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 median is basically the value that separates the 50% lowest from the 50% highest values.

Example: a median income of $2,500 means that 50% of all people earn less and 50% earn more than that amount.

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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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A frequency distribution is an overview of all values in some variable and how often these occur.

Like so, a frequency distribution shows how *frequencies* are *distributed* over values. This tutorial quickly makes things clear with some simple 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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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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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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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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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 null hypothesis is an exact statement about a population that we try to reject with sample data.

Example: 20% of some population carry virus *X*. If a sample from this population shows a *very* different percentage, then we reject this null hypothesis.

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Measurement levels are types of variables that tell you how they should be analyzed. There's 4 types:

This tutorial quickly walks you through with a simple flowchart and some examples.

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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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SPSS FILTER excludes a selection of cases from all subsequent analyses until you switch it off again.

Using a filter comes down to creating a (new) filter variable and activating it. This tutorial shows the easy way to do so and points out some nice alternatives as well.

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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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The right way to compute means over variables is SPSS’ MEAN function.

For example: COMPUTE MEAN_SCORE = MEAN(Q1 TO Q5). computes mean_score as the mean over variables Q1 to Q5. This method also works correctly if you there's any missing values in your data.

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## THIS TUTORIAL HAS 69 COMMENTS:

## By Nwopoke Ebenezer M on November 27th, 2020

This is great. I appreciate immersly.

## By giduma gadisa on January 4th, 2021

good

## By adugna bihonegn on January 4th, 2021

pleace add brief technique about data entery and analysis in spss software

## By Ruben Geert van den Berg on January 5th, 2021

Hi Adugna, thanks for your comment!

Please tell us more about what kind of "data entry" you'd like us to cover.

Thanks!

SPSS tutorials