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SPSS For Research

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

4.6

(2203)

45438 enrolled on this course

Last updated 11/2021

Bogdan Anastasiei

₹ 2999.0

₹ 4499.0

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30-Day Money-Back Guarantee

Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

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?
Who this course is for:
students
PhD candidates
academic researchers
business researchers
University teachers
anyone looking for a job in the statistical analysis field
anyone who is passionate about quantitative research

What you'll 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)

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Getting Started

1.1-Introduction

1.2-Course Outline

2-The Basics

2.1-Guide 1: Working With SPSS Files

2.2-Guide 2: Defining Variables

2.3-Guide 3: Variable Recoding

2.4-Guide 4: Dummy Variables

2.5-Guide 5: Selecting Cases

2.6-Guide 6: File Splitting

2.7-Guide 7: Data Weighting

3-Creating Charts in SPSS

3.1-Guide 8: Column Charts

3.2-Guide 9: Line Charts

3.3-Guide 10: Scatterplot Charts

3.4-Guide 11: Boxplot Diagrams

4-Simple Analysis Techniques

4.1-Guide 12: Frequencies Procedure

4.2-Guide 13: Descriptives Procedure

4.3-Guide 14: Explore Procedure

4.4-Guide 15: Means Procedure

4.5-Guide 16: Crosstabs Procedure

5-Assumption Checking. Data Transformations

5.1-Guide 17: Checking for Normality - Numerical Methods

5.2-Guide 17: Checking for Normality - Graphical Methods

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

5.4-Guide 18: Detecting Outliers - Graphical Methods

5.5-Guide 18: Detecting Outliers - Numerical Methods

5.6-Guide 18: Detecting Outliers - How to Handle the Outliers

5.7-Guide 19: Data Transformations

6-One-Sample Tests

6.1-Guide 20: One-Sample T Test - Introduction

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

6.3-Guide 21: Binomial Test

6.4-Guide 21: Binomial Test with Weighted Data

6.5-Guide 22: Chi Square for Goodness-of-Fit

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

7-Association Tests

7.1-Guide 23: Pearson Correlation - Introduction

7.2-Guide 23: Pearson Correlation - Assumption Checking

7.3-Guide 23: Pearson Correlation - Running the Procedure

7.4-Guide 24: Spearman Correlation - Introduction

7.5-Guide 24: Spearman Correlation - Running the Procedure

7.6-Guide 25: Partial Correlation - Introduction

7.7-Guide 25: Partial Correlation - Practical Example

7.8-Guide 26: Chi Square For Association

7.9-Guide 26: Chi Square For Association with Weighted Data

7.10-Guide 27: Loglinear Analysis - Introduction

7.11-Guide 27: Loglinear Analysis - Hierarchical Loglinear Analysis

7.12-Guide 27: Loglinear Analysis - General Loglinear Analysis

8-Tests For Mean Difference

8.1-Guide 28: Independent-Sample T Test - Introduction

8.2-Guide 28: Independent-Sample T Test - Assumption Testing

8.3-Guide 28: Independent-Sample T Test - Results Interpretation

8.4-Guide 29: Paired-Sample T Test - Introduction

8.5-Guide 29: Paired-Sample T Test - Assumption Testing

8.6-Guide 29: Paired-Sample T Test - Results Interpretation

8.7-Guide 30: One-Way ANOVA - Introduction

8.8-Guide 30: One-Way ANOVA - Assumption Testing

8.9-Guide 30: One-Way ANOVA - F Test Results

8.10-Guide 30: One-Way ANOVA - Multiple Comparisons

8.11-Guide 31: Two-Way ANOVA - Introduction

8.12-Guide 31: Two-Way ANOVA - Assumption Testing

8.13-Guide 31: Two-Way ANOVA - Interaction Effect

8.14-Guide 31: Two-Way ANOVA - Simple Main Effects

8.15-Guide 32: Three-Way ANOVA - Introduction

8.16-Guide 32: Three-Way ANOVA - Assumption Testing

8.17-Guide 32: Three-Way ANOVA - Third Order Interaction

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

8.19-Guide 32: Three-Way ANOVA - Simple Main Effects

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

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

8.22-Guide 33: Multivariate ANOVA - Introduction

8.23-Guide 33: Multivariate ANOVA - Assumption Checking (1)

8.24-Guide 33: Multivariate ANOVA - Assumption Checking (2)

