0.6 Writing up parametric dependent t test

Writing up your parametric dependent t test

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 Parametric Dependent Samples t test 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

Differences Between Boys’ College-Readiness Rates in Reading and in Math

Research question

The following research question was addressed in this study:

• What is the difference between boys’ college-readiness rates in reading and in math?

Results

Prior to conducting inferential statistics to determine whether a statistically significant difference was present between boys’ college-readiness rates in reading and in math, checks were conducted to determine the extent to which the data were normally distributed. Of the standardized skewness coefficients (i.e., the skewness value divided by its standard error) and the standardized kurtosis coefficients (i.e., the kurtosis value divided by its standard error), all were within the limits of normality, +/- 3 (Onwuegbuzie&Daniel, 2002). Readers are directed to Table 1 for the specific values of these standardized coefficients. Because the college-readiness rates in reading and in math were normally distributed, a parametric dependent samples t -test was conducted to answer the research question.

The parametric dependent samples t -test analysis yielded a statistically significant result, t (1006) = -52.76, p <.001, Cohen’s d = 0.69. The effect size for this difference was moderate (Cohen, 1988). Boys had a statistically significantly higher college-readiness rate in math than they did in reading. Depicted in Table 2 are the descriptive statistics for boys’ college-readiness rates in reading and in math.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.) . Hillsdale, NJ: Lawrence Erlbaum.
• Onwuegbuzie, A. J.,&Daniel, L. G. (2002). Uses and misuses of the correlation coefficient. Research in the Schools, 9 (1) , 73-90.

Spss statistical output

Figure 1. statistics

Figure 2. paired samples test