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Quantitative Data Analysis With SPSS

10 Quantitative Analysis with SPSS: Getting Started

Mikaila Mariel Lemonik Arthur

This chapter focuses on getting started with SPSS. Note that before you can start to work with SPSS, you need to get your data into an appropriate format, as discussed in the chapter on Preparing Quantitative Data and Data Management . It is possible to enter data directly into SPSS, but the interface is not conducive to data entry and so researchers are better off entering their data using a spreadsheet program and then importing it.

Importing Data Into SPSS

In some cases, existing data will be able to be downloaded in SPSS format (*.sav is the file extension for an SPSS datafile), in which case it can be opened in SPSS by going to File → Open → Data and then locating the location of the file.  However, in most cases, researchers will need to import data stored in another file format into SPSS. To import data, go to the file menu, then select import data. Next, choose the type of data you wish to import from the menu that appears. In most cases, researchers will be importing Excel or CSV data (when they have entered it themselves or are downloading it from a general-purpose site like the Census Bureau) or SAS or Stata data (when they are downloading it from a site that makes prepared statistical data files available).

Once you click on a data type, a window will pop up for you to select the file you wish to import. Be sure it is of the file type you have chosen. If you import a file in a format that is already designed to work with statistical software, such as Stata, the importation process will be as seamless as opening a file. Researchers should be sure that immediately after importing, they save their file (File → Save As) so that it is stored in SPSS format and can be opened in SPSS, rather than imported, in the future. It is essential to remember that SPSS is not cloud-resident software and does not have an autosave function, so any time a file is changed, it must be manually saved.

If you import a file in Excel, CSV (comma-separated values) or text format, SPSS will open an import wizard with a number of steps. The steps vary slightly depending on which file type you are importing. For instance, to import an Excel file, as shown in Figure 2, you first need to specify the worksheet (if the file has multiple worksheets—SPSS can only import one worksheet at a time). You can choose to specify a limited range of cells. Checking the checkbox next to “Read variable names from first row of data” will replace the V1, V2, V3, and so on column headers with whatever appears in the top row of data in the Excel file. You can also choose to change the percentage of values that are used to determine data type, remove leading and trailing spaces from string values, and—if your Excel file has hidden rows or columns—you can choose to ignore them. Below the options, a preview of your Excel file will be shown; you can scroll through the preview to see that data is being displayed correctly. Clicking OK will finalize the import.

A different set of options appears when you import a CSV file, as shown in Figure 3. The top of the popup window shows a preview of the data in CSV format. While toggles related to whether the first line contains variable names, removing leading and trailing spaces, and indicating the percentage of values that determine the data type are the same as for importing data from Excel, there are additional options that are important for the proper importing of CSV data. First of all, the user must specify whether values are delimited by a comma, a semicolon, or a tab. While commas are the most common delimiters in CSV files, the other delimiters are possible, and looking at the preview should make clear which of the delimiters is being used in a given file, as shown in the example below.

Second, the user must specify whether the period or the comma is the decimal symbol. Data produced in the United States typically uses the period (as in 1238.67), as does data produced in many other English-speaking countries, while most of Europe and Latin America use the comma. Third, the user must specify the text qualifier (single quotes, double quotes, or none). This is the character used to note that the contents of a particular entry in the CSV file are textual (string variables) in nature, not numerical. If your data includes text, it should be clear from the preview which qualifier is being used. Users can also toggle whether data is cached locally or not; caching locally speeds the importation process.

Finally, there is a button for Advanced Options (Text Wizard). The text wizard offers the same window and options that users see if they are importing a text file directly, and this wizard offers more direct control over the importation process over a series of six steps. First, users can specify a predefined format if they have a *.tpf file on their computers (this is rare) and see a preview of what the data in the file looks like. In step two, they can indicate if the file is delimited (as above) or fixed-width (where values are stored in columns of constant size specified within the file); which—if any—row contains the variable names; and the decimal symbol. Note that some forms of fixed-width files may not be supported. Third, they indicate which line of the file contains the first line of data, whether each line represents a case or a specific given number of variables represents a case, and how many cases to import. This last choice includes the option to import a random sample of cases. Fourth, users specify the delimiter and the text qualifier and determine how to handle leading and trailing spaces in string values. Fifth, users can double-check variable names and formats. Finally, before clicking the “Finish” button, users can choose to save their selections as a *.tpf file to be reused or to paste the syntax (to be discussed later in this chapter).

