By Ruben Geert van den Berg on April 28, 2013 under SPSS Data Preparation Tutorial.

SPSS Data Preparation 1 – Overview Main Steps

When we start analyzing a data file, we first inspect our data for a number of common problems. For instance, we want to be sure that variables have the right formats, don't contain any weird values and have plausible distributions. The table below proposes which steps should be taken and in which order.
For getting the most out of these tutorials, we encourage you to follow along by downloading and opening hotel_evaluation.sav.

SPSS Data Preparation - Practice File

SPSS Data Preparation - Overview Main Steps

Check forHow?Fix
1Case count and variable countInspect data view(Not applicable)
2Unique Case IdentifierUse AGGREGATE commandCreate unique ID variable
3Undesirable Variable TypesInspect variable view and data viewConvert Variables
4Presence of user missing valuesFrequency table or histogram per variableSpecify user missing values
5Variables with many missing valuesBar chart or histogram per variableDrop variables or exclude from analyses
6Inconvenient distributionsBar chart or histogram per variableDrop variables or exclude from analyses
7Small categoriesBar chart per variableMerge categories
8Undesirable codingBar chart per variableReverse code variable(s)
9Cases having many missingsCompute number of missing values per caseExclude Cases from Analysis

SPSS Data Preparation - Workflow

So how do we perform these checks in practice? We propose you first perform steps 1-3 since they involve the entire data file.
Next, we inspect each variable in our data -from top to bottom- separately. If the variable is categorical, we create a frequency table with a bar chart. If the variable is metric, we run a histogram. Based on this output, we perform steps 4-8.
All output for these steps is created with simple FREQUENCIES commands, which can take multiple variables at once. You may consider running these for groups of (similarly coded) variables rather than variables separately.
After inspecting -and possibly correcting- each variable, we round up with step 9.

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