1. Measurement Why do we need measurement? We need to measure the concepts in our hypotheses. When we measure those concepts, they become variables. Measurement is important for collecting data. So measurement comes before data collection. What is measurement? It is assigning numerical values or numerals to concepts according to some rule.

2. Measurement Example: Take, for example, the concept gender. When we measure this concept, we assign 1 for male and 0 for female. Conclusion: When we assign values to concepts, they become variables which have values. We must have variation in the variable values. Otherwise, we cannot use the variable for any study or research

3. Measurement Example: Suppose I want to study the relationship between gender and hard work. The following is my data: GenderWork Hanafi7 hours per day Hamid8 hours per day Humaid9 per hours day Hashim5 per hour day

4. Measurement In this case, we cannot do the study; there is no variation in our variable gender. There are no females in the data. Conclusion: No variation, no study or research

5. Measurement Measurement: Types of Measurement: Nominal or Categorical Level of Measurement (Nominal or Categorical Variables): In this type, the values of the variable are in the form of categories. For example, gender is a nominal variable. Comparison in terms of the variable is not meaningful. The categories are needed to differentiate between them. We cannot perform mathematical operations with type of variables.

6. Measurement Ordinal Level of Measurement: The values of an ordinal variable are also in the form of categories. However, we can compare among those categories. So comparison is meaningful. Democracy is an example of an ordinal variable. We can compare countries in terms of democracy. Thus, we can say, for example, that one country has more democracy than another one. The values of an ordinal variable are comparable in terms of more or less. With ordinal variables, mathematical operations are not possible

7. Measurement Interval Level of Measurement: Here the values of the variable are numerical values such as 3, 7, 8, etc. We can compare among those numerical values. In addition, we can tell by how much exactly one value is more or less than another value. In this type of variable, zero does not indicate the absence of the phenomenon in question. Temperature is an interval variable. Mathematical operations such as division and multiplication are not meaningful. In this type of measurement, zero is arbitrary, meaning that it is not defined.

8. Measurement Ratio Level of Measurement: As in the case of interval level of measurement, the values of a ratio variable are also real numbers. We can compare among those values. In addition, we can tell by how much exactly one value is more or less than another value. In this type of measurement, zero is defined; it indicates the absence of the phenomenon in question. Examples of ratio variables are income, weight, inflation rate, crime rate, etc. All mathematical operations are possible with type of measurement.

9. Measurement Criteria of Measurement These are the standards or benchmarks by which we evaluate our measures. Reliability: A reliable measure or measuring instrument yields consistent, the same or similar results each it is used. Validity: Measurement Validity: Measurement validity is the extent to which a measure really measures what it is supposed to measure. Measurement validity is also called construct validity.

10. Measurement Examples: Inflation is an abstract construct. However, we can measure it, how? It is measured by the inflation rate. So the inflation rate is the measure of inflation. Another example: Intelligence is an abstract construct. The measure for it is IQ test. Does IQ test really measure intelligence? If the answer is yes, then we can say that IQ test has measurement validity or that it is a valid measure.

11. Measurement A third example: Suppose that we are measuring the attitudes of Al Ghurair students toward the environment. We measure attitudes through questionnaires. If we use a questionnaire to measure the attitudes of Al Ghurair University students, then we can ask this question: does our questionnaire really measure the attitudes of the students. If the answer is yes, then our questionnaire is a valid measure.

12. Measurement Internal Validity Internal validity is also called causality. Internal validity or causality is the extent to which one thing is caused by another. It also means causal relationship. There are two things in internal validity: cause and effect. The cause is also called independent variable. The effect is called dependent variable. Example: TQM increases employee productivity. In this hypothesis, we have a causal relationship. Here the IV or cause is TQM and increased productivity is DV or the effect.

13. Measurement Example: TQM increases employee productivity. In this hypothesis, we have a causal relationship. Here the IV or cause is TQM and increased productivity is DV or the effect. Mathematically, this relationship is expressed as follows: Y= f (X). Y is always the DV if it is put on the left side of the equality sign (=). X is always the IV if it is put to the right of the equality sign. f stands for function of which means caused by. For our example, the relationship looks mathematically as follows: Increased productivity= f (TQM).

14. Measurement Threats to Internal Validity: These are factors that destroy internal validity: History: History means any event that happens and destroys internal validity. Maturation: This refers to changes that happen to people over time. As time passes, people become mature both intellectually and physically. These changes, if not controlled for, may destroy internal validity. Example: Suppose I have a one-year on the job training to increase labor productivity. Experimental Mortality:

15. Measurement External Validity: This is called generalizibility. It means the extent to which research findings can be generalized to other settings. If research findings can be applied to other settings, then the research has external validity. Example: Suppose I have conducted research on the relationship between inflation and political protest in Sudan. My research finding is that Inflation does cause political protest in Sudan. If this finding is also true in other settings (Syria, Iraq, Peru, Argentina, Ukraine, Russia, and Nigeria), then our research has external validity. However, if the research finding is not applicable beyond Sudan, then it has no external validity.