0.4 Writing up nonparametric spearman rho correlation

Writing up your nonparametric spearman rho correlation

About the Authors

• John R. Slate is a Professor at Sam Houston State University where he teaches Basic and Advanced Statistics courses, as well as professional writing, to doctoral students in Educational Leadership and Counseling. His research interests lie in the use of educational databases, both state and national, to reform school practices. To date, he has chaired and/or served over 100 doctoral student dissertation committees. Recently, Dr. Slate created a website, Writing and Statistical Help to assist students and faculty with both statistical assistance and in editing/writing their dissertations/theses and manuscripts.
• Ana Rojas-LeBouef is a Literacy Specialist at the Reading Center at Sam Houston State University where she teaches developmental reading courses. She recently completed her doctoral degree in Reading, where she conducted a 16-year analysis of Texas statewide data regarding the achievement gap. Her research interests lie in examining the inequities in achievement among ethnic groups. Dr. Rojas-LeBouef also assists students and faculty in their writing and statistical needs on the Writing and Statistical website, Writing and Statistical Help

The following is an example of how to write up (in manuscript text) your Spearman rho Correlation statistics. This module is used with a larger Collection (Book) authored by John R. Slate and Ana Rojas-LeBouef from Sam Houston State University and available at: Calculating Basic Statistical Procedures in SPSS: A Self-Help and Practical Guide to Preparing Theses, Dissertations, and Manuscripts

Relationships of Economically Disadvantaged and Minority Student Enrollment in Texas Middle Schools

Research questions

The following research questions were addressed in this study:

1. What is the relationship between the percent of economically disadvantaged students and the percent of minority students enrolled at Texas middle schools for the 2003-2004 school year?;
2. What is the relationship between the percent of economically disadvantaged students and the percent of minority students enrolled at Texas middle schools for the 2004-2005 school year?; and
3. What is the relationship between the percent of economically disadvantaged students and the percent of minority students enrolled at Texas middle schools for the 2005-2006 school year?

Results

Sample sizes, means, and standard deviations pertaining to the two variables of interest (i.e., percent of economically disadvantaged students and percent of minority students) for all three years are presented in Table 1. An examination of the scatterplots (not presented) suggested the presence of linearity for the two variables for each of the three years of data analyzed. The presence of linearity permitted the use of correlation coefficients. With respect to the distribution of scores underlying these measures, the standardized skewness coefficients (i.e., skewness divided by the standard error of skewness) and the standardized kurtosis coefficients (i.e., kurtosis divided by the standard error of kurtosis) revealed serious departures from normality for the two variables of interest for all three years of data analyzed. Specifically, for the percent of economically disadvantaged students, the standardized skewness coefficients were -202.38, -146.81, and -146.52, for each of the three years respectively. Similarly, the standardized kurtosis coefficients for the percent of economically disadvantaged students were -6.65, -6.48, and -10.86 for each of the three years respectively.