Quantitative Data Analysis Edouard Manet: In the Conservatory, 1879

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Transcript of Quantitative Data Analysis Edouard Manet: In the Conservatory, 1879

  • Slide 1
  • Quantitative Data Analysis Edouard Manet: In the Conservatory, 1879
  • Slide 2
  • Quantification of Data 1.Introduction To conduct quantitative analysis, responses to open-ended questions in survey research and the raw data collected using qualitative methods must be coded numerically.
  • Slide 3
  • Quantification of Data 1.Introduction (Continued) Most responses to survey research questions already are recorded in numerical format. In mailed and face-to-face surveys, responses are keypunched into a data file. In telephone and internet surveys, responses are automatically recorded in numerical format.
  • Slide 4
  • Quantification of Data 2.Developing Code Categories Coding qualitative data can use an existing scheme or one developed by examining the data. Coding qualitative data into numerical categories sometimes can be a straightforward process. Coding occupation, for example, can rely upon numerical categories defined by the Bureau of the Census.
  • Slide 5
  • Quantification of Data 2.Developing Code Categories (Continued) Coding most forms of qualitative data, however, requires much effort. This coding typically requires using an iterative procedure of trial and error. Consider, for example, coding responses to the question, What is the biggest problem in attending college today. The researcher must develop a set of codes that are: exhaustive of the full range of responses. mutually exclusive (mostly) of one another.
  • Slide 6
  • Quantification of Data 2.Developing Code Categories (Continued) In coding responses to the question, What is the biggest problem in attending college today, the researcher might begin, for example, with a list of 5 categories, then realize that 8 would be better, then realize that it would be better to combine categories 1 and 5 into a single category and use a total of 7 categories. Each time the researcher makes a change in the coding scheme, it is necessary to restart the coding process to code all responses using the same scheme.
  • Slide 7
  • Quantification of Data 2.Developing Code Categories (Continued) Suppose one wanted to code more complex qualitative data (e.g., videotape of an interaction between husband and wife) into numerical categories. How does one code the many statements, facial expressions, and body language inherent in such an interaction? One can realize from this example that coding schemes can become highly complex.
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  • Quantification of Data 2.Developing Code Categories (Continued) Complex coding schemes can take many attempts to develop. Once developed, they undergo continuing evaluation. Major revisions, however, are unlikely. Rather, new coders are required to learn the existing coding scheme and undergo continuing evaluation for their ability to correctly apply the scheme.
  • Slide 9
  • Quantification of Data 3.Codebook Construction The end product of developing a coding scheme is the codebook. This document describes in detail the procedures for transforming qualitative data into numerical responses. The codebook should include notes that describe the process used to create codes, detailed descriptions of codes, and guidelines to use when uncertainty exists about how to code responses.
  • Slide 10
  • Quantification of Data 4.Data Entry Data recorded in numerical format can be entered by keypunching or the use of sophisticated optical scanners. Typically, responses to internet and telephone surveys are entered directly into a numerical data base. 5.Cleaning Data Logical errors in responses must be reconciled. Errors of entry must be corrected.
  • Slide 11
  • Quantification of Data 6.Collapsing Response Categories Sometimes the researcher might want to analyze a variable by using fewer response categories than were used to measure it. In these instances, the researcher might want to collapse one or more categories into a single category. The researcher might want to collapse categories to simplify the presentation of the results or because few observations exist within some categories.
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  • Quantification of Data 6.Collapsing Response Categories: Example ResponseFrequency Strongly disagree2 Disagree22 Neither agree nor disagree45 Agree31 Strongly Agree1
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  • Quantification of Data 6.Collapsing Response Categories: Example One might want to collapse the extreme responses and work with just three categories: ResponseFrequency Disagree24 Neither agree nor disagree45 Agree32
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  • Quantification of Data 7.Handling Dont Knows When asking about knowledge of factual information (Does your teenager drink alcohol?) or opinions on a topic the subject might not know much about (Do school officials do enough to discourage teenagers from drinking alcohol?), it is wise to include a dont know category as a possible response. Analyzing dont know responses, however, can be a difficult task.
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  • Quantification of Data 7.Handling Dont Knows (Continued) The research-on-research literature regarding this issue is complex and without clear-cut guidelines for decision-making. The decisions about whether to use dont know response categories and how to code and analyze them tends to be idiosyncratic to the research and the researcher.
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  • Quantitative Data Analysis Descriptive statistics attempt to explain or predict the values of a dependent variable given certain values of one or more independent variables. Inferential statistics attempt to generalize the results of descriptive statistics to a larger population of interest.
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  • Quantitative Data Analysis 1.Data Reduction The first step in quantitative data analysis is to calculate descriptive statistics about variables. The researcher calculates statistics such as the mean, median, mode, range, and standard deviation. Also, the researcher might choose to collapse response categories for variables.
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  • Quantitative Data Analysis 2.Measures of Association Next, the researcher calculates measures of association: statistics that indicate the strength of a relationship between two variables. Measures of association rely upon the basic principle of proportionate reduction in error (PRE).
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  • Quantitative Data Analysis 2.Measures of Association (Continued) PRE represents how much better one would be at guessing the outcome of a dependent variable by knowing a value of an independent variable. For example: How much better could I predict someones income if I knew how many years of formal education they have completed? If the answer to this question is 37% better, then the PRE is 37%.
  • Slide 20
  • Quantitative Data Analysis 2.Measures of Association (Continued) Statistics are designated by Greek letters. Different statistics are used to indicate the strength of association between variables measured at different levels of data. Strength of association for nominal-level variables is indicated by (lambda). Strength of association for ordinal-level variables is indicated by (gamma). Strength of association for interval-level variables is indicated by correlation (r).
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  • Quantitative Data Analysis 2.Measures of Association (Continued) Covariance is the extent to which two variables change with respect to one another. As one variable increases, the other variable either increases (positive covariance) or decreases (negative covariance). Correlation is a standardized measure of covariance. Correlation ranges from -1 to +1, with figures closer to one indicating a stronger relationship.
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  • Quantitative Data Analysis 2.Measures of Association (Continued) Technically, covariance is the extent to which two variables co-vary about their means. If a persons years of formal education is above the mean of education for all persons and his/her income is above the mean of income for all persons, then this data point would indicate positive covariance between education and income.
  • Slide 23
  • Statistics 1.Introduction To make inferences from descriptive statistics, one has to know the reliability of these statistics. In the same sense that the distribution of one variable has a standard deviation, a parameter estimate has a standard errorthe distribution of the estimate from its mean with respect to the normal curve.
  • Slide 24
  • Statistics 1.Introduction (Continued) To better understand the concepts standard deviation and standard error, and why these concepts are important to our course, please review the presentation regarding standard error. Presentation on Standard Error.Standard Error
  • Slide 25
  • Statistics 2.Types of Analysis The presentation on inferential statistics will cover univariate, bivariate and multivariate analysis. Univariate Analysis: Mean. Median. Mode. Standard deviation.
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  • Statistics 2.Types of Analysis (Continued) Bivariate Analysis Tests of statistical significance. Chi-square. Multivariate Analysis: Ordinary least squares (OLS) regression. Path analysis. Time-series analysis. Factor analysis. Analysis of variance (ANOVA).
  • Slide 27
  • Univariate Analysis 1.Distributions Data analysis begins by examining distributions. One might begin, for example, by examining the distribution of responses to a question