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Overview SPSS Data Analysis Tutorials

Data Analysis Tutorials

SPSS Data Analysis - Introduction

Which Statistical Test Should I Use?

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

  1. Univariate Tests
  2. Within-Subjects Tests
  3. Between-Subjects Tests
  4. Association Measures
  5. Prediction Analyses
  6. 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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SPSS Data Analysis Roadmap

When analyzing data in SPSS, which steps should we take in which order?

This roadmap walks you through our basic data analysis routines -from inspecing and editing your data through your final tables, charts and tests.


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Univariate Analysis - Categorical Variables

Creating APA Style Frequency Tables in SPSS

Basic frequency tables created in SPSS look monstrous. This tutorial shows how to create nice and clean APA format tables with a simple trick.
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Univariate Analysis - Metric Variables

SPSS DESCRIPTIVES – Problems and Fixes

For APA format descriptives tables, avoid DESCRIPTIVES. Instead, use MEANS and transpose the resulting tables. This tutorial guides you through.
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Bivariate Analysis - Categorical Variables

Creating APA Style Contingency Tables in SPSS

Running simple contingency tables in SPSS is easy enough. However, the default format is very inconvenient and doesn't meet APA standards. This tutorial shows 3 ways to create better tables.
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SPSS CROSSTABS Command

SPSS CROSSTABS produces contingency tables: frequencies for one variable for each value of another variable separately. If assumptions are met, a chi-square test may follow to test whether an association between the variables is statistically significant. This tutorial, however, aims at quickly walking through the main options for CROSSTABS.
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Bivariate Analysis - Metric by Categorical Variables

SPSS MEANS – Statistics by Category

SPSS MEANS produces tables containing means and/or other statistics for different groups of cases. These groups are defined by one or more categorical variables. If assumptions are met, MEANS can be followed up by an ANOVA.
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Bivariate Analysis - Metric Variables

SPSS – Correlations in APA Format

How to create an APA format correlation matrix in SPSS? This simple tutorial shows the easy way to do so and offers a Python script that processes one or many tables in one go.
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Associations Between Variables

Association between Categorical Variables

This tutorial walks through running nice tables and charts for investigating the association between categorical or dichotomous variables. We'll demonstrate some cool SPSS tricks along the way.
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Association between Metric and Dichotomous Variable

This tutorial shows how to create nice tables and charts for studying the association between a dichotomous and a metric variable. If statistical assumptions are met, these may be followed up by an independent samples t test.
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Comparing Variables

Descriptive Statistics – One Metric Variable

A previous tutorial introduced some summary statistics appropriate for both categorical as well as metric variables. Now it's time to turn to some measures that apply to metric variables exclusively. The most important ones are the mean (or average), variance and standard deviation.
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Analyzing Categorical Variables Separately

When analyzing your data, you sometimes just want to gain some insight into variables separately. The first step in doing so is creating appropriate tables and charts. This tutorial shows how to do so for dichotomous or categorical variables.
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Comparing Dichotomous Variables

This tutorial shows how to create nice tables and charts for comparing multiple dichotomous variables. We'll use some cool tricks for doing so.
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Comparing Dichotomous or Categorical Variables

This tutorial shows how to create nice tables and charts for comparing multiple categorical variables. This approach is suitable for variables having identical value labels and comparable contents.
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Comparing Metric Variables

This tutorial shows how to create proper tables and means charts for multiple metric variables. We'll use some cool tricks along the way for getting the most out of our output.
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