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Chapter Fourteen: Quantitative Analysis: Descriptive Statistics …

- - - - 2. Data Entry
        Coded data can be entered into a spreadsheet, database, text file, or directly into a statistical program
        
        3. Missing Values
        problems should be detected during pretests and corrected before the main data collection process begins
        
        4. Data Transformation
        Sometimes, it is necessary to transform data values before they can be meaningfully interpreted
        
        may include: creating scale measures by adding individual scale items, creating a weighted index from a set of observed measures, and collapsing multiple values into fewer categories
        
        while entering data, the coder should watch out for obvious evidence of bad data
      - codebook:
        comprehensive document with detailed description of each variable, items or measures for that variable, the format, the response scale, and how to code each value into a numeric format
- - - - any value that is estimated from a sample, such as mean, median, mode, or any of the later estimates are called a statistic
  - - - the square of the standard deviation is called the variance of a distribution
- - - - The probability that a statistical inference is caused by pure chance is called the p-value
        
        The p-value is compared with the significance level, which represents the maximum level of risk that we are willing to take that our inference is incorrect
        
        degree of freedom:
        the number of values that can vary freely in any calculation of a statistic