Statistics is a discipline of applied mathematics concerned with the gathering, description, analysis, and derivation of conclusions from quantitative data. Statistics' mathematical theories rely largely on differential and integral calculus, linear algebra, and probability theory.

This SPSS tutorial for beginners will teach you how to perform statistical operations using the SPSS software. Whether you are learning statistics for academic or business purposes, this course is for you.

It is prepared in simple and clear terms with image illustrations.

### Course Objectives

#### SPSS Introduction

#### SPSS Software

#### SPSS Operations

#### SPSS Statistics

#### SPSS Descriptive

#### SPSS Chi-Square

#### SPSS T-Test

#### SPSS Correlation

#### SPSS ANOVA

#### SPSS Regression

There will be video links to enhance learning. If you get value from this SPSS tutorial for beginners, please support us by donating to us.

# SPSS Introduction

SPSS means “Statistical Package for the Social Sciences” and was first launched in 1968. Since SPSS was acquired by IBM in 2009, it's officially known as IBM SPSS Statistics but most users still just refer to it as “SPSS”.

## SPSS Compatibility

SPSS is software for editing and analyzing all sorts of data. These data may come from basically any source: scientific research, a customer database, Google Analytics, or even the server log files of a website. SPSS can open all file formats that are commonly used for structured data such as

spreadsheets from MS Excel or OpenOffice;

plain text files (.txt or .csv);

relational (SQL) databases;

Stata and SAS.

Let's now quickly look at what SPSS looks and feels like.

# SPSS Software

In this section, we are going to look at the different interfaces of the SPSS.

## Data View

After opening data, SPSS displays them in a spreadsheet-like fashion as shown in the screenshot below.

This sheet -called data view- always displays our data values. For instance, our first record seems to contain a male respondent from 1979. A more detailed explanation of the exact meaning of our variables and data values is found in a second sheet shown below.

## Variable View

An SPSS data file always has a second sheet called variable view. It shows the metadata associated with the data. Metadata is information about the meaning of variables and data values. This is generally known as the “codebook” but in SPSS it's called the dictionary.

For non-SPSS users, the look and feel of SPSS’ Data Editor window probably come closest to an Excel workbook containing two different but strongly related sheets.

## Data Analysis

Right, so SPSS can open all sorts of data and display them -and their metadata- in two sheets in its Data Editor window. So how to analyze your data in SPSS? Well, one option is using SPSS’ elaborate menu options.

For instance, if our data contain a variable holding respondents’ incomes over 2010, we can compute the average income by navigating to Descriptive Statistics as shown below.

Doing so opens a dialog box in which we select one or many variables and one or several statistics we'd like to inspect.

## Output Window

After clicking Ok, a new window opens up: SPSS’ output viewer window. It holds a nice table with all statistics on all variables we chose. The screenshot below shows what it looks like.

As we see, the Output Viewer window has a different layout and structure than the Data Editor window we saw earlier. Creating output in SPSS does not change our data in any way; unlike Excel, SPSS uses different windows for data and research outcomes based on those data.

For non-SPSS users, the look and feel of SPSS’ Output Viewer window probably come closest to a Powerpoint slide holding items such as blocks of text, tables, and charts.

## Reporting

SPSS Output items, typically tables and charts, are easily copy-pasted into other programs. For instance, many SPSS users use a word processor such as MS Word, OpenOffice, or GoogleDocs for reporting. Tables are usually copied in rich text format, which means they'll retain their stylings such as fonts and borders. The screenshot below illustrates the result.

## Syntax Editor Window

The output table we showed was created by running Descriptive Statistics from the SPSS menu. Now, SPSS has a second option for running this (or any other) command: we can open a third window, known as the syntax editor window. Here we can type and run SPSS code known as SPSS syntax. For instance, running descriptives income_2010. has the exact same result as running this command from the SPSS menu as we did earlier.

Besides typing commands into the Syntax Editor window, most of them can also be pasted into it by clicking through the SPSS menu options. As so, SPSS users unfamiliar with syntax can still use it. But why use syntax if SPSS has such a nice menu?

