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Dichotomous Data:
- Data that fall into only two categories are called dichotomous data.
- Although these data ordinarily might be considered nominal level (e.g. pass-fail, qualified-unqualified), they also can be treated as interval level in many statistical tests because the variable either has an underlying continues characteristics such as pass-fail or is conceptually changed to represent the presence or absence of a characteristic.
Levels of data can be measured in various ways, e.g. multi-model measurement, occurs when several measures instead of just one are selected, specially when measures are indirect or complicated, such as growth, compliance. In these cases, indicators are used to measure the phenomena. For example, height, weight, head circumference and arm measure can be used as indicators of growth.
The best approach in planning research is to select, if possible, variables that can be measured at interval and ratio level so that all options are available to the researcher for statistical analysis.
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Dichotomous Data: - Data that fall into only two categories are called dichotomous data. - Although these data ordinarily might be considered nominal level (e.g. pas
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