![]() ![]() Negative kurtosis indicates a flat distribution of the data points, with many data points on the extreme tails.Positive kurtosis indicates high peakedness of the data points, with long thin tails.There are two types of kurtosis: positive and negative.Kurtosis measures the peakedness of the data, that is, how high or flat the data points are distributed.The kurtosis statistic and its standard error for each variable.Zero skewness: data points are symmetrical and hence the distribution is normal.Right skewness: data points are concentrated on the right, with the tail towards the right of the distribution graph.Left skewness: data points are concentrated on the left, with the tail towards the left of the distribution graph.There are three types of skewness: left (or positive) skewness, right (or negative) skewness, and zero skewness.Skewness measures the symmetrical nature of the data.The skewness statistic and its standard error for each variable.The standard deviation for each variable.The minimum and maximum values for each variable.The number of valid cases for each variable.The output has the following key information: Interpreting the Descriptives output for continuous variables Tick the mean, standard deviation, minimum, maximum, skewness and kurtosis.From the dialogue box that opens, select all the continuous variables of interest and move them to the variables box.Click on Analyze menu > Descriptive statistics > Descriptives.Number and percentage of valid and missing cases in each category of the variable.ĭescriptive statistics for continuous variablesīesides the codebook, you can also use the Descriptives feature in SPSS to obtain more information about continuous variables.The percentage of cases in each category of the variable.The frequency of cases in each category of the variable.The output from the Frequencies table has the following key information: Interpreting the Frequencies output for categorical variables This is demonstrated in the images below: Select the variables of interest and move them to the variables box.Click Analyze menu > Descriptive statistics > Frequencies.Some examples are shown in the images below:ĭescriptive statistics for categorical variablesīesides the Codebook, one can get information on categorical variables using the frequencies feature. The output will show different statistics for categorical and numerical variables.įor categorical variables, only the count and percentages will be displayed.įor numerical variables, the mean, standard deviation and quartiles will be displayed. The procedure is demonstrated in the images below: In the same dialogue box, click on the statistics tab and select all the options listed under counts and percents and central tendency and dispersion.In the same dialogue box, click on the output tab and select Label, Value Labels and Missing Values from the variable information list.In the Codebook dialogue box, click on the variables tab, select the variables you want and move them to the codebook variables box.Click Analyze menu > Reports > Codebook.To perform the Codebook function, follow the procedure below: ![]() The Codebook feature allows one to quickly get a summary of the data. The assumptions vary from one statistical technique to another and it is important for a researcher to know them and to know how to check for their possible violation.ĭescriptive statistics in SPSS using Codebook.They help to check for violation of assumptions behind the statistical techniques.They describe the nature of the data and the variables.This post focuses on descriptive statistics in SPSS.ĭescriptive statistics play two major roles: One can conduct preliminary analysis using descriptive statistics or graphs. After checking for and correcting errors in your dataset, the next important step before running your analysis is to conduct preliminary analysis to explore the nature of your data. ![]()
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