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Udemy - Spss Masterclass A Comprehensive Course For Uni Students - OneDDL - 06-30-2025
![[Image: fe274dd3c7579c504815ceba9af06265.avif]](https://i125.fastpic.org/big/2025/0630/65/fe274dd3c7579c504815ceba9af06265.avif)
Free Download Udemy - Spss Masterclass A Comprehensive Course For Uni Students
Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.39 GB | Duration: 5h 13m
Learn to run analyses on SPSS, interpret outputs with confidence, and report results in APA style like a pro.
What you'll learn
Enter Data Into SPSS
Run Analyses
Interpret Results
Reports Results in APA Style
Process Questionnaire Data
Reverse Coding
Assess Internal Reliability
Create Graphs
Check Assumptions
Independent T-tests
Paired T-Tests
Mann-Whitney U Tests
Wilcoxon Signed-Rank Tests
Chi-Squared Goodness of Fit Tests
Chi-Squared Tests of Independence
One-Way Independent ANOVA
One-Way Repeated-Measures ANOVA
Two-Way Independent ANOVA
Kruskal-Wallis Test
Friedman Test
Pearson Correlation
Spearman Correlation
Two-Way Mixed ANOVA
One-Way Independent ANCOVA
One-Way Independent MANOVA
Simple Linear Regression
Multiple Linear Regression
Binary Logistic Regression
Requirements
Students should have access to SPSS. The course videos were created in 2025 with version 30 of SPSS. There are only minor differences between versions, so the course is also suitable for students with other versions released around the same time. The student only needs to have knowledge of basic concepts, such as means, medians, and statistical significance. A free glossary defines all of the terms used in the course.
Description
Drawing on my BSc, MSc, and PhD degrees in psychology and neuroscience and over a decade of experience working with university students, especially those studying social sciences, I designed this course to cover the majority of analyses that students usually encounter in during their degrees.The Analyses Covered By the CourseThe course covers the following analyses: Independent t-testsMann-Whitney U tests Paired t-testsWilcoxon signed-rank testsChi-squared goodness of fit testsChi-squared tests of independenceOne-way independent (i.e., between-subjects) ANOVAsKruskal-Wallis testsOne-way repeated measures (i.e., within-subjects) ANOVAsFriedman testsTwo-way independent (i.e., between-subjects) ANOVAsPearson correlation analysesSpearman correlation analysesTwo-way mixed ANOVAsOne-way independent (i.e., between-subjects) ANCOVAsOne-way independent (i.e., between-subjects) MANOVAsSimple linear regressionMultiple linear regressionBinary logistic regressionAdditionally, the course covers how to process questionnaire data, focusing on entering data, identifying and labelling invalid responses, reverse coding, internal reliability, and creating mean (i.e., average) and sum (i.e., total) scores. What You'll LearnFor each of the analyses, you'll receive clear, step-by-step guidance on when to use them, how to enter the data, how to check assumptions, how to run the test, how to create a graph, how to interpret the results, and how to report the results in APA style. With this knowledge, you'll be a step ahead of your university peers!ResourcesEach section of the course focuses on a different analysis and comes with a range of valuable resources, including the data files used in the videos, information sheets with key details about the analyses (e.g., when to use them, example hypotheses, assumptions), and example APA results sections. You'll also receive a glossary explaining all the terms used in the videos.AssignmentsEach section comes with an additional data set (not used in the videos) that you can use to practice running the analysis and reporting the results. If you choose to complete the assignments, you can download example results sections based on these data sets to assess whether you ran the test correctly and how accurately you reported the results.