In all cases, once the importation options have been selected and OK or Finish has been clicked, the data is imported. An output window (see Figure 4) may open with various warnings and details about the importation process, and the Data View window (see Figure 5) will show the data, with variable names at the top of each column. At this point, be sure to save the dataset in a location and with a name you will be able to locate later.

Before users are done setting up their dataset, they must be sure that appropriate variable information is included. When datasets are imported from other statistical programs, they will typically come with variable information. But when they are imported from Excel or CSV files, the variable information must be manually entered, typically from a codebook or related document. Variable information is entered using Variable View. Users can switch between Data View and Variable View by clicking the tabs at the bottom of the screen or using the Ctrl+T key combination. As you can see in Figure 6, a screenshot of a completed dataset, Variable View shows each variable in a row, with a variety of information about that variable. When a dataset is imported, each of these pieces of information need to be entered by hand for each variable. To move between columns by key commands, use the tab key; to open variable information that requires a menu for entry, click the space bar twice.

• Name requires that each variable be given a short name, without any spaces. There are additional rules about names, but in short, names should be primarily alphanumeric in nature and cannot be words or use symbols that have meaning for the underlying computer processing. Names can be entered directly.
• Type specifies the variable type. To open up the menu allowing the selection of variable types, click on the cell, then click on the three dots [.…] that appear on the right side of the cell. Users can then choose from among numeric, dollar, date, numeric with leading zeros, string, and other variable types.
• Width specifies the number of characters of width for the variable itself in data storage, while decimals specifies how many decimal places the variable will have. These can both be entered or edited directly or in the dialog box for Type.

more completely what the variable is measuring. It can be entered directly.

• Missing provides for the indication that particular values—like “refused to answer”—should be treated by the SPSS software as missing data rather than as analytically useful categories. Clicking the three dots [.…] opens a dialog box for specifying missing values. When there are no missing values, “no missing values” should be selected. Otherwise, users can select “discrete missing values” and then enter three specific missing values—the numerical values, not the value labels—or they can elect “range plus one optional discrete missing value” to specific a range from low to high of missing values, optionally adding an additional single discrete value.
• Columns specifies the width of the display column for the variable. It can be entered directly.
• Align specifies whether the variable data will be aligned right, center, or left. Users can click in the cell to make a menu appear or can press spacebar twice and then use arrows to select the desired alignment.
• Measure permits the indication of level of measurement from among nominal, ordinal, and scale variables. Users can click in the cell to make a menu appear or can press spacebar twice and then use arrows to select the desired level of measurement. Note that measure is often wrong in datasets and analysts should not rely on it in determining the level of measurement for selection of statistical tests; SPSS does not use this characteristic when running tests.
• Some datasets will have additional criteria. For example, the dataset shown in Figure 6 has a column called origsort which displays the original sort order of the dataset, so that if an analyst sorts the variables they can be returned to their original order.

When entering variable information, it is especially important to include Name, Label, and Values and be sure Type is correct and any Missing values are specified. Other variable information is less crucial, though clearly it is better to fully specify all variable information. Once all variable information is entered and double-checked and the dataset has been saved, it is ready for use.

When a user first opens SPSS, they are greeted with the “Welcome Dialog” (see figure 9). This dialog provides tips, links to help resources, and options for creating a new file (by selecting “new dataset”) or opening recently used files. There is a checkbox for turning off the Welcome Dialog so that it will not be shown in the future.

When the Welcome Dialog is turned off, SPSS opens with a blank file. Going to File → Open → Data (Alt+F, O, D) brings up the dialog for opening a data file; the Open menu also provides for opening other types of files, which will be discussed below. Earlier in this chapter, the differences between Data View and Variable view were discussed; when you open a data file, be sure to observe which view you are using.

