Parametric and nonparametric methods Example Paper
Parametric vs. Nonparametric Methods
The purpose of this assignment is to differentiate between parametric and nonparametric statistical methods. In addition, this assignment will help you understand and implement parametric or nonparametric statistical methods.
Researches the following statistical topics:
• Levels of measurement
• Parametric and nonparametric methods
On the basis of your research and understanding, respond to the following:
• Find and state the definition of levels of measurement that distinguishes the five types of data used in statistical analysis.
• In your own words, compare the five types of data and explain how they differ.
• Find and state a definition of parametric and nonparametric methods that distinguishes between the two.  Parametric and nonparametric methods Example Paper
• In your own words, explain the difference between parametric and nonparametric methods.
• Explain which types of data require parametric statistics to be used and which types of data require nonparametric statistics to be used and why.
• Compare the advantages and disadvantages of using parametric and nonparametric statistics.
• Describe how the level of measurement helps determine which of these methods to use on the data being analyzed.

Parametric and nonparametric methods

Question 1.

There are four levels of measurement. The first level is the nominal scale that does not have a rational zero. Also, it does not show magnitude or relationship between the objects within the scale. It only shows a philosophical difference between the objects thus making it not useful for presenting central tendencies. The second level is ordinal scale that shows difference, direction and magnitude. However, it does not show intervals. It uses ranks to assign values to the objects. It is not useful for presenting central tendencies. The third level is interval scale that shows difference, direction, magnitude and interval. This means that the assigned value will not change even when new objects are added. It is useful for presenting central tendencies. The final level is ratio scale that shows difference, direction, magnitude and interval, as well as presenting a rational zero (Howell, 2013).

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Question 2.

There are five types of data. The first type is integer that presents a whole number that can be subjected to mathematical operation, which include addition, subtraction, multiplication and division. For example, 90 is an integer that can represent the number of participants in a study. The second type is floating point value that is a proportion of a while. For example, 3 can represent the multiple differences between the populations in two countries. The third type is double-precision floating point value (Boolean data type) where the value can only be presented as one of two forms. For example, yes/no and true/false answers are Boolean data types. The fourth type is alphanumeric strings that whereby special characters are used to represented data with no numerical values. For example, nucleic acids in RNA and DNA can be presented as G, C, T, A and U. The final types is characters that are simply instructional information units. For instance, a comma between numerical figures can represent value is thousands, million, billions and so on (Anderson et al., 2014).

Question 3.

Paramedic methods refers to data manipulation approaches that present graphical results as bell-shaped curves. In fact, they are used on ratio and interval scales to present homogenous variance and mean as central measures. In contrast, non-parametric methods can present any type of curve and variance can either be homogenous or non-homogenous. It uses ordinal and nominal scales while presenting median as the central measure (Johnson & Kuby, 2012).

Question 4.

Parametric methods are used on normally distributed data with homogenous variance. The data must be from interval and ratio scales with the data sets having an independent relationship. On the other hand, nonparametric methods are not restricted to any distribution type or variance. They use ordinal and nominal scales with the relationship between data sets either being dependent or independent (Johnson & Kuby, 2012).

Question 5.

Parametric statistics is used on ratio and interval scales with a focus on the independent relationship between data sets. This allows for presentation of magnitude, direction and differences between the datasets. On the other hand, nonparametric statistics is used on ordinal and nominal scales. This allows it to only present the direction and differences between the data sets (Howell, 2013).

Question 6.

Parametric statistics presents many conclusions since it has more opportunities for data manipulation. However, this makes it easier for outliers to affect the results thus restricting its usage to data sets with independent relationships. In contrast, non-parametric statistics shows the relationship between data sets so that outliers do not affect the results. However, it is not useful for drawing conclusions and inferences for large populations (Howell, 2013).

Question 7.

The level of measurement has an effect on the data analysis method. Parametric tests should be used on interval and ratio levels of measurement since they present difference, direction, and magnitude. In contrast, nonparametric test should be used on ordinal and nominal levels of measurement since they focus on direction (Howell, 2013).

References

Anderson, D., Sweeney, D., Williams, T., Camm, J. & Cochran, J. (2014). Statistics for business & economics (12^th ed.). Stamford, CT: Cengage Learning.

Howell, D. (2013). Fundamental statistics for the behavioral sciences. Belmont, CA: Wadsworth.

Johnson, R. & Kuby, P. (2012). Elementary statistics (11^th ed.). Boston, MA: Brooks/Cole.  Parametric and nonparametric methods Example Paper