You find outliers in SPSS three ways: read a boxplot from Analyze > Descriptive Statistics > Explore, where SPSS marks a mild outlier with a circle and an extreme value with a star; save standardised z-scores from Descriptives and flag any absolute value above 3.29; and for several variables at once, save Mahalanobis distance from a linear regression and compare it against a chi-square critical value. Finding the point is step one. This page also shows how to check whether it is real and what to do with it.
An outlier is a value that sits far away from the rest of your data. It matters because a single extreme point can pull a mean, inflate a standard deviation, weaken a correlation or tilt a regression line, so most marking schemes expect you to screen for outliers before you run the main test. This guide is part of our SPSS homework help. It gives the exact menu path for each screening method, points at the number that decides whether a case is an outlier, and shows how to check the point and report your decision.
One idea to keep straight from the start. Finding an outlier is not the same as deleting one. SPSS flags candidate points for you, but the flag is a prompt to investigate, not an instruction to erase the row. The second half of this page is about that judgement, because that is where the marks and the honest analysis live.
Match how many variables you are screening to the right SPSS method, then jump to its steps.
| Your situation | What you are checking | Method |
|---|---|---|
| One continuous variable, want a visual flag | Values outside 1.5 and 3 box-lengths | Boxplot in Explore |
| One continuous variable, want a number | Standardised distance from the mean | Standardised z-scores |
| Several continuous variables together | Unusual combination across variables | Mahalanobis distance |
A point can be ordinary on each variable alone yet unusual in combination, which is why multivariate work needs Mahalanobis distance, not just a column of z-scores.
Outlier screening applies to continuous variables, so check the Measure column in Variable View first. SPSS tags every numeric variable as Scale by default, and that is what you want for a genuine continuous measure such as height, score or reaction time. A variable that is really a category coded as a number, for example one for male and two for female, is not a candidate for outlier screening at all, so set it to Nominal and leave it out of this process.
Everything below happens through the Analyze menu on the data you can see in Data View. The boxplot and z-score methods look at one variable at a time, and Mahalanobis distance looks at a set of variables together. None of them change your data unless you choose to act on what they find.
Menu path, the rule SPSS uses, the exact value to read, and the recreated output. Example values follow standard teaching datasets.
The fastest visual screen for one continuous variable. SPSS labels the suspect cases for you with their case numbers.
The rule SPSS uses. SPSS builds the box from the first and third quartiles, so the box length is the interquartile range, the IQR. It then draws whiskers to the most extreme values that are still within 1.5 box-lengths of the box. Beyond that, a value between 1.5 and 3 box-lengths from the edge of the box is a mild outlier, drawn as an open circle with its case number beside it. A value more than 3 box-lengths out is an extreme value, drawn as a star or asterisk with its case number. The number next to the symbol is the row in Data View, not the value, so you can find and inspect that case.
What to read. The boxplot itself for the circles and stars, and the Extreme Values table, which lists the five highest and five lowest cases with their case numbers and actual values so you can confirm what the plot flagged.
| Case Number | Value | ||
|---|---|---|---|
| Highest | 1 | 27 | 842 |
| 2 | 14 | 561 | |
| 3 | 8 | 548 | |
| Lowest | 1 | 19 | 301 |
| 2 | 3 | 318 |
Here case 27 is drawn as a star on the boxplot, well beyond 3 box-lengths above the box, so it is an extreme value to investigate. Case 14 sits as a circle, a milder outlier.
Common mistakes
Turns each value into its distance from the mean in standard deviations, giving a single number you can threshold.
The rule. A z-score says how many standard deviations a value sits from the mean, so the sign shows direction and the size shows distance. The widely taught cut-off is an absolute z above 3.29, because a value that far out lies beyond the middle 99.9 percent of a normal distribution, which corresponds to p below .001. Some courses use a rounder cut-off of 3. Sort the new Z column, or use Data > Select Cases with the condition ABS(Zreaction) > 3.29, and inspect every case that passes it.
What to read. The new Z column in Data View. Any case whose absolute z-score exceeds your threshold is a potential univariate outlier.
| Case Number | Reaction (ms) | Zreaction | Flag |
|---|---|---|---|
| 27 | 842 | 3.71 | Outlier, p < .001 |
| 14 | 561 | 2.18 | Within range |
| 19 | 301 | -1.94 | Within range |
Common mistakes
Finds a case that is unusual across several variables at once, even when it looks ordinary on each variable alone. Needed before regression or factor analysis.
