0.8 Conducting a parametric one-way analysis of variance  (Page 4/4)

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 CH005TC09R CL005TC09R CW005TC09R N Valid 3125 1805 1877 Missing 5197 6517 6445 Skewness -1.129 -.479 -2.197 Std. Error of Skewness .044 .058 .056 Kurtosis 1.818 -.42 6.991 Std. Error of Kurtosis .088 .115 .113
• Copy skewness and kurtosis information into the skewness and kurtosis calculator

Step two

• Compute Descriptive Statistics on the Dependent Variable
• * Do so via the ANOVA procedure
• * Note. Do not use the ANOVA statistical significance information provided in the output. Use only the M s, SD s, and n s.
• * The screen shot will occur in the next step (Mean and standard deviation)

Step three

• Conduct Analysis of Variance
• √ Analyze
• √ General Linear Model
• √ Univariate

• √ Dependent variable is sent over to the top box, titled dependent variable
• √ Grouping Variable is sent over to the fixed factor box

• √ Options
• √ Descriptive Statistics
• √ Estimate of effect size
• √ Continue

• √ Post Hoc
• √ Scheffé
• √ Click on variables on which you want the Post Hoc Tests
• √ Continue
• √ OK

Step four

• Check for Statistical Significance
• 1. Go to the ANOVA table and look at the far right column labeled Sig to check for statistical significance.
• 2. If you have any value less than .05 then you have statistical significance. Remember to replace the third zero with a 1, if the sig value is .000 (i.e., if the sig value reads as .000, replace the third 0, so it reads as .001).
• 3. Numerical Sentence = F ( df between, df within) sp = sp F value, sp p sp < sp .001.
• 4. The outcome of the ANOVA, F (2,1179) = 503.22, p = .001, was . . . .

Dependent Variable: Verbal IQ (Wechsler Verbal Intelligence 3)

 Source Type III Sum of Squares Df Mean Square F Sig. Partial Eta Squared Corrected 101503.093 a 2 50751.547 503.219 .000 .461 Model Intercept 6366052.170 1 6366052.170 63121.595 .000 .982 group 101503.093 2 50751.547 503.219 .000 .461 Error 118906.620 1179 100.854 Total 7405615.000 1182 Corrected Total 220409.713 1181
• a. R Squared = .461 (Adjusted R Square = .460)

Step five

• 1. Partial Eta 2 is the effect size n 2
• 2. Cohen (1988)
• .01 - .059 = small effect size
• .06 - .139 = moderate effect size
• .14 and above = large effect size
• Note. n 2 cannot be greater than 1.00. Therefore, a 0 should not be placed in front of the decimal point.

Step six:

• Narrative and Interpretation
• 1. F value
• 2. degrees of freedom for groups and for participants
• 3. p value
• 4. Post hoc results
• 5. M , SD , and n for each group (in a table)

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.

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 parametric ANOVA statistics."

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.) . Hillsdale, NJ: Lawrence Erbaum
• Onwuegbuzie, A. J.,&Daniel, L. G. (2002). Uses and misuses of the correlation coefficient. Research in the Schools, 9 (1) , 73-90.
• StatSoft, Inc. (2011). Electronic statistics textbook. Tulsa, OK: StatSoft. WEB: (External Link)

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