Good Example Of Data Analysis And Statistical Tests Essay

Question 1: The most important aspect for transcriptions is accuracy that reflects the true experience. To increase accuracy researchers can repeat listening and double check recording. What are the three categories of error?
Error is defined as the difference between the measured value and the actual value. It is categorically divided into three – 1) personal, 2) systematic, and 3) random. Personal error comes into play because of faulty procedure adopted by the observer, e.g. making an error in reading a scale. On the other hand, systematic error arises due to a defect in the measuring device or instrument. This type of error may be a positive or negative error and it can be corrected by calibrating the measuring device. Lastly, random error is produced due to a sudden change in workplace conditions. It is an accidental error and is beyond the control of the person making the observation (Exell, 2001).

Question 2: How do you analyze data when consecutive sampling was the sampling plan used?

Consecutive sampling is a type of non-probability sampling where every available subject is selected and studied (Lunsford et al, 1995). The main characteristic of non-probability sampling designs is that each member of the population does not have an equal chance of being selected. Therefore, it may not be assumed that the sample fully represents the target. As a result, standard errors cannot be computed and methods of statistical inference cannot be applied. Therefore, results based on this type of sampling design are best utilized only for descriptive purposes rather than for making generalizations about the target population (Mendoza, 2010).

Question 3: The chi-square and the analysis of variance: when is it appropriate to use each one?

The chi-square (X2) test of association is used to investigate whether distributions of categorical variables differ from one another (The Mathbeans Project, n.d). It is appropriate to use X2 test when exploring the existence of an association or relationship between two qualitative variables. A quantitative variable may also be used but the data needs to be transformed first into the nominal form. On the other hand, analysis of variance (ANOVA) is used to see if there is any difference between groups on some variable. It is used for quantitative variables specifically parametric (score data) and non-parametric (ranking/ordering) data (CERG, n.d).
Question 4: Choose 2 statistical tests that measure relationships. How are they similar? How are they different? When is it appropriate to use each one?
Correlation and regression analyses are two statistical tests that measure the relationship between variables. The former describes the relationship of two quantitative variables by means of a single number called the correlation coefficient (Google sites, n.d), while the latter describes the relationship of the independent (X) variable and dependent (Y) variable by means of an equation which best fits the data (Google sites, n.d). It is appropriate to use correlation analysis when one is interested in simply measuring the strength and direction of the linear relationship between two quantitative variables, say A and B, without necessarily assuming that and B is dependent on A. On the other hand, regression is used in verifying or validating the nature of the relationship between the independent (X) variable and dependent (Y) variable. An example of which is when one is interested in identifying if X is a predictor of Y (Mendoza, 2010).

References:

CERG. (n.d). "Analysis of Variance (ANOVA)". Web. 20 February 2015. Retrieved from http://www.csse.monash.edu.au/~smarkham/resources/anova.htm
Exell, R. (2001). "Error Analysis". King Mongkut's University of Technology Thonburi. Web. 20 February 2015. Retrieved from http://www.jgsee.kmutt.ac.th/exell/PracMath/ErrorAn.htm
Google sites. (n.d). "Chapter 10: Correlation". Fundamental Statistics. Web. 20 February 2015. Retrieved from https://sites.google.com/site/fundamentalstatistics/chapter-10
Google sites. (n.d). "Chapter 11: Regression". Fundamental Statistics. Web. 20 February 2015. Retrieved from https://sites.google.com/site/fundamentalstatistics/chapter-11
Mendoza, O. (2010). "Foundations of Statistical Analysis for the Health Sciences". Manila, Philippines: College of Public Health, University of the Philippines Manila.
Lunsford, T. et al. (1995). “Research Forum--The Research Sample, Part I: Sampling”. Washington DC: Journal of Prosthetics and Orthotics. Web. 20 February 2015. Retrieved from http://www.oandp.org/jpo/library/1995_03_105.asp
The Mathbeans Project. (n.d). "The Chi Square Statistic". Web. 20 February 2015. Retrieved from http://math.hws.edu/javamath/ryan/ChiSquare.html