It can be useful to be able to search for a variable or case in the datafile. There are two main ways to do this, both under the Edit menu (Alt+E). [1] The Edit menu offers Find and Go To. Find, which can also be accessed by pressing Ctrl+F, allows users to search for all or part of a variable name. Figure 10 displays the Search dialog, with options shown after clicking on the “show options” button. (Users can also use the Replace function, but this carries the risk of writing over data and so should be avoided in almost all cases.) Be sure to select the column you wish to search—the Find function can only examine one column in Variable View at a time. Most typically, users will want to search variable names or labels. The checkbox for Match Case toggles whether or not case (in other words, capitalization) matters to the search. Expanding the options permits users to specify how much and which part of a cell must be matched as well as search order.

Users can also navigate to specific variables by using the Edit → Go to Case (to navigate to a specific case—or row in data view) and Edit → Go to Variable (to navigate to a specific variable—a row in variable view or a column in data view). Users can also access detailed variable information via the tool Utilities → Variables.

Another useful feature is the ability to sort variables and cases. Both types of sorting can be found in the data menu. Variables can be sorted by any of the characteristics in variable view; when sorting, the original sort order can be saved as a new characteristic. Cases can be sorted on any variable.

SPSS Options

The Options dialog can be reached by going to Edit → Options (or Alt+E, Alt+N). There are a wide variety of options available to help users customize their SPSS experience, a few of which are particularly important. First of all, using various dialogs and menus in the program is much easier if the options Variable List—Display Names (Alt+N) and Alphabetical (Alt+H) are selected under General. You can also change the display language for both the user interface and for output under Language, change fonts and colors for output under Viewer, set number options under Data; change currency options under Currency; set default output for graphs and charts under Charts; and set default file locations for saving files under File locations. While most of these options can be left on their default settings, it is really important for most users to set variables to display names and alphabetical before use. Options will be preserved if you use the same computer and user account, but if you are working on a public computer you should get in the habit of checking every time you start the program.

Getting More Out of SPSS

So far, we have been working only with Data View and Variable View in the main dataset window. But when researchers produce the results of an analysis, these results appear in a new window called Output—IBM SPSS Statistics Viewer. New Output windows can be opened from the File menu by going to Open → Output or from the Window menu by selecting “Go to Designated Viewer Window” (the later command also brings the output window to the foreground if one is already open). Output will be discussed in more detail when the results of different tests are discussed. For now, note that output can be saved in *.spv format, but this format can only be viewed in SPSS. To save output in a format viewable in other applications, go to File → Export, where you can choose a file location and a file format (like Word, PowerPoint, HTML, or PDF). Individual output items can also be copied and pasted.

SPSS also offers a Syntax viewer and editor, which can also be accessed from both the File and Window menus. While syntax is beyond the scope of this text, it provides the option for writing code (kind of like a computer program) to control SPSS rather than using menus and buttons in a graphical user interface. Experienced users, or those doing many similar repetitive tasks, often find working via syntax to be faster and more efficient, but the learning curve is quite steep. If you are interested in learning more about how to write syntax in SPSS, Help → Command Syntax Reference brings up a very long document detailing the commands available.

Finally, the Help menu in SPSS offers a variety of options for getting help in using the program, including links to web resource guides, PDF documentation, and help forums. These tools can also be reached directly via the SPSS website. In addition, many dialog boxes contain a “Help” button that takes users to webpages with more detail on the tool in question.

Go to https://www.baseball-reference.com/ and select 10 baseball players of your choice. In an Excel or other spreadsheet, enter the name, position, batting arm, throwing arm, weight in pounds, and height in inches, as well as, from the Summary: Career section, HR (home runs) and WAR (wins above replacement). Each player should get one row of the Excel spreadsheet. Once you have entered the data, import it into SPSS. Then use Variable View to enter the relevant information about each variable—including value labels for position, batting arm, and throwing arm. Sort your cases by home runs. Finally, save your file.

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• import excel © IBM SPSS is licensed under a All Rights Reserved license
• import csv © IBM SPSS is licensed under a All Rights Reserved license
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• spss data view © IBM SPSS is licensed under a All Rights Reserved license
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• find and replace © IBM SPSS is licensed under a All Rights Reserved license
• Note that "Search," another option under the Edit menu, does not search variables or cases but instead launches a search of SPSS web resources and help files. ↵

A data type that represents non-numerical data; string values can include any sequence of letters, numbers, and spaces.

