title

jamovi for Data Analysis - Full Tutorial

description

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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detail

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'start': 97.9, 'weight': 1, 'content': [{'end': 102.764, 'text': 'Go to jamovi.org and then simply click on this link download.', 'start': 97.9, 'duration': 4.864}, {'end': 106.406, 'text': "Now the beautiful thing is it's free and it's open source.", 'start': 103.424, 'duration': 2.982}, {'end': 111.632, 'text': 'Click the system that you need, Windows, Mac OS, or Linux.', 'start': 107.367, 'duration': 4.265}, {'end': 118.519, 'text': "Download the file, double click on it, and you'll be ready to start working in Jamovi in just a few seconds.", 'start': 112.312, 'duration': 6.207}, {'end': 123.261, 'text': "When you open Jamovi, this is the window that you're going to see.", 'start': 119.679, 'duration': 3.582}], 'summary': 'Visit jamovi.org to download the free and open-source software. choose your system and start working in just a few seconds.', 'duration': 25.361, 'max_score': 97.9, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY97900.jpg'}, {'end': 184.576, 'src': 'embed', 'start': 138.823, 'weight': 3, 'content': [{'end': 144.971, 'text': "Now over here on the left, of course, where we have rows and columns arranged in a grid, this is where your data goes, it's your data window.", 'start': 138.823, 'duration': 6.148}, {'end': 152.02, 'text': "Right now we have three variables just called A, B and C, you're of course going to replace those with something else and you put the data in.", 'start': 145.571, 'duration': 6.449}, {'end': 156.285, 'text': "So it's one column per variable, one row per case.", 'start': 152.44, 'duration': 3.845}, {'end': 160.679, 'text': 'Over here on the right is the output window.', 'start': 157.757, 'duration': 2.922}, {'end': 162.881, 'text': "It's where we'll see the results of our analyses.", 'start': 160.779, 'duration': 2.102}, {'end': 166.063, 'text': 'Up at the top, we have two tabs here.', 'start': 164.042, 'duration': 2.021}, {'end': 170.786, 'text': "The first one that's open by default is analyses, which gives you options for exploration.", 'start': 166.103, 'duration': 4.683}, {'end': 179.693, 'text': "If I click on that and come to descriptives, you'll see, for instance, it opens up the menu that allows you to choose what you want to do,", 'start': 170.826, 'duration': 8.867}, {'end': 184.576, 'text': 'and you get a blank version of the results over here on the right.', 'start': 179.693, 'duration': 4.883}], 'summary': 'Data input in left grid, analysis results in right window, options for exploration available.', 'duration': 45.753, 'max_score': 138.823, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY138823.jpg'}], 'start': 0.629, 'title': 'Jamovi for data analysis', 'summary': 'Introduces jamovi, a free and open-source data analysis application based on r, as an alternative to expensive proprietary programs like spss and sas. it emphasizes its user-friendly nature, provides instructions for downloading and installing the software, and introduces its layout and functionalities for data analysis, including its single-window interface, data organization, options for analyses, similarities to spss, and specific functionalities like defining variables and customizing plot themes.', 'chapters': [{'end': 123.261, 'start': 0.629, 'title': 'Introduction to jamovi', 'summary': 'Introduces jamovi, a free and open-source data analysis application based on the programming language r, as an alternative to expensive proprietary programs like spss and sas. it emphasizes the user-friendly nature of jamovi and provides instructions for downloading and installing the software.', 'duration': 122.632, 'highlights': ['Jamovi is a free and open-source data analysis application based on the programming language R, offering a compelling alternative to expensive proprietary programs like SPSS and SAS and 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on the left allows input of data with one column per variable and one row per case, facilitating data organization.', 'The output window on the right displays the results of analyses, and users can access various analysis options such as descriptives, t tests, ANOVA, regression, and frequencies.']}, {'end': 1579.354, 'segs': [{'end': 545.905, 'src': 'embed', 'start': 518.756, 'weight': 2, 'content': [{'end': 524.382, 'text': 'And so use any of these four data sets as a way to start exploring how you can work with Jamovi.', 'start': 518.756, 'duration': 5.626}, {'end': 529.306, 'text': "You'll see how quick and easy you can get into the data, get the exploration,", 'start': 524.662, 'duration': 4.644}, {'end': 533.27, 'text': 'start doing some analyses and start getting some meaning out of the data that you have in Jamovi.', 'start': 529.306, 'duration': 3.964}, {'end': 540.423, 'text': 'One of the things I truly love about Jamovi is how easy it makes it to collaborate with other people.', 'start': 534.682, 'duration': 5.741}, {'end': 545.905, 'text': "And the reason it's easy is because when you share things with people, you're sharing a single file.", 'start': 540.884, 'duration': 5.021}], 'summary': 'Start exploring jamovi with 4 data sets. easily collaborate and share single files.', 'duration': 27.149, 'max_score': 518.756, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY518756.jpg'}, {'end': 668.625, 'src': 'embed', 'start': 625.859, 'weight': 0, 'content': [{'end': 635.327, 'text': "Jamovi makes it easy to share files by simply emailing or putting in a shared box the file that you've been working on, because it has the data,", 'start': 625.859, 'duration': 9.468}, {'end': 637.068, 'text': 'it has the transformation, has the analyses.', 'start': 635.327, 'duration': 1.741}, {'end': 642.172, 'text': 'There is, however, a really interesting, more sophisticated alternative to that.', 'start': 637.709, 'duration': 4.463}, {'end': 644.874, 'text': "And it's called the open science framework.", 'start': 642.673, 'duration': 2.201}, {'end': 652.661, 'text': 'And if you want to go here to osf.io, I can show you a little bit about how it works and how Jamovi is very well adapted to it.', 'start': 645.435, 'duration': 7.226}, {'end': 663.08, 'text': 'The open science framework is a free service that really facilitates collaboration and reproducibility and research, you can create a free account.', 'start': 653.752, 'duration': 9.328}, {'end': 668.625, 'text': 'And one of the important things here is that it allows you to integrate a number of other services.', 'start': 663.24, 'duration': 5.385}], 'summary': 'Jamovi facilitates file sharing and is well integrated with the open science framework for collaboration and reproducibility in research.', 'duration': 42.766, 'max_score': 625.859, 'thumbnail': 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{'end': 1377.981, 'text': 'you only have to learn one package.', 'start': 1376.321, 'duration': 1.66}, {'end': 1385.405, 'text': 'And that makes it extremely efficient and extremely user friendly when it comes to learning a powerful language like R.', 'start': 1378.442, 