Data And Measurements In This Unit You're Starting To Look M

Data And Measurementsin This Unit You Start Looking More Closely At D

Data and Measurements in this unit, you start looking more closely at data and making assessments regarding its perceived level of usefulness to your own research endeavors. For the first part of the assignment, go to the National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) at the link below (bookmark this page; you will be going here a lot throughout the course). National Center for Education Statistics. (n.d.). Statistical standards program. Retrieved from [URL]. From the left-hand menu options, review the material and answer the following question.

In your review of the NCES Statistical Standards, what evidence do you see that the data acquired is reliable and valid; that is, which standards suggest that reliability and validity are priorities? Next, go to the IPEDS Report Your Data page from the link below. National Center for Education Statistics. (n.d.). Report your data. Retrieved from [URL]. Click on the Answer Current Survey link.

Answer the following questions. At first glance, what appears to be the primary means that IPEDS uses to collect data? What threats to reliability and validity does this technique pose? Would 12-month enrollment likely be a very sensitive measure of student satisfaction? Why, or why not? How appropriate do you think a time-series analysis of graduation rates would be within a research study? Would it be more or less appropriate than a time-series analysis of institutional characteristics? Why, or why not? Your scholarly activity must be at least two pages in length, not counting title and reference pages. You are not required to include an introduction or conclusion.

You may number your paper and answer each question. Please use at least the textbook and the required website in the development of your assignment. APA formatting applies. The purpose of this assignment is to gauge your understanding of the content, so focus on writing original content rather than simply regurgitating the textbook or other sources, whether by paraphrasing or using direct quotes. Paraphrasing is acceptable, but try to keep paraphrasing to a minimum. this is the text book .. Textbook: O'Sullivan, E., Rassel, G. R., & Taliaferro, J. D. (2011). Practical research methods for nonprofit and public administrators. New York, NY: Routledge.

Paper For Above instruction

The evaluation of data reliability and validity within the context of the National Center for Education Statistics (NCES) and the Integrated Postsecondary Education Data System (IPEDS) reveals several standards emphasizing the importance of accurate, consistent, and trustworthy data collection methods. According to O'Sullivan, Rassel, and Taliaferro (2011), reliability refers to the consistency of measurement, while validity is concerned with whether the data accurately captures what it is intended to measure. NCES demonstrates its commitment to these principles through stringent standards that emphasize data clarity, consistency, and accuracy to ensure high levels of reliability. For instance, NCES standards advocate for standardized data collection procedures, training of data collectors, and validation processes that include cross-checks and audits, all of which reinforce the reliability of the data (O'Sullivan et al., 2011). Moreover, validity is supported through clear operational definitions of variables and the use of multiple data sources that corroborate findings, ensuring the data truly reflects the intended educational metrics (NCES, n.d.).

When exploring how IPEDS reports data, it becomes evident that the primary data collection method involves self-report submissions by postsecondary institutions via surveys or reporting forms. This approach relies heavily on institutional compliance and the accuracy of the data provided by the respondents. Such a technique introduces potential threats to reliability and validity, including reporting bias, inconsistencies in data entry, or misunderstanding of definitions and instructions. For example, institutions may differ in how they interpret survey questions or the timing of data submission, which can introduce variability and compromise data integrity. Furthermore, if institutions intentionally or unintentionally misreport data to portray favorable outcomes, this could threaten the validity of the dataset (O'Sullivan et al., 2011).

Regarding the measurement of 12-month enrollment as an indicator of student satisfaction, it is generally considered a limited proxy. Enrollment data primarily reflect students’ registration patterns rather than their satisfaction or engagement with the institution. While fluctuations in enrollment can signal satisfaction or dissatisfaction, they are also influenced by external factors such as economic conditions, marketing efforts, or administrative policies. Therefore, 12-month enrollment is unlikely to be a highly sensitive measure of student satisfaction because it does not directly assess students’ perceptions, experiences, or outcomes. More nuanced measures, such as surveys of student satisfaction or retention rates, would better capture this construct.

In the context of research, performing a time-series analysis of graduation rates can be highly appropriate, especially if the goal is to observe trends or measure the impact of policy changes over time. Graduation rates tend to be stable and quantifiable, allowing researchers to identify patterns and anomalies in student success over successive periods. Compared to institutional characteristics, which can vary widely in nature, scope, and measurement, graduation rates provide more concrete and comparable data points. Institutional characteristics such as faculty size, facilities, or funding levels may be less amenable to straightforward time-series analysis due to their complexity and the potential influence of numerous external variables (O'Sullivan et al., 2011). Therefore, analyzing graduation rates over time offers a more direct and meaningful assessment of institutional effectiveness and student outcomes.

In conclusion, the standards set forth by NCES and observations of IPEDS data collection practices reveal a focus on ensuring data reliability and validity through standardized procedures, validation, and operational clarity. While self-reporting introduces some risks, these can be mitigated through rigorous oversight and validation protocols. The measurement tools used, such as enrollment figures, have their limitations when it comes to capturing abstract constructs like student satisfaction, but are valuable for tracking tangible outcomes like graduation rates. Time-series analysis of graduation rates is particularly suited for longitudinal studies aimed at assessing institutional performance, whereas analysis of institutional characteristics may require more nuanced approaches due to their complexity. Ultimately, understanding these data attributes helps researchers determine the appropriateness of various metrics for specific research questions, enhancing the overall quality of educational data analysis.

References

• O'Sullivan, E., Rassel, G. R., & Taliaferro, J. D. (2011). Practical research methods for nonprofit and public administrators. Routledge.
• National Center for Education Statistics. (n.d.). Statistical standards program. Retrieved from [URL]
• National Center for Education Statistics. (n.d.). Report your data. Retrieved from [URL]
• Babbie, E. (2010). The practice of social research. Wadsworth Cengage Learning.
• Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
• Fink, A. (2013). How to conduct surveys: A step-by-step guide. Sage Publications.
• Patton, M. Q. (2002). Qualitative research & evaluation methods. Sage Publications.
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