The possible levels or response choices of a given variable.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

IBM SPSS Statistics provides a powerful suite of data analytics tools which allows you to quickly analyze your data with a simple point-and-click interface and enables you to extract critical insights with ease. During these times of rapid change that demand agility, it is imperative to embrace data driven decision-making to improve business outcomes. Organizations of all kinds have relied on IBM SPSS Statistics for decades to help solve a  wide range of business and research problems .

Explore SPSS Statistics with our interactive tutorials

SPSS Statistics offers a  comprehensive set of capabilities  in support of the entire analytical process from data preparation to analysis and reporting. It simplifies and accelerates data analytics by offering a simple menu-driven user interface that allows you to get to insights with just a few clicks, without any coding.

Interactive, hands-on tutorials are one of the best ways to experience SPSS Statistics. Here are a few SPSS Statistics learning resources that can get you started:

Statistics 101

If you’re just starting out with IBM Statistics, this introductory tutorial can help you get up to speed. You’ll learn about descriptive statistics, variance, probability, correlation and data visualization. It starts you off gently with a coverage of the fundamentals including descriptive statistics and moves you through five self-paced modules that take you through the steps to data wrangling and more.

Get more information on the SPSS Statistics 101 tutorial  here .

View SPSS Statistics in action

IBM experts have put together an array of demo videos and assets that allow you to deep-dive into powerful statistical procedures and tools included in this versatile statistical software. We recommend starting with the overview video below to explore the power of statistical analysis to enable timely and accurate decisions for your organization. 

To help you along your learning journey, we have provided a detailed video library that includes demo videos  around advanced statistics, data preparation and  popular procedures like Regression. Visit the video library .

Are you wondering if SPSS Statistics enables you to deliver visualizations and other output? Watch this video about the output and visualization capabilities of SPSS Statistics to learn how to customize pivot tables and create publication-ready charts, tables and decision trees. Visit the  IBM media center  to view it.

These are just a few of the tutorials available to help you learn and become proficient with SPSS Statistics. For more basic to advanced tutorials and feature documentation, visit the SPSS product documentation .

Get more from SPSS Statistics with new algorithms and visualization tools

IBM recently launched SPSS Statistics 29. The latest version includes new statistical algorithms, enhancements to existing statistical procedures, new Relationship Maps for data visualization, and several usability improvements to make SPSS Statistics more user friendly for novices and experts alike. You can read all about the new release in this data sheet .

Sign up for our tech-talk series to stay up to date with the latest developments around SPSS Statistics. Register here

Ready to dive deeper into SPSS Statistics on your own and start turbocharging your research and business analysis?

Try SPSS Statistics at no cost for 30 days.

Get yearly subscription and save more

IBM offers simple  subscription options to help you easily get started with SPSS Statistics and scale as your requirements grow. You can even choose the 12 months auto-renewal plan and  save 10% on subscription and add-ons .

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How to Analyse Data Using SPSS

Last Updated: July 18, 2023 Fact Checked

wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. To create this article, volunteer authors worked to edit and improve it over time. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 291,373 times. Learn more...

SPSS (The Statistical Package for the Social Sciences) software has been developed by IBM and it is widely used to analyse data and make predictions based on specific collections of data. SPSS is easy to learn and enables teachers as well as students to easily derive results with the help of a few commands. The implications of the results are fairly evident and are statistically valid. Using the software, one can conduct a series of studies quickly and effectively. If you are worried about conducting your data analysis on SPSS, here are a few guidelines and an overview of the process.

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• No study can be incomplete without the right research and therefore, SPSS plays a vital role in the life of a researcher. Thanks Helpful 2 Not Helpful 8

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• ↑ https://scholar.valpo.edu/cgi/viewcontent.cgi?article=1000&context=psych_oer
• ↑ https://students.shu.ac.uk/lits/it/documents/pdf/analysing_data_using_spss.pdf
• ↑ https://med.und.edu/research/daccota/_files/pdfs/berdc_resource_pdfs/data_analysis_using_spss.pdf

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Excellent graphic user interface makes statistics analysis easier, including many most complex models. At the same time, the package still supports syntax programs which offers flexibility and time-effectiveness.

