Practical Data Analysis With Spss

Quickly get started with data analysis using SPSS for theses, dissertations, lab reports, research projects, and more.

What you'll learn

Understand the Basic Statistical Concepts

Use SPSS to Enter, Manipulate and Clean a Dataset

Analyze Survey and Experimental Data

Answer Research Questions Using Appropriate Statistical Analyses

Present Results in APA Style

Requirements

Access to SPSS software

Description

Data analysis is an integral part of academic research. It is important to ensure that you are conducting the right analysis in the right way. However, as a beginner, it can feel overwhelming to know where to start or how to conduct an analysis and present the findings.This course is designed to equip you with essential data analysis skills so that you can carry out analysis independently. If you are taking a statistics course, this will serve as a supplement to your class, helping you complete homework and assignments with ease. For those working on a thesis, dissertation, or research project, the course will walk you through the entire analytical process—from data cleaning to reporting results—helping you produce high-quality, reliable, and valid findings.What You Will Learn:Statistics is a vast field with countless analyses and methods. However, this course focuses on the analyses you're most likely to need in your academic work. I’ve carefully selected techniques that undergraduate students, graduate students, and researchers commonly use.The course begins with basic statistical concepts to help you better understand the subsequent materials. You'll then learn how to work with SPSS, including data management and manipulation techniques. As you progress, you'll move into preliminary analysis before exploring three core areas of statistical testing:Exploring Relationships – Learn techniques like correlation and regression to understand the relationships between variables.Comparing Groups – Explore methods such as ANOVA and t-tests to investigate differences between groups or conditions.Non-Parametric Tests – Learn how to analyze data that doesn't meet the assumptions required for parametric analysis.Some Features of the Course:Coverage of the most commonly used analysesClearly organized sections on different topicsGuidance on selecting appropriate statistical testsConcepts explained in simple, easy-to-understand languagePractical demonstrations with realistic datasets and scenariosGuidance on presenting results in APA Style (templates included)Recommendations for additional resources for further learningBy the end of this course, you'll have a solid understanding of data analysis and feel more confident in handling your research project. Start learning today and see how straightforward it can be!

Overview

Section 1: Introduction

Lecture 1 Downloading and Installing SPSS

Section 2: Basics of Statistics

Lecture 2 Statistics and Its Types

Lecture 3 Levels of Measurement

Lecture 4 Basics of Hypothesis Testing

Lecture 5 How to Develop Testable Hypothesis

Lecture 6 Type 1 vs Type 2 Error

Lecture 7 Essential Terminologies

Lecture 8 How to Choose the Right Analysis?

Section 3: SPSS Essentials

Lecture 9 Introduction to SPSS Data Editor

Lecture 10 Introduction to Output Viewer

Lecture 11 Entering Data in SPSS

Lecture 12 Editing Data in SPSS

Lecture 13 Importing Data from Excel

Lecture 14 Automatic Recode

Lecture 15 Manual Recode

Lecture 16 Reverse Coding Items

Lecture 17 Categorizing Variables

Lecture 18 Combining Categories

Lecture 19 Compute Variable

Lecture 20 Spilt File

Lecture 21 Select Cases

Lecture 22 Define Missing Values

Lecture 23 Calculating Date

Lecture 24 Create Dummy Variables

Lecture 25 Introduction to Table Editor

Lecture 26 Introduction to Graph Editor

Lecture 27 Basic of Syntax

Lecture 28 Additional Tips

Section 4: Preliminary Analysis

Lecture 29 Preparing a Dataset

Lecture 30 Data Cleaning

Lecture 31 Reliablity analysis

Lecture 32 Basics of Descriptive Statistics

Lecture 33 Descritibe Statsitics

Lecture 34 Analyzing Multiple Response Questions

Section 5: Data Visualization

Lecture 35 Introduction to Data Visualization

Lecture 36 Histogram

Lecture 37 Bar Chart

Lecture 38 Clustered Bar Chart

Lecture 39 Line Chart

Lecture 40 Scatterplot

Section 6: Introduction to Statistical Assumption

Lecture 41 Overview of Statistical Assumptions

Lecture 42 Basics of Normality

Lecture 43 Outliers (Detection and Handling)

Lecture 44 Dealing with Missing Values

Section 7: Correlation Analysis

Lecture 45 Introduction to Correlation Analysis

Lecture 46 Basics of Pearson’s correlation analysis

Lecture 47 Pearson's Correlation Example

Lecture 48 Spearman's Correlation Example

Lecture 49 Partial Correlation Example

Section 8: Linear Regression Analysis

Lecture 50 Overview of Linear Regression

Lecture 51 Linear Regression Analysis

Lecture 52 Multiple Linear Regression Analysis

Lecture 53 Regression with Categorical Variables

Lecture 54 Hierarchical Multiple Regression Analysis

Section 9: Logistic Regression Analysis

Lecture 55 Overview of logistic Regression

Lecture 56 Binary Logistic Regression

Lecture 57 Ordinal Logistic Regression

Section 10: Mediation and Moderation

Lecture 58 Introduction to Mediation and Moderation

Lecture 59 Installing PROCESS

Lecture 60 Simple Mediation Analysis

Lecture 61 Moderation example 1: Continuous*Continuous Interaction

Section 11: T test

Lecture 62 Overview of T Tests

Lecture 63 Independent sample t test

Lecture 64 Paired Sample t test

Section 12: Anlysis of Variance (ANOVA)

Lecture 65 Overview of Anlaysis of Variance (ANOVA)

Lecture 66 One Way Between Groups ANOVA

Lecture 67 One Way Repeated Measures ANOVA

Lecture 68 Two Way Between Groups ANOVA

Lecture 69 Mixed Design ANOVA

Lecture 70 Analylsis of Covariance (ANCOVA)

Lecture 71 Multivariate Analysis of Variance (MANOVA)

Section 13: Non-Parametric Tests

Lecture 72 Overview of Non Parametric Tests

Lecture 73 Chi-square test for goodness of fit

Lecture 74 Chi-square test for independence

Lecture 75 McNemar’s Test

Lecture 76 Cochran’s Q Test

Lecture 77 Kappa Measure of Agreement

Lecture 78 Mann-Whitney U Test

Lecture 79 Wilcoxon Signed Rank Test

Lecture 80 Kruskal-Wallis Test

Lecture 81 Friedman Test

Graduate and PhD students who need to analyze data for theses, dissertations, or research projects,Existing or aspiring researchers working on, or planning to work on, academic research projects,University students seeking help to successfully complete data analysis homework, assignments, and lab reports,Industry professionals who need to improve data analysis skills for work