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SPSS Certification Training - SPSS Training


40 hours | 57K Participants

Plans & Pricing



40 Hours of Videos.
Flexible Schedule.
Free Demo
Technical Support
Lifetime free Upgrade
Course completion Certificate

Instructor Led-Live


Instructor Led Training
40 Hours E-Learning Videos.
Flexible Schedule.
Free Demo
Learn Whenever & Wherever.
Technical Support
Lifetime free Upgrade
Course Completion Certificate



4 Hours E-Learning Videos.

Flexible Schedule.
Free Demo
Learn Whenever & Wherever

Lifetime free Upgrade

NO instructor led training
NO Technical Support

About Course

In SPSS Training, students learn introductory classes on how to use SPSS effectively.

For those new to SPSS, the course covers every aspect of using the software, from data input and export to data manipulation, descriptive statistics, data analysis techniques, and charting and reporting findings.

The online SPSS course is structured into sections that address everything from various file types and data sources to creating data files, setting up analysis parameters, building data reports, and analyzing their results.

The training emphasizes the development of practical skills and real-world applications, providing hands-on examples of each topic.

After completing the course, students will be able to confidently utilize SPSS in their research and data analysis projects.

Students will also be equipped to evaluate data, write valuable reports, and effectively communicate their findings to a broader audience.

SPSS training serves as an introductory course for students who aim to master the use of SPSS for reliable data analysis.

The course focuses on building practical skills and real-world applications through extensive, hands-on education.

It is an excellent option for individuals who wish to gain a thorough understanding of SPSS and develop expertise in data analysis.

SPSS Online Training is an online course that teaches the fundamentals of data analysis using the SPSS software.

Designed for students, researchers, and professionals, the SPSS course aims to teach participants how to analyze data using the SPSS program.

The SPSS class is intended to provide a comprehensive introduction to this sophisticated statistical software.

The curriculum will cover the fundamentals of data input, hypothesis testing, data analysis, and the creation of charts and graphs.

Upon completion, students will be able to understand and report SPSS results and develop SPSS models and datasets.

This course is suitable for students with a background in introductory statistics who want to enhance their knowledge and skills in data-driven decision-making.

Those without a foundational knowledge in statistics are encouraged to take an introductory statistics course before enrolling in this SPSS seminar.

The SPSS online course is available in both online and in-person formats and requires a computer with access to the SPSS software.

The online class includes video lectures, a discussion room, and quizzes, while the in-person sessions feature weekly lectures and hands-on exercises.

The SPSS Certification program allows users to demonstrate their software expertise while enhancing their skills and marketability.

It includes tutorials, quizzes, and certifications at the mastery level, aimed at providing professionals with the necessary skills and knowledge to utilize SPSS software for data analysis.

The SPSS interview Q & A offers a complete set of questions and answers about the Statistical Package for Social Sciences (SPSS) program.

It covers installation, data types, importing data, data manipulation, statistical analyses, exporting results, and other topics.

The guide also features videos, links, blogs, and materials for these tutorials, designed to help researchers, analysts, and data scientists become familiar with the SPSS software.

CloudCertification.io offers SPSS training for predictive and data analysis. Our SPSS Training teaches data analysis, interpretation, and application through self-paced video lessons and activities.

You’ll also learn to utilize SPSS for advanced logistic regression and cluster analysis methods.

Our SPSS Training will boost your confidence in SPSS and enhance your data analysis skills.


SPSS Training from CloudCertification.io offers comprehensive online and in-person courses designed to teach students, researchers, and professionals how to effectively use SPSS for data analysis. The course covers a range of topics, including data input, manipulation, statistical analysis, and reporting. Participants will gain practical skills through hands-on examples and activities, enhancing their ability to utilize SPSS in various research and data analysis projects. The program also includes a certification option for those looking to validate their skills and improve their professional marketability.


Material Include





What Will I Learn?

