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Chapter 15 Social Research
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Chapter 15 Social Research

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  • 1. Quantitative and Qualitative Data Analysis Chapter 15
  • 2. Introduction
    • Quantitative or Qualitative?
      • What is the difference been qualitative and quantitative?
        • The distinction between qualitative and quantitative data is not as important as the distinction between the strategies driving their collection
  • 3. Introduction
    • Quantitative data analysis
      • Analysis that tends to be based on the statistical summary of data
      • Quantitative researchers typically focus on the relationship between or among variables, with a natural science-like view of social science in the backs of their minds.
  • 4. Introduction
    • Qualitative data analysis
      • Analysis that tends to results in the interpretation of action or representations of meanings in the researcher's own words
      • Empathic understanding or an in-depth, thick description
  • 5. Quantitative Data Analysis
    • Presumes one has collected data about a reasonably large, and sometimes representative, group of subjects, whether these subjects are individuals, groups, organizations, social artifacts, etc.
    • The data does not always come in the form of numerical data
  • 6. Quantitative Data Analysis
    • Sources of Data for Quantitative Analysis
      • When data is collected by researcher, coding is an important first step
      • Coding is the process by which raw data are given a standardized form. This means making data computer usable.
        • For example, if you are coding gender – you may have Male = 1 and Female = 2
      • The assignment of numbers to words is arbitrary
  • 7. Quantitative Data Analysis
    • Elementary Quantitative Analyses
      • Descriptive statistics
        • Statistics used to describe and interpret sample data
        • Example
          • Fifty-five percent of the people sampled were married.
  • 8. Quantitative Data Analysis
    • Elementary Quantitative Analyses
      • Inferential statistics
        • Statistics used to make inferences about the population from which the sample was drawn
        • Example
          • Men are significantly more likely than women to have been employed full-time.
  • 9. Quantitative Data Analysis
    • Univariate analyses
      • Analyses that tell us something about one variable
  • 10. Quantitative Data Analysis
    • Bivariate analyses
      • Analyses that focus on the association between two variables
  • 11. Quantitative Data Analysis
    • Multivariate analyses
      • Analyses that permit researchers to examine the relationship between variables while investigating the role of other variables
  • 12. Univariate Analysis
    • Measures of Central Tendency
      • Mode
        • The measure of central tendency designed for nominal level variables. The value or category that occurs most frequently. It can be computed for any variable because all ordinal and interval level variables are also nominal.
  • 13. Univariate Analysis
    • Measures of Central Tendency
      • Median
        • The measure of central tendency designed for ordinal level variables. The middle value when all values are arranged in order. Can also be used for interval variables because they are also ordinal variables.
  • 14. Univariate Analysis
    • Measures of Central Tendency
      • Mean
        • The measure of central tendency designed for interval level variables. The sum of all values divided by the number of values.
  • 15. Univariate Analysis
    • How does a researcher know which measure of central tendency (mode, median, or mean) to use to describe a given variable?
      • Do not use a measurement that is inappropriate for a given level of measurement
        • Example: Mean or Median for a nominal level variable like gender
  • 16. Univariate Analysis
    • Variation
      • Frequency Distribution
        • A way of showing that number of times each category of a variable occurs in a sample
        • Assume we have 20 people in our sample, with 17 females and 3 males
  • 17. Frequency Distribution 100 N = 20 Total 15 3 Male 85 17 Female % FREQUENCY GENDER
  • 18. Univariate Analysis
    • Variation
      • Examining frequency distribution, and their percentage distribution is a good way of understanding variation in nominal or ordinal variables
      • Example
        • If you are looking at gender and discern that 100% of your sample is female and 0% is male, you know that there is no variation in gender in your sample.
  • 19. Univariate Analyses
    • Measures of Dispersion of Variation for Interval Scale Variables
    • Measures of dispersion
      • Measures that provide a sense of how spread out cases are over categories of a variable
  • 20. Univariate Analyses
    • Measures of Dispersion of Variation for Interval Scale Variables
      • Range
        • A measure of dispersion or spread designed for interval-level variables. The difference between the highest and lowest values.
  • 21. Univariate Analyses
    • Standard Deviation
      • A measure of dispersion designed for interval-level variables and that accounts for every value's distance from the sample mean
      • The standard deviation has properties that make it useful in measuring variation when the variable is normally distributed
  • 22. Univariate Analyses
    • The graph of a normal distribution is bell-shaped and symmetric
    • In a normal distribution 68% of cases would fall between one standard deviation above the mean and one standard deviation below the mean
    • Standard deviation is not as useful if the variable is not normally distributed.
  • 23. Bivariate Analyses
    • Examining the relationship between variables
    • Crosstabulation is the process of making a bivariate table to examine a relationship between two variables
  • 24. Bivariate Analyses
    • Measures of association
      • Measures that give a sense of the strength of a relationship between two variable – or how strongly two variables “go together”
  • 25. Bivariate Analyses
    • Measures of correlation
      • Measures that provide a sense not only of the strength of the relationship between two variables, but also the direction of the association
      • Pearson’s r is a measure of correlation designed for examining relationships between interval level variables.
  • 26. Stop and Think
    • Would you expect the association between education and income for adults in the US to be positively or negatively correlated?
