Mastering SPSS from A to Z session 2: Normality and correlation test скачать в хорошем качестве

Mastering SPSS from A to Z session 2: Normality and correlation test

Welcome to this comprehensive SPSS training course! 🌟 If you're a student, researcher, or professional looking to master SPSS for your data analysis, you've come to the right place! In this video, you'll learn step-by-step how to use SPSS, a powerful and essential statistical software used by thousands of researchers and analysts to process and analyze data. What you'll learn in this comprehensive training course: --- 1. Introduction to SPSS Discover the SPSS user interface and learn how to navigate the different views: Data View and Variable View. Install and configure SPSS: How to configure the software to start an analysis without any hassle. Learn how to import data from Excel, CSV, or other formats. 2. Data Management Basics Creating and Managing Variables: Variable types (numeric, string, date, etc.) and managing labels. Data Manipulation: Sort, filter, recode variables, and add new calculated variables. Missing Value Management: How to handle missing data in SPSS. 3. Descriptive Statistics Calculate measures of central tendency: Mean, median, and mode to summarize data. Measures of Dispersion: Standard deviation, variance, range, and quartiles to understand data variability. Data Visualization: Create histograms, box plots, scatter plots, and other useful graphs. Normality Tests: Learn how to test whether your data follows a normal distribution using tests such as Shapiro-Wilk and Kolmogorov-Smirnov. 4. Analyzing Relationships Pearson and Spearman Correlation: Measure the linear or monotonic relationship between two variables. Simple Linear Regression: Learn how to establish a predictive relationship between two continuous variables. Analysis of Variance (ANOVA): Compares means across multiple groups to test for significant differences. 5. Reliability and Psychometric Tests Cronbach's Alpha: Measures the internal reliability of measurement scales and assesses item consistency. Exploratory Factor Analysis (EFA): Identifies underlying factors that explain relationships between variables. Principal Component Analysis (PCA): Reduces the dimensionality of data to simplify interpretation while retaining essential information. 6. Multiple Regression and Other Advanced Analyses Multiple Linear Regression: Explores the relationships between a dependent variable and multiple independent variables. Advanced Hypothesis Testing: Test hypotheses on means, variances, and distributions to deepen your analysis. Why take this course? Simple and clear concepts: Whether you are a beginner or advanced, this course guides you through each step with detailed explanations and concrete examples. Practical exercises: You'll learn through real-life exercises to better understand how to use SPSS in a research or project context. Learning approach: Each section is designed so you can directly apply the concepts learned to your own data. Ongoing support: Ask your questions in the comments, and I'll be happy to help! Download the exercise files here: 📥 [Download the Excel files for the practical exercises.] Subscribe to the channel to stay up-to-date on our upcoming training courses and tutorials on SPSS and data analysis! Turn on the notification bell 🔔 to receive updates as soon as a new video is posted! 👉 Ask your questions in the comments and share this video with your friends and colleagues interested in data analysis! #SPSS #Statistics #DataAnalysis #SPSSTraining #Research #StatisticalMethods #PerformingAnalysis #AdvancedStatistics #SPSSTutorial Why will this video help you? If you're looking to understand how to analyze data with SPSS, run statistical tests, or perform advanced analyses, this training is perfect for you. Join a community of statistics and data science enthusiasts and develop your skills to succeed in your research or projects!