'duration': 6.963}, {'end': 1390.241, 'text': 'Our next chapter is about wrangling data in Jamovi.', 'start': 1387.139, 'duration': 3.102}], 'summary': 'Jamovi allows easy transition from spss, making learning r efficient and user-friendly.', 'duration': 43.872, 'max_score': 1346.369, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY1346369.jpg'}], 'start': 307.196, 'title': 'Jamovi software benefits and data analysis tools', 'summary': 'Introduces the benefits of using jamovi software, such as duplicating analyses from spss and hints at future tutorials. it also discusses sample datasets for data exploration, collaboration, and sharing, along with the integration of open science framework. furthermore, it covers various statistical analysis tools and their applications in jamovi, emphasizing the bridging of r with menu-driven applications.', 'chapters': [{'end': 354.435, 'start': 307.196, 'title': 'Introduction to jamovi software', 'summary': 'Introduces the benefits of using jamovi software, such as the ability to duplicate analyses from spss, and hints at future tutorials on how to use the software effectively and read its output.', 'duration': 47.239, 'highlights': ['The JMV package in Jamovi allows users to duplicate analyses from SPSS, providing an enormous benefit for those familiar with SPSS and trying to learn Jamovi.', 'Future tutorials will cover how to use each analysis function and read the output from Jamovi.', "The fastest way to get started with Jamovi is to explore its example data sets by accessing the menu and selecting 'open'."]}, {'end': 942.069, 'start': 355.776, 'title': 'Jamovi data analysis and collaboration', 'summary': "Discusses four sample datasets available in jamovi for data exploration and analysis, including big five personality characteristics, tooth growth in guinea pigs, human reactions to insects, and anderson's iris data, emphasizing the ease of collaboration and sharing through jamovi's file sharing capabilities and the integration of the open science framework. additionally, it highlights the use of modules to extend jamovi's functionality with extra features and the potential for integrating r packages as modules.", 'duration': 586.293, 'highlights': ["Jamovi facilitates easy data exploration and analysis with four sample datasets, including big five personality characteristics, tooth growth in guinea pigs, human reactions to insects, and Anderson's iris data.", "Jamovi's file sharing capabilities streamline collaboration by allowing the sharing of a single file containing data, calculations, transformations, and analyses, enabling easy collaboration with others.", 'The integration of the Open Science Framework with Jamovi enables seamless collaboration, reproducibility, and sharing of research results, with the ability to integrate various services and share work with specific collaborators or the wider research community.', "Jamovi's use of modules extends its functionality, similar to R packages, providing additional features for data analysis, such as correlation matrices, regression analysis, and density plots."]}, {'end': 1579.354, 'start': 942.69, 'title': 'Data analysis tools overview', 'summary': 'Covers a range of statistical analysis tools and their applications in jamovi, including power analysis, scatters, mediation, moderation, survival analysis, and data wrangling, with emphasis on the bridging of r with menu-driven applications like jamovi.', 'duration': 636.664, 'highlights': ['The various statistical analysis tools in Jamovi, such as power analysis, scatters, mediation, moderation, survival analysis, and data wrangling, are discussed in detail, showcasing their applications and potential benefits for data analysis.', 'The bridging of R with menu-driven applications like Jamovi is highlighted as a significant advantage, easing the transition for individuals accustomed to SPSS and similar applications to learn and use R for statistical analysis, making data analysis more accessible and user-friendly.', 'The demonstration of using the Jamovi package to reproduce commands in R, enabling users to seamlessly transfer analyses from Jamovi to R, illustrates the interoperability and flexibility offered by these tools, enhancing the efficiency and adaptability of statistical analysis processes.', 'The focus on data wrangling and its significance in preparing data for analysis, including entering data, importing data, defining variables, computing new variables, and filtering cases, emphasizes the crucial role of data preparation in the success of a project and the time-consuming nature of this process.']}], 'duration': 1272.158, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY307196.jpg', 'highlights': ["Jamovi's file sharing capabilities streamline collaboration by allowing the sharing of a single file containing data, calculations, transformations, and analyses, enabling easy collaboration with others.", 'The integration of the Open Science Framework with Jamovi enables seamless collaboration, reproducibility, and sharing of research results, with the ability to integrate various services and share work with specific collaborators or the wider research community.', 'The various statistical analysis tools in Jamovi, such as power analysis, scatters, mediation, moderation, survival analysis, and data wrangling, are discussed in detail, showcasing their applications and potential benefits for data analysis.', 'The bridging of R with menu-driven applications like Jamovi is highlighted as a significant advantage, easing the transition for individuals accustomed to SPSS and similar applications to learn and use R for statistical analysis, making data analysis more accessible and user-friendly.', 'The demonstration of using the Jamovi package to reproduce commands in R, enabling users to seamlessly transfer analyses from Jamovi to R, illustrates the interoperability and flexibility offered by these tools, enhancing the efficiency and adaptability of statistical analysis processes.']}, {'end': 3263.891, 'segs': [{'end': 1625.647, 'src': 'embed', 'start': 1597.743, 'weight': 7, 'content': [{'end': 1602.344, 'text': "Google Sheets is good because it's online and several people can be working on the same sheet at once.", 'start': 1597.743, 'duration': 4.601}, {'end': 1611.188, 'text': "And then you can import that file, which is what I'm going to show you in the next video, the much better way to get your data into Jamovi.", 'start': 1603.385, 'duration': 7.803}, {'end': 1617.203, 'text': "While it's possible to enter data manually into Jamovi, it's an awkward process.", 'start': 1612.501, 'duration': 4.702}, {'end': 1625.647, 'text': "And it's much better to save the data in a spreadsheet either from Google Sheets, Excel or some other program, and then import it directly.", 'start': 1617.303, 'duration': 8.344}], 'summary': 'Google sheets allows multiple users to work concurrently and is recommended for importing data into jamovi.', 'duration': 27.904, 'max_score': 1597.743, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY1597743.jpg'}, {'end': 1755.031, 'src': 'embed', 'start': 1727.458, 'weight': 0, 'content': [{'end': 1731.699, 'text': "And you'll see that it's able to open