  • 27. Bivariate Analyses
    • Inferential Statistics
      • P-value
        • Allows the reader to make an inference about the relationship between variables.
      • The typical cut off is 0.05, p<.05
  • 28. Multivariate Analysis and the Elaboration Model
    • Why would a researcher want to examine more than two variables at a time?
  • 29. Multivariate Analysis and the Elaboration Model
    • Elaboration
      • The process of examining the relationship between two variables by introducing the control for another variable or variables
  • 30. Multivariate Analysis and the Elaboration Model
    • Control variable
      • A variable that is held constant to examine the relationship between two other variables
  • 31. Multivariate Analysis and the Elaboration Model
    • Partial relationship
      • The relationship between an independent and a dependent variable for that part of a sample defined by one category of a control variable
  • 32. Multivariate Analysis and the Elaboration Model
    • Four kinds of elaboration
      • Replication
      • Explanation
      • Specification
      • Interpretation
  • 33. Multivariate Analysis and the Elaboration Model
    • Replication
      • A kind of elaboration in which the original relationship is replicated by all of the partial relationships
  • 34. Multivariate Analysis and the Elaboration Model
    • Explanation
      • A kind of elaboration in which the original relationship is explained away as spurious by a control for an antecedent variable
  • 35. Multivariate Analysis and the Elaboration Model
    • Specification
      • A kind of elaboration that permits the researcher to specify conditions under which the original relationship is particularly strong or weak
  • 36. Multivariate Analysis and the Elaboration Model
    • Interpretation
      • A kind of elaboration that provides an idea of the reasons why an original relationship exist without challenging the belief that the original relationship is causal.
  • 37. Qualitative Data Analysis
    • The outputs of qualitative data analyses are usually words, the inputs are also usually words – typically in the form of extended texts
    • Data is almost always derived from what the researcher has observed, heard in interviews, or found in documents
  • 38. Qualitative Data Analysis
    • Social anthropological versus interpretivist approaches
      • Social anthropologists (and others, like grounded theorists and life historians) believe that there exist behavioral regularities (for example, rules, rituals, relationships, and so on) that affect everyday life and that it should be the goal of researchers to uncover and explain those regularities.
  • 39. Qualitative Data Analysis
    • Social anthropological versus interpretivist approaches
      • Interpretivists (including phenomenologists and symbolic interactionists) believe that actors, including researchers themselves, are forever interpreting situations, and that these, often quite unpredictable, interpretations largely affect what goes on.
  • 40. Qualitative Data Analysis
    • Does qualitative data analysis emerge from or generate the data collected?
      • The question of which comes first
        • Data or ideas about data
  • 41. Qualitative Data Analysis
    • The strengths and weaknesses of qualitative data analysis revisited
      • Strengths
        • Can produce theories
        • More likely to be grounded in the immediate experiences of those participants than in the speculations of researchers.
  • 42. Qualitative Data Analysis
    • The strengths and weaknesses of qualitative data analysis revisited
      • Weaknesses
        • Generalizability
  • 43. Qualitative Data Analysis
    • Are there predictable steps in qualitative data analysis?
      • First researchers code their own data or acquire computer-ready data
      • Other steps are much more fluid
      • Typical flow includes data collection –data reduction—data displaying—conclusion drawing and verification
  • 44. Qualitative Data Analysis
    • Data Collection and Transcription
      • Several software packages exist to facilitate the processing of qualitative data
      • Qualitative data software packages have many pros an cons and should be considered carefully before adopting.
  • 45. Qualitative Data Analysis
    • Data Reduction
      • The various ways in which a researcher orders collected and transcribed data
      • Coding and memoing are common data reduction techniques
  • 46. Qualitative Data Analysis
    • Coding
      • The process of assigning observations, or data, to categories
      • In qualitative analysis, coding is more open-ended because both the relevant variables and their significant categories are apt to remain in question longer
  • 47. Qualitative Data Analysis
    • Coding
      • The goal of coding is to create categories that can be used to organize information about different cases
      • Assigning a code to a piece of data is the first step in coding
      • The second step is putting the coded data together with other data coded the same way
  • 48. Qualitative Data Analysis
    • Coding
      • Types of Coding
        • One purpose of coding is to keep facts straight – called descriptive coding
        • Coding to advance your analysis is analytical coding
        • The preliminary phase of analytical coding is called initial coding
        • Initial coding eventually becomes focused coding , which is concentrating or elaborating on codes specific to analysis
  • 49. Qualitative Data Analysis
    • Coding
      • Memos
        • Extended notes that the researcher writes to help herself or himself understand the meaning of codes
  • 50. Qualitative Data Analysis
    • Data displays
      • Visual images that summarize information
  • 51. Summary
    • Quantitative data analyses
    • Qualitative data analyses
  • 52. Quiz – Question 1
    • Measures of central tendency do not include
      • the mode.
      • median.
      • mean.
      • standard deviation.
  • 53. Quiz – Question 2
    • In a frequency distribution, we are
      • displaying the number of cases that fall in categories.
      • showing the connections between descriptive statistics.
      • examining the central tendencies of variables.
      • testing out our coding schemes.
  • 54. Quiz – Question 3
    • As a measure of dispersion, a _______ tells us how far the mean is from individual scores.
      • range
      • standard deviation
      • mode
      • regular distribution