Quantitative Analysis of Data Using SPSS V29

Quantitative Analysis, SPSS Software, Tests and Conclusions

Created byAlain Tannous

Last updated 10/2024

English

What you'll learn

Explain the six SPSS windows and their specific uses.
Calculate the mean, sum, standard deviation, variance, and more.
Examine relationships using Scatter Plot and Scatter Plot Matrices.
Test for Normality and Homoscedasticity.
Explain numerical measures such as Central Location, Dispersion, Skewness, Kurtosis and Linearity.
Identify and differentiate between the different forms of outcomes.
Differentiate between the statistical types I and II errors.
Construct a multiple regression model, identify its elements and list its assumptions.
Learn to use the Ordinary Least Squares (OLS) regression method to estimate the coefficients.
Define the Standard Error of Estimates (SEE), explain its role in regression model and understand its visual representation.
Relate SEE to R-squared and the overall performance of the regression model.
Define ANOVA, explain its assumptions and significance in regression analysis, set its hypotheses, conduct ANOVA analysis using SPSS and explain its outcomes.
Formulate the null and alternative hypotheses.
Conduct a statistical T-test to assess and interpret the significance of the coefficients.
Define autocorrelation, identify its impact on regression models and test for autocorrelation using Durbin-Watson Test.
Explain the importance of forecasting time series data and perform a forecast seasonal time series data using SPSS V29.
Define MANOVA, explain its assumptions and significance in regression analysis, set its hypotheses, conduct MANOVA test using SPSS and explain its outcomes.
Generate different tests related to MANOVA such as: Box's M Test, Hotelling's T Squared, Pillai's Trace Test and Wilk's Lambda Test.
Formulate and test the hypotheses of all MANOVA tests using SPSS.
Compose and test the hypotheses for Levene's Test of homogeneity of variances.
Define Discriminant analysis and identify its variables, assumptions and hypotheses.
Test for Multivariate Normality, Equality of Covariances and Vectors of Means.
Perform a stepwise discriminant analysis and analyze its outcomes using the cross-validation method.
Define logistic regression, identify its variables and assumptions, compose its hypotheses, perform a stepwise logistic regression test and analyze its outputs.
Define the Principal Component Analysis (PCA), identify its key variables and assumptions, and conduct a PCA analysis following a detailed procedure on SPSS.
Define Factor Analysis (FA), identify its assumptions, variables and hypotheses, and conduct all FA tests and analyze their outcomes.
Define Cluster Analysis, identify its assumptions, variables and hypotheses, and conduct all FA tests and analyze their outcomes.
Apply all tests using practical exercises on SPSS

Course content

10 sections • 21 lectures • 4h 19m total length

Introduction1:48
By the end of this lesson, you will be able to:
Define SPSS and its uses.
List the SPSS Forms.
Identify the SPSS versions.
Lesson 1.1 Quiz
Lesson 2.1 SPSS Basics16:15
By the end of this lesson, you will be able to:
Explain the six SPSS windows and their specific uses.
Lesson 2.1 SPSS Basics

Descriptive Statistics10:20
By the end of this lesson, you will be able to:
Calculate the mean, sum, standard deviation, variance, and other key statistical measures.

This sets the stage for what you'll learn, highlighting the practical skills and knowledge you'll gain. As we delve into these concepts, you'll discover how they serve as the building blocks for understanding and analyzing data, providing you with the tools needed to interpret real-world datasets effectively.
Descriptive Statistics
Relationships5:24
By the end of this session, you will be able to:
Use tools such as scatter plots and correlation coefficients to explore and analyze the relationships between two numerical variables.

These objectives set the stage for a deep dive into the methodologies that allow us to visualize and quantify the strength and direction of relationships, essential skills for anyone looking to understand the dynamics within their data.
Relationships
Normality and Homoscedasticity13:48
By the end of this lesson, you will prepare to:
Dive into the methodologies for testing the assumptions of Normality and Homoscedasticity, their implications for statistical modeling, and strategies for addressing violations.
Normality and Homoscedasticity
Numerical Measures14:33
This session covers the essential metrics that form the backbone of statistical analysis such as:
Central location
Dispersion
Skewness
Kurtosis
Linearity.
Numerical Measures

Null and Alternate Hypotheses3:43
By the end of this lesson, you will be able to:
Set and differentiate between the null and the alternative hypotheses.
Null and Alternate Hypothesis
Outcomes and Types of Errors3:43
By the end of this lesson, you'll be equipped to:
Define outcomes, differentiate between Type I and Type II errors, and apply this knowledge to enhance the accuracy of research findings.
Outcomes and Types of Errors

