Step five:
-
Check for Statistical Significance
- 1. Go to the Chi-Square Test Box
- 2. Find Pearson Chi-Square row and Asymp. Sig. (2-sided) column cell
Chi-square tests
|
Value |
df |
Asymp.Sig.(2-sided) |
| Pearson Chi-Square |
833.549
a |
118 |
.000 |
| Likelihood Ratio |
907.609 |
118 |
.000 |
| Linear-by-Linear |
16.845 |
1 |
.000 |
| Association |
|
|
|
| N of Valid Cases |
1182 |
|
|
a. 81 cells (45.0%) have expected count less than 5. The minimum expected count is .23.
Step six:
-
Check Effect Size
- 1. Go to the Symmetric Measures Box
- 2. Find the Nominal by Nominal Cramer’s
V row and Value column cell
- 3. The effect size is there and must be related to Cohen (1998)
- Small effect size = .10 (range of .10 to .299)
- Medium effect size = .30 (range of .30 to .499)
- Large effect size = .50 (range of .50 to 1.00)
Cramer's
V cannot be greater than 1.00
Symmetric measures
|
Value |
Approx Sig. |
| Nominal by Phi |
.840 |
.000 |
| Nominal Cramer's V |
.94 |
.000 |
| N of Valid Cases |
1182 |
|
Step seven:
-
Numerical Sentence
- 1.
X
2 (df)
sp =
sp Pearson Chi-Square/Value Cell,
sp
p
sp <
sp .001
-
X
2 (1)= 833.55,
p <.001
- [Note. The sp refers to a space being present where the sp is located.]
Step eight:
- 1. Go to the IV by DV table (i.e., the one above the Chi-Square Tests table)
- 2. Examine the percentages to determine where the statistically significant differences are
Step nine:
-
Narrative and Interpretation Outline
- 1. Let the reader know what statistical procedure was conducted.
- 2. Explain how the assumptions for this statistical procedure were met.
- 3. Report the results from the test
- 4. Interpret the findings
Writing up your statistics
So, how do you "write up" your Research Questions and your Results? Schuler W. Huck (2000) in his seminal book entitled,
Reading Statistics and Research, points to the importance of your audience understanding and making sense of your research in written form. Huck further states:
This book is designed to help people
decipher what researchers are trying to communicate in the written or oral summaries of their investigations. Here, the goal is simply to distill meaning from the words, symbols, tables, and figures included in the research report. To be competent in this arena, one must not only be able to decipher what's presented but also to "fill in the holes"; this is the case because researchers typically assume that those receiving the research report are familiar with unmentioned details of the research process and statistical treatment of data.
A Note from the Editors
Researchers and Professors John Slate and Ana Rojas-LeBouef understand this critical issue, so often neglected or not addressed by other authors and researchers. They point to the importance of doctoral students "writing up their statistics" in a way that others can understand your reporting and as importantly, interpret the meaning of your significant findings and implications for the preparation and practice of educational leadership. Slate and LeBouef provide you with a model for "writing up your Chi-square statistics."
Click here to view:
Writing Up Your Chi-square Staistics
References
- Cohen, J. (1988).
Statistical power analysis for the behavioral sciences (2nd ed.)
. Hillsdale, NJ: Lawrence Erbaum
- Hyperstats Online Statistics Textbook. (n.d.) Retrieved from
(External Link)
- Kurtosis. (n.d.). Definition. Retrieved from
(External Link)&term_id=326
- Kurtosis. (n.d.).
Definition of normality . Retrieved from
(External Link)
- Onwuegbuzie, A. J.,&Daniel, L. G. (2002). Uses and misuses of the correlation coefficient.
Research in the Schools, 9 (1)
, 73-90.
- Skewness. (n.d.) Retrieved from
(External Link)&term_id=356
- Skewness. (n.d.).
Definition of normality . Retrieved from
(External Link)
- StatSoft, Inc. (2011).
Electronic statistics textbook. Tulsa, OK: StatSoft. WEB:
(External Link)