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Course Curriculam

  • Statistics
  • The Research Process
  • Initial Observation
  • Generate Theory
  • Generate Hypotheses
  • Data collection to Test Theory
  1. What to measure
  2. How to Measure
  • Analyze data
  • Descriptive Statistics: Overview
  • Central Tendency
  • Measure of variation
  • Coefficient of Variation
  • Fitting Statistical Models
  • Conclusion
  • Building statistical models
  • Types of statistical models
  • Populations and samples
  • Simple statistical models
  • The mean as a model
  • The variance and standard deviation
  • Central Limit Theorem
  • The standard error
  • Confidence Intervals
  • Test statistics
  • Non-significant results and Significant results:
  • One- and two-tailed tests
  • Type I and Type II errors
  • Effect Sizes
  • Statistical power
  • Accessing SPSS
  • To explore the key windows in SPSS
  1. Data editor
  2. The viewer
  3. The syntax editor
  • How to create variables
  • Enter Data and adjust the properties of your variables
  • How to Load Files and Save
  • Opening Excel Files
  • Recoding Variables
  • Deleting/Inserting a Case or a Column
  • Selecting Cases
  • Using SPSS Help
  • The art of presenting data
  • The SPSS Chart Builder
  1. Histograms: a good way to spot obvious problems
  2. Boxplots (box–whisker diagrams)
  • Graphing means: bar charts and error bars
  1. Simple bar charts for independent means
  2. Clustered bar charts for independent means
  3. Simple bar charts for related means
  4. Clustered bar charts for related means
  5. Clustered bar charts for ‘mixed’ designs
  • Line charts
  • Graphing relationships: the scatterplot
  1. Simple scatterplot
  2. Grouped scatterplot
  3. Simple and grouped -D scatterplots
  4. Matrix scatterplot
  • Simple dot plot or density plot
  • Drop-line graph
  • Editing graphs
  • What are assumptions?
  • Assumptions of parametric data
  • The assumption of normality
  • Quantifying normality with numbers
  • Exploring groups of data
  • Testing whether a distribution is normal
  • Kolmogorov–Smirnov test on SPSS
  1. Output from the explore procedure
  2. Reporting the K–S test
  • Testing for homogeneity of variance
  1. Levene’s test
  2. Reporting Levene’s test
  • Correcting problems in the data
  1. Dealing with outliers
  2. Dealing with non-normality and unequal variances
  3. Transforming the data using SPSS
  • Looking at relationships
  • How do we measure relationships?
  1. Covariance
  2. Standardization and the correlation coefficient
  • The significance of the correlation coefficient
  • Confidence intervals for r
  • Correlation in SPSS
  1. Bivariate correlation
  2. Pearson’s correlation coefficient
  3. Spearman’s correlation coefficient
  4. Kendall’s tau (non-parametric)
  5. Biserial and point–biserial correlations
  6. Partial correlation
  7. The theory behind part and partial correlation
  8. Partial correlation using SPSS
  9. Semi-partial (or part) correlations
  • Comparing correlations
  • Comparing independent rs
  • dependent rs
  • Calculating the effect size
  • How to report correlation coefficients