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• SPSS is available from OIT as a $210 recharge for one year
• Campus - UCI's Virtual Computing Lab  (VCL) and check the Computer Lab Software List

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• SPSS Tutorials Modules address the basics, data analysis, ANOVA, T-Test, and Chi-Square Test.
• Getting Started with SPSS for Windows It's a tutorial by Indiana University. Its intro to the SPSS interface, data preparation, output are excellent for newbies to software. The tutorial also covers descriptive statistics and OLS.
• SPSS Learning Modules The tutorial is prepared by Academic Technology Services in UCLA. It has a starter kit for beginners, and also topics for a range of data analysis models. more... less... data analysis models, including, Probit Regression , Logistic Regression , Poisson Regression , One-way MANOVA , Canonical Correlation Analysis , etc.
• SPSS Syntax Many users choose SPSS for its user-friendly interface, however, don't satisfy yourself with it. The Syntax will provide great productivity, and this website will offer effectiveness in learning SPSS Syntax.
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Quantitative Data Analysis

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SPSS is a software package used for statistical analysis. It can

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How to Use IBM SPSS for Research

Learn how to use IBM SPSS for research in this beginner's guide

Table of contents

Step 1: installing and entering data, step 2: preparing your data, step 3: understanding your data with descriptive statistics, step 4: visualizing your data, step 5: testing your hypotheses, step 6: analyzing and interpreting results, step 7: reporting your findings.

If you’re new to the world of data analysis and research, IBM SPSS (Statistical Package for the Social Sciences) can be a valuable tool to help you make sense of your data. In this beginner’s guide, iLovePhD walks you through the steps of using IBM SPSS for research, from data entry and preparation to statistical analysis and reporting. Don’t worry; we’ll keep it simple and easy to understand!

A Beginner’s Guide to Using IBM SPSS for Research

Before we dive into data analysis, you’ll need to install IBM SPSS on your computer. Once you have it set up, create a new data file or import your existing data from sources like Excel or CSV files. You can manually enter your data or import it with a few clicks.

Visit: https://www.ibm.com/products/spss-statistics/campus-editions

Data needs to be clean and error-free before analysis. Check for missing data or outliers and handle them appropriately. You can also recode variables or create new ones if needed. It’s a good idea to label your variables and their values to make the data more understandable.

Descriptive statistics help you get a clear picture of your data’s basic characteristics. IBM SPSS can calculate mean, median, standard deviation, and frequency distributions, making it easier for you to interpret your data.

Seeing is believing! IBM SPSS allows you to create charts and graphs to visually represent your data. Whether it’s bar charts, line graphs, or scatterplots, visualization can reveal patterns and trends that might not be apparent in raw numbers.

To answer your research questions, you’ll need to perform hypothesis testing. Don’t worry; you don’t have to be a statistician! IBM SPSS offers various tests, such as t-tests, ANOVA, chi-square, and correlation. Pick the appropriate one for your research and interpret the results it provides.

Once you’ve done the statistical tests, it’s time to analyze the results. Look for patterns and relationships in your data that can help you draw meaningful conclusions. Consider the practical implications of your findings and discuss their significance in your research context.

Now that you have your results, it’s time to present them in a clear and organized manner. IBM SPSS can help you generate comprehensive reports with charts and tables. You can export the results to other software, such as Microsoft Word or Excel, for further formatting and presentation.

Using IBM SPSS for research doesn’t have to be intimidating. With its user-friendly interface and powerful statistical tools, you can gain valuable insights from your data without being a statistics expert. Remember, practice makes perfect! As you become more familiar with IBM SPSS, you’ll be better equipped to tackle more complex research questions and contribute to the world of knowledge. Happy analyzing!

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A Step-by-Step Guide to Data Analysis Using SPSS: Iron Study Data

• First Online: 13 December 2023

Cite this chapter

• Amal K. Mitra 2  

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The purpose of this chapter is to provide a step-by-step guide for beginners on how to use SPSS to analyze health data. An example of the Iron Supplementation Study conducted by the author will be used to demonstrate data analysis techniques. The readers will first get familiar with a few basic steps before starting the data analysis process, including the study’s objectives, variable characteristics, data entry, data cleaning, and a data analysis plan. An in-depth, step-by-step method of data analysis and interpretation of the data output will be provided. Data analysis will include basic descriptive statistics, data distribution, normality tests, and commonly used parametric and nonparametric tests, depending on the data distribution. The statistical tests include: independent sample t -test, paired t -test, confidence intervals, chi-square test, ANOVA tests, repeated analysis, correlation, linear regression, logistic regression, and selected nonparametric tests.

