1. The Practice of Social Research Chapter 14 – Quantitative Data Analysis

2. Chapter Outline  Quantification of Data  Univariate Analysis  Subgroup Comparisons  Bivariate Analysis  Introduction to Multivariate Analysis  Sociological Diagnostics  Ethics and Quantitative Data Analysis  Quick Quiz

3. Quantification of Data  Quantification Analysis – the numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect.

4. Quantification of Data  Age  1 = 1  2 = 2  3 = 3  4 = 4  5 = 5  Sex  Male = 1  Female = 2  Political Affiliation  Democrat = 1  Republican = 2  Independent = 3  Region of Country  West = 1  Midwest = 2  South = 3  Northeast = 4

5. Quantification of Data  Develop Code Categories 1. Use well-developed coding scheme. 2. Generate codes from your data.

6. Quantification of Data  Codebook Construction  Codebook – the document used in data processing and analysis that tells the location of different data items in a data file.  The codebook also identifies the locations of data items and the meaning of the codes used.  Purposes of the Codebook 1. Primary guide in the coking processes 2. Guide for locating variables

7. ATTEND How often do you attend religious services? 0. Never 1. Less than once a year 2. About once or twice a year 3. Several times a year 4. About once a month 5. 2-3 times a month 6. Nearly every week 7. Every week 8. Several times a week 9. Don’t know, No answer Abbreviated Variable Name Numerical Lable Definition of the Variable Variable Attributes

8. Univariate Analysis  Univariate Analysis – the analysis of a single variable, for purposes of description (examples: frequency distribution, averages, and measures of dispersion).  Example: Gender  The number of men in a sample/population and the number of women in a sample/population.

9. Univariate Analysis  Distributions  Frequency Distributions – a description of the number of times the various attributes of a variable are observed in a sample.

10. Univariate Analysis  Central Tendency  Average – an ambiguous term generally suggesting typical or normal – a central tendency (examples: mean, median, mode).

11. Univariate Analysis  Mean – an average computed by summing the values of several observations and dividing by the number of observations.  Mode- an average representing the most frequently observed value or attribute.  Median – an average representing the value of the “middle” case in a rank-ordered set of observations.

12. Univariate Analysis  Practice: The following list represents the scores on a mid-term exam.  100, 94, 88, 91, 75, 61, 93, 82, 70, 88, 71, 88  Determine the mean.  Determine the mode.  Determine the median.

13. Univariate Analysis  Dispersion – the distribution of values around some central value, such as an average.  Standard Deviation – a measure of dispersion around the mean, calculated so that approximately 68 percent of the cases will lie within plus or minus one standard deviation from the mean, 95 percent within two, and 99.9 percent within three standard deviations.

14. Univariate Analysis  Continuous Variable – a variable whose attributes form a steady progression, such as age of income.  Discrete Variable – a variable whose attributes are separate from one another, such as gender or political affiliation.

15. Univariate Analysis  Detail versus Manageability  Provide reader with fullest degree of detail, balanced with presenting data in a manageable form.

16. Subgroup Comparisons  Description of subsets of cases, subjects or respondents.  “Collapsing” Response Categories  Handling “Don’t Knows”

17. Bivariate Analysis  Bivariate Analysis – the analysis of two variables simultaneously, for the purpose of determining the empirical relationship between them.

18. Bivariate Analysis  Constructing a Bivariate Table 1. Determine logical direction of relationship (independent variable and dependent variable). 2. Percentage down versus percentage across.

19. Bivariate Analysis  Constructing and Reading Bivariate Tables  Example: Gender and Attitude toward Sexual Equality 1. The cases are divided into men and women. 2. Each gender subgroup is described in terms of approval or disapproval of sexual equality. 3. Men and women are compared in terms of the percentages approving of sexual equality.

20. Bivariate Analysis  Contingency Table – a format for presenting the relationship among variables as percentage distributions.

21. Bivariate Analysis  Guidelines for Presentation of Tables 1. A table should have a heading or title that describes what is contained in the table. 2. Original content should be clearly presented. 3. The attributes of each variable should be clearly indicated. 4. The base on which percentage are computed should be indicated. 5. Missing data should be indicated in the table.

22. Introduction to Multivariate Analysis  Multivariate Analysis – the analysis of the simultaneous relationships among several variables.

23. Quick Quiz

24. Chapter 14 Quiz 1.To conduct a quantitative analysis, researchers often must engage in a _____ after the data have been collected. A. coding process B. case-oriented analysis C. experimental analysis D. field research study

25. Chapter 14 Quiz Answer: A. To conduct a quantitative analysis, researchers often must engage in a coding process after the data have been collected.

26. Chapter 14 Quiz 2. Which of the following describes the analysis of more than two variables? A. experimental designs B. quasi-experimental designs C. qualitative evaluations D. multivariate analysis

27. Chapter 14 Quiz Answer: D. Multivariate analysis describes the analysis of more than two variables.

28. Chapter 14 Quiz 3. The process of converting data to numerical format is called _____. A. feminist research B. qualification C. quantification

29. Chapter 14 Quiz ANSWER: C. The process of converting data to numerical format is called quantification.

30. Chapter 14 Quiz 4. Which of the following are basic approaches to the coding process? A. You can begin with a well developed coding scheme. B. You can generate codes from your data. C. both of the above D. none of the above

31. Chapter 14 Quiz ANSWER: C. The following are basic approaches to the coding process: you can begin with a well developing coding scheme and/or you can generate codes from your data.

32. Chapter 14 Quiz 5. A _____ is a document that describes the locations of variables and lists the assignments of codes to the attributes composing those variables. A. cross-case analysis B. codebook C. constant comparative method D. monitoring study

33. Chapter 14 Quiz ANSWER: B. A codebook is a document that describes the locations of variables and lists the assignments of codes to the attributes composing those variables.

34. Chapter 14 Quiz 6. The _____ is an average computed by summing the values of several observations and divided by the number of observations. A. frequency B. mean C. median D. mode

35. Chapter 14 Quiz ANSWER: B. The mean is an average computed by summing the values of several observations and divided by the number of observations.