The basic point is that syntax can be saved, corrected, rerun, and shared between projects or users. Your syntax makes your SPSS work replicable. If anybody raises any doubts regarding your outcomes, you can show exactly what you did and -if needed- correct and rerun it in seconds.

For non-SPSS users, the look and feel of SPSS’ Syntax Editor window probably come closest to Notepad: a single window basically just containing plain text.

# SPSS Operations

Now that we have a basic idea of how SPSS works, let's take a look at what it can do. Following a typical project workflow, SPSS is great for

Opening data files, either in SPSS’ own file format or many others;

editing data such as computing sums and means over columns or rows of data. SPSS has outstanding options for more complex operations as well.

creating tables and charts containing frequency counts or summary statistics over (groups of) cases and variables.

running inferential statistics such as ANOVA, regression, and factor analysis.

saving data and output in a wide variety of file formats.

We'll now take a closer look at each one of these features.

## Opening Data Files

SPSS has its own data file format. Other file formats it easily deals with include MS Excel, plain text files, SQL, Stata, and SAS.

## Editing Data

In real-world research, raw data usually need some editing before they can be properly analyzed. Typical examples are creating means or sums as new variables, restructuring data, or detecting and removing unlikely observations.

SPSS performs such tasks -and more complex ones- with amazing efficiency.

## Saving Data and Output

SPSS data can be saved in a variety of file formats, including

MS Excel;

plain text (.txt or .csv);

Stata;

SAS.

The options for output are even more elaborate: charts are often copy-pasted as images in .png format. Rich text format is often used for tables because it retains the tables’ layout, fonts, and borders.

Besides copy-pasting individual output items, all output items can be exported in one go to .pdf, HTML, MS Word, and many other file formats. A terrific strategy for writing a report is creating an SPSS output file with nicely styled tables and charts. Then export the entire document to Word and insert explanatory text and titles between the output items.

Right, I hope that gives at least a basic idea of what SPSS is and what it does. Let's now explore SPSS in some more detail, starting off with the Data Editor window. We'll present many more examples in the next couple of tutorials as well.

## Tables and Charts

All basic tables and charts can be created easily and fast in SPSS. Typical examples are demonstrated under data analysis. A real weakness of SPSS is that its charts tend to be ugly and often have a clumsy layout.

A great way to overcome this problem is to develop and apply SPSS chart templates. Doing so, however, requires a fair amount of effort and expertise.

# SPSS Stat Data

Before we begin to show how to use SPSS, it is important we understand the basics of statistics.

It is basically about data analysis. Now let us look at different statistical data.

## Nominal Data

In nominal data, there is no numerical or quantitative value, and there is no ranking of attributes. It is merely labels or categories allocated to other variables. Nominal data are best thought of as non-numerical information about a variable.

Examples: White House, Red House, Blue House, Yellow House, etc.

In Nigeria for instance, these categories are usually used to group secondary school students during inter-house sports.

## Ordinal Data

In ordinal data, results can be organized in any order, but all data values are equal in importance. Even though they are numerical, ordinal level statistics measures cannot be subtracted from one another since only the location of the data point matters. Ordinal levels are frequently contrasted with the entire variable group when using nonparametric statistics.

Examples: 1st position, 2nd position, 3rd position, 4th position, 5th position, etc.

## Interval Data

In interval data, results can be arranged in a particular order, but differences in data values may now be meaningful. Two different data points are often used to compare the passing of time or changing conditions within a data set. A meaningful intrinsic zero value for calendar dates or temperatures may not always exist, and there is frequently no "starting point" for the range of data values.

Examples: temperature, mark grading, time, IQ test, CGPA, etc.

## Ratio Data

Results may be placed in a hierarchy, and disparities in data values are now meaningful. However, a statistical value may now be further enhanced by using a beginning point, or "zero value." The ratio of the data values now has significance, including how far it is from zero.

Examples: Income, height, weight, annual sales, market share, product defect rates, time to repurchase, unemployment rate, and crime rate.

Note: Try to first study statistics to help you better understand SPPS.