![]() There are two ways of correcting errors in a dataset, depending on whether you have access to the original questionnaires or not.īefore correcting the errors, you need to find where the errors are in the dataset. Once you have identified the errors, the next step is to correct them. In the example above, the variable of interest is “annual interest rate” on loans/credit and the maximum value is 600, which does not make sense. Do the minimum and maximum values make sense, given the nature of the variable? Do the mean and standard deviation make sense? If there is an outlier in the data, the mean will be affected. ![]() The above procedure is demonstrated in the images below:Ĭheck if the statistics make sense. Click on the Options button > select mean, standard deviation, minimum and maximum.Choose the variable of interest and click on the arrow button to move it to the variables box.Click on Analyze > Descriptive statistics > Descriptives.To check for errors in continuous variables, follow the procedure below: Checking for errors in continuous variables It is important to correct this error before data analysis is done. However, the dataset shows 2 instances where a code “16” was entered into the dataset, which is an error because there was no category 16. In the above example, the variable of interest is called “source of credit over the last 12 months” and has 14 different categories of sources of credit and a category for other sources of credit. The images below demonstrate the procedure: Click on statistics button > select minimum and maximum from the Dispersion section.Choose the variables that you want to check for errors for > click on the arrow button to move the variable to the variables box.Click on Analyze > Descriptive statistics > Frequencies.To check for errors in categorical variables, follow the procedure below: It is important to turn on the value labels so that you can see clearly where the errors are for categorical variables. The following images show value labels turned off and value labels turned on, respectively. Under the Output tab, go to the last box titled “variable values in labels shown as:” and select “values and labels” from the drop-down menu, then click OK.Go to the Edit menu and choose Options.To check for errors in categorical variables, follow the steps below:įirst ensure that the value labels for the categorical variables are turned on by following the procedure below: Checking for errors in categorical variables Checking for errors in continuous variablesĬhecking for errors in SPSS depends on whether the variable is categorical or continuous.Checking for errors in categorical variables.This post will demonstrate these two steps of data cleaning in SPSS. Data cleaning in SPSS involves two steps: checking whether the dataset has any errors, then correcting those errors. Before you start analysing your data, it is important to clean it first so that you start with a clean dataset.
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