several different kinds of files.", 'start': 1727.458, 'duration': 4.241}, {'end': 1736.261, 'text': 'Of course, Jamovi files and then CSV and text files.', 'start': 1732.219, 'duration': 4.042}, {'end': 1745.164, 'text': "It's able to open SPSS files, Stata files, SAS files and JASP, which is another program created by several of the same developers of Jamovi.", 'start': 1737.021, 'duration': 8.143}, {'end': 1748.325, 'text': 'Looks very similar but operates a little differently.', 'start': 1746.024, 'duration': 2.301}, {'end': 1751.107, 'text': 'And so these are the options.', 'start': 1749.145, 'duration': 1.962}, {'end': 1755.031, 'text': "Now, let's come up here to browse and I'm going to go to my desktop.", 'start': 1751.828, 'duration': 3.203}], 'summary': 'Jamovi can open various files including jamovi, csv, spss, stata, sas, and jasp.', 'duration': 27.573, 'max_score': 1727.458, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY1727458.jpg'}, {'end': 1909.968, 'src': 'embed', 'start': 1887.137, 'weight': 1, 'content': [{'end': 1894.827, 'text': 'And so you can see that if you have the data in a spreadsheet, you got it in SPSS or SAS or some other format.', 'start': 1887.137, 'duration': 7.69}, {'end': 1898.192, 'text': "it's a very quick and easy process to import that data into Jamovi.", 'start': 1894.827, 'duration': 3.365}, {'end': 1901.396, 'text': "It reads the format, it reads the labels, and then you're good to go.", 'start': 1898.572, 'duration': 2.824}, {'end': 1909.968, 'text': "Your computer can analyze data without knowing what it is, if it has numbers, it'll work with numbers, if that's text, it'll it'll do something.", 'start': 1902.805, 'duration': 7.163}], 'summary': 'Jamovi can easily import data from various formats and analyze it without prior knowledge.', 'duration': 22.831, 'max_score': 1887.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY1887137.jpg'}, {'end': 2875.63, 'src': 'heatmap', 'start': 2148.225, 'weight': 0.797, 'content': [{'end': 2152.249, 'text': "And the last one here, I'll just put like, like product.", 'start': 2148.225, 'duration': 4.024}, {'end': 2156.892, 'text': "And I'm going to leave this one as nominal, but I'm now I'm going to change these levels.", 'start': 2153.21, 'duration': 3.682}, {'end': 2159.854, 'text': "So I'm actually going to change these labels.", 'start': 2157.993, 'duration': 1.861}, {'end': 2165.077, 'text': "So for instance, I click right here at one, and that's usually strongly disagree.", 'start': 2159.894, 'duration': 5.183}, {'end': 2169.039, 'text': 'And then the two is disagree.', 'start': 2166.317, 'duration': 2.722}, {'end': 2173.021, 'text': 'The three might be neither.', 'start': 2171.06, 'duration': 1.961}, {'end': 2176.823, 'text': 'Four would be agree.', 'start': 2175.062, 'duration': 1.761}, {'end': 2182.526, 'text': 'And five might be strongly agree.', 'start': 2178.744, 'duration': 3.782}, {'end': 2185.888, 'text': 'A lot of people call this a likert scale.', 'start': 2182.546, 'duration': 3.342}, {'end': 2187.449, 'text': "it's actually a response scale.", 'start': 2185.888, 'duration': 1.561}, {'end': 2193.012, 'text': 'likert scales have more to do with how you choose the questions as opposed to the format in which you respond to them.', 'start': 2187.449, 'duration': 5.563}, {'end': 2197.475, 'text': "But call it likert scale if you want, it's a one to five rating scale.", 'start': 2194.153, 'duration': 3.322}, {'end': 2204.06, 'text': "And now you can see that these labels all show up down here, that's really the convenient thing.", 'start': 2198.116, 'duration': 5.944}, {'end': 2207.662, 'text': 'And remember, the numbers are still underneath there.', 'start': 2204.92, 'duration': 2.742}, {'end': 2210.624, 'text': 'So you can still do numerical operations on these variables.', 'start': 2207.702, 'duration': 2.922}, {'end': 2214.606, 'text': 'The last one I want to show you is this text variable at the end.', 'start': 2211.084, 'duration': 3.522}, {'end': 2220.368, 'text': 'You see it says nominal text because I actually typed in the letters Y and N.', 'start': 2214.646, 'duration': 5.722}, {'end': 2222.209, 'text': 'And you can do the same thing with these.', 'start': 2220.368, 'duration': 1.841}, {'end': 2224.891, 'text': 'I can click on the Y and I can put subscribed.', 'start': 2222.309, 'duration': 2.582}, {'end': 2231.834, 'text': "And I can maybe put they're not subscribed but they came to your website so we'll call them a visitor.", 'start': 2227.372, 'duration': 4.462}, {'end': 2238.645, 'text': 'And so now you have new names for the variables.', 'start': 2232.901, 'duration': 5.744}, {'end': 2245.89, 'text': 'I changed the names, I changed the level of measurement or the type of the variable, for some of them say, for instance,', 'start': 2238.645, 'duration': 7.245}, {'end': 2252.194, 'text': 'from continuous or quantitative to ordinal, to nominal or categorical.', 'start': 2245.89, 'duration': 6.304}, {'end': 2253.955, 'text': 'And then I changed labels.', 'start': 2252.754, 'duration': 1.201}, {'end': 2258.839, 'text': 'And so this is a very important step in terms of preparing the data into movie,', 'start': 2254.015, 'duration': 4.824}, {'end': 2263.883, 'text': "because it's going to make it much easier for you to interpret the analyses.", 'start': 2258.839, 'duration': 5.044}, {'end': 2270.708, 'text': "And then in turn to make sense of it, especially when you're collaborating with a colleague or potentially working for a client.", 'start': 2264.303, 'duration': 6.405}, {'end': 2277.408, 'text': 'One of the most common transformations on data is averaging several variables.', 'start': 2272.367, 'duration': 5.041}, {'end': 2279.789, 'text': 'That way you can get, for instance, a scale score.', 'start': 2277.668, 'duration': 2.121}, {'end': 2289.031, 'text': "It's also a great way of helping balance out the error variance of different scores and really get you more generalizable information.", 'start': 2280.209, 'duration': 8.822}, {'end': 2291.692, 'text': 'This is easy to do in Chamovie.', 'start': 2289.852, 'duration': 1.84}, {'end': 2298.074, 'text': "What I'm going to do is I'm going to take this data set that has ID and it has these three rating scale questions.", 'start': 2292.152, 'duration': 5.922}, {'end': 2300.234, 'text': "And they're all in a one to five scale.", 'start': 2298.994, 'duration': 1.24}, {'end': 2304.316, 'text': 'And they asked people how much they liked different elements of for instance, your website.', 'start': 2300.254, 'duration': 4.062}, {'end': 2310.958, 'text': "And I'm going to click on each of these first, we'll go to like website, and we have a little bit descriptive text.", 'start': 2304.976, 'duration': 5.982}, {'end': 2315.499, 'text': "And it's a continuous or quantitative variable that's just on a one to five.", 'start': 2311.158, 'duration': 4.341}, {'end': 2320, 'text': "If we go to like price, you see it's ordinal.", 'start': 2316.219, 'duration': 3.781}, {'end': 2325.442, 'text': "And even though it's ordinal, you get to specify levels now it just has the one to five, that's easy to do.", 'start': 2320.22, 'duration': 5.222}, {'end': 2332.459, 'text': 'then like product, you