# SPSS Training – The only Course you need

**⏰36 hours | ▶️ 36 Videos | 📣 48 Participants | 🔥 27 Reviews**

# Choose a Plan that Works for You

## Self Paced

Unlimited Access- Advanced sessions
- Interview Q&A
- Free study Materials
- Premium Technical support

## Instructor Led Live Training

Unlimited Access- Live Instructor
- Advanced sessions
- Interview Q&A
- Premium Technical Support

## Corporate Training

Unlimited Access- Live Instructor
- Advanced sessions
- Interview Q&A
- Premium Technical Support
- Contact Us

# Upcoming Batches PST

Weekday

Jan 21 (1 HR A DAY) |

07:00 PM PST |

Enroll Now → |

Weekday

Jan 25 (1 HR A DAY) |

06:00 PM PST |

Enroll Now → |

Weekend

Jan 23 (1 HR A DAY) |

07:30 AM PST |

Enroll Now → |

# Upcoming Batches IST

Weekday

Jan 20 (1 HR A DAY) |

07:30 AM IST |

Enroll Now → |

Weekday

Jan 26 (1 HR A DAY) |

06:30 AM IST |

Enroll Now → |

Weekend

Jan 23 (1 HR A DAY) |

08:00 PM IST |

Enroll Now → |

# Course Description

So you want to learn SPSS ? Great job! Do you know SPSS is the most trending SPSS course? There are oceans of opportunities in SPSS as it leads the SPSS market. Our Workday Training course is a job oriented course i.e at the end of the course you can easily clear interviews or on board into an ongoing SPSS project.

# Features

✅Lifetime access | ✅Lifetime video access |

✅Real-time case studies | ✅The project integrated into the Curriculum |

✅24*7 Support from our team of administrators |

# Course Content

### 1.Research methods

- Statistics
- The Research Process
- Initial Observation
- Generate Theory
- Generate Hypotheses
- Data collection to Test Theory

- What to measure
- How to Measure

- Analyze data
- Descriptive Statistics: Overview
- Central Tendency
- Measure of variation
- Coefficient of Variation
- Fitting Statistical Models
- Conclusion

### 2.Statistics

- 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

### 3.SPSS Environment

- Accessing SPSS
- To explore the key windows in SPSS

- Data editor
- The viewer
- 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

### 4. Exploring data with graphs

- The art of presenting data
- The SPSS Chart Builder

- Histograms: a good way to spot obvious problems
- Boxplots (box–whisker diagrams)

- Graphing means: bar charts and error bars

- Simple bar charts for independent means
- Clustered bar charts for independent means
- Simple bar charts for related means
- Clustered bar charts for related means
- Clustered bar charts for ‘mixed’ designs

- Line charts
- Graphing relationships: the scatterplot

- Simple scatterplot
- Grouped scatterplot
- Simple and grouped -D scatterplots
- Matrix scatterplot

- Simple dot plot or density plot
- Drop-line graph
- Editing graphs

### 5. Exploring assumptions

- 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

- Output from the explore procedure
- Reporting the K–S test

- Testing for homogeneity of variance

- Levene’s test
- Reporting Levene’s test

- Correcting problems in the data

- Dealing with outliers
- Dealing with non-normality and unequal variances
- Transforming the data using SPSS

### 6.Correlation

- Looking at relationships
- How do we measure relationships?

- Covariance
- Standardization and the correlation coefficient

- The significance of the correlation coefficient
- Confidence intervals for r
- Correlation in SPSS

- Bivariate correlation
- Pearson’s correlation coefficient
- Spearman’s correlation coefficient
- Kendall’s tau (non-parametric)
- Biserial and point–biserial correlations
- Partial correlation
- The theory behind part and partial correlation
- Partial correlation using SPSS
- Semi-partial (or part) correlations

- Comparing correlations
- Comparing independent rs
- dependent rs
- Calculating the effect size
- How to report correlation coefficients

### 7.Regression

- 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

- Interpreting a simple regression
- Overall fit of the model
- Model parameters
- Using the model

- Multiple regression: the basics

- An example of a multiple regression model
- Sums of squares, R and R2
- Methods of regression
- How accurate is my regression model?
- Assessing the regression model, I: diagnostics
- Assessing the regression model II: generalization