• SPSS data analysis
• Test of normality
• Student’s t -test
• Paired t -test
• Confidence intervals
• Chi-square test
• Repeated analysis
• Correlation
• Regression tests

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Mitra, A.K. (2024). A Step-by-Step Guide to Data Analysis Using SPSS: Iron Study Data. In: Mitra, A.K. (eds) Statistical Approaches for Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-031-41784-9_20

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6 Steps To Follow When Analyzing Data Using SPSS

SPSS, which stands for Statistical Package for Social Sciences is a strong statistical tool mainly used for analysing the data in different ways. No matter if you’re working on a research project, using SPSS in data analysis for a dissertation, or a business report, or just exploring something on your own, SPSS can help you arrange, change, and visualize your data. It also helps you check your ideas and make decisions based on the data. In this article, you’ll discover six steps to use SPSS for understanding and analyzing data.

1. Step 1: Define your research questions/ hypotheses

Before you dive into SPSS data analysis , it’s important to know what you’re looking for and what you think you might discover. This means, you always start with defining research questions or research hypotheses before analyzing data using SPSS . Also, when defining the research questions, you should be clear on what you wish to achieve and whether your variables are measured in your data. In other words, some of the factors you should consider when formulating research questions include:

• Relevance to Your Field : Ensure that your research questions are closely related to your field of study and contribute to existing knowledge.
• Clarity and Precision : Craft clear and specific questions that avoid ambiguity.
• Feasibility : Make sure your questions are realistic and can be answered within the scope of your dissertation.
• Originality : Aim for questions that address gaps in existing literature or offer a unique perspective.
• Measurability : Ensure that your questions can be measured or assessed using appropriate methods and data.

Your hypothesis is like a guess about how things are connected or different. For instance, if you’re curious about how gender influences how well college students do in their studies, your research question could be: “Does gender make a difference in the academic performance of college students?” Your guess, or hypothesis, could be: “Female students do better in their studies than male students.”

2. Step 2: Prepare Your Data

Once you have your research question and hypothesis, you need to collect and prepare your data. Some of the tools you can use to collect data for SPSS data analysis include, survey questionnaires, existing databases, experiments and even observations. Once you have gathered data using your preferred data collection tool, you need to prepare and preprocess your data for analysis. That is, before analyzing data using SPSS, you need to perform some data transformation and organization to ensure it fits the suitable format that SPSS can read and process.

Some of the main steps you need to follow when preprocessing your data for spss data analysis include:

• Collect all the relevant data for your study and ensure it is well-organized.
• Store data in a format that is compatible with SPSS (e.g., Excel, CSV).
• Identify and handle missing data: Decide whether to remove cases with missing data, impute missing values, or use other appropriate techniques.
• Check for and correct any data entry errors or inconsistencies.
• Assign meaningful labels to categorical variables (e.g., convert numeric codes to labels for gender: 1 = Male, 2 = Female).
• Ensure consistency in coding throughout the dataset.
• If necessary, perform transformations like log or square root to make data more normally distributed.
• Create new variables or recode existing ones to suit your analysis (e.g., calculating a mean score from multiple survey items).
• Identify outliers using descriptive statistics or visualization techniques (e.g., box plots).
• Decide whether to remove, transform, or winsorize outliers, depending on the nature of your data and research objectives.
• Determine which variables are relevant to your research question and analysis. Remove irrelevant variables to simplify your dataset.
• Generate summary statistics and explore the distribution of your variables using SPSS descriptive statistics and visualizations (e.g., histograms, scatterplots).

Once you’ve completed these preprocessing steps, your data should be well-prepared for analysis in SPSS or any other statistical software, and you can proceed with your chosen statistical tests and research objectives.

Learn How to Analyze Data Using SPSS in 6 Steps

3. Step 3: Choose the Right Statistical Tests

There are various statistical tests available, each with its specific use cases. Depending on your research question and hypothesis, you need to select the most suitable statistical test that will help you answer your question and test your hypothesis.

SPSS offers a wide range of analysis methods, such as descriptive statistics, inferential statistics, correlation, regression, ANOVA, chi-square, t-test, and more. Remember that statistical tests highly rely on the level of measurements. Therefore, you need to be well-versed with the various levels of measurements.

