It is a scale of measurement referring to quality more than quantity and where numbers lack quantity. It is the lowest level of measurement. It is also called qualitative scale. Classification in nominal scale bases on names and no ordering of cases. Examples of this level include gender, race, ethnicity, nationality, culture, and religion (Smith, Gratz, & Bousquet, 2009).
The nominal scale does not imply the process of ordering. For example, when classifying people according to their races, there is no sense in which Asians will be ahead of Africans. Analysis of data in nominal scale is by frequency distribution. Mode, cross tabulation with chi-square is its main statistical computation. Categorizing operations regarding set membership and equality apply to objects of nominal scale. The mode is a measure of central tendency for the nominal scale. The median makes no sense at this level because the ranking is not necessary here.
The ordinal level of measurement has to rank as the simplest of this scale. It shows the direction in addition to nominal information thus it helps interpret the order and not relative positions (Smith, Gratz, & Bousquet, 2009). This measurement also allows comparison of the degree of two subjects which possess dependent variables, for example, placing two situations as being pleasant or more pleasant. It is meaningful to affirm that one situation is more excellent than the other. It will be pointless for the researcher to measure the situation of being fantastic or very fantastic by choosing a number from one to three.
The difference between the responses of the people would not reflect the same differences of the situation thus ordinal data does not capture a clear difference between the data does not change the meaning of the level of measurement. Changing the responses to numbers It is essential to note that differences between two levels ordinal data are not the same as differences between two levels of another like the degree of an agreement being yes no and maybe. Examples of ordinal data high, small, fast, medium, and cheap. Ordinal data use nonparametric statistics of media and mode, rank, order correlation, and variance. Modeling techniques are necessary. The ordering of outcomes is from the least to the most. It fails to capture necessary information that presents other examined scale.
This level of measurement provides information about the order. It is the standard survey rating scale. Besides, it possesses same interpretation throughout (Smith, Gratz, & Bousquet, 2009). Temperature is an example of interval level of measurement that is either measured in Fahrenheit or degrees Celsius. Temperature represents the same amount of heat despite where it occurs on the scale. Interval level of measurement helps interpret differences in the distance along a scale. Metrics such as logarithms also define this level. The distance thus is strictly definable based on the metric used. However, it is not a perfect degree of measurement as it lacks a point of absolute zero. Interval scales statistical techniques such as mean, standard deviation, correlation, regression, variance, factor analysis and multivariate and modeling techniques.
It is the topmost level of measurement. It takes both the characteristics of nominal, ordinal and interval scale. First, it provides names or categories for each object. There is also ordering of objects regarding the ordering of numbers (Smith, Gratz, & Bousquet, 2009). Besides, the same difference at two points on the scale has the same interpretation. Measurement of length is an example of a ratio scale.
Besides having the qualities of nominal, ordinal and interval, it has a point of absolute zero which indicates the absence of quantity. It has its application in the following examples. First, because the Fahrenheit scale for temperature does not have an absolute zero, it is not a ratio scale. On the other hand, Kelvin scale is a ratio scale due to the point of absolute zero. Also, measurement of money is in ratio scale because it has the property of an interval level absolute zero point .The harmonic mean, geometric mean, mode, median, range, and coefficient are the statistical application of ratio.
Heroin Use in High School Students in China
The research took place in China among young adolescents in high school and its environment which were heroin stricken areas. An Individual-Collectivism Assessment Test was used to measure the norms and values in the context of the ally, family, and fellow students (Stallwitz, A.2012).
A total of two hundred and twenty boys and two hundred and forty-one girls participated in an interview.
Girls showed higher levels of behavioral control for the use of heroin, peer control and communication about heroin with their parents compared to boys. Thus, the number of boys using heroin was higher than that of girls in the school. This research documents those psychosocial factors such as peers influence the use of heroin. The pattern of influence varies by gender. More girls had behavioral control on heroin use hence lower number than that of boys who use heroine.
Smith, L. F., Gratz, Z. S., & Bousquet, S. (2009). The art and practice of statistics. Belmont, CA: Wadsworth Cengage Learning.
Stallwitz, A. (2012). The role of community-mindedness in the self-regulation of drug cultures: A case study of the Shetland Islands. Dordrecht: Springer.
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