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Phân tích dữ liệu với các phương pháp thống kê Chương này không có thống kê 101, nhưng thay vì nó được dự định để xem xét sử dụng tiềm năng, thực tế sử dụng, và lạm dụng các số liệu thống kê trong phân tích công lý môi trường. Phương pháp thống kê khác nhau được áp dụng đối với các khu vực khác nhau của phân tích công lý môi trường. Đó là vấn đề lựa chọn phương pháp đúng. Cả hai số liệu thống kê mô tả và suy luận đã được áp dụng để phân tích công. | 7 Analyzing Data with Statistical Methods This chapter is not Statistics 101 but rather it is intended to review potential use actual use and misuse of statistics in environmental justice analysis. Different statistical methods are applicable to different areas of environmental justice analysis. It is a matter of choosing the right method. Both descriptive and inferential statistics have been applied to environmental justice analysis. It has been shown that the type of statistics affects the results. 7.1 DESCRIPTIVE STATISTICS Descriptive statistics are procedures for organizing summarizing and describing observations or data from measurements. Different types of measurements lead to different types of data. Four types of measurements or scales in decreasing order of levels are ratio interval ordinal and nominal. A ratio scale is a scale of measure that has magnitude equal intervals and an absolute zero point. Some socioeconomic characteristics have a ratio scale such as income. Environmental data such as emission and ambient concentration are ratio measures. An interval scale has the attributes of magnitude and equal intervals but not an absolute zero point for example temperature in degrees Fahrenheit. An ordinal scale has only magnitude but does not have equal intervals or absolute zero point for example risk-ranking data are ordinal. A nominal scale is simply the classification of data into discrete groups that have no magnitude relationship to one another. For example race can be classified into African American Asian American Pacific Islanders Native American Whites and Other Races. A variable initially measured at a higher level such as a ratio scale may be reduced to a lower level of measure such as ordinal. However the reverse cannot be done. For example household income initially a ratio measure can be classified as low middle and high income. This conversion helps us grasp large data concisely but results in loss of information initially contained in the
