Fundamentals of Data Analysis - Summary
Fundamentals of Data Analysis - Summary
The first data analysis phase is to edit and code the data. Editing involves identifying omissions, ambiguities, inconsistencies, lack of cooperation. axicLineligible respondents. Coding involves deciding how the responses are going to be entered.
There are a variety of data analysis techniques available. The most basic is to analyze each question by itself, A frequency distribution provides the moaLcomplete. jrrfprmajrion and often leads to decisions to combine response categories. Reporting only the sample means or percentages is the other principal approach.
The usual next step is to_tabu]ate questions among subgroups and involves two of the questions from the questionnaire. Thus, the sample mean or.the fre,quen_cy.diMribution is obta for subgroups such as transit users and transit nonusers. Another technique involves the association between two intervally scaled variables and is termed correlation analysis. The sample correlation is a number between +1 and -1 that reflects the degree to which two variables have a linear or straight-line relationship. In all these two-question analyses, the concept of the hypothesis testing—the subject of the next chapter—is relevant.
The simultaneous analysis of three questions or variables is the subject of Chapter 15. Multivariate analysis, which involves techniques used to analyze three or more questions simultaneously, is covered in Part IV. The final data analysis phase involves the presentation of the results.
The first data analysis phase is to edit and code the data. Editing involves identifying omissions, ambiguities, inconsistencies, lack of cooperation. axicLineligible respondents. Coding involves deciding how the responses are going to be entered.
There are a variety of data analysis techniques available. The most basic is to analyze each question by itself, A frequency distribution provides the moaLcomplete. jrrfprmajrion and often leads to decisions to combine response categories. Reporting only the sample means or percentages is the other principal approach.
The usual next step is to_tabu]ate questions among subgroups and involves two of the questions from the questionnaire. Thus, the sample mean or.the fre,quen_cy.diMribution is obta for subgroups such as transit users and transit nonusers. Another technique involves the association between two intervally scaled variables and is termed correlation analysis. The sample correlation is a number between +1 and -1 that reflects the degree to which two variables have a linear or straight-line relationship. In all these two-question analyses, the concept of the hypothesis testing—the subject of the next chapter—is relevant.
The simultaneous analysis of three questions or variables is the subject of Chapter 15. Multivariate analysis, which involves techniques used to analyze three or more questions simultaneously, is covered in Part IV. The final data analysis phase involves the presentation of the results.
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