Inferential Statistics and Measures of Association (Assignment Attached)
This assignment is responding to the two post below. Requirements for each response is:
1. At least one paragraph
2. Contain intext citation where appropriate
3. At least one reference per paragraph (it could be the same reference for each)
Do not connect the two posts. They are completely separate. One reference for each paragraph. No introduction and no summary.
ASSIGNMENT: Respond to the two by providing feedback about the inferential statistics and measurements of association presented in their posts. Are these statistical approaches most appropriate for their program evaluations? Why or why not? If not, offer suggestions or ideas about statistical approaches you think are more appropriate.
Discussion – Week 9
Inferential Statistics and Measures of Association
Inferential statistics are used to make an analysis of the data collected and make a conclusion based on the analyzed results. They are statistical analysis techniques that help analysts make a generalization, based on observation, about a population from results obtained in a valid sample used during the research. The main goal of inferential statistics is to determine whether the characteristics of observed samples are defiant from the null hypothesis stipulated during the study (Macfie & Nufrio, 2006). Inferential statistics enable researchers to determine the existence and strength of the relationship existing between the dependent and independent variables in a study. Inferential statistics are essential in making inferences about a population from a sample, making a conclusion on whether or not to accept a null hypothesis, concluding whether or not a sample is significantly different from the population (Asadoorian & Kantarelis, 2005). In instances where a regression model is used, inferential statistics give insight into whether or not adding or removing a variable from the model changes the relationship between the variables.
The choice of inferential statistics to use to either accept or reject a null hypothesis depends on the data collected and the distribution of the data. Hypothesis testing is usually aimed at finding out whether or not a particular claim is true or false statistically (Macfie & Nufrio, 2006). My project focusses on the normal distribution and hence uses the z-test. Based on the sample mean and standard deviation obtained, a p-value is obtained. The p-value, at the selected level of significance (1%, 5%, or 10%), helps in deciding if to accept or reject the null hypothesis. If the calculated p-value falls within the critical area, we reject the null hypothesis and accept the alternative (Asadoorian & Kantarelis, 2005). For example, at a 5% confidence interval, the p-value to use will be 0.05. If the p-value obtained is less than 0.05, the null hypothesis is rejected.
Asadoorian, M. O., & Kantarelis, D. (2005). Essentials of inferential statistics. Lanham:
University Press of America.
Macfie, B. P., & Nufrio, P. M. (2006). Applied statistics for public policy. Armonk, N.Y:
RE: Discussion – Week 9
Week 9 Main Discussion Post
The National Association for the Advancement of Colored People (NAACP) is an organization focused on improving the lives of individuals within the Black community and creating a just and equal system for all. Specifically, my final project will focus on the current issue of police brutality and the disproportionate numbers of Blacks being victimized versus any other race. A specific research question for this topic would be: how often does race play a role in the frequency of police brutality cases and will implementing a policy to defund the police change these statistics?
Using inferential statistics in assisting with the answer to this research question would provide the researcher with a base foundation to begin the process of social justice change. Johnson (2014) defines inferential statistics as “a body of statistical computations that enable the researcher to make inferences from random sample results to the larger population as well as determine how likely it is that the results are due to chance” (p. 297). In simpler terms, inferential statistics are calculations that allow scientists to infer trends about a larger population based on a study of a sample taken from that larger population.
For my final project, it would be beneficial for the NAACP to use both online data sets as well as conduct questionnaires or surveys to determine the average number of Black lives that have been affected by police brutality and then use this information to infer the trends that lead to police brutality. Using a measure of association, the researcher would also be able to determine the strength of the cause-and-effect relationship that race has on police brutality.
Johnson, G. (2014). Research methods for public administrators (3rd ed.). Armonk, NY: M. E. Sharpe.
· Johnson, G. (2014). Research methods for public administrators (3rd ed.). Armonk, NY: M. E. Sharpe.
o Chapter 14, “Data Analysis: Exploring Relationships” (pp. 198–215)
o Chapter 16, “Data Analysis Using Inferential Statistics” (pp. 230–243)
· Trochim, W. M. K. (2006, October 20). Inferential statistics. Retrieved from http://www.socialresearchmethods.net/kb/statinf.php
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Discussion – Week 9
Discussion: Inferential Statistics and Measures of Association
One of the primary areas of focus for research and evaluation in public and nonprofit sectors is exploring the relationship between variables. For example, as a public administrator, you might be interested in determining the effectiveness of a public program. To do this, you would explore the relationship between the program and its intended outcome. Consider a program intended to reduce homelessness. You would need to reduce the number or percentage of citizens who lack suitable housing in order to be considered successful. Sometimes, you might need to consider additional variables in assessing the success of the homelessness program. In this case, measures of association and inferential statistics can help you sort out these types of relationships.
There are many types of inferential statistics (e.g., t-tests for independent samples means, t-tests for proportions, analysis of variance, etc.) and many types of measures of association (e.g., Lambda, Gamma, and Pearson’s r, etc.). A skilled researcher must have the ability to select the most appropriate statistic for a given situation. As noted in your course text, selecting the appropriate statistic is based on the purpose of the research, the measurement level of the variables, the measure’s sensitivity, and the researcher’s familiarity with the statistic. Therefore, there is no “perfect” statistic. Instead, you must determine the “most appropriate” statistic for your research.
To prepare for this Discussion, review Chapters 13 and 15 in your course text, paying particular attention to the selection and use of inferential statistics and measures of association. Review the four criteria for selecting the best measure, keeping in mind the purpose for which you might use measures of association. Finally, think about which inferential statistics and measures of association you might use in your Final Evaluation Design (Final Project) to answer your research question. Use the four criteria to help you make a selection.
Post by Day 4 a description of the inferential statistics and measures of association that would be most appropriate for your evaluation and answer your research question. Explain how you would use them, and justify why these statistics and measures are most appropriate for your Final Evaluation Design (Final Project).
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