0.5 Conducting a parametric dependent samples t-test (paired samples  (Page 4/4)

• √ Click on second dependent variable
• √ Arrow to send over to Paired Variables Side, Variable 2

• √ OK

Step four:

• Check for Statistical Significance
• Go to the Paired Samples Test Box and look at the very last cell labeled Sig. (2-tailed) to check for significance.
• If you have any value less than .05 then you have statistical significance. Remember to replace the third zero with a 1 to a .000 value (i.e., for a value of .000, you would write it as .001).

• 1. Numerical sentence is written as :
• Numerical Sentence = t (df) _sp = _sp t , _sp p _sp < _sp .001 (or Bonferroni-adjusted alpha).
• - df is located in Paired Samples Box
• - t is located in Paired Samples Box
• 2. The outcome of the paired samples t -test, t (477) = 164.27 p <.001, was statistically significant.

Step five:

• Check for Effect Size
• * Use the web-based calculator for effect size using the following websites:
• Effect Size Calculators for Basic and Multivariate Statistical Procedures

• Cohen’s d (1988)
• d of 0.20 = small effect size (range 0.20 to 0.49)
• d of 0.50 = moderate effect size (range 0.50 to 0.79)
• d of 0.80 = large effect size (range 0.80 and above)
• Note. Cohen’s d can be greater than 1.00. Therefore, a 0 should be placed in front of the decimal when the value is lower than 1.00.

Step six:

• Narrative and Interpretation
• 1. type of t -test conducted and assumptions met
• 2. t value
• 3. degrees of freedom
• 4. p value

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.

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 dependent sample t-test statistics."

Click here to view: Writing Up Your Parametric Dependent Samples t-test Statistics

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)