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CHAPTER 14, QUANTITATIVE DATA ANALYSIS. Chapter Outline  Quantification of Data  Univariate Analysis  Subgroup Comparisons  Bivariate Analysis  Introduction.

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Presentation on theme: "CHAPTER 14, QUANTITATIVE DATA ANALYSIS. Chapter Outline  Quantification of Data  Univariate Analysis  Subgroup Comparisons  Bivariate Analysis  Introduction."— Presentation transcript:

1 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 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  Develop Code Categories 1. Use well-developed coding scheme. 2. Generate codes from your data.

6  Codebook Construction  Codebook – The document used in data processing and analysis that tells the location of different data items in a data file.

7 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

8 Figure 14.1

9 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 Label Definition of the Variable Variable Attributes

10  Data Entry  Excel  SPSS

11 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.

12  Distributions  Frequency Distributions – A description of the number of times the various attributes of a variable are observed in a sample.

13 Figure 14.3

14 Figure 14.4

15  Central Tendency  Average – An ambiguous term generally suggesting typical or normal – a central tendency (examples: mean, median, mode).

16  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.

17  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.

18 Figure 14.5

19  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.

20 Figure 14.6

21  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.

22  Detail versus Manageability  Provide reader with fullest degree of detail, balanced with presenting data in a manageable form.

23 Subgroup Comparisons  Description of subsets of cases, subjects or respondents.  “Collapsing” Response Categories  Handling “Don’t Knows”  Numerical Descriptions in Qualitative Research

24 Bivariate Analysis  Bivariate Analysis – The analysis of two variables simultaneously, for the purpose of determine the empirical relationship between them.

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

26 Figure 14.7  Percentaging a Table

27  Constructing and Reading Bivariate Tables  Example: Gender and Attitude toward Sexual Equality 1. The cases are divided into men and women. 2. Each gender subgrouping 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.

28  Contingency Table – A format for presenting the relationship among variables as percentage distributions.

29  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.

30 Introduction to Multivariate Analysis  Multivariate Analysis – The analysis of the simultaneous relationships among several variables.

31 Quick Quiz

32 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

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

34 2. Which of the following describe the analysis of more than two variables? A. experimental designs B. quasi-experimental designs C. qualitative evaluations D. multivariate analysis

35 Answer: D. Multivariate analyses describe the analysis of more than two variables.

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

37 ANSWER: C. The process of converting data to numerical format is called quantification.

38 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

39 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.

40 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

41 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.

42 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

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

44 7. Which of the following are aimed at explanation? A. multivariate analysis B. bivariate analysis C. univariate analysis D. both A and B

45 ANSWER: D. Multivariate analysis and bivariate analysis are aimed at explanation.

46 8. The multivariate techniques can serve as power tools for A. predicting behavior. B. diagnosing social problems. C. reacting to issues. D. all of the above

47 ANSWER: B. The multivariate techniques can serve as powerful tools for diagnosing social problems.


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