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Writing Up Chi-square

Module by: John R. Slate, Ana Rojas-LeBouef. E-mail the authors

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Note:

This module is published by NCPEA Press and is presented as an NCPEA/Connexions publication. Each chapter has been peer-reviewed, accepted, and endorsed by the National Council of Professors of Educational Administration (NCPEA) as a significant contribution to the scholarship and practice of education administration. Formatted and edited in Connexions by Theodore Creighton and Brad Bizzell, Virginia Tech and Janet Tareilo, Stephen F. Austin State University.

Writing Up Your Chi-Square

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 Chi-Square 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

Gender Differences in Reading Group Membership

Research Question

The following research question was addressed in this investigation: What is the difference between boys and girls in their reading group membership?

Results

To ascertain whether a difference was present in reading group membership (i.e., Excellent, Good, Extremely Poor) between boys and girls, a Pearson chi-square was conducted. This statistical procedure was viewed as the optimal statistical procedure to use because frequency data were present for reading group membership and for gender. As such, chi-squares are the statistical procedure of choice when both variables are categorical. In addition, with the large sample size, the available sample size per cell was more than five. Therefore, the assumptions for utilizing a chi-square were met.

For this research question in which the focus was placed on reading group membership between boys and girls, the result was statistically significant, χ2(2) = 122.86, p < .001. The effect size for this finding, Cramer’s V, was moderate, .32 (Cohen, 1988). As can be seen in Table 1, 47.30% of the girls were in the Excellent Reader group, compared to only 21.4% of the boys. Most of the boys were in the Extremely Poor Reader group, 57.40%, compared to only 27.30% of the girls.

Reference

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Note:

To be compliant with APA 6th edition, students and faculty are to be aware that Table titles are placed "above" the table entry. Titles here are placed below the tables because of special formatting templates and for conciseness of visual presentation.
Table 1: Frequencies and Percentages of Reading Group Membership by Gender
Reading Group Girls n and %ageof Total Boys n and %ageof Total
Excellent Reader 47.30% (n = 279) 21.40% (n = 126)
Good Reader 25.40% (n = 150) 21.20% (n = 125)
Extremely Poor Reader 27.30% (n = 161) 57.40% (n = 338)

SPSS Statistical Output

Note:

Tables 2, 3, and 4 below came directly from SPSS output. As such, they are not compliant with APA 6th edition and should not be used in theses, dissertations, or manuscripts. Only Table 1 above the Output from SPSS is compliant with APA format.
Table 2: Chi-Square Tests
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 137.38.
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 122.856a 2 .000
Likelihood ratio 125.706 2 .000
Linear-by-Linear Association 121.427 1 .000
N of Valid Cases 1179    
Table 3: Symmetric Measures
    Value Approx. Sig.
Nominal by Nominal Phi .323 .000
  Cramer's V .323 .000
N of Valid Cases   1179  
Table 4: Gender of Persons in Study
    Excellent Reader Good Reader Extremely Poor Reader Total
Gender of Persons in Study Boys Count 126 125 338 589
  % within Gender 21.4% 21.2% 57.4% 100%
  Girls Count 279 150 161 590
  % within Gender 47.3% 25.4% 27.3% 100%
Total Count 405 275 499 1179
  % of total 34.4% 23.3% 42.3% 100%

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