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SPSS For Research

SPSS data analysis made easy. Become an expert in advanced statistical analysis with SPSS.

4.47 (1279 reviews)

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\$2.95/month

What you will learn

perform simple operations with data: define variables, recode variables, create dummy variables, select and weight cases, split files

built the most useful charts in SPSS: column charts, line charts, scatterplot charts, boxplot diagrams

perform the basic data analysis procedures: Frequencies, Descriptives, Explore, Means, Crosstabs

test the hypothesis of normality (with numeric and graphic methods)

detect the outliers in a data series (with numeric and graphic methods)

transform variables

perform the main one-sample analyses: one-sample t test, binomial test, chi square for goodness of fit

perform the tests of association: Pearson and Spearman correlation, partial correlation, chi square test for association, loglinear analysis

execute the analyses for means comparison: t test, between-subjects ANOVA, repeated measures ANOVA, nonparametric tests (Mann-Whitney, Wilcoxon, Kruskal-Wallis etc.)

perform the regression analysis (simple and multiple regression, sequential regression, logistic regression)

compute and interpret various tyes of reliability indicators (Cronbach's alpha, Cohen's kappa, Kendall's W)

use the data reduction techniques (multidimensional scaling, principal component analysis, correspondence analysis)

use the main grouping techniques (cluster analysis, discriminant analysis)

Description

Become an expert in statistical analysis with the most extended SPSS course at Udemy: 146 video lectures covering about 15 hours of video!

Within a very short time you will master all the essential skills of an SPSS data analyst, from the simplest operations with data to the advanced multivariate techniques like logistic regression, multidimensional scaling or principal component analysis.

The good news – you don't need any previous experience with SPSS. If you know the very basic statistical concepts, that will do.

And you don't need to be a mathematician or a statistician to take this course (neither am I). This course was especially conceived for people who are not professional mathematicians – all the statistical procedures are presented in a simple, straightforward manner, avoiding the technical jargon and the mathematical formulas as much as possible. The formulas are used only when it is absolutely necessary, and they are thoroughly explained.

Are you a student or a PhD candidate? An academic researcher looking to improve your statistical analysis skills? Are you dreaming to get a job in the statistical analysis field some day? Are you simply passionate about quantitative analysis? This course is for you, no doubt about it.

Very important: this is not just an SPSS tutorial. It does not only show you which menu to select or which button to click in order to run some procedure. This is a hands-on statistical analysis course in the proper sense of the word.

For each statistical procedure I provide the following pieces of information:

• a short, but comprehensive description (so you understand what that technique can do for you)
• how to perform the procedure in SPSS (live)
• how to interpret the main output, so you can check your hypotheses and find the answers you need for your research)

The course contains 56 guides, presenting 56 statistical procedures, from the simplest to the most advanced (many similar courses out there don't go far beyond the basics).

The first guides are absolutely free, so you can dive into the course right now, at no risk. And don't forget that you have 30 full days to evaluate it. If you are not happy, you get your money back.

So, what do you have to lose?

Content

Getting Started

Introduction

Course Outline

The Basics

Guide 1: Working With SPSS Files

Guide 2: Defining Variables

Guide 3: Variable Recoding

Guide 4: Dummy Variables

Guide 5: Selecting Cases

Guide 6: File Splitting

Guide 7: Data Weighting

Creating Charts in SPSS

Guide 8: Column Charts

Guide 9: Line Charts

Guide 10: Scatterplot Charts

Guide 11: Boxplot Diagrams

Simple Analysis Techniques

Guide 12: Frequencies Procedure

Guide 13: Descriptives Procedure

Guide 14: Explore Procedure

Guide 15: Means Procedure

Guide 16: Crosstabs Procedure

Assumption Checking. Data Transformations

Guide 17: Checking for Normality - Numerical Methods

Guide 17: Checking for Normality - Graphical Methods

Guide 17: Checking for Normality - What to Do If We Do Not Have Normality?

