Chapter 14

• Coding


  The process of assigning a numerical score or other character symbol to previously edited data
• Codes


  Rules for interpreting, classifying, and recording data in the coding process; also, the actual numerical or other character symbols assigned to raw data
• Dummy Coding


  Numeric "1" or "0" coding where each number represents an alternate response such as "female" or "male"
• Effects Coding


  An alternative to dummy coding using the values of -1 and 1 to represent two categories of responses
• Descriptive Analysis


  The elementary transformation of raw data in a way that describes the basic characteristics such as central tendency, distribution, and variability

  
• Histogram


  A graphical way of showing a frequency distribution in which the height of a bar corresponds to the observed frequency of the category
• Tabulation


  The orderly arrangement of data in a table or other summary format showing the number of responses to each response category; tallying
• Frequency Table


  A table showing the different ways respondents answered a question
• Cross-Tabulation


  The appropriate technique for addressing research questions involving relationships among multiple less-than interval variables; results in a combined frequency table displaying one variable in rows and another variable in columns
• Contingency Table


  A data matrix that displays the frequency of some combination of possible responses to multiple variables; cross-tabulation results
• Marginals


  Row and column totals in a contingency table, which are shown in its margins
• Statistical Base


  The number of respondents or observations (in a row or column) used as a basis for computing percentages
• Elaboration Analysis


  An analysis of the basic cross-tabulation for each level of a variable not previously considered, such as subgroups of the sample
• Moderator Variable


  A third variable that changes the nature of a relationship between the original independent and dependent variables
• Data Transformation


  Process of changing the data from their original form to a format suitable for performing a data analysis addressing research objectives
• Median Split


  Dividing a data set into two categories by placing respondents below the median in one category and respondents above the median in another
• The median split approach is best applied only when the data do indeed exhibit bimodal characteristics
• Inappropriate collapsing of continuous variables into categorical variables ignores the information contained within the untransformed values
• Index Numbers


  Scores or observations recalibrated to indicate how they relate to a base number
• Computer Programs for Analysis

  • Spreadsheets
  • Statistical software: SAS, SPSS, MINITAB
• Interpretation


  The process of drawing inferences from the analysis results
• Univariate Statistical Analysis


  Tests of hypotheses involving only one variable
• Bivariate Statistical Analysis


  Tests of hypotheses involving two variables
• Multivariate Statistical Analysis


  Statistical analysis involving three or more variables or sets of variables
• Hypothesis Testing Procedure


  1. The specifically stated hypothesis is derived from the research objectives

  2. A sample is obtained and the relevant variable is measured

  3. The measured sample value is compared to the value either stated explicitly or implied in the hypothesis
• Significance Level


  A critical probability associated with a statistical hypothesis test that indicates how likely an inference supporting a difference between an observed value and some statistical expectation is true. The acceptable level of Type I error
• 1. Value


  Probability value, or the observed or computed significance level; p-values are compared to significance levels to test hypotheses
• Type I Error


  An error caused by rejecting the null hypothesis when it is true; has a probability of alpha (α). Practically, a Type I error occurs when the researcher concludes that a relationship or difference exists in the population when in reality it does not exist
• Type II Error


  An error caused by failing to reject the null hypothesis when the alternative hypothesis is true; has a probability of beta (β). Practically, a Type II error occurs when a researcher concludes that no relationship or difference exists when in fact one does exist