Determining The Function Of The Ordinal Scale

Distinguishing between scale and proportion is significant in depicting a specific image in which the size is questionable and cannot be visualized accurately. According to the textbook, scale is the size relation of one thing to another. However, Proportion is

2. Data from Likert scales and continuous (e.g. 1-10) rating scales are quantitative. Allows you to measure their feeling on a scale of 1

Measurement where a number is assigned to represent something or someone else. An example of nominal could be credit card numbers, social security numbers, or zip codes.

1. The researchers analyzed the data they collected as though it were at what level of measurement?- The correct answer is Interval/ratio.

b. Ordinal: This is a measurement that represent the order of a particular stat. A good example of this would the placement in a contest, 1st, 2nd, and 3rd.

Now I want to be clear, though this scale i've created can be indicative of quality. It isn't necessarily indicative of

This concept is manifested in the inquiry as I will be reviewing the levels of rainfall and the sizes of stalactites from smallest to largest in Princess Margaret Rose Cave.

Answer: The variable is nominal because the question is asking for what type of diabetes a person has and is put into 3 categories.

A nominal variable is qualitative, or word orientated, distinct, mutually exclusive, completely exhaustive, and cannot be ordered (Walker, 2009).  Meaning, the variable is a descriptive word, i.e. color, can be distinguished from each other, its value coincides with only the category in which it was designed, has a designated category for each characteristic, and has no specific order (Walker, 2009).  A qualitative variable is strictly descriptive.  Whereas, a quantitative variable is numeric.  Therefore, the color of law enforcement officers’ uniforms is considered a nominal variable.

Nominal data is the most basic level of measurement. It is also known as categorical. The numbers do not imply an order. Basically nominal data is used for frequency and the only number property of the nominal scale of measurement is identity. An everyday example of the use of nominal data would be classifying people according to gender is a common application of the nominal scale. When you first meet someone, an observation is generally made on the specific gender of the person you are meeting for the first time.

Without designed or determined variables, a research cannot be conducted. As denoted in Meyers et al. (2013) “As a rather conceptual but important characterization, a variable is an obstruction or construct that can take on different values.” The values of variables could be numbers expressing quantitative meaning (Meyers et al., 2012). “Quantitative” relates to numerical values, it may also justify the weight or variability of any population; it also can be anything represented by numerical values. Some values may be represented by names of people or animals. Such values are used to determine “qualitative” or categorical differences between cases (Meyers et al., 2013). In terms of measurement, I have apprehended that there are five scales of measurements. There are as follows: Ordinal, Nominal, Summative response, Interval, and Ratio scales (GCU, 2012). From the PSYC 845, I have also recall of learning about the ANOVA research design. As noted by Santayana (2011): “Measurement is at the core of doing

Specific measure instruments are used in quantitative research. Gathered measurements are recorded on a chart, which can reveal how small changes between individual measurements may equal to a more noticeable change over a period of time.

The instrument measured physical and mental health dimensions through eleven questions and thirty-six items covering eight components that were represented by functional capacity, physical pain, general health, vitality, social functioning, emotional aspects, mental health, and a related question about the current general health a year before the interview (Girardi et al., 2015).

The following are examples of nominal, ordinal, interval and ratio levels variables with explanations of why. Examples of nominal level data would include different medications classifications such as ACE or ARB inhibitors, hypoglycemic agents, neuroleptics, antibiotics, etc. They are considered nominal because they cannot be placed in an order different categories, there is no measureable distance between them and there is no relationship in list order (Marateb, et al., 2014). An example of ordinal level data would include Likert-type scale variables, pain level scales, or social status. They are considered ordinal because they indicate a direction as well as provide nominal information (Marateb, et al., 2014). Interval level data examples

|only the ordinal scale | |b. |only the nominal scale | |c. |the rank