Data Preparation

SPSS String or Numeric Variables

Before you start analyzing real-world data, make sure you have a basic idea what your data look like. How many cases and variables do you have? Do their frequency distributions look plausible? Do you have any string variables and do you need to set any missing values?
A quick data inspection takes little time and effort and may save you a lot. Analyzing your data becomes faster and more satisfying when you know you're in control. The tutorials below will get you there fast.

/

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. This tutorial proposes which steps should be taken and in which order. Read More

SPSS Data Preparation 2 – Initial Data Checks

This tutorial shows how to get a quick case count and variable count. Next, we'll inspect whether our case identifier variable does not contain duplicate values. Read More

SPSS Data Preparation 3 – Inspect Variable Types

A problem with some data files is that they contain string variables that should have been numeric. This tutorial shows how to detect and correct such variables. Read More

SPSS Data Preparation 4 – Specify Missing Values

Before you can do anything at all with your variables, you need to inspect them for user missing values. This tutorial shows how to find them quickly. Read More

SPSS Data Preparation 5 – Inspect Variables

Scanning your variables for unusual distributions is easy and facilitates later steps in the analysis of your data. This tutorial shows how to do so quickly. Read More

SPSS Data Preparation 6 – Inspect Cases

Before analyzing our data, we check whether they contain any bad cases. If these are present, we need to exclude them from our analyses. Read More

Creating Histograms in SPSS

Among the very best SPSS practices is running histograms over your metric variables. Doing so is a super fast way to detect problems such as extreme values and gain a lot of insight into your data. This tutorial quickly walks you through. Read More