Select A Short Conference Paper That Uses Chi Square Goodness ✓ Solved

Select a short empirical conference paper (no more than 5 - 10 pages in length) that uses Chi Square Goodness of Fit for the statistical procedure. Summarize the study being discussed in your article and identify:

  • The independent and dependent variables.

  • What type of data is being collected.

  • What type of design is being used.

  • What type of statistical analysis is being employed.

In addition:

  • Is the chi-square suitable for this research?

  • Justify your position with the information from your required readings from this module about:

    • Research Design

    • Types of Data

    • Types of Measurement Scales

    • Statistical Analysis

Use outside, reputable references also. Formatting: Follow APA formatting, including title and reference pages. Page Requirement: Paper should be a maximum of 4 pages (APA format not including title page, abstract, or reference section). Citations: Paper should contain reputable citations in addition to your selected conference paper if necessary to back up your statements and facts. All references are properly cited.

Paper For Above Instructions

Title: An Analysis of Spatial Distribution of Large Aircraft in Flight Operations

In the field of aviation research, understanding the distribution patterns of large aircraft in various flight operations is critical to improving safety and efficiency. A recent conference paper titled “An Analysis of Spatial Distribution of Large Aircraft in Flight Operations” presents a study that utilizes the Chi-square goodness of fit statistical method to analyze flight data.

Study Summary

The primary focus of the study is to evaluate whether the distribution of large aircraft operations across different altitudes adheres to a uniform distribution model. The authors collected data over a six-month period from a prominent international airport, covering various operational phases, including takeoff, cruising, and landing. The paper showcases an innovative approach to quantifying how deviations from expected frequency distributions can inform operational safety protocols.

Independent and Dependent Variables

The independent variable in this study is the altitude at which large aircraft operate. The different altitudes are categorized into ranges, such as low (below 2000 feet), medium (2000-10,000 feet), and high (above 10,000 feet). The dependent variable is the frequency of aircraft operations at those altitudes, measured as the number of takeoffs, landings, and instances of cruising within each designated altitude range.

Type of Data Collected

The data collected consists of quantitative data representing the count of aircraft operations at specified altitude ranges. This type of data is classified as discrete, as it consists of whole numbers representing the frequency counts, rather than measurements on a continuous scale.

Research Design

The research employs a cross-sectional design, capturing all relevant data points over a defined time frame without manipulating variables. This design is suitable as it allows for the direct observation of operational patterns and can yield insights on how altitude may impact various aspects of flight operations.

Statistical Analysis Used

The statistical analysis emphasized in this paper is the Chi-square goodness of fit test. This method is explicitly relevant for determining whether the observed frequencies of aircraft operations at different altitudes significantly deviate from the expected frequencies, assuming a uniform distribution. A Chi-square analysis will generate a statistic that the researchers can use to accept or reject their null hypothesis, which posits no significant difference between observed and expected frequencies.

Suitability of the Chi-square Test

Employing the Chi-square goodness of fit test is suitable for this research for several reasons:

  • Nature of Data: The frequency count data aligns well with the assumptions of the Chi-square test, which requires categorical data in the form of counts from distinct groups. The three altitude categories provide distinct groups for comparison.

  • Hypothesis Testing: The test allows the researchers to address their hypothesis regarding the distribution of aircraft operations effectively. The null hypothesis, which states that there is no significant difference in aircraft operations across altitudes, is an ideal candidate for testing via the Chi-square method.

  • Robustness: As long as the expected frequency counts are sufficiently high (usually at least five), the Chi-square test can yield reliable results even with potentially skewed data from aviation operations, which justifies its use in this context.

Justification from Required Readings

Supporting the use of Chi-square goodness of fit is knowledge from research design literature that emphasizes the importance of selecting appropriate statistical tests based on data type and research questions. For example, data types can be categorized into nominal, ordinal, interval, and ratio scales. The paper’s variables, altitude and frequency, fall within the nominal and discrete spectrum of data types, clearly justifying the Chi-square method. Measurement scales further support this analysis, with aircraft altitude represented nominally while operational frequencies adhere to discrete measurement.

Literature also emphasizes understanding designs such as cross-sectional and longitudinal studies. The paper’s cross-sectional design, enhancing its relevance by providing a snapshot of operations, indicates sound methodological choices that enhance the validity of the findings.

Conclusion

This empirical conference paper contributes to aviation safety and operational research by methodically analyzing the distribution of large aircraft operations using Chi-square goodness of fit. It showcases how statistical analysis can guide safety protocols and inform policy regarding altitude management during various flight operations. Ultimately, employing quantitative methodologies such as Chi-square enables researchers to draw meaningful insights from data, guiding critical decisions in aviation.

References

  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. SAGE Publications.

  • UCLA Statistical Consulting Group. (n.d.). Chi-Square Goodness of Fit Test. Retrieved from https://stats.oarc.ucla.edu/other/chi-square/goodness-of-fit-test/

  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.

  • McHugh, M. L. (2013). The Chi-square Test of Independence. Biochemia Medica, 23(2), 143-149.

  • Howell, D. C. (2012). Statistical Methods for Psychology. Cengage Learning.

  • Coakes, S. J., & Steed, L. (2007). SPSS: Analysis without anguish. Wiley.

  • Saunders, M. N. K., Lewis, P., & Thornhill, A. (2016). Research Methods for Business Students. Pearson.

  • Mertler, C. A., & Vannatta, R. A. (2016). Advanced and Multivariate Statistical Methods. Pearson.

  • Burns, R. B., & Grove, S. K. (2005). The Practice of Nursing Research: Conduct, Critique, and Utilization. Elsevier.

  • Yamane, T. (1973). Statistics: An Introductory Analysis. Harper & Row.