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Step one:

  • Check for Skewness and Kurtosis values falling within/without the parameters of normality (-3 to +3)
  • √ Split your file on the basis on your independent variable/fixed factor/grouping variable

After you do this, your screen should resemble the following:

Your screen will show that all cases are going to be analyzed and a “do not create groups”. You will need to click the compare groups and move the independent variable over to the “Group Based on”. For most ANOVA procedures, your independent or grouping variable will have more than two groups.

  • √ Analyze
  • * Descriptive Statistics
  • * Frequencies

  • √ Move over the dependent (outcome) variable

  • √ Click on Statistics
  • Your screen will now look like this

  • * Skewness [Note. Skewness refers to the extent to which the data are normally distributed around the mean. Skewed data involve having either mostly high scores with a few low ones or having mostly low scores with a few high ones.] Readers are referred to the following sources for a more detailed definition of skewness: http://www.statistics.com/index.php?page=glossary&term_id=356 and http://www.statsoft.com/textbook/basic-statistics/#Descriptive%20statisticsb
  • To standardize the skewness value so that its value can be constant across datasets and across studies, the following calculation must be made: Take the skewness value from the SPSS output and divide it by the Std. error of skewness. If the resulting calculation is within -3 to +3, then the skewness of the dataset is within the range of normality (Onwuegbuzie&Daniel, 2002). If the resulting calculation is outside of this +/-3 range, the dataset is not normally distributed.
  • * Kurtosis [Note. Kurtosis also refers to the extent to which the data are normally distributed around the mean. This time, the data are piled up higher than normal around the mean or piled up higher than normal at the ends of the distribution.] Readers are referred to the following sources for a more detailed definition of kurtosis: http://www.statistics.com/index.php?page=glossary&term_id=326 and http://www.statsoft.com/textbook/basic-statistics/#Descriptive%20statisticsb
  • To standardize the kurtosis value so that its value can be constant across datasets and across studies, the following calculation must be made: Take the kurtosis value from the SPSS output and divide it by the Std. error of kurtosis. If the resulting calculation is within -3 to +3, then the kurtosis of the dataset is within the range of normality (Onwuegbuzie&Daniel, 2002). If the resulting calculation is outside of this +/-3 range, the dataset is not normally distributed.
  • * Continue
  • * OK
  • Note: Before you continue to another application you must “UNSPLIT” the files before moving on to other steps:
  • √ Data
  • √ Split Files
  • √ Analyze all cases, do not create groups
  • √ OK

Check for Skewness and Kurtosis values falling within/without the parameters of normality (-3 to +3). Note that each variable below has its own skewness value and its own kurtosis value. Thus, a total of three standardized skewness coefficients and three standardized kurtosis coefficients can be calculated from information in the table below.

Questions & Answers

marginal cost formula
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Source:  OpenStax, Calculating basic statistical procedures in spss: a self-help and practical guide to preparing theses, dissertations, and manuscripts. OpenStax CNX. Apr 28, 2011 Download for free at http://cnx.org/content/col11292/1.6
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