7. Draft a set of questions for each part above, the answer to which would provide the information you need. Indicate whether your questions ask for factual or subjective information and whether the resulting data will have nominal, ordinal, interval, or ratio properties.
9. What are the various levels of measurement? Why are the differences between the levels of measurement important? Give an example of data that can be transformed from one level to another and another example of data that cannot be so transformed.
Levels of measurement are defined by the nominal, ordinal, interval or ratio properties. The various levels of measurement are the relationship among the values of data. These values or attributes characterize the variable. Understanding the difference among the levels of measurement is important because it allows the researcher to determine whether the data needs to be processed or transformed from one level to another. For example a data can be said to transform if it is ordinal or interval. Hence, income of individuals can be transformed into low, medium or high group. Alternatively, there are some data which cannot be transformed. Such data falls into the nominal category. Nominal data merely represents the alternate name or denomination of the said data and do not have statistical value even when calculated. For example quality health care cannot be measured.
A good evaluation question implies that the levels of measurement are used to identify the significance of the data. Variables have values to be assigned. For example assumptions at nominal level cannot be used to generate meaningful data that would support analysis of the hypothesis. On the other hand, if levels of measurement are integrated within the evaluation question then it would enable the researcher to transform data to meaningful data that would provide meaningful results. Good evaluation questions also assign