Available for: Windows / Mac OS
License: Generally available to all students and faculty
1. Go to www.portal.bi.no and log in with s-number and password.
2. Go to the menu in the top right corner and click Digital services.
3. Select Software
4. Scroll down until you find SPSS. Click the link «Download the software from here».
5. List of all available software for faculty and students at BI. Choose the newest version of SPSS: IBM SPSS Statistics 29.
6. You will then see this window with different tabs from left to right.
It is important that you read the information in these tabs to learn how to download the software.
In the first tab About, you will find information about how long the current license is valid.
7. Click the next tab - License agreement. Read the information and accept the license agreement.
8. When you have accepted the license agreement, select software for Windows or MacOS (choose one of the tabs).
9. FOR WINDOWS
Read the instructions and click the link Receive license-key and download the Windows 64-bit version of IBM SPSS Statistics 29.
10. You will then receive the license key and the zip-file. Again, remember to read the Installation instructions as seen in the picture below.
If you click on [New Dataset] in the top left corner, you open a new dataset. You see the rows and the columns of the SPSS data window. By default, this is the window that shows up when you open the SPSS application. SPSS has normally two or three different windows that are open simultaneously. They show you different features of your dataset.
In the data window, you have some important options up in the menu:
The toolbar buttons that are used most frequently:
“Recall recently used dialogs” (icon in red circle to the left) – if you click on that you see all the functions you have used recently.
One of the great things about SPSS is the "A-1" (value labels) (icon in red circle to the right)– this turns on the value labels.
The output window is where SPSS puts the results – numerical results, graphs and any logs or history of commands that are used to produce those. Over to the left in the output-file you have a navigation-window which makes it easy for you to go to the different elements of the output.
If you click on the icon with the big star in the output-window, it takes you back to the data-window:
SPSS Syntax Files make more efficient and organized as a SPSS user.
What are SPSS Syntax Files? They are text files that contain a series of commands and instructions for SPSS to execute. Instead of manually clicking through menus and dialog boxes, you can automate your analyses by running these syntax files.
Why you should get familiar with SPSS Syntax Files:
Save Time: Once you've created a syntax file for a specific analysis, you can reuse it as many times as you need. You won't have to try to remember what you did days or weeks back.
Enhance Reproducibility: Syntax files serve as a record of your analysis steps. By sharing these files with others or future you, you ensure that your analyses can be easily reproduced, fostering transparency.
Customize Your Analyses: SPSS Syntax Files give you control over your analyses. You can fine-tune every aspect of your statistical procedures, allowing you to tailor them to your specific research needs.
See more detailed descriptions below:
Syntax Window
There is an optional third window in SPSS. In the output window you can see that you have a written command. This may or may not show depending on the way you have SPSS set up. You can get the syntax in the output window, but you can also have a syntax file.
If you click PASTE when conducting an analysis, SPSS will put that as a written command in the syntax file that you can reuse several times. This allows you to recreate your commands, share them, copy and modify them.
If you have saved the syntax used in a SPSS file, you have a tracking of everything you have done (statstical analyses, visualizations etc.) Later on you can run the syntax file to either remind yourself what you did and how, or copy from the syntax file in order to carry out the same procedure in another SPSS file. Your syntax file will also in many cases be relevant to attach to a publication or assignments as documentation of the steps in your analyses.
You should first look at the row with the variable names on top of the spread sheet of the document. And next to each one there is an icon that indicates the level of measurement:
If we go to variable view at the bottom left, we can see each of the variables in the data set.
Scale: A quantitative variable
Ordinal: Ordered categorie
Nominal: Categorical
In data window:
In many cases in survey research, not all elements in a sample have an equal probability of being selected. This is where weighting cases in SPSS comes into play. Weighting allows you to give different levels of importance to different observations in your dataset, ensuring that your analysis reflects as accurately as possible the characteristics of the entire population, not just the sample.
Case weighting, or population weighting, is a statistical technique used to adjust the influence of each case (individual or unit) in your dataset based on its relative importance or representation in the population you are studying. The purpose is to correct for any sampling biases or uneven probabilities of selection, making your analysis more accurate and representative.
IHere we describe how you can activate a weight already in your dataset.
Go to Data - Weight Cases.
Now click "Weight Cases by" and select the correct variable. If you want to deactivate weighting, remove the variable and click "Do not weight cases"
Now you will see "Weight On" down to the right in the "Weight status area".
You can also use the command WEIGHT to activate or deactivate weighting. Use WEIGHT BY and then choose the weight variable to activate the weight variable, and WEIGHT OFF to deactivate it.
The easiest way to start looking at your data is to start with frequencies. The frequency table tells you how common each of the categories are, both in frequency and percent.