  • An introduction to regression
  • Some important information about straight lines
  • The method of least squares
  • Assessing the goodness of fit: sums of squares, R and R2
  • Doing simple regression on SPSS
  1. Interpreting a simple regression
  2. Overall fit of the model
  3. Model parameters
  4. Using the model
  • Multiple regression: the basics
  1. An example of a multiple regression model
  2. Sums of squares, R and R2
  3. Methods of regression
  4. How accurate is my regression model?
  5. Assessing the regression model, I: diagnostics
  6. Assessing the regression model II: generalization
  • How to do multiple regression using SPSS
  1. Some things to think about before the analysis
  2. Main options
  3. Statistics
  4. Regression plots
  5. Saving regression diagnostics
  6. Interpreting multiple regression
  • Descriptive
  1. Summary of model
  2. Model parameters
  3. Excluded variables
  4. Assessing the assumption of no multicollinearity
  5. Case wise diagnostics
  • Checking assumptions
  1. What if I violate an assumption?
  2. to report multiple regression
  • Dummy coding
  • SPSS output for dummy variables
  • Background to logistic regression
  • What are the principles behind logistic regression?
  • Assessing the model: the log-likelihood statistic
  • Assessing the model: R and R2
  1. The Wald statistic
  2. The odds ratio: Exp (B)
  • Methods of logistic regression
  1. Assumptions
  2. Incomplete information from the predictors
  3. Complete separation
  4. Overdispersion
  5. Binary logistic regression
  6. The main analysis
  7. Method of regression
  8. Categorical predictors
  9. Obtaining residuals
  • Interpreting logistic regression
  1. The initial model
  2. Step: intervention
  3. Listing predicted probabilities
  4. Interpreting residuals
  5. Calculating the effect size
  • How to report logistic regression
  • Testing assumptions
  1. Testing for linearity of the logit
  2. Testing for multicollinearity
  • Predicting several categories: multinomial logistic regression
  • Running multinomial logistic regression in SPSS
  1. Statistics
  2. Other options
  3. Interpreting the multinomial logistic regression output
  4. Reporting the results



  • Looking at differences
  • A problem with error bar graphs of repeated-measures designs
  1. Step: calculate the mean for each participant
  2. Step: calculate the grand mean
  3. Step: calculate the adjustment factor
  4. create adjusted values for each variable
  • The t-test
  • Rationale for the t-test
  1. Assumptions of the t-test
  2. The dependent t-test
  3. Sampling distributions and the standard error
  4. The dependent t-test equation explained
  5. The dependent t-test and the assumption of normality
  6. Dependent t-tests using SPSS
  7. Output from the dependent t-test
  8. Calculating the effect size
  • Reporting the dependent t-test
  1. The independent t-test
  2. The independent t-test equation explained
  3. The independent t-test using SPSS
  4. Output from the independent t-test
  5. Calculating the effect size
  • Reporting the independent t-test
  • Between groups or repeated measures?
  • The t-test as a general linear model
  • The theory behind ANOVA
  • Inflated error rates
  • Interpreting f-test
  • ANOVA as regression
  1. Logic of the f-ratio
  2. Total sum of squares (SST)
  3. Model sum of squares (SSM)
  4. Residual sum of squares (SSR)
  5. Mean squares
  6. The f-ratio
  • Assumptions of ANOVA
  • Planned contrasts
  • Post hoc procedure
  • Running one-way ANOVA on SPSS
  • Planned comparisons using SPSS
  • Post hoc tests in SPSS
  1. Output from one-way ANOVA
  2. Output for the main analysis
  3. Output for planned comparisons
  4. Output for post hoc tests
  5. Calculating the effect size
  6. Reporting results from one-way independent ANOVA
  7. Violations of assumptions in one-way independent ANOVA
  • Analyzing categorical data
  • Theory of analyzing categorical data
  1. Pearson’s chi-square test
  2. Fisher’s exact test
  • The likelihood ratio
  • Yates’ correction
  • Assumptions of the chi-square test
  • Doing chi-square on SPSS
  1. Running the analysis
  2. Output for the chi-square test
  3. Breaking down a significant chi-square test with standardized residuals
  4. Calculating an effect size
  • Reporting the results of chi-square

Question and Answer

Yes, Cloudcertification provides a 100% money-back guarantee on fulfilling all the below checkpoints
1. This offers is only on instructor-led training
2. 95% class attendance
3. In the Test, you have to score 95% marks conducted by Cloudcertification.

At Cloudcertification, you'll never miss a Session! You will be provided with the recorded class of that day.

Post-enrollment will provide access immediately, and you can start the course right away.

SPSS Training Certification

The certification process plays a crucial role in getting the job you want. It shows that you've gained the necessary skills to manage crucial jobs in the real world with minimal or no help from your colleagues. Companies are focusing on hiring those with qualifications and are providing huge salary packages. We will provide all required courses to earn in obtaining the certification. Cloud Certification Trainings encompasses every aspect of shaping you into a professional certified by providing you with the professional and technical knowledge that will help your career to be successful.

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