Statistical Levels of Measurement

In statistics, there are four levels of measurement, which represent the different ways in which data can be categorized and analyzed. These levels are hierarchical, with each level including all the characteristics of the levels below it. The four levels of measurement are:

• Nominal data represents categories or labels for different groups or items.
• Data at this level cannot be quantitatively compared or ordered.
• Examples include gender (male, female), colors, types of animals, and yes/no responses.
• Statistical operations: Mode (most frequent category), frequency counts.
• Ordinal data includes categories with a specific order or ranking.
• While you can rank and order the categories, the intervals between them are not equal or meaningful.
• Examples include education levels (e.g., high school, bachelor’s, master’s, Ph.D.), Likert scale responses (e.g., strongly disagree, disagree, neutral, agree, strongly agree).
• Statistical operations: Mode, median, percentiles, non-parametric tests.
• Interval data has equal intervals between values, but it lacks a true zero point.
• You can perform arithmetic operations like addition and subtraction on interval data, but multiplication and division do not make sense in the absence of a meaningful zero.
• Examples include temperature in Celsius or Fahrenheit and IQ scores.
• Statistical operations: Mode, median, mean, standard deviation, parametric tests (e.g., t-tests, ANOVA).
• Ratio data has equal intervals between values and a true zero point.
• With a true zero, you can perform all arithmetic operations, including multiplication and division.
• Examples include height, weight, age, income, and counts (e.g., number of books).
• Statistical operations: Mode, median, mean, standard deviation, parametric tests, and ratio-based measures (e.g., income-to-debt ratio).

The level of measurement determines the types of statistical analysis that can be applied to the data. Typically, as you move up the hierarchy from nominal to ratio, you gain more analytical capabilities and precision. However, it’s important to choose the appropriate level of measurement for your data to ensure meaningful and accurate analysis. Using an inappropriate level of measurement can lead to incorrect conclusions and misinterpretation of results in statistical analysis.

Furthermore, you need to ensure that the assumptions for each statistical tests are met before proceeding with your analysis. For example, if you want to compare the means of two groups, you can use a t-test, but you need to check if your data is normally distributed and if the groups have equal variances. You will also need to understand the difference between parametric and non-parametric tests and when to use them.

4. Step 4: Analyzing Data Using SPSS

Once you’ve selected the most suitable statistical test, the next step is to conduct data analysis using SPSS and check your output. You can use the menus, the dialog boxes, or the syntax editor to run your analysis in SPSS. You can also customize your output options, such as tables, charts, graphs, or reports. You need to examine your output carefully and look for any errors, warnings, or messages that might indicate a problem with your data or analysis. You also need to interpret your output and look for the relevant statistics, such as means, standard deviations, p-values, coefficients, or effect sizes.

5. Step 5: Report your results and findings

Once you’ve figured out what your data is trying to tell you, it’s time to share your findings in a clear and simple way. You’ll want to follow the referencing guidelines such as APA, MLA, or Chicago style, to make it all look nice and organized.

You can share your results by writing about them, making tables or charts, and using the right words. Make sure to connect what you found to your original questions and hypothesis. For example, you might say something like this: The results of the t-test showed that female students had significantly higher academic performance than male students (M = 85.6, SD = 9.8; M = 79.4, SD = 10.2; t(98) = 3.12, p < 0.01). This means that the proposed research hypothesis has been proven thus confirming the hypothesis.

6. Step 6: Evaluate your analysis and interpretation

The last step involves carefully looking at your analysis and interpretation while thinking about their strengths and weaknesses. You should consider whether your analysis and interpretation are trustworthy and applicable to a broader context. It’s also essential to be aware of any potential biases, mistakes, or uncertainties that could affect your work and figure out how to minimize them. Additionally, you should suggest what your findings mean and any recommendations or future research ideas that might come from them.

For example, you could say: “Our study shows that gender plays a role in how well college students perform academically. However, we should keep in mind that our study had some limitations, like a small group of participants, only using one way to measure academic performance, and not taking into account other things that could affect academic success. So, it’s important to do more research to understand why there are differences between genders in how well students do in college.”

