# How to Run Statistical Analysis in SPSS

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## Introduction

When it comes to data analysis, SPSS stands out as one of the most widely used statistical software programs. Nevertheless, the task of conducting a statistical analysis using SPSS might seem daunting to individuals unfamiliar with the program. In this article, we will walk you through the process of running a basic statistical analysis in SPSS step-by-step.  Alongside this, we will offer valuable insights into result interpretation. As you progress through this article, you’ll gain the proficiency needed to run statistical analyses in SPSS with confidence!

## 1. Measures of Central Tendency

Measures of Central Tendency are ways of working out the most representative or central value within a data set. In statistics, there are three common measures of central tendency: The mean, The median, and The mode. Please click the following SPSS Video Tutorials to see how to run Measures of Central Tendency Tests in SPSS.

## 2. Parametric Tests

Parametric tests are used when data follow a particular distribution (e.g., a normal distribution—a bell-shaped distribution where the median, mean, and mode are all equal). These tests are generally more powerful. Please click the following SPSS Tutorial Videos to see how to run Parametric Tests in SPSS.

## 3. Non-parametric Tests

Nonparametric tests are used when a particular distribution cannot be assumed; they rank data rather than taking absolute differences into account.  Please click the following SPSS Video Tutorials to see how to run Nonparametric Tests in SPSS.

## 4. Analysis of Variance

We use the Analysis of variance (ANOVA) in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study. Please click the following Free SPSS Video Tutorials to see how to run Analysis of Variance Tests in SPSS.

## 5. Correlation Analysis

Correlation analysis is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables. There are two types of correlation analysis. The Pearson correlation evaluates the linear relationship between two continuous variables while the Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Please click the following free SPSS Tutorials to see how to run Correlayin Analysis Tests in SPSS.

## 6. Regression Analysis

Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable. Please click the following SPSS Video Tutorials to see how to run Regression Analysis in SPSS.

## 7. Exploratory Factor Analysis

Factor analysis is a technique that we can use to reduce a large number of variables into fewer numbers factors. While Reliability analysis provides a measure of how well a group of observed variables goes together. Please click the following SPSS Video Tutorials to see how to Factor and Reliability Analysis in SPSS.

## 8. Survival Analysis

Survival analysis is a set of statistical approaches for data analysis where the outcome variable of interest is time until an event occurs. Please click the following SPSS Video Tutorials to see how to run Survival Analysis in SPSS.

## How To Perform A Statistical Analysis In SPSS

Statistical analysis is a process of using mathematical techniques to extract meaning from data. In SPSS, we perform statistical analysis can a variety of different techniques. The most common type of statistical analysis is Regression analysis, which we use to identify relationships between variables. Other types of statistical analysis include factor analysis, which we use to identify the underlying factors that influence a variable. Cluster analysis, which we use to group together data points that are similar to each other. Lastly, discriminant analysis, which we use to identify which variables are most important for predicting a particular outcome.  This page provides a step-by-step guide on how to run statistical analysis in SPSS. It covers all the different types of statistical analyses that can be performed in SPSS, as well as how to interpret the results of the analysis.

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