ANALYZE → DESCRIPTIVE STATISTICS → FREQUENCIES
For categorical variables (nominal and ordinal):
But the frequencies command can do more than nominal and categorical variables.
For quantitative variables:
If you have quantitative data, then you want to have some basic descriptive statistics, like the mean or the standard deviation. It is possible to do this in frequencies, but SPSS has a special command for that. And that is descriptives.
ANALYSE → DESCRIPTIVE STATISTICS → DESCRIPTIVES
In data window:
We get several numerical descriptions for one variable in the output window:
ANALYZE - DESCRIPTIVE STATISTICS - CROSSTABS
ANALYZE - TABLES - CUSTOM TABLES
Chi-square test is a statistical test for examining the association between categorical variables. It is used in the analysis of contingency tables (also known as crosstabs = a table in a matrix format), which display the frequency distribution of the variables. Chi-square tests are used a lot in the analysis of surveys.
Go to:
CROSSTABS: Chi Square test – the whole table (not individual procentages)
CROSSTABS: Test of proportions
CUSTOM TABLES: Chi-square test
a) Analyze → tables → custom tables
Correlation analyses are used to examine whether changes in one continuous variable are associated - or correlated - with changes in one or more other continuous variables. It is a statistical method which assesses the strength and direction of relationships between variables.
A wiedely used measure of correlation is Pearson's correlation coefficient, which quantifies the linear relationship between two continuous variables.
For Pearson's correlation coefficient
go to:
Analyze -> Correlate -> Bivariate.
Select the variables you want to want to include in the analysis and click the arrow to transfer them to the variables window.
Make sure "Pearson" is checked.
Regression analysis examines the relationships between a dependent variable and one or more independent variables. It is a statistical method which helps you predict a response variable (the dependent variable) based on one or more explanatory (independendent variables).
Regression analysis encompasses various types, including linear and logistic regressions. Linear regression is suitable when the dependent variable is numeric, and it aims to predict a continuous outcome. In contrast, logistic regression is used when the dependent variable is categorical, often representing a binary outcome, such as True or False.
Both may be simple (only one independent variable) or multiple (several independent variables).
For Linear Regression analysis
Go to:
Analyze -> Regresion -> Linear
Select the dependent variable and click the arrow
Do the same for the independent variable
Click OK
If you got for instance 10 variables that are measuring approximately the same thing, then you probably do not need all 10. If you are able to find how the variables go together, then maybe you can combine those 10 and get a factor that is more useful than the individual variables.
Factor analysis is based on covariance between the different variables.
1) ANALYSE → DIMENSION REDUCTION → FACTOR
2) Choose a collection of variables that may have something in common.
3) Click OK (for default analysis)
Communalities
What we get first is a table with communalities. When each variable is standardized, it has one unit of variance, and that is the initial value. The Extraction has to do with how much they have in common with each other and that feeds into the rest of the analysis.
Total variance explained
If each of the 12 variables has one unit of variance, there is 12 units of variance total. But if the variables run parallel to each other, then you can find a single factor that accounts for more than one unit of variance. And that is what we have in the table "Total variance explained".
When we look at the column "Cumulative%", those listed over there - these factors accounts for over 3/4 of the variance in the original variables. So in the example we are able to go from 12 to 4. And that is a benefit for us because it is fewer things we have to deal with, and they are probably going to be more stable, more reliable, and more generalizable than the individual variables would be.
Component matrix
In terms of what goes in to these 4 variables, we look at the component matrix. We have the four components listed across the top, we got the 12 variables down the side, and we have these numbers that are like correlation coefficients. They go from -1 (negative one) to +1 (positive one). And high absolute values indicate strong relationships between that variable and that component. So for example: Under component 1 we have a -.759 - that means that Facebook and component 1 are strongly connected. And other ones are less connected - for example -.080 between GDPR and component 4.
Box plots, also known as box-and-whisker plots, provide a concise summary of the distribution and variability of numerical data. Box plots highlight the central tendency, spread, and potential outliers within your dataset. If you are examining continuous variables, you may visualize the distributional characteristics effectively using a box plot.
Go to: Graphs → Chart builder
Drag in a template chart – choose Boxplot.
Use a Simple Boxplot (the one to the left) when you want to visualize the distribution of a single continuous variable in relation to another variable. Simple boxplots are ideal for providing insights into the spread, central tendency, and potential outliers of one continuous variable, making them suitable for comparing the distribution of that variable across different groups or categories.
Opt for a Clustered Boxplot (the one in the middle) when you need to explore how two variables covariate in relation to a third variable. Clustered boxplots allow you to simultaneously display the distributions of two continuous variables while considering the influence t of a categorical or grouping variable.
Start putting the variables in.
Read more about the concise information a Boxplot provides here.