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Psychology Curricular Materials

Using SPSS to Understand Research and Data Analysis

Daniel Arkkelin , Valparaiso University Follow

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Download Chapter 1 - First Contact: Overview of Book & SPSS Help (143 KB)

Download Chapter 2 - Close Encounter: The Data Editor, Syntax & Output Viewer (446 KB)

Download Chapter 3 - Point-and Click: Conducting Analyses in the Data Editor (272 KB)

Download Chapter 4 - Variables in the EZDATA File: Sex Roles, Work Motives & Leadership (213 KB)

Download Chapter 5 - Housekeeping: Transforming Variables and Adding Labels (447 KB)

Download Chapter 6 - The Frequencies Procedure: Summarizing with Descriptive Statistics (107 KB)

Download Chapter 7 - Crosstabulation: Understanding Bivariate Relationships Between Categorical Variables (238 KB)

Download Chapter 8 - Correlation: Bivariate Relationships Between Continuous Variables (116 KB)

Download Chapter 9 - Independent Samples t-test: Testing Differences Between Two Groups (135 KB)

Download Chapter 10 - Paired Samples t-test: Testing Differences Between Correlated Groups (185 KB)

Download Chapter 11 - One-Way ANOVA: Differences Between Multiple Independent Groups (280 KB)

Download Chapter 12 - Repeated Meansures ANOVA: Differences Between Correlated Groups (302 KB)

Download Chapter 13 - Factorial Independent Groups ANOVA: Two Independent Variables (271 KB)

Download Chapter 14 - Mixed-Model ANOVA: Combining Between & Within-Groups Factors (295 KB)

Download Chapter 15 - Epilogue: SPSS, Data Analysis & The EZ Research Project (163 KB)

Download Appendix - Reassurance & Help for the Nervous Novice (773 KB)

Download List of Review Videos (20 KB)

Download EZDATA.sav file (18 KB)

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Introduction to SPSS

Spss – what is it.

IBM SPSS Statistics (or “SPSS” for short) is super easy software for editing and analyzing data.

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SPSS Data Editor Window

SPSS’ main window is the data editor. It shows our data so we can visually inspect it.

This tutorial explains how the data editor works: we'll walk you through its main parts and point out some handy tips & tricks.

SPSS Syntax Introduction

SPSS syntax is computer code used by SPSS for analyzing data , editing data, running statistical tests and more.

Using SPSS syntax is super easy and saves tons of time and effort. This tutorial quickly gets you started!

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This condition -known as the homogeneity of variance assumption- is required by t-tests and ANOVA.

So how to run and interpret this test in SPSS? This simple tutorial quickly walks you through.

SPSS Factor Analysis – Beginners Tutorial

Factor analysis examines which variables in your data measure which underlying factors.

This tutorial illustrates the ideas behind factor analysis with a simple step-by-step example in SPSS.

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• system missing values: values that are absent from the data;
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We'll quickly walk you through both types. We'll also show how to detect, set and deal with missing values in SPSS.

Normality Tests in SPSS

Spss shapiro-wilk test – quick tutorial with example.

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SPSS Kolmogorov-Smirnov Test for Normality

The Kolmogorov-Smirnov test examines if a variable is normally distributed in some population.

This “normality assumption” is required for t-tests, ANOVA and many other tests. This tutorial shows how to run and interpret a Kolmogorov-Smirnov test in SPSS with some simple examples.

Selecting Cases in SPSS

Spss if – a quick tutorial.

In SPSS, IF computes a new or existing variable but for a selection of cases only.

For example: IF(GENDER = 0) SCORE = MEAN(Q1 TO Q5). computes “score” as the mean over variables Q1 to Q5 but only for cases whose gender is 0 (female).

SPSS FILTER – Quick & Simple Tutorial

SPSS FILTER excludes a selection of cases from all subsequent analyses until you switch it off again.

Using a filter comes down to creating a (new) filter variable and activating it. This tutorial shows the easy way to do so and points out some nice alternatives as well.

SPSS SELECT IF – Tutorial & Examples

In SPSS, SELECT IF removes a selection of cases from your data.

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Effect size measures are useful for comparing effects across and within studies. This tutorial helps you to choose, obtain and interpret an effect size for each major statistical procedure.

Cohen’s D – Effect Size for T-Test

Cohen’s D is the effect size measure of choice for t-tests.

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