see here in the data, it has text, it says neither strongly disagree, and so on.', 'start': 2326.636, 'duration': 5.823}, {'end': 2334.579, 'text': "And it's coded as nominal.", 'start': 2333.079, 'duration': 1.5}, {'end': 2339.161, 'text': "On the other hand, it's important to say that it's an integer variable, even if it's nominal.", 'start': 2334.82, 'duration': 4.341}, {'end': 2343.923, 'text': 'And then over here, we have levels where we have the 12345.', 'start': 2339.662, 'duration': 4.261}, {'end': 2346.465, 'text': "And we've put labels on each of those levels.", 'start': 2343.923, 'duration': 2.542}, {'end': 2349.666, 'text': 'So one is strongly disagree to disagree and so on.', 'start': 2346.545, 'duration': 3.121}, {'end': 2356.587, 'text': 'The reason this is important is because the numbers still are there underneath those labels.', 'start': 2350.146, 'duration': 6.441}, {'end': 2359.448, 'text': "And that's what makes it possible for us to average them.", 'start': 2356.967, 'duration': 2.481}, {'end': 2361.648, 'text': "So I'm going to close this for just a moment.", 'start': 2359.828, 'duration': 1.82}, {'end': 2364.469, 'text': "And we're going to create a new variable.", 'start': 2362.108, 'duration': 2.361}, {'end': 2366.689, 'text': "I'm going to double click right here.", 'start': 2365.329, 'duration': 1.36}, {'end': 2372.23, 'text': 'And I can either enter a new data variable, a new computed variable or a new transform variable.', 'start': 2367.449, 'duration': 4.781}, {'end': 2374.991, 'text': "we're going to do the middle one right here a new computed variable.", 'start': 2372.23, 'duration': 2.761}, {'end': 2378.532, 'text': "and it's going to ask for the name of that new variable.", 'start': 2376.471, 'duration': 2.061}, {'end': 2380.354, 'text': "I'm simply going to call it the mean.", 'start': 2378.552, 'duration': 1.802}, {'end': 2382.935, 'text': 'And we can give it a description if we want.', 'start': 2381.134, 'duration': 1.801}, {'end': 2386.157, 'text': "I'm going to put down here the average of three rating scale variables.", 'start': 2383.376, 'duration': 2.781}, {'end': 2396.007, 'text': "The easiest way to do this is to come to the function window And if you're used to SPSS or to Excel, you know you've got a lot of different choices.", 'start': 2386.818, 'duration': 9.189}, {'end': 2400.714, 'text': "There's a much smaller range in Jamovi, but they're basically the ones that you need.", 'start': 2396.107, 'duration': 4.607}, {'end': 2404.319, 'text': "I'm going to scroll down a little bit and get to Mean.", 'start': 2401.275, 'duration': 3.044}, {'end': 2406.322, 'text': "I'm going to double-click on that to put it in the box.", 'start': 2404.359, 'duration': 1.963}, {'end': 2410.065, 'text': 'And then I need to tell it what variables I want to include.', 'start': 2407.343, 'duration': 2.722}, {'end': 2414.127, 'text': "I'm going to come over here to the variable list and simply double click on the first one.", 'start': 2410.665, 'duration': 3.462}, {'end': 2418.41, 'text': "And when I double click on it, it puts it up here and you'll see that it puts it in back ticks.", 'start': 2414.348, 'duration': 4.062}, {'end': 2422.012, 'text': 'Those are sort of back leaning apostrophes.', 'start': 2418.95, 'duration': 3.062}, {'end': 2426.615, 'text': "That's because there's a space or a non-text character in here.", 'start': 2422.413, 'duration': 4.202}, {'end': 2429.197, 'text': 'If you had an underscore or a dash, it might do the same thing.', 'start': 2426.655, 'duration': 2.542}, {'end': 2435.772, 'text': "It's actually a nice reason to not have spaces or other things in your variable names because then you don't have to do the backticks.", 'start': 2429.898, 'duration': 5.874}, {'end': 2438.658, 'text': 'But Jamovi is going to do that for us automatically.', 'start': 2436.293, 'duration': 2.365}, {'end': 2443.864, 'text': 'Now, when you have a range of variables that are all next to each other in SPSS,', 'start': 2439.56, 'duration': 4.304}, {'end': 2448.428, 'text': 'you can give the name of the first one and then write TO capital two and the last one.', 'start': 2443.864, 'duration': 4.564}, {'end': 2451.111, 'text': 'In Jamovi, you need to specify each of them separately.', 'start': 2448.889, 'duration': 2.222}, {'end': 2457.837, 'text': "So I'm going to put a comma and then I'll click the second one and a comma and I'll click the third one.", 'start': 2451.191, 'duration': 6.646}, {'end': 2461, 'text': "And now I've got that and I can close this.", 'start': 2458.918, 'duration': 2.082}, {'end': 2464.748, 'text': 'And you can see it automatically fills in with the mean.', 'start': 2462.287, 'duration': 2.461}, {'end': 2469.951, 'text': 'Even though there are three different levels of measurement, it knows that they all have numerical information.', 'start': 2465.348, 'duration': 4.603}, {'end': 2475.833, 'text': 'Now I said it was important that this one right here was specified as integer.', 'start': 2470.451, 'duration': 5.382}, {'end': 2477.214, 'text': 'Let me double click on that again.', 'start': 2475.853, 'duration': 1.361}, {'end': 2485.251, 'text': "And then you'll see, if I come down here and I say it's not integer but it's text, then you see the mean disappears,", 'start': 2478.689, 'duration': 6.562}, {'end': 2486.932, 'text': 'even though those levels still remain.', 'start': 2485.251, 'duration': 1.681}, {'end': 2490.754, 'text': "But if I come back and tell it again, no, it's integer, then the mean reappears.", 'start': 2487.332, 'duration': 3.422}, {'end': 2492.814, 'text': "It's able to treat it as numeric information.", 'start': 2490.794, 'duration': 2.02}, {'end': 2500.657, 'text': "And so that's how you can average several variables in Jamovi, which gets you a long way towards getting more reliable scores,", 'start': 2493.134, 'duration': 7.523}, {'end': 2505.019, 'text': 'averaging out some of the variants and getting the scale scores that you may need for your further analyses.', 'start': 2500.657, 'duration': 4.362}, {'end': 2512.103, 'text': "Sometimes your data comes to you on scales that don't have any inherent meaning or may not be familiar.", 'start': 2506.639, 'duration': 5.464}, {'end': 2518.507, 'text': "A 1 to 5 or 1 to 7 agreement scale, while it's common, doesn't have inherent meaning.", 'start': 2512.703, 'duration': 5.804}, {'end': 2524.451, 'text': "And if you're comparing income information from Nepal to Turkey to Mexico,", 'start': 2518.867, 'duration': 5.584}, {'end': 2529.973, 'text': "you're going to be dealing with different currencies and you're going to need a way of comparing relative standing.", 'start': 2525.071, 'duration': 4.902}, {'end': 2534.255, 'text': 'The easiest way to do that is with z-scores, or standardized scores.', 'start': 2530.434, 'duration': 3.821}, {'end': 2541.439, 'text': "Now, if you've had statistics, you know that that simply takes the score, subtract the mean of the variable, and divide it by the standard deviation.", 'start': 2534.896, 'duration': 6.543}, {'end': 2547.782, 'text': "And a lot of people show you how you can do that manually, and you can set that up in Jamovi that way, but there's a much easier way