Statistical Package for the Social Sciences (SPSS) is a software widely used for statistical analysis in various fields, such as social sciences, finance, and healthcare. With the increasing demand for data analysis skills, many individuals and organizations seek to enhance their SPSS proficiency. Online courses offering SPSS instruction have gained popularity due to their flexibility and accessibility. In this article, we will provide an overview of some of the best SPSS courses available online, highlighting their features and benefits.
Here’s a look at the Best Spss Courses and Certifications Online and what they have to offer for you!
Spss Online Certificate Course
- Spss Online Certificate Course
- 1. SPSS For Research by Bogdan Anastasiei (Udemy) (Our Best Pick)
- 2. SPSS Masterclass: Learn SPSS From Scratch to Advanced by Scholarsight Learning (Udemy)
- 3. SPSS Basics by Bogdan Anastasiei (Udemy)
- 4. Statistics / Data Analysis in SPSS: Inferential Statistics by Quantitative Specialists (Udemy)
- 5. Statistics/Data Analysis with SPSS: Descriptive Statistics by Quantitative Specialists (Udemy)
- 6. IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced by Scholarsight Learning (Udemy)
- 7. SPSS Beginners: Master SPSS by SPSS Statisticians MyProjects (Udemy)
- 8. The 16-hour SPSS Pro: Analysis, Interpretation, and Write-Up by Todd Bottom, Ph.D. – Founder Research Learning Center (Udemy)
- 9. Introduction to SPSS by Quantitative Specialists (Udemy)
- 10. IBM SPSS Modeler: Getting Started by Sandy Midili (Udemy)
1. SPSS For Research by Bogdan Anastasiei (Udemy) (Our Best Pick)
The SPSS for Research course at Udemy covers advanced statistical analysis techniques using SPSS software in 146 video lectures over approximately 15 hours of video. The course is designed for individuals with no previous experience in SPSS or professional mathematics or statistics. The course’s 56 guides present statistical procedures in a simple and straightforward manner, avoiding technical jargon and mathematical formulas as much as possible. Each procedure is accompanied by a brief description, live demonstrations on how to perform it in SPSS, and interpretation of the main output. The course includes 14 sections covering topics such as Getting Started, Creating Charts in SPSS, Simple Analysis Techniques, and Addenda. The first guides are free, and the course includes a 30-day money-back guarantee.
Scholarsight Learning offers a comprehensive course called SPSS Masterclass: Learn SPSS From Scratch to Advanced with the aim of equipping learners with the skills to carry out advanced research, statistics, and data analysis. The course is intended for students, researchers, teachers, and corporate professionals who wish to develop their data analysis skills using IBM SPSS Statistics.
The course is designed to help participants develop the ability to analyze and treat data independently and accurately, plan and conduct new research based on their interests, and write scholarly articles or develop high-quality thesis/project work. It covers most of the major research techniques employed in academic and professional research in a comprehensive, in-depth and stepwise manner.
The training program will focus on developing practical skills in data analysis using SPSS and its various options. Participants will learn to accurately decide which statistical tests are appropriate for a particular research objective and how to write the obtained output from SPSS in APA format.
The course requires a love for data analysis and statistics, research aptitude, and motivation to do great research work. It covers a range of topics, including descriptive statistics, linear and multiple regression, exploratory factor analysis, Chi-Square test, reliability analysis, logistic regression, moderation and mediation analysis, General Linear Modelling (GLM) and Generalized Linear Modelling (GLIM), and bug fixing in SPSS.
The course is divided into several sections covering topics such as data entry and transformation, comparisons between sample means and groups, correlation and measures of association, ANOVA, ANCOVA, MANOVA, and python for SPSS users. The program will also provide references and further readings on research methodology.
On completion of the course, learners should have the ability to independently carry out in-depth data analysis accurately, plan and carry out research work based on their research interests, and develop scholarly articles or high-quality thesis/project work.
The SPSS Basics course is designed to provide a foundational understanding of statistical analysis using SPSS. The course is intended for beginners, providing an introduction to SPSS data sets, data management, and visualization techniques. Additionally, it covers basic statistical indicators and tests such as the chi-square test for association and the independent samples t test.
The course comprises 27 lectures and approximately 20 topics, with practical exercises that reinforce the concepts taught in each lecture. The course assumes a basic understanding of statistics, but is not a Statistics 101 course.
The course is structured as follows: Getting Started, Basic Operations With Data, Building Charts in SPSS, Simple Analysis Techniques, Bonus Package 1: Assumption Checking, Bonus Package 2: More Advanced Analyses, and What’s Next? The course materials include PDF files with exercises attached to each lecture.