Guide 18: Detecting Outliers - Graphical Methods

Guide 18: Detecting Outliers - Numerical Methods

Guide 18: Detecting Outliers - How to Handle the Outliers

Guide 19: Data Transformations

One-Sample Tests

Guide 20: One-Sample T Test - Introduction

Guide 20: One-Sample T Test - Running the Procedure

Guide 21: Binomial Test

Guide 21: Binomial Test with Weighted Data

Guide 22: Chi Square for Goodness-of-Fit

Guide 22: Chi Square for Goodness-of-Fit with Weighted Data

Association Tests

Guide 23: Pearson Correlation - Introduction

Guide 23: Pearson Correlation - Assumption Checking

Guide 23: Pearson Correlation - Running the Procedure

Guide 24: Spearman Correlation - Introduction

Guide 24: Spearman Correlation - Running the Procedure

Guide 25: Partial Correlation - Introduction

Guide 25: Partial Correlation - Practical Example

Guide 26: Chi Square For Association

Guide 26: Chi Square For Association with Weighted Data

Guide 27: Loglinear Analysis - Introduction

Guide 27: Loglinear Analysis - Hierarchical Loglinear Analysis

Guide 27: Loglinear Analysis - General Loglinear Analysis

Tests For Mean Difference

Guide 28: Independent-Sample T Test - Introduction

Guide 28: Independent-Sample T Test - Assumption Testing

Guide 28: Independent-Sample T Test - Results Interpretation

Guide 29: Paired-Sample T Test - Introduction

Guide 29: Paired-Sample T Test - Assumption Testing

Guide 29: Paired-Sample T Test - Results Interpretation

Guide 30: One-Way ANOVA - Introduction

Guide 30: One-Way ANOVA - Assumption Testing

Guide 30: One-Way ANOVA - F Test Results

Guide 30: One-Way ANOVA - Multiple Comparisons

Guide 31: Two-Way ANOVA - Introduction

Guide 31: Two-Way ANOVA - Assumption Testing

Guide 31: Two-Way ANOVA - Interaction Effect

Guide 31: Two-Way ANOVA - Simple Main Effects

Guide 32: Three-Way ANOVA - Introduction

Guide 32: Three-Way ANOVA - Assumption Testing

Guide 32: Three-Way ANOVA - Third Order Interaction

Guide 32: Three-Way ANOVA - Simple Second Order Interaction

Guide 32: Three-Way ANOVA - Simple Main Effects

Guide 32: Three-Way ANOVA - Simple Comparisons (1)

Guide 32: Three-Way ANOVA - Simple Comparisons (2)

Guide 33: Multivariate ANOVA - Introduction

Guide 33: Multivariate ANOVA - Assumption Checking (1)

Guide 33: Multivariate ANOVA - Assumption Checking (2)

Guide 33: Multivariate ANOVA - Result Interpretation

Guide 34: Analysis of Covariance (ANCOVA) - Introduction

Guide 34: Analysis of Covariance (ANCOVA) - Assumption Checking (1)

Guide 34: Analysis of Covariance (ANCOVA) - Assumption Checking (2)

Guide 34: Analysis of Covariance (ANCOVA) - Results Intepretation

Guide 35: Repeated Measures ANOVA - Introduction

Guide 35: Repeated Measures ANOVA - Assumption Checking

Guide 35: Repeated Measures ANOVA - Results Interpretation

Guide 36: Within-Within Subjects ANOVA - Introduction

Guide 36: Within-Within Subjects ANOVA - Assumption Checking

Guide 36: Within-Within Subjects ANOVA - Interaction

Guide 36: Within-Within Subjects ANOVA - Simple Main Effects (1)

Guide 36: Within-Within Subjects ANOVA - Simple Main Effects (2)

Guide 36: Within-Within Subjects ANOVA - Case of Nonsignificant Interaction

Guide 37: Mixed ANOVA - Introduction

Guide 37: Mixed ANOVA - Assumption Checking

Guide 37: Mixed ANOVA - Interaction

Guide 37: Mixed ANOVA - Simple Main Effects (1)

Guide 37: Mixed ANOVA - Simple Main Effects (2)