to do that.", 'start': 2541.879, 'duration': 5.903}, {'end': 2549.343, 'text': "Let's do this one.", 'start': 2548.522, 'duration': 0.821}, {'end': 2556.426, 'text': "We're going to come here and double-click on this empty variable here, and we're going to choose new computed variable.", 'start': 2549.483, 'duration': 6.943}, {'end': 2561.028, 'text': "And what I'm going to do is I'm going to change this to say z-score.", 'start': 2557.707, 'duration': 3.321}, {'end': 2570.652, 'text': "And then I'm going to use the function window and scroll down to the statistical functions to the last one on that particular list.", 'start': 2562.609, 'duration': 8.043}, {'end': 2571.792, 'text': "It's z.", 'start': 2570.932, 'duration': 0.86}, {'end': 2572.933, 'text': 'I just double click on that.', 'start': 2571.792, 'duration': 1.141}, {'end': 2577.114, 'text': 'It brings up the function and then I need to tell it what I want the z-score of.', 'start': 2573.813, 'duration': 3.301}, {'end': 2579.035, 'text': 'In this case, I want the mean.', 'start': 2577.615, 'duration': 1.42}, {'end': 2581.736, 'text': "That's the mean score of those three rating scale questions.", 'start': 2579.095, 'duration': 2.641}, {'end': 2582.937, 'text': 'I double click on that.', 'start': 2581.976, 'duration': 0.961}, {'end': 2585.818, 'text': 'And I can close the window.', 'start': 2584.037, 'duration': 1.781}, {'end': 2587.419, 'text': 'And there it is.', 'start': 2585.838, 'duration': 1.581}, {'end': 2588.32, 'text': "That's all I need to do.", 'start': 2587.459, 'duration': 0.861}, {'end': 2591.862, 'text': 'A negative z-score indicates that somebody is below the mean.', 'start': 2588.92, 'duration': 2.942}, {'end': 2594.543, 'text': "A positive z-score says they're above the mean.", 'start': 2592.042, 'duration': 2.501}, {'end': 2598.105, 'text': 'And the numbers themselves are units of standard deviations.', 'start': 2595.004, 'duration': 3.101}, {'end': 2603.509, 'text': "So this very first score that's highlighted is 0.4 standard deviations below the mean.", 'start': 2598.245, 'duration': 5.264}, {'end': 2605.29, 'text': 'quick and easy.', 'start': 2604.549, 'duration': 0.741}, {'end': 2607.791, 'text': 'And Jamovi makes it a cinch.', 'start': 2605.87, 'duration': 1.921}, {'end': 2621.478, 'text': 'And that helps you get on the way to taking your variables that are in different scales or arbitrary ones and putting them into something that may be more meaningful and is certainly more comparable from one variable to another.', 'start': 2607.911, 'duration': 13.567}, {'end': 2625.623, 'text': "When you're getting your data ready for analysis.", 'start': 2622.881, 'duration': 2.742}, {'end': 2632.588, 'text': 'sometimes you need to do the same transformation to many different scores, like doing reverse coding or logarithmic transformations.', 'start': 2625.623, 'duration': 6.965}, {'end': 2638.672, 'text': 'Or maybe you need to take variables and convert them into ordinal categories.', 'start': 2633.168, 'duration': 5.504}, {'end': 2647.158, 'text': "I want to show you an example of how to use Jamovi's transform function to take z-scores and identify whether they are extreme or not,", 'start': 2639.153, 'duration': 8.005}, {'end': 2650.501, 'text': 'using the plus or minus two standard deviations criterion.', 'start': 2647.158, 'duration': 3.343}, {'end': 2660.964, 'text': "What I'm going to do is I'm going to come over here and click on this blank space and then I get to choose a new transformed variable.", 'start': 2651.221, 'duration': 9.743}, {'end': 2667.309, 'text': "Now, what I'm going to show you can be done with a computed variable.", 'start': 2663.246, 'duration': 4.063}, {'end': 2672.532, 'text': 'But the advantage of doing it with a transform variable is it saves that transformation function.', 'start': 2667.329, 'duration': 5.203}, {'end': 2676.955, 'text': 'And then you can apply it to multiple variables at the same time if you want.', 'start': 2672.852, 'duration': 4.103}, {'end': 2683.9, 'text': "The first thing we need to do is we need to define the actual function that we're going to use the transformation function.", 'start': 2677.476, 'duration': 6.424}, {'end': 2686.422, 'text': "And so I'm going to come right here to transform.", 'start': 2683.94, 'duration': 2.482}, {'end': 2687.963, 'text': "I'm going to click on that.", 'start': 2687.162, 'duration': 0.801}, {'end': 2692.666, 'text': "And right now I don't have any transforms, I'm going to come down here to create new transform.", 'start': 2688.063, 'duration': 4.603}, {'end': 2695.907, 'text': 'And that brings up another dialogue from the bottom.', 'start': 2693.406, 'duration': 2.501}, {'end': 2698.448, 'text': "And now I'm going to label the transformation.", 'start': 2696.467, 'duration': 1.981}, {'end': 2701.749, 'text': 'And again, this is something that can be applied to multiple variables.', 'start': 2698.568, 'duration': 3.181}, {'end': 2706.27, 'text': "And so it's not the name of a variable, but it's the name of what you're going to do to variables.", 'start': 2701.989, 'duration': 4.281}, {'end': 2707.43, 'text': "And I'm going to call it extreme.", 'start': 2706.33, 'duration': 1.1}, {'end': 2715.093, 'text': "And I'm going to say is score more than two standard deviations from mean.", 'start': 2708.291, 'duration': 6.802}, {'end': 2718.274, 'text': "And I'm going to be using the z score so I don't have to compute everything else.", 'start': 2715.593, 'duration': 2.681}, {'end': 2721.575, 'text': 'And then what you have here is a variable suffix.', 'start': 2719.214, 'duration': 2.361}, {'end': 2731.718, 'text': 'And the idea is that if you have many different variables, like q1, q2, q3, for question 123, and doing a logarithmic transformation all of them,', 'start': 2722.295, 'duration': 9.423}, {'end': 2739.02, 'text': 'it can create a new variable that includes, for instance, q1, underscore, log or log and then, in parentheses, q1,', 'start': 2731.718, 'duration': 7.302}, {'end': 2740.34, 'text': 'there are a lot of different ways to do it.', 'start': 2739.02, 'duration': 1.32}, {'end': 2744.001, 'text': "I'm just going to put this right here and say, extreme.", 'start': 2740.92, 'duration': 3.081}, {'end': 2750.02, 'text': "And then I'm going to do the recode condition.", 'start': 2746.997, 'duration': 3.023}, {'end': 2752.882, 'text': "Now, right now, it's asking me just to replace it with something else.", 'start': 2750.12, 'duration': 2.762}, {'end': 2755.204, 'text': 'I have to do add recode condition.', 'start': 2752.942, 'duration': 2.262}, {'end': 2758.507, 'text': "And source means the variable that you're starting with.", 'start': 2755.965, 'duration': 2.542}, {'end': 2763.992, 'text': "And again, you can leave that there because you're going to choose that variable in the next dialog box.", 'start': 2758.547, 'duration': 5.445}, {'end': 2772.597, 'text': "I'm simply going to say, if it's greater than two, Then I'm going to say assign the text.", 'start': 2764.592, 'duration': 8.005}, {'end': 2779.679, 'text': "Yes And you have to put text in single quotes so it knows otherwise it's going to try to read it as a variable name.", 'start': 2773.217, 'duration': 