We may doubt if this respondent filled out the questionnaire seriously. This is the Valid N (listwise) we saw in the descriptives table earlier on.Īlso note that 1 case has 8 missing values out of 9 variables. In this table, 0 means zero missing values over q1 to q9.
*Inspect frequency distribution missing values. variable labels mis_1 'Missing values over q1 to q9'. *Set description of mis_1 as variable label. *Create new variable holding number of missing values over q1 to q9. Just make sure you add a description of what's in it -the number of missing.- as a variable label. We'll use a short and simple variable name: mis_1 is fine. In the example below, that'll be q1 to q9. This variable holds the number of missing values over a set of variables that we'd like to analyze together. Inspecting Missing Values per Caseįor inspecting if any cases have many missing values, we'll create a new variable. Let's now see if any cases -rows of cells in data view- have many missing values. Some procedures will use only those 309 cases -known as listwise exclusion of missing values in SPSS.Ĭonclusion: none of our variables -columns of cells in data view- have huge percentages of missingness. These are the cases without any missing values on all variables in this table. Importantly, note that Valid N (listwise) = 309. If any variables have high percentages of missingness, you may want to exclude them from -especially- multivariate analyses. Since we've 464 cases in total, (464 - N) is the number of missing values per variable. The N column shows the number of non missing values per variable. *Note: (464 - N) = number of missing values. The easiest way for doing so is running the syntax below. Make sure the output tables show both values and value labels. User Missing Values for Categorical VariablesĪ quick way for inspecting categorical variables is running frequency distributions and corresponding bar charts.
Let's now see if any values should be set as user missing and how to do so. For metric variables, unlikely values -a reaction time of 50ms or a monthly salary of € 9,999,999- are usually set as user missing.įor bank.sav, no user missing values have been set yet, as can be seen in variable view.for categorical variables, answers such as “don't know” or “no answer” are typically excluded from analysis.So which -if any- values must be excluded? Briefly, Hey, that's you! So it's you who may need to set some values as user missing. “User” in user missing refers to the SPSS user. User missing values are values that are excluded So how to detect and handle missing values in your data? We'll get to that after taking a look at the second type of missing values. Therefore, you should try toįind out why some values are system missing Something may or may not have gone wrong.
In other cases, however, it may not be clear why there's system missings in your data. In the data, we'll probably see system missing values on color for everyone who does not own a car.
Well, then my survey software should skip the next question:
For example, say I askĪnd somebody answers “ no”. In some cases system missing values make perfect sense.
System missing values are shown as dots in data view as shown below. System missing values are values that are You'll get the most out of this tutorial if you try the examples for yourself after downloading and opening this file. We'll use bank.sav -partly shown below- throughout. The SPSS user specifies which values -if any- must be excluded.