8.25-Guide 33: Multivariate ANOVA - Result Interpretation

8.26-Guide 34: Analysis of Covariance (ANCOVA) - Introduction

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

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

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

8.30-Guide 35: Repeated Measures ANOVA - Introduction

8.31-Guide 35: Repeated Measures ANOVA - Assumption Checking

8.32-Guide 35: Repeated Measures ANOVA - Results Interpretation

8.33-Guide 36: Within-Within Subjects ANOVA - Introduction

8.34-Guide 36: Within-Within Subjects ANOVA - Assumption Checking

8.35-Guide 36: Within-Within Subjects ANOVA - Interaction

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

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

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

8.39-Guide 37: Mixed ANOVA - Introduction

8.40-Guide 37: Mixed ANOVA - Assumption Checking

8.41-Guide 37: Mixed ANOVA - Interaction

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

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

8.44-Guide 37: Mixed ANOVA - Case of Nonsignificant Interaction

8.45-Guide 38: Mann-Whitney Test - Introduction

8.46-Guide 38: Mann-Whitney Test - Results Interpretation

8.47-Guide 39: Wilcoxon and Sign Tests - Wilcoxon Test

8.48-Guide 39: Wilcoxon and Sign Tests - Sign Test

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

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

8.51-Guide 41: Friedman Test

8.52-Guide 42: McNemar Test

9-Predictive Techniques

9.1-Guide 43: Simple Regression - Introduction

9.2-Guide 43: Simple Regression - Assumption Checking (1)

9.3-Guide 43: Simple Regression - Assumption Checking (2)

9.4-Guide 43: Simple Regression - Results Interpretation

9.5-Guide 44: Multiple Regression - Introduction

9.6-Guide 44: Multiple Regression - Assumption Checking

9.7-Guide 44: Multiple Regression - Results Interpretation

9.8-Guide 45: Regression with Dummy Variables

9.9-Guide 46: Sequential Regression

9.10-Guide 47: Binomial Regression - Introduction

9.11-Guide 47: Binomial Regression - Assumption Checking

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

9.13-Guide 47: Binomial Regression - Coefficient Interpretation (1)

9.14-Guide 47: Binomial Regression - Coefficient Interpretation (2)

9.15-Guide 47: Binomial Regression - Classification Table

9.16-Guide 48: Multinomial Regression - Introduction

9.17-Guide 48: Multinomial Regression - Assumption Checking

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

9.19-Guide 48: Multinomial Regression - Coefficient Interpretation (1)

9.20-Guide 48: Multinomial Regression - Coefficient Interpretation (2)

9.21-Guide 48: Multinomial Regression - Coefficient Interpretation (3)

9.22-Guide 48: Multinomial Regression - Classification Table

9.23-Guide 49: Ordinal Regression - Introduction

9.24-Guide 49: Ordinal Regression - Assumption Testing

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

9.26-Guide 49: Ordinal Regression - Coefficient Interpretation (1)

9.27-Guide 49: Ordinal Regression - Coefficient Interpretation (2)

9.28-Guide 49: Ordinal Regression - Classification Table

10-Scaling Techniques

10.1-Guide 50: Reliability Analysis - Cronbach's Alpha

10.2-Guide 50: Reliability Analysis - Cohen's Kappa

10.3-Guide 50: Reliability Analysis - Kendall's W

10.4-Guide 51: Multidimensional Scaling - Introduction

10.5-Guide 51: Multidimensional Scaling - ALSCAL procedure (1)

10.6-Guide 51: Multidimensional Scaling - ALSCAL procedure (2)

10.7-Guide 51: Multidimensional Scaling - PROXSCAL procedure (1)

10.8-Guide 51: Multidimensional Scaling - PROXSCAL procedure (2)

11-Data Reduction

11.1-Guide 52: Principal Component Analysis - Introduction

11.2-Guide 52: Principal Component Analysis - Running the Procedure

11.3-Guide 52: Principal Component Analysis - Testing For Adequacy

11.4-Guide 52: Principal Component Analysis - Obtaining a Final Solution

11.5-Guide 52: Principal Component Analysis - Interpreting the Final Solutions

11.6-Guide 52: Principal Component Analysis - Final Considerations

11.7-Guide 53: Correspondence Analysis - Introduction

11.8-Guide 53: Correspondence Analysis - Running the Procedure

11.9-Guide 53: Correspondence Analysis - Results Interpretation

11.10-Guide 53: Correspondence Analysis - Imposing Category Constraints

12-Grouping Methods

12.1-Guide 54: Cluster Analysis - Introduction

12.2-Guide 54: Cluster Analysis - Hierarchical Cluster

12.3-Guide 54: Cluster Analysis - K-Means Cluster

12.4-Guide 55: Discriminant Analysis - Introduction

12.5-Guide 55: Discriminant Analysis - Simple DA

12.6-Guide 55: Discriminant Analysis - Multiple DA

13-Addenda

13.1-Guide 56: Multiple Response Analysis

14-Course Materials

14.1-Download Links

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