Qualitative Analysis In Spss – Logistic Regression Full Study

Free Download Qualitative Analysis In Spss – Logistic Regression Full Study
Published 8/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.53 GB | Duration: 3h 0m
Master: Qualitative Research in SPSS – Logistic Regression, Dimension Reduction Methods, Descriptives

What you’ll learn
Understanding Logistic Regression: Students will learn the fundamentals of logistic regression and how to apply it using SPSS.
Qualitative Data Analysis Techniques: Learners will explore various methods for analyzing qualitative data.
Analyzing Relationships Between Variables: The course will teach students how to examine and interpret the relationships between different variables-categories.
Creating a Thesis Model: Students will be guided through the process of creating a model for their thesis. How to conduct the bachelor degree final project.
Requirements
No prior knowledge is required.
You just need to have a laptop or PC to follow along.
You need to have installed Microsoft Excel and IBM SPSS.
Description
Unlock the full potential of your bachelor’s research with our comprehensive course, "Qualitative Analysis in SPSS: Logistic Regression Full Study." This course is meticulously designed to equip you with the essential skills and knowledge required to conduct in-depth qualitative data analysis using SPSS.In this course, you will learn to:Analyze Variables: Gain a thorough understanding of different types of variables and how to analyze them effectively. Understand Correlations: Learn how to identify and interpret the relationships between variables, crucial for drawing meaningful conclusions from your data.Master Logistic Regression: Delve into logistic regression analysis, a powerful statistical method for modeling the relationship between a dependent variable and one or more independent variables.Dimension Reduction: Explore advanced techniques like Factorial Correspondence Analysis (FCA) and Multiple Correspondence Analysis (MCA) to simplify complex data sets and uncover hidden patterns.By the end of this course, you will have a solid foundation in qualitative data analysis and be proficient in using SPSS to conduct sophisticated statistical analyses. Whether you are working on your bachelor’s thesis or preparing for future research projects, this course will provide you with the tools and confidence to excel. Join us and transform your data into impactful insights! Yuhu!
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Overview of This Course. What We Will Cover?
Lecture 3 Resources
Section 2: Research Design: Outlining the Logical Framework for Analysis
Lecture 4 Explaining the Database
Lecture 5 Key Elements of a Statistical Study: From Design to Analysis
Lecture 6 Two Ways of Thinking
Section 3: Getting Started: Preparing the Database for Analysis
Lecture 7 Basic Theory
Lecture 8 Data Cleaning
Lecture 9 Creating A New Variable
Lecture 10 Excel to SPSS
Lecture 11 Labeling Variables
Lecture 12 From Numerical to Ordinal
Lecture 13 Recomandations Of Chi-Square
Section 4: Descriptive Analysis
Lecture 14 Theory of Descriptive Analysis
Lecture 15 Creating Different Charts in SPSS
Lecture 16 Descriptive Analysis – Numerical Methods
Lecture 17 Creating Different Charts in Microsoft Excel
Lecture 18 Creating a Dashboard in Microsoft Excel
Lecture 19 Creating a Dashboard in Power BI
Section 5: Correspondence Factor Analysis (CFA)
Lecture 20 What is Correspondence Factor Analysis (CFA)
Lecture 21 Factorial Correspondence Analysis
Section 6: Multiple Correspondence Analysis (MCA)
Lecture 22 Theory of Multiple Factor Correspondence
Lecture 23 Quick Fix for a Common Error in Multiple Correspondence Analysis (MCA)
Lecture 24 Recoding of The Variables
Lecture 25 Multiple Correspondence Analysis
Lecture 26 Merging Two Variables into One
Section 7: Logistic Regression: Binary Logistic
Lecture 27 Regression Techniques for Categorical Variables | Binary Logistic
Lecture 28 Practical Logistic
Lecture 29 Check for Multicoliniarity
Lecture 30 Performance of the Model
Students and Academics,Beginner to Intermediate Statisticians,Lifelong Learners,Data Analysts and Researchers
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