0.6 Conducting a nonparametric independent samples t-test  (Page 2/4)

Editors Information

• Theodore B. Creighton , is a Professor at Virginia Tech and the Publications Director for NCPEA Publications , the Founding Editor of Education Leadership Review, and the Senior Editor of the NCPEA Connexions Project.
• Brad E. Bizzell , is a recent graduate of the Virginia Tech Doctoral Program in Educational Leadership and Policy Studies, and is a School Improvement Coordinator for the Virginia Tech Training and Technical Assistance Center. In addition, Dr. Bizzell serves as an Assistant Editor of the NCPEA Connexions Project in charge of technical formatting and design.
• Janet Tareilo , is a Professor at Stephen F. Austin State University and serves as the Assistant Director of NCPEA Publications. Dr. Tareilo also serves as an Assistant Editor of the NCPEA Connexions Project and as a editor and reviewer for several national and international journals in educational leadership.
• Thomas Kersten is a Professor at Roosevelt University in Chicago. Dr. Kersten is widely published and an experienced editor and is the author of Taking the Mystery Out of Illinois School Finance , a Connexions Print on Demand publication. He is also serving as Editor in Residence for this book by Slate and LeBouef.

Conducting a nonparametric independent samples t -test

In this set of steps, readers will calculate either a parametric or a nonparametric statistical analysis, depending on whether the data for the dependent variable reflect a normal distribution. A parametric statistical procedure requires that its data be reflective of a normal curve whereas no such assumption is made in the use of a nonparametric procedure. Of the two types of statistical analyses, the parametric procedure is the more powerful one in ascertaining whether or not a statistically significant difference, in this case, exists. As such, parametric procedures are preferred over nonparametric procedures. When data are not normally distributed, however, parametric analyses may provide misleading and inaccurate results. According, nonparametric analyses should be used in cases where data are not reflective of a normal curve. In this set of steps, readers are provided with information on how to make the determination of normally or nonnormally distributed data. For detailed information regarding the assumptions underlying parametric and nonparametric procedures, readers are referred to the Hyperstats Online Statistics Textbook at http://davidmlane.com/hyperstat/ or to the Electronic Statistics Textbook (2011) at http://www.statsoft.com/textbook/

For this nonparametric independent samples t -test to be appropriately used, at least half of the standardized skewness coefficients and the standardized kurtosis coefficients must be outside the normal range (+/-3, Onwuegbuzie&Daniel, 2002). Research questions for which nonparametric independent samples t -tests are appropriate involve asking for differences in a dependent variable by group membership (i.e., only two groups are present for t -tests). The research question, “What is the difference between boys and girls in their science performance among middle school students?” could be answered through use of a nonparametric independent samples t -test.