IBM SPSS Statistics (or “SPSS” for short) is super easy software for editing and analyzing data.
This tutorial presents a quick overview of what SPSS looks like and how it basically works.
SPSS’ main window is the data editor. It shows our data so we can visually inspect it.
This tutorial explains how the data editor works: we'll walk you through its main parts and point out some handy tips & tricks.
SPSS’ output window shows the tables, charts and statistical tests you run while analyzing your data.
This tutorial walks you through some basics such as exporting tables and charts to WORD or Excel. We'll also point out some important tricks such as batch editing and styling tables and charts.
The Shapiro-Wilk test examines if a variable is normally distributed in a population. This assumption is required by some statistical tests such as t-tests and ANOVA.
The SW-test is an alternative for the Kolmogorov-Smirnov test. This tutorial shows how to run and interpret it in SPSS.
The Kolmogorov-Smirnov test examines if a variable is normally distributed in some population.
This “normality assumption” is required for t-tests, ANOVA and many other tests. This tutorial shows how to run and interpret a Kolmogorov-Smirnov test in SPSS with some simple examples.
In SPSS, missing values refer to
We'll quickly walk you through both types. We'll also show how to detect, set and deal with missing values in SPSS.
Factor analysis examines which variables in your data measure which underlying factors.
This tutorial illustrates the ideas behind factor analysis with a simple step-by-step example in SPSS.
Levene’s test examines if 2+ populations have equal variances on some variable.
This condition -known as the homogeneity of variance assumption- is required by t-tests and ANOVA.
So how to run and interpret this test in SPSS? This simple tutorial quickly walks you through.
In SPSS, IF computes a new or existing variable but for a selection of cases only.
For example: IF(GENDER = 0) SCORE = MEAN(Q1 TO Q5). computes “score” as the mean over variables Q1 to Q5 but only for cases whose gender is 0 (female).
Effect size is an interpretable number that quantifies the difference between data and some hypothesis.
Effect size measures are useful for comparing effects across and within studies. This tutorial helps you to choose, obtain and interpret an effect size for each major statistical procedure.
The nth percentile is the value that separates the lowest n% from all other values.
Example: the 10th percentile for body weight is 60 kilos. This means that 10% of all people weigh less than 60 kilos and 90% of people weigh more.
Simple tutorial with examples in Excel & SPSS and (interpolation) formulas.
Statistical significance is roughly the probability of finding your data under some null hypothesis.
If this probability (or “p”) is low -usually p < 0.05- then your data contradict your null hypothesis. In this case, you conclude that the hypothesis is not true.