and multivariate analyses. Discover the nuances of continuous and discrete variables, as well as the role of contingency tables in understanding interdependent variable values.

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Quantitative Data Analysis: Exploring Phenomena Through Numerical Manipulation

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  1. Chapter 14 Quantitative Data Analysis Key Terms

  2. Quantitative analysisNumerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect. CodebookDocument that describes the locations of variables and lists the assignments of codes to the attributes composing those variables.

  3. Univariate analysisDescribes a case in terms of a single variable - the distribution of attributes that comprise it. Frequency distributionDescription of the number of times that the various attributes of a variable are observed in a sample.

  4. AverageMeasure of central tendency. MeanResult of diving the sum of the values by the total number of cases.

  5. ModeThe most frequently occurring attribute. MedianMiddle attribute in the ranked distribution of observed attributes.

  6. DispersionRefers to the way values are distributed around some central value. Standard deviationIndex of the amount of variability in a set of data.

  7. Continuous variableIncreases steadily in tiny fractions. Discrete variableJumps from category to category without intervening steps.

  8. Bivariate analysisAnalysis of two variables simultaneously. Focus is on the variables and the empirical relationships. Contingency tablesValues of the dependent variable are contingent on values of the independent variable.

  9. Multivariate analysisAnalysis of more than two variables simultaneously.

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