One Of The Primary Areas Of Focus For Research And Evaluatio

One Of The Primary Areas Of Focus For Research And Evaluation In Publi

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. 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.

Paper For Above instruction

Research in public administration and nonprofit management heavily emphasizes understanding the relationships between variables to evaluate the success and effectiveness of policies and programs. This approach involves selecting appropriate statistical methods—namely, measures of association and inferential statistics—to analyze data accurately and draw meaningful conclusions. When assessing a program, such as one aimed at reducing homelessness, it is vital to measure the impact quantitatively and consider additional factors that may influence the outcomes. For example, evaluating the reduction in homeless populations requires analyzing the relationship between program implementation and housing outcomes, often controlled for other variables like demographic attributes or economic factors.

Choosing the suitable statistical tools depends on several crucial criteria. First, the purpose of the analysis guides whether the focus is on measuring strength, direction, or significance of relationships between variables. For example, if the goal is to determine whether a reduction in homelessness is statistically significant, inferential tests such as t-tests or analysis of variance (ANOVA) are appropriate. Second, the measurement level of variables influences the choice—nominal, ordinal, interval, or ratio data—each requiring different statistical approaches. For instance, Pearson’s r is suitable for interval or ratio data to measure correlation, while Gamma or Lambda may be preferable for ordinal data.

Third, the sensitivity of the measure pertains to its ability to detect true relationships amidst variability, which guides the selection of measures like Pearson’s r or Gamma depending on data distribution and measurement level. Lastly, the researcher’s familiarity with specific statistical tests affects feasibility and accuracy, emphasizing the importance of choosing tests aligned with one’s expertise. Given these criteria, no single measure is universally perfect. Instead, the most appropriate choice depends on aligning the research question, data characteristics, and analysis purpose.

In planning for a final evaluation or project, applying these considerations enables the systematic selection of both measures of association and inferential statistics. For example, if evaluating the effectiveness of a homelessness reduction program with continuous data on the number of homeless individuals pre- and post-intervention, a paired t-test might be suitable. Conversely, for categorical data, a chi-square test for independence could assess the relationship between program participation and housing status. For capturing the strength and direction of relationships, Pearson’s r might be used if data are interval or ratio, while measures like Gamma may better suit ordinal data.

Ultimately, the goal is to use the most appropriate statistical methods to produce valid, reliable, and meaningful insights into program effectiveness and variable relationships. Consistently applying the four criteria for selecting statistical measures ensures that research findings are both accurate and useful for guiding future policy decisions and program improvements.

References

• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
• Groves, R. M., et al. (2009). Survey methodology (2nd ed.). Wiley.
• Gliner, J. A., et al. (2017). Principles and practice of structural equation modeling. Guilford Publications.
• Levine, R. A., et al. (2017). Statistics for managers using Microsoft Excel. Pearson.
• Moorman, R. H. (2017). Principles of statistical analysis. Routledge.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
• Upton, G., & Cook, I. (2014). Understanding statistics. Oxford University Press.
• Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594–604.
• McDonald, R. P. (2014). Test theory: A unified treatment. Psychology Press.
• Salkind, N. J. (2017). Statistics for people who (think they) hate statistics. Sage.