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.

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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.

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SPSS syntax is computer code used by SPSS for analyzing data, editing data, running statistical tests and more.

Using SPSS syntax is super easy and saves tons of time and effort. This tutorial quickly gets you started!

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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.

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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.

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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.

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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.

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Cohen’s D is the effect size measure of choice for t-tests.

This simple tutorial quickly walks you through

- rules of thumb for small, medium and large effects;
- formulas for computing Cohen’s D and;
- software options for obtaining it.

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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.

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A Pearson correlation is a number between -1 and +1 that indicates how strongly two variables are *linearly* related.

This simple tutorial quickly explains the basics with outstanding illustrations and examples.

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In SPSS, missing values refer to

- system missing values: values that are absent from the data;
- user missing values: values that are present in the data but must be excluded from analyses.

We'll quickly walk you through both types. We'll also show how to detect, set and deal with missing values in SPSS.

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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.

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SPSS has 2 types of variables:

- numeric variables contain only numbers and can be used for calculations;
- string variables contain text and cannot be used for calculations.

Numeric variables come in several formats such as plain numbers, dates and percentages. Working with SPSS becomes much faster and easier if you're aware of variable types and formats.

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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).

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In SPSS, SELECT IF removes a selection of cases from your data.

This tutorial walks you through the basics and some FAQ's such as

- how to remove cases based on 2 variables instead of one?
- how to remove cases based on (number of) missing values?
- how to visually inspect only those cases that will be removed?

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Three common ways to find outliers are

- inspecting histograms;
- inspecting boxplots;
- inspecting z-scores.

So how to find precisely which values to exclude? This tutorial walks you through all 3 methods.

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The vast majority of statistical tests fall into one of 6 basic types:

- Univariate Tests
- Within-Subjects Tests
- Between-Subjects Tests
- Association Measures
- Prediction Analyses
- Classification Analyses

Look up which *type* of test is right for your data and you'll see which test you should use.

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The correct way to convert a string variable into a numeric one is the ALTER TYPE command.

This tutorial walks you through with some examples. We'll point out some tricks, pitfalls and alternatives as well.

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## THIS TUTORIAL HAS 101 COMMENTS:

## By Ogaramos on May 4th, 2022

The package is unique, I need a tutor