SPSS Data Preparation 1 – Overview Main Steps
SPSS Data Preparation 2 – Initial Data Checks
SPSS Data Preparation 3 – Inspect Variable Types
SPSS Data Preparation 4 – Specify Missing Values
SPSS Data Preparation 5 – Inspect Variables
SPSS Data Preparation 6 – Inspect Cases
9. Missing Values per Case (Overview and data file are are found here) Missing values complicate some analyses or may sometimes indicate that respondents didn’t fill out the questionnaire very seriously. In any case, it’s a good idea to at least check how missing values are distributed over respondents and we often do so for sections […]
6. Inconvenient Distributions (Overview and data file are are found here) Let’s first state this clearly: we don’t exclude variables simply because their distributions are not to our liking. What we mean by “inconvenient distributions” are variables that don’t really provide us with useful information. This point is perhaps best explained by an example. Let’s inspect q4. […]
5. Presence of User Missing Values (Overview and data file are are found here) User missing values are values that we want to exclude from analysis. We do so by specifying (ranges of) values as “missing” in SPSS. For ordinal variables, we typically exclude answers such as “Don’t know” or “Not applicable”. For metric variables, we exclude values that are not […]
Summary (Overview and data file are are found here) SPSS has two variable types: string variables and numeric variables. String variables have “String” under Type in Variable View. All other variables are numeric. The screenshot below illustrates this point for hotel_evaluation.sav. 3. Undesirable Variable Types A problem with some data files is that they contain string variables that should have been numeric. A […]
1. SPSS Case Count and Variable Count (Overview and data file are are found here) The very first thing we want to know about basically any data file, are its dimensions: how many cases and how many variables does it contain? For a quick case count, select any cell in data view and press the CTRL + shortkey. Alternatively, just […]
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 using bank_clean.sav, part of which is shown below. Option 1: FREQUENCIES without Frequency Tables If you’re running […]
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 […]