Guide 37: Mixed ANOVA - Case of Nonsignificant Interaction

Guide 38: Mann-Whitney Test - Introduction

Guide 38: Mann-Whitney Test - Results Interpretation

Guide 39: Wilcoxon and Sign Tests - Wilcoxon Test

Guide 39: Wilcoxon and Sign Tests - Sign Test

Guide 40: Kruskal-Wallis and Median Tests - Kruskal-Wallis Test

Guide 40: Kruskal-Wallis and Median Tests - Median Test

Guide 41: Friedman Test

Guide 42: McNemar Test

Predictive Techniques

Guide 43: Simple Regression - Introduction

Guide 43: Simple Regression - Assumption Checking (1)

Guide 43: Simple Regression - Assumption Checking (2)

Guide 43: Simple Regression - Results Interpretation

Guide 44: Multiple Regression - Introduction

Guide 44: Multiple Regression - Assumption Checking

Guide 44: Multiple Regression - Results Interpretation

Guide 45: Regression with Dummy Variables

Guide 46: Sequential Regression

Guide 47: Binomial Regression - Introduction

Guide 47: Binomial Regression - Assumption Checking

Guide 47: Binomial Regression - Goodness-of-Fit Indicators

Guide 47: Binomial Regression - Coefficient Interpretation (1)

Guide 47: Binomial Regression - Coefficient Interpretation (2)

Guide 47: Binomial Regression - Classification Table

Guide 48: Multinomial Regression - Introduction

Guide 48: Multinomial Regression - Assumption Checking

Guide 48: Multinomial Regression - Goodness-of-Fit Indicators

Guide 48: Multinomial Regression - Coefficient Interpretation (1)

Guide 48: Multinomial Regression - Coefficient Interpretation (2)

Guide 48: Multinomial Regression - Coefficient Interpretation (3)

Guide 48: Multinomial Regression - Classification Table

Guide 49: Ordinal Regression - Introduction

Guide 49: Ordinal Regression - Assumption Testing

Guide 49: Ordinal Regression - Goodness-of-Fit Indicators

Guide 49: Ordinal Regression - Coefficient Interpretation (1)

Guide 49: Ordinal Regression - Coefficient Interpretation (2)

Guide 49: Ordinal Regression - Classification Table

Scaling Techniques

Guide 50: Reliability Analysis - Cronbach's Alpha

Guide 50: Reliability Analysis - Cohen's Kappa

Guide 50: Reliability Analysis - Kendall's W

Guide 51: Multidimensional Scaling - Introduction

Guide 51: Multidimensional Scaling - ALSCAL procedure (1)

Guide 51: Multidimensional Scaling - ALSCAL procedure (2)

Guide 51: Multidimensional Scaling - PROXSCAL procedure (1)

Guide 51: Multidimensional Scaling - PROXSCAL procedure (2)

Data Reduction

Guide 52: Principal Component Analysis - Introduction

Guide 52: Principal Component Analysis - Running the Procedure

Guide 52: Principal Component Analysis - Testing For Adequacy

Guide 52: Principal Component Analysis - Obtaining a Final Solution

Guide 52: Principal Component Analysis - Interpreting the Final Solutions

Guide 52: Principal Component Analysis - Final Considerations

Guide 53: Correspondence Analysis - Introduction

Guide 53: Correspondence Analysis - Running the Procedure

Guide 53: Correspondence Analysis - Results Interpretation

Guide 53: Correspondence Analysis - Imposing Category Constraints

Grouping Methods

Guide 54: Cluster Analysis - Introduction

Guide 54: Cluster Analysis - Hierarchical Cluster

Guide 54: Cluster Analysis - K-Means Cluster

Guide 55: Discriminant Analysis - Introduction

Guide 55: Discriminant Analysis - Simple DA

Guide 55: Discriminant Analysis - Multiple DA

Guide 56: Multiple Response Analysis

Course Materials

Reviews

S
Shreya10 September 2020

I think the course covers most of the technicalities very well. I return to the course again and again. I am half-way through the course and I like the approach.

S
Steven25 August 2020

While the instructor and class is quite informative, it is quite heavy on actual statistics and not very pertinent to marketing research use of SPSS.

s
salah-eddine20 August 2020

Honestly, this is one of the best course I've done on Udemy so far. The instructor really takes his time to explain every step in the process. If you want to have a good grasp on the powerful package of SPSS, then I highly recommend to you this course.

I
Isse18 August 2020

Thank you for giving me this wonderful opportunity to introduce myself to you.  My full name is Isse Osman Adam.  I come from Somalia  I live in Bangladesh  We are twenty-one members in my family including me.  I’m student at the university of Rajshahi  My major is Statistics  My favourite subjects are Time series analysis and forecasting, Biostatistics, multivariate analysis and statistical computing softwares.  My hobbies are reciting and listening Holy Quran, reading books, lifting weights, traveling and visiting historical places and chatting with my best friends.  In my free time I would like to spend to attend online courses especially related courses in my specialization and also I enjoy doing exercise and reading books and watching motivational videos.  My long term goal is to become extremely experienced analyst, information researcher, and science scholastic author.  I am a self-motivated, and disciplined soul. I am always keen to up-skill myself by learning new things whenever I get a chance.  My strengths are my analytical approach, my human touch to the situations, my appreciable communication, and presentation skills. I believe in being realistic.  Thank you. If you have any specific questions, please ask, I will be glad to answer that.”

J
Johanna13 August 2020

So far it has been very good, clear explanations and good material which I work on along with the tutorials

R
Rutuja27 February 2020

The data excel sheet is not attached to this course. There sholud be an option to download the excel file, so that it becomes more relatable.

H
Hector10 February 2020

Excellent course with very detailed explanations. The only thing I wish it included is the formulas for each type of test and a brief explanation of what they mean. But this course will teach you how to perform the test and how to analyze the results. There is a folder with the data analyZed in the lecture and another folder with additional exercises. Is there a solution for the excellent problems suggested?

A
Ali26 January 2020

Clear explanations with sufficient practical examples. The course covers a wide range of topics and techniques. Very satisfied.

P
Pablo19 January 2020

Explanations are detailed and the professor provides a theoretical background to all analysis. Moreover, the SPSS procedures are thoroughly explained in a way which is simple and easy-to-understand. The SPSS courses by this professor are the best I’ve found online! (I’ve already taken the Advanced Data Science Techniques in SPSS course).

M

I really like the course, because there are very details and the lecturer explained it slowly. Thanks so much.

M
Mohamed20 April 2019

The course is excellent, and i learned a lot, and the instructor paid much effort to collect much resources concerning data analysis specially spss, and i believe the coerces is good to university students as well as those who want to improve their capacity of data analysis.thank you Udmy and voluble instructor.

H
Hrishikesh16 March 2019

It gives you basic understanding of how to use as well as interpret using SPSS which is quite required at novice level. Thanks a ton!

O
Odeony2 March 2019

Because the professor is very clear in his explanation. The structure of the course is ideal to people that are starting to learn statistical methods.

L
Loh15 November 2018

I would strongly recommend anyone who want to learn SPSS especially in doing their postgraduate research studies to take this must course! I learned much more from here than what being taught at my university.

K
Kevin16 August 2018

I find this course is not worthy in terms of money and time. The teaching given is like a robots reading a thick textbook to the audience. What I learned from this course is that there are a variety of ANOVA tests. This course definitely will be beneficial if Dr, Bogdan could provide at least one examples of how those statistical tests would be used. The lack of real-world examples is a hindrance to the appreciation of the importance pf each test for research purposes. Since Dr. Bogdan is not very active in research, I believe this may be his inability to provide such examples. I hope future research students do not fail into his trap of liking this long-winded lectures. SPSS is a powerful tolls that require extensive explanation that Dr. Bogdan fails to deliver to the audience. I believe his newer courses are designed to be similar styles of delivery as well. To me, this is not beneficial to the learning at all.

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Udemy ID

11/11/2014

Course created date

9/11/2019

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