Multiple Regression Model and Assumptions4:09
By the end of this lesson, you will be able to:
Define a multiple regression model.
Construct a multiple regression model and identify its elements.
List the conditions required to construct a multiple regression model.
Multiple Regression Model and Assumptions
Estimating the Coefficients11:02
By the end of this lesson, you will be able to:
Learn to use the Ordinary Least Squares (OLS) regression method to estimate the coefficients.
Estimating the Coefficients
Assessing the Model8:05
By the end of this lesson, you will be able to:
Understand what SEE is.
Its significance in regression models.
How it relates to R-squared.
How to evaluate it both numerically and graphically.
Assessing the Model
Assessing the Model: Analysis of Variance (ANOVA)10:11
By the end of this lesson, you will be able to:
Define ANOVA and its significance in regression analysis
Identify and explain the assumptions of ANOVA.
Learn how to set the hypotheses in ANOVA.
Understand the process of performing ANOVA.
Analysis of Variance (ANOVA)
Testing and Interpreting the Coeficients6:08
By the end of this lesson, you will be able to:
Formulate hypotheses for model coefficients.
Conduct statistical T-tests to assess their significance.
Interpret the results effectively.
Testing and Interpreting the Coefficients
Correlation of Errors: Durbin Watson Test6:13
By the end of this lesson, you will be able to:
Define autocorrelation of errors.
Identify the impact of autocorrelation on the accuracy of the regression model results.
Test for autocorrelation using Durbin-Watson Test.
Correlation of Errors: Durbin Watson Test
Forecasting Seasonal Time Series20:19
By the end of this lesson, you will be able to:
Explain the importance of forecasting time series data and give examples.
Forecast seasonal time series data using SPSS v. 29.
Forecasting Seasonal Time Series

Multivariate Analysis of Variance (MANOVA)26:35
By the end of this lesson, you will be able to:
Understand what MANOVA is and identify the variables involved.
Understand what MANOVA is and identify the variables involved.
Understand what MANOVA is and identify the variables involved.
List and differentiate among the tests that are part of the MANOVA Process (Box’s M Test, Hotelling’s T squared, Pillai’s Trace Test, Wilk’s Lambda Test and Levene’s Test).
Understand how to generate these tests using SPSS.
Formulate the hypotheses of each test.
Analyze the output of each test.
What is the primary purpose of MANOVA?

Requirements

No prerequisites are required. You will learn all steps in this course.

Description

This course offers a detailed exploration of quantitative data analysis using SPSS V29, a powerful software tool widely used in research fields such as social sciences, business, healthcare, and education. Designed for both beginners and intermediate users, the course covers essential statistical techniques and guides learners through the process of managing, analyzing, and interpreting quantitative data.

Over 4 hours and 20 minutes of recorded lectures, accompanied by PDF notes for each session, will introduce you to core concepts in data analysis, such as:

Data entry, management, and cleaning techniques using SPSS V29
Descriptive statistics, including measures of central tendency and variability
Inferential statistics such as t-tests, ANOVA, regression analysis, and chi-square tests
Multivariate analysis techniques, including factor analysis, discriminant analysis, and cluster analysis
Reporting and interpreting statistical outputs effectively
Using visual tools like graphs and charts for data presentation

The course also includes SPSS data files for hands-on practice, ensuring that students gain practical experience working with real datasets.

Additionally, a quiz at the end of each lesson allows students to assess their understanding and apply the skills learned. By the end of this course, participants will be able to effectively use SPSS V29 to perform complex statistical analyses, create meaningful data visualizations, and report results professionally and clearly, equipping them with the tools needed for academic research or professional projects.

Who this course is for:

Graduate and Postgraduate students who are engaged in writing their thesis or dissertation which is based on a quantitative analysis of data.
Beginner researchers who require a tool to analyze their findings.

Quantitative Analysis of Data Using SPSS V29

What you'll learn

Explore related topics

Course content

Introduction & SPSS Basics2 lectures • 18min

Descriptive Statistics4 lectures • 44min

Hypotheses, Outcomes and Errors2 lectures • 7min

Multiple Regression Model7 lectures • 1hr 6min

Multivariate Analysis of Variance (MANOVA)1 lecture • 27min

Discriminant Analysis (DA)1 lecture • 29min

Logistic Regression (LR)1 lecture • 27min

Principal Component Analysis (PCA)1 lecture • 12min

Factor Analysis1 lecture • 18min

Cluster Analysis (CA)1 lecture • 11min

Requirements

Description

Who this course is for: