Data analysis in the field of social sciences, business, and health research heavily relies on SPSS descriptive statistics in providing descriptions about a particular data set. Be it survey data or you are looking to investigate what a big dataset has to offer, descriptive statistics in SPSS is a nice way of summarizing large amounts of data in measures such as mean, median, mode, standard deviation, and the like.
What are Descriptive Statistics?
Descriptive statistics summarize the data with:
- Central tendency: Mean, Median, and Mode.
- Measures of dispersion: Standard Deviation, Variance, and Range.
- Measures of distribution shape: Kurtosis and Skewness.
- Frequency counts and percentages of categorical data.
The above statistics aid in investigating patterns before carrying out sophisticated analyses such as regression or ANOVA.
Running Descriptive Statistics in SPSS
In SPSS, it is easy to run descriptive statistics. Follow the steps below:
Procedure:
Step 1. Open your dataset in SPSS (.sav format).
Step 2: Click on the top menu bar:
Analyze → Descriptive Statistics → Descriptives…
Step 3: A dialogue box will emerge. Select the numeric variables you want to summarize and move them into the “Variable(s)” box.
Step 4: Select the Options button. Here, you may select:
- Mean
- Standard Deviation
- Minimum & Maximum
- Variance, Skewness, Kurtosis
Step 5: Click on Continue and OK to generate the output.
Interpretation of Descriptive Statistics in SPSS
It is important to interpret your SPSS output. Here is the interpretation of the main metrics:
1. Mean: Average value. Applicable to normal distributions.
2. Median: A midpoint. Best when the data is skewed.
3. Mode: The most frequent value. Applicable to categorical or multimodal data.
4. Standard Deviation (SD):
- Low SD = data close to the mean
- High SD = the data is widely spread
5. Skewness:
- 0 = absolutely symmetrical
- > 0 = positively skewed (tail to the right)
- < 0 = negatively skewed (tail to the left)
6. Kurtosis:
- 0 = normal distribution
- > 0 = peaked distribution (leptokurtic)
- < 0 = flat distribution (platykurtic)
Hint: Compare the mean and median:
- If mean > median, then the data is positively skewed
- When the mean < the median, the data is said to be negatively skewed.
Writing Descriptive Statistics Results
In academic writing (APA or otherwise), presenting results should be clear and concise. Following is an example format:
“The average age of participants was M = 29.56, SD = 4.72. The distribution was slightly skewed to the right (skewness = 0.64) and had a relatively normal kurtosis (kurtosis = -0.12).”
Writing Tips:
- Always report mean and SD.
- Mention n (sample size) when summarizing.
- When giving a report on many variables, use tables.
- Neatly align figures to 2 decimal places or as recommended in the instructions.
Example of APA-style table:
| Variable | N | Mean | Std. Deviation |
| Age | 150 | 29.56 | 4.72 |
| Income (USD) | 150 | 45,230 | 12,350 |
How to Interpret Descriptive Statistics (In General)
Descriptive statistics can assist you in going further than SPSS by:
- Identify errors or outliers during data entry
- Comprehend shapes of distribution
- Inform your decision regarding the inferential test
Normal Data: Use parametric tests (t-test, ANOVA)
Skewed Data: Consider non-parametric tests (Mann-Whitney, Kruskal-Wallis)
Tips and Tricks of SPSS Descriptive Statistics
- Use “Explore…” to find more choices such as histograms, normality plots, and percentiles, which are available under Descriptive Statistics.
- Use “Frequencies…” for categorical data such as gender or location.
- Use “Compare Means” to evaluate group differences side-by-side.
- The mean is not suitable for ordinal or skewed data—use the median instead.
- Always inspect missing values and outliers before interpreting the results.
Key Errors to Prevent
| Mistake | Why It’s a Problem |
| Ignoring missing values | Skews mean, SD, etc. |
| Using mean on ordinal data | Mean is not valid for ranks |
| Impact test choice | Impacts test choice |
| Misreporting statistics | Affects research credibility |
Visualizing Descriptive Statistics in SPSS
Visuals assist readers in interpreting the patterns of data simply by looking.
- Use histograms for distribution shape
- Use boxplots for detecting outliers
- Use bar charts for categorical variables
In SPSS: Go to Graphs ⇢ Chart Builder ⇢ Histogram/Boxplot
Conclusion
Descriptive statistics in SPSS are crucial for examining and summarizing your data before proceeding to specific analyses. Whether preparing your findings in a thesis, journal, or report, knowing how to run, interpret, and write descriptive statistics will make your research accurate and influential.
FAQs
Q1: What is the procedure used to report non-normal descriptive statistics?
A: Use the median and interquartile range (IQR) instead of the mean and SD.
Q2: But what happens when my SPSS results indicate high skewness?
A: You may transform the data or employ nonparametric tests.
Q3: Can I compute descriptives on categorical variables?
A: Yes. Use frequencies for counts and percentages.
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