6.462}, {'end': 2785.582, 'text': "Then I'm going to add another recode condition and say if it is less than negative two.", 'start': 2780.2, 'duration': 5.382}, {'end': 2788.043, 'text': 'And then that is also extreme.', 'start': 2786.602, 'duration': 1.441}, {'end': 2788.623, 'text': "So I'll say yes.", 'start': 2788.083, 'duration': 0.54}, {'end': 2795.832, 'text': "And then else says otherwise just do this, otherwise it's no.", 'start': 2790.911, 'duration': 4.921}, {'end': 2797.393, 'text': "So that's simple.", 'start': 2796.713, 'duration': 0.68}, {'end': 2802.074, 'text': "So I'm going to close that and now I've defined that transform function.", 'start': 2797.973, 'duration': 4.101}, {'end': 2804.635, 'text': "And you can see that it's available now.", 'start': 2802.954, 'duration': 1.681}, {'end': 2806.075, 'text': "There's extreme right there.", 'start': 2804.975, 'duration': 1.1}, {'end': 2810.696, 'text': "And I'm going to use extreme, but it wants to know what variable it is I want to transform.", 'start': 2806.535, 'duration': 4.161}, {'end': 2812.037, 'text': "That's why there's the question mark there.", 'start': 2810.717, 'duration': 1.32}, {'end': 2815.96, 'text': "So I'm going to come right here, and I'm going to choose z score.", 'start': 2812.757, 'duration': 3.203}, {'end': 2823.725, 'text': 'And once I do that, you see it fills in immediately, where we have the nose and down here, we have a z score of 2.097.', 'start': 2816.94, 'duration': 6.785}, {'end': 2826.067, 'text': "That's greater than two.", 'start': 2823.725, 'duration': 2.342}, {'end': 2828.108, 'text': "So it says yes, that's extreme.", 'start': 2826.167, 'duration': 1.941}, {'end': 2832.752, 'text': "And this is a function that I could apply to other variables if they're on the same scale.", 'start': 2828.309, 'duration': 4.443}, {'end': 2839.537, 'text': 'And so it makes it easy to prepare a lot of variables, sometimes using rather complex functions, but rather quickly,', 'start': 2833.052, 'duration': 6.485}, {'end': 2844.317, 'text': "in a way that's easy to set up and easy to understand what's happening.", 'start': 2839.537, 'duration': 4.78}, {'end': 2853.351, 'text': 'One of the important steps in analysis is the ability to drill down, to focus on specific cases in your dataset.', 'start': 2846.909, 'duration': 6.442}, {'end': 2856.613, 'text': 'Jamovi allows you to do that by filtering cases.', 'start': 2853.932, 'duration': 2.681}, {'end': 2864.356, 'text': "To do this, I'm opening this dataset that I've called Filtering Cases, and I'm showing you a little bit of descriptive statistics here.", 'start': 2857.093, 'duration': 7.263}, {'end': 2870.798, 'text': 'The number of cases, 173, and the minimum and maximum for two of the calculated variables at the end of the dataset.', 'start': 2864.396, 'duration': 6.402}, {'end': 2875.63, 'text': 'To get to filters, come here to data and click on the filter.', 'start': 2871.909, 'duration': 3.721}], 'summary': 'Using jamovi, data variables are transformed and analyzed to prepare for further interpretations and analyses. functions include averaging, z-scores, standard deviations, transformations, and filtering.', 'duration': 727.405, 'max_score': 2148.225, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY2148225.jpg'}, {'end': 2310.958, 'src': 'embed', 'start': 2277.668, 'weight': 4, 'content': [{'end': 2279.789, 'text': 'That way you can get, for instance, a scale score.', 'start': 2277.668, 'duration': 2.121}, {'end': 2289.031, 'text': "It's also a great way of helping balance out the error variance of different scores and really get you more generalizable information.", 'start': 2280.209, 'duration': 8.822}, {'end': 2291.692, 'text': 'This is easy to do in Chamovie.', 'start': 2289.852, 'duration': 1.84}, {'end': 2298.074, 'text': "What I'm going to do is I'm going to take this data set that has ID and it has these three rating scale questions.", 'start': 2292.152, 'duration': 5.922}, {'end': 2300.234, 'text': "And they're all in a one to five scale.", 'start': 2298.994, 'duration': 1.24}, {'end': 2304.316, 'text': 'And they asked people how much they liked different elements of for instance, your website.', 'start': 2300.254, 'duration': 4.062}, {'end': 2310.958, 'text': "And I'm going to click on each of these first, we'll go to like website, and we have a little bit descriptive text.", 'start': 2304.976, 'duration': 5.982}], 'summary': 'Using chamovie to analyze a dataset with id and three 1-5 scale rating questions about website elements.', 'duration': 33.29, 'max_score': 2277.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY2277668.jpg'}, {'end': 2561.028, 'src': 'embed', 'start': 2530.434, 'weight': 3, 'content': [{'end': 2534.255, 'text': 'The easiest way to do that is with z-scores, or standardized scores.', 'start': 2530.434, 'duration': 3.821}, {'end': 2541.439, 'text': "Now, if you've had statistics, you know that that simply takes the score, subtract the mean of the variable, and divide it by the standard deviation.", 'start': 2534.896, 'duration': 6.543}, {'end': 2547.782, 'text': "And a lot of people show you how you can do that manually, and you can set that up in Jamovi that way, but there's a much easier way to do that.", 'start': 2541.879, 'duration': 5.903}, {'end': 2549.343, 'text': "Let's do this one.", 'start': 2548.522, 'duration': 0.821}, {'end': 2556.426, 'text': "We're going to come here and double-click on this empty variable here, and we're going to choose new computed variable.", 'start': 2549.483, 'duration': 6.943}, {'end': 2561.028, 'text': "And what I'm going to do is I'm going to change this to say z-score.", 'start': 2557.707, 'duration': 3.321}], 'summary': 'Using z-scores to standardize data, simplifying calculation process.', 'duration': 30.594, 'max_score': 2530.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY2530434.jpg'}, {'end': 2650.501, 'src': 'embed', 'start': 2625.623, 'weight': 2, 'content': [{'end': 2632.588, 'text': 'sometimes you need to do the same transformation to many different scores, like doing reverse coding or logarithmic transformations.', 'start': 2625.623, 'duration': 6.965}, {'end': 2638.672, 'text': 'Or maybe you need to take variables and convert them into ordinal categories.', 'start': 2633.168, 'duration': 5.504}, {'end': 2647.158, 'text': "I want to show you an example of how to use Jamovi's transform function to take z-scores and identify whether they are extreme or not,", 'start': 2639.153, 'duration': 8.005}, {'end': 2650.501, 'text': 'using the plus or minus two standard deviations criterion.', 'start': 2647.158, 'duration': 3.343}], 'summary': "Using jamovi's transform function to identify extreme z-scores based on plus or minus two standard deviations criterion.", 'duration': 24.878, 'max_score': 2625.623, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY2625623.jpg'}, {'end': 2883.574, 'src': 'embed', 'start': 2853.932, 'weight': 6, 'content': [{'end': 2856.613, 'text': 'Jamovi allows you to do that by filtering cases.', 'start': 2853.932, 'duration': 2.681}, {'end': 2864.356, 'text': "To do this, I'm opening this dataset that I've called Filtering Cases, and I'm showing you a little bit of descriptive