0.2 Calculating a nonparametric pearson chi-square  (Page 2/3)

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 pearson chi-square

In this set of steps, readers are provided with directions on calculating a statistical procedure in which the independent variable and the dependent variable are categorical variables. As such, the only descriptive statistics that can be obtained are frequencies, percentages, and sums. Because the data on which this chi-square procedure is used are grouped data, skewness and kurtosis values are not appropriate. Readers should ensure that the assumptions described in the steps below are met prior to conducting this nonparametric procedure. For more detailed information about the statistical and conceptual underpinnings of this statistical technique, readers are referred to the Hyperstats Online Statistics Textbook at (External Link) or to the Electronic Statistics Textbook (2011) at (External Link)

Step one:

Check to make sure that both variables are categorical in nature. That is, the variables must have values that are in a restricted range (e.g., 1 or 2 for gender; 1 – 5 for Strongly Agree through Strongly Disagree; 1 – 5 for ethnicity categories).

Step two:

Check to verify that you have available per cell at least 5 responses (i.e., divide the sample size by the number of cells [number of categories for the IV times the number of categories for the DV] and have a value of at least 5).

Step three:

Verify that only one response per participant is present. Once these assumptions have been checked and validated, then the Pearson chi-square procedure can be calculated.

Step four:

• √ Analyze
• * Descriptive Statistics
• * Crosstabs

• √ Independent Variable (e.g., gender) in Row
• √ Dependent Variable (e.g., responses to a survey item) in Column

• √ Cells
• √ In the Percentages Box
• √ Row

• √ Continue
• √ Statistics
• √ Chi Square
• √ Phi and Cramer's V

• √ Continue
• √ OK