Overview
Section 1: Processing Questionnaire Data
Lecture 1 Introduction
Lecture 2 Processing Questionnaire Data Introduction
Lecture 3 Entering Questionnaire Data
Lecture 4 Invalid Responses, Reverse Code, Internal Reliability, Mean & Sum Scores
Section 2: Independent T-Tests
Lecture 5 An Introduction to Independent T-Tests and Entering Data
Lecture 6 Check Assumptions, Run T-test, Create Graph
Lecture 7 Interpret and Report T-Test Results
Section 3: Mann-Whitney U Tests
Lecture 8 An Introduction to Mann-Whitney U Tests and Entering Data
Lecture 9 Run Mann-Whitney U Test and Create a Graph
Lecture 10 Interpret and Report Mann-Whitney U Test Results
Section 4: Paired T-Tests
Lecture 11 An Introduction to Paired T-Tests and Entering Data
Lecture 12 Check Assumptions, Run T-test, Create Graph
Lecture 13 Interpret and Report Paired T-Test Results
Section 5: Wilcoxon Signed-Rank Tests
Lecture 14 An Introduction to Wilcoxon Signed-Rank Tests and Entering Data
Lecture 15 Run Wilcoxon Signed-Rank Test
Lecture 16 Interpret and Report Wilcoxon Signed-Rank Test Results
Section 6: Chi-Squared Goodness of Fit Tests
Lecture 17 An Introduction to Chi-Squared Goodness of Fit Tests and Entering Data
Lecture 18 Run Chi-Squared Goodness of Fit Test and Make a Graph
Lecture 19 Check Assumptions and Interpret and Report Chi-Squared Test Results
Section 7: Chi-Squared Tests of Independence
Lecture 20 An Introduction to Chi-Squared Tests of Independence and Entering Data
Lecture 21 Run Chi-Squared Test of Independence and Make a Graph
Lecture 22 Check Assumptions and Interpret and Report Chi-Squared Test Results
Section 8: One-Way Independent ANOVA
Lecture 23 An Introduction to One-Way Independent ANOVAs and Entering Data
Lecture 24 Check Normality Assumption, Run One-Way Independent ANOVA, Create Graph
Lecture 25 Check HoV Assumption, Interpret and Report One-Way Independent ANOVA Results
Section 9: Kruskal-Wallis Test
Lecture 26 An Introduction to Kruskal-Wallis Tests and Entering Data
Lecture 27 Run Kruskal-Wallis Test and Create a Graph
Lecture 28 Interpret and Report Kruskal-Wallis Test Results
Section 10: One-Way Repeated Measures ANOVA
Lecture 29 An Introduction to One-Way Repeated Measures ANOVAs and Entering Data
Lecture 30 Check Normality Assumption, Run One-Way Repeated Measures ANOVA and Make a Graph
Lecture 31 Check Sphericity Assumption, Interpret and Report ANOVA Results
Section 11: Friedman Test
Lecture 32 An Introduction to Friedman Tests and Entering Data
Lecture 33 Run Friedman Test and Make a Graph
Lecture 34 Interpret and Report Friedman Test Results
Section 12: Two-Way Independent ANOVA
Lecture 35 An Introduction to Two-Way Independent ANOVAs and Entering Data
Lecture 36 Check Normality Assumption, Run ANOVA, and Make a Graph
Lecture 37 Check HoV Assumption, Interpret and Report Two-Way Independent ANOVA Results
Section 13: Two-Way Mixed ANOVA
Lecture 38 An Introduction to Two-Way Mixed ANOVAs and Entering the Data
Lecture 39 Check Assumptions, Create Graph, Run Analysis, Interpret Results
Lecture 40 Reporting the Results of the Two-Way Mixed ANOVA
Section 14: One-Way Independent ANCOVA
Lecture 41 An Introduction to One-Way Independent ANCOVAs and Entering the Data
Lecture 42 Check Assumptions, Create Graph, Run Analysis, Interpret Results
Lecture 43 Reporting the Results of the One-Way Independent ANCOVA
Section 15: One-Way Independent MANOVA
Lecture 44 An Introduction to One-Way Independent MANOVAs and Entering the Data
Lecture 45 Check Normality, Outliers, and Linearity Between the Dependent Variables
Lecture 46 Check Other Assumptions, Run Analysis, Create Graph, Interpret Results
Lecture 47 Reporting the Results of the One-Way Independent MANOVA
Section 16: Pearson Correlation
Lecture 48 An Introduction to Pearson Correlation and Entering Data
Lecture 49 Check Assumptions, Create Figure, and Run Pearson Correlation Analysis
Lecture 50 Interpret and Report Pearson Correlation Analysis Results
Section 17: Spearman Correlation
Lecture 51 Introduction to Spearman Correlation and Entering Data
Lecture 52 Run Spearman Correlation Analysis and Create a Figure
Lecture 53 Interpret and Report Spearman Correlation Analysis Results
Section 18: Simple Linear Regression
Lecture 54 An Introduction to Simple Linear Regression and Entering the Data
Lecture 55 Check Assumptions, Create Graph, Run Analysis, Interpret Results
Lecture 56 Reporting the Results of the Simple Linear Regression
Section 19: Multiple Linear Regression
Lecture 57 An Introduction to Multiple Linear Regression and Entering the Data
Lecture 58 Check Assumptions, Create Graph, Run Analysis, Interpret Results
Lecture 59 Reporting the Results of the Multiple Linear Regression
Section 20: Binary Logistic Regression
Lecture 60 An Introduction to Binary Logistic Regression and Entering the Data
Lecture 61 Check Assumptions, Run Analysis, Interpret Results
Lecture 62 Reporting the Results of the Binary Logistic Regression
University students, especially those studying social science subjects (e.g., psychology), who need to use SPSS for their research methods classes.
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