statistics here.", 'start': 2857.093, 'duration': 7.263}, {'end': 2870.798, 'text': 'The number of cases, 173, and the minimum and maximum for two of the calculated variables at the end of the dataset.', 'start': 2864.396, 'duration': 6.402}, {'end': 2875.63, 'text': 'To get to filters, come here to data and click on the filter.', 'start': 2871.909, 'duration': 3.721}, {'end': 2877.251, 'text': "It looks like you're pouring filter in a kitchen.", 'start': 2875.65, 'duration': 1.601}, {'end': 2883.574, 'text': 'And when you click on that, you get the opportunity to enter your text for a filter.', 'start': 2878.592, 'duration': 4.982}], 'summary': 'Using jamovi, 173 cases are filtered with descriptive statistics shown.', 'duration': 29.642, 'max_score': 2853.932, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY2853932.jpg'}, {'end': 2966.367, 'src': 'embed', 'start': 2937.735, 'weight': 5, 'content': [{'end': 2940.977, 'text': "And there's our nine cases from our first filter.", 'start': 2937.735, 'duration': 3.242}, {'end': 2944.615, 'text': 'But you probably want to do more than that.', 'start': 2942.774, 'duration': 1.841}, {'end': 2950.478, 'text': 'And so Jamovi allows you to use more than one filter in combination or trading off with another one.', 'start': 2944.675, 'duration': 5.803}, {'end': 2954.341, 'text': 'And it allows you to do more sophisticated filter commands.', 'start': 2950.859, 'duration': 3.482}, {'end': 2956.222, 'text': 'Let me come back to filters here.', 'start': 2954.861, 'duration': 1.361}, {'end': 2959.263, 'text': "And I'm going to add a new filter.", 'start': 2957.723, 'duration': 1.54}, {'end': 2960.964, 'text': "I'm simply going to press the plus.", 'start': 2959.283, 'duration': 1.681}, {'end': 2966.367, 'text': "By the way, what the eye here does is it shows or hides the filter column that's on the left.", 'start': 2961.445, 'duration': 4.922}], 'summary': 'Jamovi allows using multiple filters in combination and more sophisticated filter commands.', 'duration': 28.632, 'max_score': 2937.735, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY2937735.jpg'}], 'start': 1579.394, 'title': 'Importing and transforming data in jamovi', 'summary': 'Covers the importance of importing data into jamovi from various formats like xlsx, csv, text, and spss dot sav files, and demonstrates the ease of the process. it also discusses data transformation, including changing variable types, averaging variables, creating z-scores, and using transform functions to make data more interpretable and reliable for further analyses.', 'chapters': [{'end': 1707.195, 'start': 1579.394, 'title': 'Importing data into jamovi', 'summary': "Explains that manually entering data into jamovi is not recommended, and it's much better to save the data in a spreadsheet, such as google sheets, excel, or csv, and then import it into jamovi, which can be done easily. the chapter also demonstrates importing data from various formats like xlsx, csv, text, and spss dot sav files.", 'duration': 127.801, 'highlights': ["Manually entering data into Jamovi is not recommended, and it's much better to save the data in a spreadsheet.", 'Google Sheets is good for collaborative work, and data can be easily imported into Jamovi from various formats like xlsx, CSV, text, and SPSS dot SAV files.']}, {'end': 2207.662, 'start': 1707.215, 'title': 'Importing data into jamovi', 'summary': 'Covers the process of importing various file types into jamovi, including csv, text, and spss, highlighting the ease of the process and the ability to define variable types and labels for data analysis.', 'duration': 500.447, 'highlights': ['Jamovi can import various file types including CSV, text, and SPSS, making it a versatile tool for data analysis.', 'The process of importing data into Jamovi is quick and easy, with the software automatically formatting the data types to match the imported file.', 'Users can define variable types and labels for data analysis in Jamovi, allowing for customization and improved data interpretation.']}, {'end': 2739.02, 'start': 2207.702, 'title': 'Data transformation in jamovi', 'summary': 'Discusses the process of transforming data in jamovi, including changing variable types, averaging variables, creating z-scores, and using transform functions, aiming to make data more interpretable and reliable for further analyses.', 'duration': 531.318, 'highlights': ['The chapter focuses on transforming data in Jamovi, including changing variable types, averaging variables, creating z-scores, and using transform functions.', 'Transforming data into different variable types, such as ordinal, nominal, and categorical, is discussed to make data interpretable for collaborative and client-based work.', 'The importance of averaging several variables to obtain scale scores and balance out error variance is highlighted, emphasizing its role in providing more generalizable information.', 'Creating z-scores is discussed as a method to standardize and compare different scales of data, aiming to make data more meaningful and comparable across variables and contexts.', 'The use of transform functions in Jamovi to identify extreme scores, such as those more than two standard deviations from the mean, is demonstrated, providing a method to effectively apply transformations to multiple variables.']}, {'end': 3263.891, 'start': 2739.02, 'title': 'Jamovi: transform and filter data', 'summary': 'Introduces how to transform data using the recode function and how to filter cases in jamovi, allowing users to focus on specific cases in the dataset and perform complex filter commands.', 'duration': 524.871, 'highlights': ["Jamovi allows users to transform data by using the recode function to create new variables based on conditions, making it easy to prepare a lot of variables in a way that's easy to set up and understand.", 'The ability to filter cases in Jamovi allows users to focus on specific cases in the dataset, with the example demonstrating how to filter cases based on specific conditions such as values less than or greater than a certain threshold.', 'Using multiple filters in Jamovi, users can perform more sophisticated filter commands and combine filters to analyze data more effectively.']}], 'duration': 1684.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY1579394.jpg', 'highlights': ['Jamovi can import various file types including CSV, text, and SPSS, making it a versatile tool for data analysis.', 'The process of importing data into Jamovi is quick and easy, with the software automatically formatting the data types to match the imported file.', 'The chapter focuses on transforming data in Jamovi, including changing variable types, averaging variables, creating z-scores, and using transform functions.', 'Creating z-scores is discussed as a method to standardize and compare different scales of data, aiming to make data more meaningful and comparable across variables and contexts.', 'The importance of averaging several variables to obtain scale scores and balance out error variance is highlighted, emphasizing its role in providing more generalizable information.', 'Using multiple filters in Jamovi, users can perform more sophisticated filter commands and combine filters to analyze data more effectively.', "Jamovi allows users to transform data by using the recode function to create new variables based on conditions, making it easy to prepare a lot of variables in a way that's easy to set up and understand.", 'Google Sheets is good for collaborative work, and data can be easily imported into Jamovi from various formats like xlsx, CSV, text, and SPSS dot SAV files.', 'Users can define variable types and labels for data analysis in Jamovi, allowing for customization and improved data interpretation.', 'The ability to filter cases in Jamovi allows users to focus on specific cases in the dataset, with the example demonstrating how to filter cases based on specific conditions such as values less than or greater than a certain threshold.']