Reference no: EM133195392

1) 100 Words response

After reading that article, what did you learn about additional job opportunities in the field for those who know statistics? Also, did you agree or disagree with the author of the article when she said, "It turns out that the importance of understanding and being able to apply and interpret statistics in psychological research cannot be understated" (Tessler, n.d., para. 1). 

Reference

Tessler, J. (n.d.) On the importance of learning statistics for psychology students.

Article is the reference provided to answer the question.

2) 100 Words response

The importance of understanding statistical value in research methods is not only for the student's value, but the profession, as the results obtained can lead to new hypothesis for a deeper understanding and better realization of information and areas of study. Research is expanded based on results that can be replicated. This means the stastical analysis, and methods implemented during the research have validity, and can support further investigation to offer more knowledge in the area of study (Jackson, 2017).

The construction of a study is critical and has a major impact on replication ability based on the results and conclusions of a research study. The population sample, the question being asked to determine a true or null hypothesis, the methods of research and the statistical analysis used for calculations provide support for replication. Support of a hypothesis with repeatable statistical data allows individuals in the field of practice or research, such as behavioral science, to expand upon methods and generate new alternatives to theories. Growth and development of the field is necessary, as human behavior is complex. We need complex studies and research to continue to understand behavior; the why and how of responses and actions that lead to the ultimate behavior (Jackson, 2017). 

There are many measures as to how we view the data collection for review and relation in the field. Four primary areas include Ordinal, Interval, Ratio, and Nominal scales of measurement. In each scale there are examples of what is measured, the properties of measurement, how these are calculated (equations), and the statistical representation of each. As humans, measuring behavior requires multiple methods of value composition for evaluation. We cannot measure an individual's sex, religion or ethnicity the same as we might determine how they score on a test, or answer questions to how they feel in response to an emotional situation that requires identifying with a personality type. Nor can we assimilate physical attributes, such as body height, weight or time of reactions in the same method as others. As this is the case, scientifically we must categorize such values for appropriate measurement (Jackson, 2017).

In organizing such means of study, we measure implementing key scales based on what is being measured. We ask what property are we looking to identify so that we can apply the appropriate mathematical operation that will produce the most efficient statistical analysis for the data we are looking to obtain. By implementing experimental means for research, we are allowing for a description of behavior that supports a possible prediction that one variable has a cause-effect- on the other variable (s), seeking an explanation of the behavior. When we analyze the results from a study appropriately, we can organize a collection of information that may be further assessed statically to gain a deeper understanding of what the data represents. If we can replicate a study, then averages of the information provided leads to deeper representation of the cause-effect relationship among variables when mean, median and modes can be acquired, based on the method of research used and calculation of the data. Depending on the nature of the data collected, quantitative or qualitative, ways to organize the data include using a histogram, a bar graph, frequency polygon or frequency distribution (Jackson, 2017).

References

Jackson, S. L. (2017).Statistics plain and simple (4th ed.). Boston, MA: Cengage Learning.