0.3 Calculating correlations: parametric and non parametric  (Page 2/4)

Calculating correlations: parametric and nonparametric

In this set of steps, readers will calculate either a parametric or a nonparametric statistical analysis, depending on whether the data reflect a normal distribution. A parametric statistical procedure requires that its data be reflective of a normal curve whereas no such assumption is made in the use of a nonparametric procedure. Of the two types of statistical analyses, the parametric procedure is the more powerful one in ascertaining whether or not a statistically significant relationship, in this case, exists. As such, parametric procedures are preferred over nonparametric procedures. When data are not normally distributed, however, parametric analyses may provide misleading and inaccurate results. Accordingly, nonparametric analyses should be used in cases where data are not reflective of a normal curve. In this set of steps, readers are provided with information on how to make the determination of normally or nonnormally distributed data. For detailed information regarding the assumptions underlying parametric and nonparametric procedures, readers are referred to the Hyperstats Online Statistics Textbook at (External Link) or to the Electronic Statistics Textbook (2011) at (External Link)

Research questions for which correlations are appropriate involve asking for relationships between or among variables. The research question, “What is the relationship between study skills and grades for high school students?” could be answered through use of a correlation.

Step one:

• Perform ScatterPlots
• √ Graphs
• √ Legacy Dialogs
• √ Scatter/Dot
• √ The Simple Scatter icon should be highlighted

• √ Define
• √ Drag one of the two variables of interest to the first box (Y axis) on the right hand side and the other variable of interest to the second box (X axis) on the right hand side. It does not matter which variable goes in the X or Y axis because your scatterplot results will be the same.
• Once you have a variable in each of the two boxes, click on the OK tab on the bottom left hand corner of the screen.

• √ Look at the scatterplots to see whether a linear relationship is present.
• In the screenshot below, the relationship is very clearly linear.

Step two:

• Calculate Descriptive Statistics on Variables
• √ Analyze
• * Descriptive Statistics
• * Frequencies
• * Click on the variables for which you want descriptive statistics (your dependent variables)
• * You may click on each variable separately or highlight several of them
• * * Once you have a variable in the left hand cell highlighted, click on the arrow in the middle to send the variable to the empty cell titled Variable(s)

• √ Statistics
• * Click on as many of the options you would like to see results
• * At the minimum, click on: M , SD , Skewness, and Kurtosis

• * Continue
• * Charts (these are calculated only if you wish to have visual depictions of skewness and of kurtosis-they are not required)
• * Histograms (not required, optional) with Normal Curve

• * Continue
• * OK

Step three:

Check for Skewness and Kurtosis values falling within/without the parameters of normality (-3 to +3)