0.4 Conducting a parametric independent samples t-test  (Page 4/4)

• Standardized Coefficients Calculator
• Copy variable #1 and #2 into the skewness and kurtosis calculator

Note. Prior to calculating parametric independent t -tests, at least half of your standardized coefficients should be within the +/- 3 range.

Step three

• Calculate a Parametric Independent Samples t -test on Data (after you have unsplit your file)
• √ Analyze
• √ Compare Means
• √ Independent Samples t -test

• √ Test Variable would be your Dependent Variable (e.g., test scores)
• √ Grouping Variable would be your dichotomous Independent Variable

• √ Define Groups
• √ Group One is No. 1 and Group Two is No. 2 (or whatever numbers you used to identify each group)
• Note: Click on view than value labels to find the code for each group.
• √ Continue

Step four

• Check for Statistical Significance
• * Go to the Independent Samples Test Box (bottom row~ Equal variances not assumed) and look at the cell labeled Sig. (2-tailed) to check for significance. Always use the bottom row.
• * If you have any value less than .05 then you have statistical significance, unless you have adjusted for multiple statistical analyses using the Bonferroni procedure. 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). If you calculate more than one t -test, you must use the

• 1. Numerical Sentence = t ( df ) _sp = _sp t , _sp p _sp < _sp .001(or Bonferroni-adjusted alpha significance error rate).
• - df is located in Independent Samples Box
• - t is located in Independent Samples Box
• 2. Numerical sentence is written as: t (686.95) = 34.67 p <.001, example was statistically significant.

Writing up you statistcs

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 Independent Samples t -test statistics."

Click here to view: Writing Up Your Independent 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)