jamovi for Data Analysis - Full Tutorial

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Learn jamovi in this full tutorial course. jamovi is a free, open-source application that makes data analysis easy and intuitive. jamovi menus and commands are designed to simplify the transition from programs like SPSS but, under the hood, jamovi is based on the powerful statistical programming language R. jamovi has a clean, human-friendly design that facilitates insight into your data and makes it easy to share your work with others. In this introductory course, you’ll learn how you can use jamovi to refine, analyze, and visualize your data to get critical insights.

🔗 Course files: https://drive.google.com/drive/folders/1gJJ9di69FBZPhAdG9dWqXHZnR_JJry0G
🔗 Preview course files on preview them on OSF.io: https://osf.io/4k9rx/?view_only=de565278f386463dacf16a0047a90a31

💻 Course created by Barton Poulson from datalab.cc.
🔗 Check out the datalab.cc YouTube channel: https://www.youtube.com/user/datalabcc
🔗 Watch more free data science courses at http://datalab.cc/

⭐️ Course Contents ⭐️
GETTING STARTED
⌨️ (0:00:00) Welcome
⌨️ (0:01:26) Installing jamovi
⌨️ (0:02:00) Navigating jamovi
⌨️ (0:05:43) Sample data
⌨️ (0:08:54) Sharing files
⌨️ (0:10:26) Sharing with OSF.io
⌨️ (0:13:54) jamovi modules
⌨️ (0:18:05) The jmv package for R

WRANGLING DATA
⌨️ (0:23:07) Wrangling data: chapter overview
⌨️ (0:24:36) Entering data
⌨️ (0:26:52) Importing data
⌨️ (0:31:43) Variable types & labels
⌨️ (0:37:52) Computing means
⌨️ (0:41:47) Computing z-scores
⌨️ (0:43:43) Transforming scores to categories
⌨️ (0:47:25) Filtering cases

EXPLORATION
⌨️ (0:55:51) Exploration: chapter overview
⌨️ (0:56:56) Descriptive statistics
⌨️ (1:02:22) Histograms
⌨️ (1:06:47) Density plots
⌨️ (1:10:10) Box plots
⌨️ (1:13:35) Violin plots
⌨️ (1:16:13) Dot plots
⌨️ (1:19:20) Bar plots
⌨️ (1:23:08) Exporting tables & plots

T-TESTS
⌨️ (1:24:28) t-tests: chapter overview
⌨️ (1:33:24) Independent-samples t-test
⌨️ (1:40:03) Paired-samples t-test
⌨️ (1:45:16) One-sample t-test

ANOVA
⌨️ (1:52:23) ANOVA: chapter overview
⌨️ (1:54:20) ANOVA
⌨️ (2:06:31) Repeated-measures ANOVA
⌨️ (2:16:21) ANCOVA
⌨️ (2:30:14) MANCOVA
⌨️ (2:37:26) Kruskal-Wallis test
⌨️ (2:43:26) Friedman test

REGRESSION
⌨️ (2:48:55) Regression: chapter overview
⌨️ (2:51:03) Correlation matrix
⌨️ (2:58:34) Linear regression
⌨️ (3:13:36) Variable entry
⌨️ (3:20:51) Regression diagnostics
⌨️ (3:27:11) Binomial logistic regression
⌨️ (3:36:12) Multinomial logistic regression
⌨️ (3:45:03) Ordinal logistic regression

FREQUENCIES
⌨️ (3:53:28) Frequencies: chapter overview
⌨️ (3:55:47) Binomial test
⌨️ (4:00:39) Chi-squared goodness-of-fit
⌨️ (4:07:06) Chi-squared test of association
⌨️ (4:12:26) McNemar test
⌨️ (4:17:19) Log-linear regression

FACTOR
⌨️ (4:23:05) Factor: chapter overview
⌨️ (4:24:54) Reliability analysis
⌨️ (4:32:20) Principal component analysis
⌨️ (4:40:18) Exploratory factor analysis
⌨️ (4:43:49) Confirmatory factor analysis

CONCLUSION
⌨️ (4:52:42) Next steps

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data science
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r programming
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