- How to do multiple regression using SPSS

- Some things to think about before the analysis
- Main options
- Statistics
- Regression plots
- Saving regression diagnostics
- Interpreting multiple regression

- Descriptive

- Summary of model
- Model parameters
- Excluded variables
- Assessing the assumption of no multicollinearity
- Case wise diagnostics

- Checking assumptions

- What if I violate an assumption?
- to report multiple regression

### 8.Categorical predictor in multiple regression

- Dummy coding
- SPSS output for dummy variables

### 9.Logistic regression

- Background to logistic regression
- What are the principles behind logistic regression?
- Assessing the model: the log-likelihood statistic
- Assessing the model: R and R2

- The Wald statistic
- The odds ratio: Exp (B)

- Methods of logistic regression

- Assumptions
- Incomplete information from the predictors
- Complete separation
- Overdispersion
- Binary logistic regression
- The main analysis
- Method of regression
- Categorical predictors
- Obtaining residuals

- Interpreting logistic regression

- The initial model
- Step: intervention
- Listing predicted probabilities
- Interpreting residuals
- Calculating the effect size

- How to report logistic regression
- Testing assumptions

- Testing for linearity of the logit
- Testing for multicollinearity

- Predicting several categories: multinomial logistic regression
- Running multinomial logistic regression in SPSS

- Statistics
- Other options
- Interpreting the multinomial logistic regression output
- Reporting the results

### 10.Comparing two means (t-test)

- Looking at differences
- A problem with error bar graphs of repeated-measures designs

- Step: calculate the mean for each participant
- Step: calculate the grand mean
- Step: calculate the adjustment factor
- create adjusted values for each variable

- The t-test
- Rationale for the t-test

- Assumptions of the t-test
- The dependent t-test
- Sampling distributions and the standard error
- The dependent t-test equation explained
- The dependent t-test and the assumption of normality
- Dependent t-tests using SPSS
- Output from the dependent t-test
- Calculating the effect size

- Reporting the dependent t-test

- The independent t-test
- The independent t-test equation explained
- The independent t-test using SPSS
- Output from the independent t-test
- Calculating the effect size

- Reporting the independent t-test
- Between groups or repeated measures?
- The t-test as a general linear model

### 11.Comparing several means: ANOVA (GLM)

- The theory behind ANOVA
- Inflated error rates
- Interpreting f-test
- ANOVA as regression

- Logic of the f-ratio
- Total sum of squares (SST)
- Model sum of squares (SSM)
- Residual sum of squares (SSR)
- Mean squares
- 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

- Output from one-way ANOVA
- Output for the main analysis
- Output for planned comparisons
- Output for post hoc tests
- Calculating the effect size
- Reporting results from one-way independent ANOVA
- Violations of assumptions in one-way independent ANOVA

### 12.Chi-square

- Analyzing categorical data
- Theory of analyzing categorical data

- Pearson’s chi-square test
- Fisher’s exact test

- The likelihood ratio
- Yates’ correction
- Assumptions of the chi-square test
- Doing chi-square on SPSS

- Running the analysis
- Output for the chi-square test
- Breaking down a significant chi-square test with standardized residuals
- Calculating an effect size

- Reporting the results of chi-square

# FAQ’s

**❓ Do you offer any discount/offer?**

✅ Yes, offers keep changing from time to time. You can chat with us or call our training coordinator for more details.

**❓ Is there any demo video which I can watch before enrolling to the course?**

✅ Yes, we have provided a Demo video section on each course page so that you can get a glimpse into the course you want to enroll.

**❓ How soon after signing up would I get access to the learning content?**

✅ Yes, we will provide access to all the learning materials after the complete payment for the course.

# Suggested Courses

MuleSoft Training

⭐⭐⭐⭐⭐

😃 221 Learners

Pega Training

⭐⭐⭐⭐⭐

😃 391 Learners

SailPoint Training

⭐⭐⭐⭐⭐

😃 106 Learners

WorkDay Training

⭐⭐⭐⭐⭐

😃 158 Learners

A few of our students