Lesson 8

Summarizing the Steps and Moving On

In the statistical tests we’ve calculated (the t-test, correlation, and X2), we’ve gone through a series of steps that you’ll go through when you compute any statistical test.

Recapping, here they are:

  1. First, determine the level of measurement you have. Are the data you have interval, ordinal, or nominal?

    2.
  2. If you have interval data, determine whether they meet the requirements of a parametric test (adequate sample size and variance similarity).

    3.
  3. Based on the determinations you made from (1) and (2), select the statistical test (t, r, X2, or whatever).

  4. Calculate the values required, plug them into the formula, and compute the test. (Now that you have gone through these calculations and understand them, the labor can be done for you by any one of the available statistical software packages.)

    5.
  5. Select the level of risk you want to take in rejecting the null hypothesis and making (or avoiding) the Type I and Type II errors. Usually that will be .05 or .01.

    6.
  6. Enter the appropriate significance table (e.g., for t, r, or X2) with the test result and the proper degrees of freedom.

  7. Determine whether your test result is large enough to reject the null hypothesis and enable you to conclude that it is statistically significant.

    8.
  8. If it is statistically significant, use whatever additional tests may be available (e.g., the effect test, the coefficient of determination, etc.) and your own reasoned judgment to determine if the result is also practically significant.

*    *    *    *

Congratulate yourself. The fact that you understand these steps and can execute them shows how far you’ve come. You now have a good grip on basic statistics. You can understand them in research journals, and you can use them in your practicum and in your own research. And you are now in a position to go on to more advanced statistics (I know you can’t wait).

References

I have not provided a set of references because there are literally dozens of introductory statistics texts, and just about any of them will do. You definitely should have one of these texts for reference purposes, especially for the significance tables they all provide. My favorite, and the one I highly recommend, is Neil Salkind's Statistics for People Who (Think They) Hate Statistics. Sage Publications, 2000.

Statistical Software

This short course has taken you through both the explanation of the major statistical concepts and the actual computation of the most common statistical tests you will be encountering in the research literature and using in your own research.

Now that you have this essential, basic understanding, you won’t need to do any computations by hand. There are software applications that will do that for you. Once you enter the data, they will compute a correlation in less than a second, and provide you with the significance levels.

There are a number of such programs. You can, in fact, do a number of statistical tests with Microsoft Excel, which is mainly a spreadsheet program. And many of you probably have this application on your computers, either as a stand-alone program or as part of Microsoft Office.

But one of the most highly regarded and user friendly statistical programs is GB-STAT, so if you don’t already have such a program, this would be a good one to get.

Good luck in all your research endeavors.

John Evans

evansj@nsu.nova.edu