Upon completing the course, participants will have a solid understanding of SPSS and be able to create SPSS data sets, manage data, summarize and visualize data with charts and tables, compute statistical indicators, and perform basic statistical tests. The course can be completed within a few days depending on the learner’s pace.
To enroll in the course, interested individuals can simply press the enroll button and start learning.
This course entitled Statistics / Data Analysis in SPSS: Inferential Statistics is designed to provide professionals with marketable and highly sought after skills in data analytics, specifically in the area of significance testing. This course covers various tests, such as t tests, ANOVAs, post hoc tests, chi-square tests, correlation, and regression. In addition, it provides in-depth examples of each test for additional practice, making it suitable for professionals who want to increase their knowledge of data analytics.
Statistics plays a key role in making sound business decisions that generate higher profits. Inferential statistics, in particular, provides a better understanding of a population’s needs, allowing for the provision of attractive products and services. This course is designed for business professionals who want to learn how to analyze data accurately using IBM SPSS to draw conclusions that benefit their customers and bottom line.
By understanding how to use inferential statistics, students can draw accurate conclusions about a large group of people based on research conducted on a sample of that population. This course is easy to follow and includes illustrative examples throughout, helping students determine the appropriate statistical test to use for a particular data set. It also provides tools to interpret effect sizes and confidence intervals and write the results of statistical analyses in APA format.
The course covers various statistical tests, including One Sample t Test, Independent Samples t Test, Dependent Samples t Test, ANOVA, Correlation and Regression, Chi-Square, and Data Management in SPSS. It provides illustrations of how to analyze each test and additional examples for more practice. By the end of the course, students will be confident in IBM SPSS and statistics, identifying consumer needs, and developing products and services that effectively address those needs.
The Statistics/Data Analysis with SPSS: Descriptive Statistics course is designed to provide students and professionals with the skills and knowledge to analyze data using descriptive statistics, an important tool for making informed business decisions. The course covers the basics of both descriptive statistics and IBM SPSS, a software program designed for analyzing data. Upon completion of the course, students will be able to create charts and graphs, measure central tendency and variability, and interpret data effectively. The course is divided into six sections, including an introduction to SPSS, creating charts and graphs, measuring central tendency and variability, and two video lectures on statistics. The course offers step-by-step instructions and is suitable for beginners. Enroll now to gain highly valued and sought after skills in data analytics.
The IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced Course, offered by Scholarsight Learning, focuses on teaching learners Structural Equation Modelling (SEM), Path Analysis, and Confirmatory Analysis using IBM SPSS AMOS from scratch. AMOS is a powerful and widely used tool for doing Structural Equation Modelling, and is ideal for researchers looking to test complex structural models. Mastery of SEM is increasingly becoming essential for researchers, as a vast majority of researches are now using SEM.
The course instructor, Sanjay Singh, has published research papers using SEM, which learners can refer to for further understanding. In this course, learners will learn how to do SEM from scratch using AMOS. AMOS is a powerful tool for confirmatory validation and is often used by researchers and psychometricians for research and high impact publishing. The software allows learners to specify, estimate, assess and present models to show hypothesized relationships among variables.
The AMOS software also enables learners to build and test complex models more accurately and efficiently than standard multivariate statistics techniques. The course offers a 30-day money-back guarantee, and the instructor is responsive to questions and clarifications. Reviews from previous learners in the SPSS Foundation course have praised the instructor’s teaching approach and the organization of the course.
The course is broken down into sections, starting with an introduction and installation of the software, followed by practice datasets, references, and resources. Learners will get familiar with the AMOS interface and terminology of SEM, and will use AMOS graphic tools to build a structural model. The course will cover issues in SEM, Exploratory Factor Analysis (EFA), Scale Validation in AMOS, Indices of Model Fit, Working with Plugins in AMOS, and the next steps for learners.
The SPSS Beginners: Master SPSS course is designed to assist individuals without a background in statistics or mathematics to analyze data in SPSS confidently. The course covers everything from entering data into SPSS to interpreting the results and provides a step-by-step guide to mastering descriptive statistics in SPSS. The course begins by introducing the SPSS interface and providing guidance on variable types and SPSS format. Next, the course delves into entering, editing, and removing data, including how to transform variables into new ones using recode functions. Descriptive statistics are covered in depth, including how to run major descriptive statistics like mean, median, mode, standard deviation, and one-samples t-test. The course also teaches how to create and manipulate graphs, plots, and charts in SPSS. Finally, the course teaches how to choose the appropriate statistical technique for analyzing data in SPSS.
