In statistics, the mode for a variable is its value or range of values with the highest frequency. Find the mode in SPSS & Excel with simple examples.

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Inferential statistics is the branch of statistics that tries to draw conclusions (inferences) about populations based on (much smaller) samples.

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A common problem with SPSS data files are unlabeled variables with huge names. These make your data unmanageable and your output ugly. This tutorial shows an easy fix for the problem.

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For reverse coding variables in SPSS, you must copy your variables, RECODE them and then adjust their value labels. Comparing the recoded variables with the copies ensures the results are correct. This tutorials quickly walks you through.

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Everything you want to know about the normal distribution: examples, formulas and normality tests in simple language with clear illustrations.

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This tutorial outlines how to get work done efficiently by combining data, syntax and output. We'll then propose how to keep your SPSS project work organized efficiently in order to save time and effort.

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The standard deviation is a number that indicates how far a set of numbers lie apart. This tutorial explains what a standard deviation is in normal language along with examples.

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A histogram is a chart showing frequencies for fixed width intervals of a metric variable. This tutorial explains what histograms are and demonstrates why they are useful with illustrations and examples.

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Skewness - Quick Introduction, Examples & Formulas

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SPSS statistical functions are mostly straightforward. This tutorial walks you through the main ones and points out some tips, tricks and pitfalls.

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In SPSS, "SUM(v1,v2)" is not always equivalent to "v1 + v2". This tutorial explains the difference and shows how to make the right choice here.

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SPSS basic operators are mainly used with IF, DO IF and COMPUTE. They work mostly as you'd expect but they do have a couple of surprises in store. No worries, we'll walk you through.

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A confidence interval is a range of values that encloses a parameter with a given likelihood. Example: the 95% confidence interval is from 35% to 45%.

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SPSS version 24 seems to have a bug in the ALTER TYPE command: it reports different values than it actually creates. This tutorial replicates the problem.

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In some cases, one would like to calculate respondents' age given their birthday. As long as birthday is an actual date variable, age is readily calculated by using the DATEDIFF function.

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If SPSS says that your license will expire in ... days, you can fix the issue by running law.exe and entering your authorization code into the wizard.

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We downloaded and tested SPSS 26’ new interface. We feel it's still very incomplete and much worse than the old interface in all respects.

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Some SPSS commands -COMPUTE, RECODE, SELECT IF and many others- are only executed when needed. EXECUTE forces SPSS to complete them right away.

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SPSS 25 promises great looking charts by default. This tutorial compares the old and new versions for the most important charts in SPSS.

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Get things done faster in SPSS by understanding its three basic command types: transformations, procedures and other SPSS commands.

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The SPSS RECODE command usually replaces values with different values. It comes in handy for merging categories, dichotomizing continuous variables and some other tasks. This tutorial walks you through its main options, best practices and pitfalls.

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A great way to dichotomize variables is with a single short compute command. This is also the fastest way to create multi variable filters.

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Keyboard shortcuts allow you to work with amazing speed. Some SPSS shortkeys may be obvious for MS Windows users but others are less so. A quick overview is presented below.

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Opening .csv data in SPSS is easy if you understand some basics. This tutorial (with downloadable .csv file) walks you through the right steps.

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Having a basic understanding of SPSS numeric variables allows you to work fast and with a sense of confidence. This tutorial aims at accomplishing just that by pointing out some essential basics.

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Overview of all SPSS commands in a single table with links, notes and SPSS version in which they were introduced. Downloadable as SPSS data file.

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Many easy options have been proposed for combining the values of categorical variables in SPSS. However, the real information is usually in the value labels instead of the values. This tutorial proposes a simple trick for combining categorical variables and automatically applying correct value labels to the result.

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Using Euro signs or other foreign currencies in SPSS is pretty easy. This tutorial shows how it's done with some tips for creating pretier output.

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This tutorial explains and demonstrates SPSS' main numeric functions. Some tips and tricks will be included along the way.

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Using SPSS keywords, especially TO and ALL greatly speeds up a myriad of typical tasks. This tutorial walks you through SPSS main keywords and points out how they can save time and effort.

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Get better results faster by learning a handful of SPSS system settings. This tutorial demonstrates the 5 most useful ones along with examples.

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SPSS dataset logic is not always logical. However, for working proficiently with datasets, just a handful of basics is sufficient. These are explained in this tutorial.

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SPSS data files may contain a single string variable holding several data points. This tutorial shows a simple but solid approach to splitting such a string variable into new variables. We'll use just plain syntax - no Python today!

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## THIS TUTORIAL HAS 3 COMMENTS:

## By Dobromir Stoyanov on March 15th, 2019

Hello, I think that your website is quite useful for researchers. I have one question however, that I could not find answer to on your website, so I will be glad to hear your opinion on it. I want to run logliner analysis on SPSS Statistics 25 but I have more than 10 variables.= 1 outcome variable and 11 predictors/factors. The big number of factor variables can be explained by the fact that a big part of them are dummy variables from a multiple response question. Is there an alternative way to run the analysis. Thank you.

## By Ruben Geert van den Berg on March 15th, 2019

Loglinear analysis on so many variables is insane. And you'll need a huge sample size to satisfy its assumptions. Besides, there's no "outcome variable" or "predictors" in loglinear analysis. There's just

variablesas with correlations.This sounds as if some kind of regression analysis is what you're looking for.

## By iftikhar hussain on May 9th, 2019

Hi,

Many thanks for such a clear writing. I suggest you should add printable version facility as well. It will help many like me. Good job