The rule. Mahalanobis distance measures how far a case sits from the centre of the cloud of points, adjusting for how the variables correlate. You compare each MAH_1 value against the chi-square critical value at a strict alpha, usually p below .001, with degrees of freedom equal to the number of predictors you entered. A case whose Mahalanobis distance is larger than that critical value is a multivariate outlier. For example, with three predictors the chi-square critical value at p below .001 is about 16.27, so any MAH_1 above 16.27 is flagged.
What to read. The MAH_1 column, sorted in descending order, against your chi-square critical value. You can also use Data > Select Cases with the condition MAH_1 > 16.27 to isolate them.
| Case Number | MAH_1 | Decision |
|---|---|---|
| 27 | 18.94 | Multivariate outlier |
| 41 | 12.06 | Retain |
| 9 | 7.55 | Retain |
Common mistakes
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A flag from any of the three methods is the start of a decision, not the end of one. Work through it in order.
How to report it. State the screening method and threshold you used, how many cases were flagged, what you decided for each and why, and whether the main result held with and without them. One or two honest sentences in the method section is what marking schemes look for. Deleting points quietly is what they penalise.
Suppose you have reaction times for 30 participants and you plan an independent-samples t-test. You run Analyze > Descriptive Statistics > Explore on the reaction-time variable and the boxplot shows one star at the top, labelled case 27, and one circle, labelled case 14. To put numbers on it, you run Descriptives with Save standardized values as variables. The new Zreaction column shows case 27 at z = 3.71, above the 3.29 cut-off, and case 14 at z = 2.18, inside it. So the boxplot and the z-scores agree on one univariate outlier, case 27.
You open case 27. The value is 842 ms, which is high but physically possible for that task, and the rest of that participant's data looks normal, so it is not an entry error. You retain it, then rerun the t-test with and without case 27. The p-value moves from .020 to .028, still significant, so the outlier is not driving the result. You keep the full sample and note the check in your method section. That is a complete, defensible outlier screen: find it, check it, decide, report.
Procedures on this page were checked against the IBM SPSS Statistics documentation, the UCLA statistical computing SPSS guides and university statistics tutorials from Laerd Statistics and Kent State University.
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In a boxplot from Analyze > Descriptive Statistics > Explore, SPSS marks a mild outlier with an open circle and the case number next to it, and an extreme value with a star or asterisk and its case number. A circle sits between 1.5 and 3 box-lengths from the edge of the box, and a star sits more than 3 box-lengths out, where a box-length is the interquartile range.
A common rule is that a standardised value with an absolute z above 3.29 is a potential outlier, because that point lies beyond the middle 99.9 percent of a normal distribution, which is p below .001. Some courses use the simpler cut-off of 3. Save z-scores with Analyze > Descriptive Statistics > Descriptives and the Save standardized values as variables tick box, then scan the new Z column.
Not by default. First investigate the value. If it is a data-entry or measurement error, correct it or remove it and say so. If it is a genuine observation, the usual practice is to keep it, run the analysis with and without it, and report both, or to use a transformation or a resistant method. Deleting a real value only because it is inconvenient is not acceptable and must be justified.
Use Mahalanobis distance. Go to Analyze > Regression > Linear, put any variable in Dependent and your set of continuous variables as Independents, then in Save tick Mahalanobis under Distances. SPSS writes a MAH_1 column. Compare each value against the chi-square critical value at your alpha, using degrees of freedom equal to the number of predictors. A case above that critical value is a multivariate outlier.
An outlier is a value far from the rest of the data on one or more variables. An influential point is a case that noticeably changes the model, for example the regression slope, when it is included. A point can be an outlier without being influential, and a case with high leverage can be influential without looking extreme on any single variable. In regression you check influence with Cook's distance, not with a z-score.
No. A large sample helps parametric tests tolerate mild non-normality through the central limit theorem, but it does not neutralise a genuine extreme value, which can still distort a mean, a standard deviation, a correlation or a regression slope. You check for outliers regardless of sample size.
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