Upon completing the course, individuals will have learned the techniques for choosing the appropriate statistical technique, including Pearson correlation, Spearman ranked order correlation, Kendall’s tau B correlation, independent samples t-test, paired samples t-test, point bi-serial correlation, Mann Whitney U Test, Kruskal Wallis, McNemar’s Test, Chi Square, linear regression, multiple regression, binary logistic regression, repeated measures ANOVA, between subject ANOVA, mixed/split-plot ANOVA, and many more. The course uses a mix of video materials, slides, template documents, SPSS data, and output files to deliver the course effectively.
The course is designed with the philosophy that if you can’t explain something simply, you don’t understand it well enough. The course is presented in simple English to ensure learners get the most out of the course. The course is expected to take a few days to complete, but the skills learned will last a lifetime.
8. The 16-hour SPSS Pro: Analysis, Interpretation, and Write-Up by Todd Bottom, Ph.D. – Founder Research Learning Center (Udemy)
The 16-hour SPSS Pro course is designed to help graduate students and early career professionals who struggle with analyzing and reporting their research data. Taught by Dr. Todd Bottom, a 13-year veteran researcher, the course covers everything from basic functions to advanced skills like cluster analysis and repeated measures ANOVA. The course is structured to progress from beginner to advanced levels, and the modules and lectures guide students through each of the program’s menu functions. The course also includes quizzes and homework assignments to test students’ knowledge and offer practical opportunities to use their newly gained skills.
The course is suitable for new SPSS users who need to learn the basics, students with some familiarity with SPSS, and experienced users who want a refresher course. The curriculum includes instruction on conducting various statistical analyses, such as descriptive statistics, reliability analysis, correlation analysis, t-tests, regression analyses, ANOVA, cluster analysis, and factor analysis. In addition to step-by-step instructions, the course also covers topics like variable set-up, item reverse scoring, subscale and full measure calculation, outlier and extreme score handling, missing data analysis, and interpretation and reporting of results in APA format. Lectures also cover topics such as checking normality of data, meeting assumptions like multicollinearity and homogeneity of data, and interpreting outputs.
The course is praised by previous clients for Todd’s coaching and consulting skills, and the curriculum is structured into 10 modules, including a bonus section. The course starts with an introduction and covers topics like SPSS description, screen views, importing data, setting up variables, file menu, edit menu, view menu, data menu, transform menu, and analyze menu. The course is designed to have a real classroom feel by offering lectures that are 30 minutes or longer. The course is a comprehensive resource for students and researchers who want to learn, conduct, and report data analysis.
The Introduction to SPSS course is designed to provide an overview of the SPSS software program and basic descriptive and inferential statistics. The course will cover topics including creating variables, entering data, modifying data files, and various descriptive statistics such as bar graphs and measures of central tendency. It will also delve into hypothesis testing with coverage of the Pearson r correlation coefficient.
The course will begin with an introduction to SPSS, providing tips on getting started with the software program. Participants will then learn how to modify the SPSS data editor and output options to customize their data analysis. The course will also cover statistical analysis in SPSS, including charts, graphs, and both descriptive and inferential statistics.
Participants will be given guidance on entering questionnaire data in SPSS, including creating value labels and entering basic data. The course will conclude with coverage of hypothesis testing, including the Pearson r correlation coefficient.
Overall, the Introduction to SPSS course is suitable for individuals seeking an introduction to the SPSS software program and statistical analysis techniques. The course is suitable for beginners and does not require prior experience with the software or statistical analysis.
The IBM SPSS Modeler: Getting Started course is designed to introduce students to data mining using IBM SPSS Modeler. This data mining workbench enables analysts to build predictive models quickly and intuitively without programming. The course is broken up into phases and each video consists of detailed instructions explaining various techniques.
The Introduction to Data Mining Phase helps students understand the idea of data mining, the CRISP-DM methodology, and how to navigate within Modeler. The Data Understanding Phase focuses on understanding data resources and assessing data quality. The Data Preparation Phase discusses how to integrate and construct data, while the Modeling Phase focuses on building a predictive model. The Evaluation Phase teaches how to take data mining results and achieve business objectives, and the Deployment Phase allows students to do something with their findings.
Throughout the course, students will learn about specific nodes or data mining topics, and the course content is designed to address different aspects of data mining. By the end of the course, students will be able to use IBM SPSS Modeler for data mining, and to build predictive models based on their findings.