SPSS Training Course Online Course

SPSS Training Course

All levels
English

Course Description

SPSS course is designed for business professionals who want to know how to analyze data. You'll learn how to use IBM SPSS to draw accurate conclusions on your research and make decisions that will benefit your customers and your bottom line. SPSS is a powerful statistical application package from IBM used for analysis of data. The training delivers the skill related to the use of SPSS environment for data understanding, data preparation, and ways of executing sequence of operations to derive the result in the required format. BIT’s SPSS training enables you to master all the essential concepts of SPSS for performing data analysis and statistics through hands-on exposure to industry use cases. By the end of the training, you will gain valuable insights into data analysis and will be able to clear the SPSS certification exam in your first attempt itself. There is a massive scope for SPSS statistics in the market and the candidates can take full advantage of it.

See more less

What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
Understanding the research process and be clear in which type of data you must collect and how to measure it
Familiar with SPSS environment
Exploring groups of data and making assumptions to work on a problem
Compare Means,and conduct post hoc test
In this course, you will gain proficiency in how to analyze a number of statistical procedures in SPSS.
Learn how to write the results of statistical analyses using APA format
Differentiating among different statistical models and types of results and errors
Know how to use SPPS to work with graphs
Use SPSS forvarious statistical procedures such as Correlation, Regression, Dependent & Independent T-test, Pearson Chi-...
Run several tests for statistical significance and interpreted the results
You will learn how to interpret the output of a number of different statistical tests

Covering Topics

Lecture -1 Research methods

Lecture -2 Statistics

Lecture -3 SPSS Environment

Lecture -4 Exploring data with graphs

Lecture -5 Exploring assumptions

Lecture -6 Correlation

Lecture -7 Regression

Lecture -8 Categorical predictor in multiple regression

Lecture -9 Logistic regression

Lecture -10 Comparing two means (t-test)

Lecture -11 Comparing several means: ANOVA (GLM)

Lecture -12 Chi-square

Case Studies

Curriculum

  Lecture -1 Research methods

·     Statistics?

·     The Research Process

·     Initial Observation

·     Generate Theory

·     Generate Hypotheses

·     Data collection to Test Theory

·     Analyze data

·     Descriptive Statistics: Overview

·     Central Tendency

·     Measure of variation

·     Coefficient of Variation

·     Fitting Statistical Models

·     Conclusion

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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 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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Casewise diagnostics

·     Checking assumptions

·     What if I violate an assumption?

·     to report multiple regression

·     Practical Exercise

  Lecture -8 Categorical predictor in multiple regression
·     Dummy coding

·     SPSS output for dummy variables

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Case Studies

Frequently Asked Questions

Basic knowledge of Computer handling and Statistics is enough to learn SPSS.

Ans: The course offers a variety of online training options, including: • Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. • 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. • Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. • Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Ans: Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

Ans: If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

Ans: The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.