}, {'end': 5515.278, 'segs': [{'end': 3361.273, 'src': 'embed', 'start': 3327.504, 'weight': 3, 'content': [{'end': 3330.406, 'text': 'That gets me the fours and the fives, the agrees and the strongly agrees.', 'start': 3327.504, 'duration': 2.902}, {'end': 3336.91, 'text': "And so that's an exploration of how filters operate many different ways in Jamovi.", 'start': 3331.806, 'duration': 5.104}, {'end': 3339.993, 'text': 'They allow you to focus on particular cases,', 'start': 3337.011, 'duration': 2.982}, {'end': 3346.899, 'text': 'to find the things that are most interesting in your particular data set and drill down to focus on those, give them the attention they deserve and,', 'start': 3339.993, 'duration': 6.906}, {'end': 3349.141, 'text': 'hopefully, get some new insights out of your data.', 'start': 3346.899, 'duration': 2.242}, {'end': 3353.805, 'text': 'Our next topic in Jamovi is exploring data.', 'start': 3350.822, 'duration': 2.983}, {'end': 3361.273, 'text': "And the reason we need to do that is because raw data is just completely overwhelming, even when it's set up in nice rows.", 'start': 3354.826, 'duration': 6.447}], 'summary': 'Exploration of filters in jamovi to focus on interesting cases, gaining new insights from data.', 'duration': 33.769, 'max_score': 3327.504, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY3327504.jpg'}, {'end': 3584.319, 'src': 'heatmap', 'start': 3402.265, 'weight': 0.856, 'content': [{'end': 3411.711, 'text': "We'll also talk about how you can export these graphs, these tables, to docs like Google Docs and Microsoft Word and to presentations,", 'start': 3402.265, 'duration': 9.446}, {'end': 3415.194, 'text': 'so you can share your insights and get your message across.', 'start': 3411.711, 'duration': 3.483}, {'end': 3425.202, 'text': 'Perhaps the quickest way to get some insight into your data is to do basic descriptive statistics, like frequencies and means and standard deviations.', 'start': 3416.519, 'duration': 8.683}, {'end': 3427.742, 'text': "Fortunately, that's really easy to do into movie.", 'start': 3425.662, 'duration': 2.08}, {'end': 3433.784, 'text': 'Now the data set that I have open here is the bugs data set, where we have a number of people.', 'start': 3428.223, 'duration': 5.561}, {'end': 3442.107, 'text': 'And they are rating how much they would like to get rid of bugs that are either high or low disgusting or in high or low frightening.', 'start': 3434.825, 'duration': 7.282}, {'end': 3445.548, 'text': "And we have the people's gender region and education.", 'start': 3442.827, 'duration': 2.721}, {'end': 3450.648, 'text': 'And all we need to do is come over here to exploration and click descriptives.', 'start': 3446.701, 'duration': 3.947}, {'end': 3452.692, 'text': "Now I've installed the scatter module.", 'start': 3451.069, 'duration': 1.623}, {'end': 3454.515, 'text': 'So these two menus show up here.', 'start': 3452.752, 'duration': 1.763}, {'end': 3456.398, 'text': "If you don't have those, that's not a problem.", 'start': 3454.555, 'duration': 1.843}, {'end': 3457.721, 'text': 'Just hit descriptives.', 'start': 3456.839, 'duration': 0.882}, {'end': 3460.927, 'text': 'then we get to pick the variables we want.', 'start': 3459.165, 'duration': 1.762}, {'end': 3465.611, 'text': 'And jamovi will either give us means and so on or frequencies.', 'start': 3461.267, 'duration': 4.344}, {'end': 3474.119, 'text': "Now, I find it useful to start with the predictor variables, the ones that you're going to use to predict the outcomes.", 'start': 3466.232, 'duration': 7.887}, {'end': 3482.166, 'text': "And in this case, that's going to be these three categorical demographic things, gender, region and education.", 'start': 3474.599, 'duration': 7.567}, {'end': 3484.448, 'text': "So I'm going to put those over here into variables.", 'start': 3482.767, 'duration': 1.681}, {'end': 3489.954, 'text': "And what we're going to get is a table that really only tells us how many cases there are.", 'start': 3485.609, 'duration': 4.345}, {'end': 3492.998, 'text': "there's 91 or 92, and we're missing one or two on each of them.", 'start': 3489.954, 'duration': 3.044}, {'end': 3500.987, 'text': "I don't need these other variables, because these have to do with quantitative or continuous variable.", 'start': 3493.819, 'duration': 7.168}, {'end': 3502.649, 'text': "So I'm just going to remove those for right now.", 'start': 3501.007, 'duration': 1.642}, {'end': 3508.398, 'text': 'On the other hand, because I have nominal or categorical variables, it would be nice to get frequency tables.', 'start': 3503.934, 'duration': 4.464}, {'end': 3510.399, 'text': "So I'm going to click this selection right here.", 'start': 3508.418, 'duration': 1.981}, {'end': 3516.845, 'text': "And then it's going to automatically expand and it's going to give me the count, the number of people in each category,", 'start': 3511.28, 'duration': 5.565}, {'end': 3520.308, 'text': 'along with the percentage of the total data and the cumulative percent.', 'start': 3516.845, 'duration': 3.463}, {'end': 3526.871, 'text': "And so, for instance, we can see that we've got about two thirds women and one third men,", 'start': 3520.848, 'duration': 6.023}, {'end': 3533.815, 'text': "and that we've got a lot of people from North America and almost 10% from Europe, but other groups are pretty small.", 'start': 3526.871, 'duration': 6.944}, {'end': 3544.12, 'text': 'And level of education, we have a spike at less and we have 15 people or 16.5% at high, which I assume means high school.', 'start': 3534.215, 'duration': 9.905}, {'end': 3550.104, 'text': "Anyhow, this is the first step, get some basic demographics or the things that you're going to use as predictors.", 'start': 3544.96, 'duration': 5.144}, {'end': 3558.509, 'text': "Now, I also want to look at the outcome variables, which are scaled, they're measured on a one to 10 or zero to 10 scale.", 'start': 3550.484, 'duration': 8.025}, {'end': 3566.194, 'text': "And I'm going to do the descriptives command over again, I'm going to hit descriptors, and then it shows up as a blank.", 'start': 3559.41, 'duration': 6.784}, {'end': 3571.998, 'text': "And what I'm going to do is I'm going to take these four outcomes and put those in the variables over here.", 'start': 3566.274, 'duration': 5.724}, {'end': 3579.298, 'text': "Now this time, because these are scaled or quantitative outcomes, it's going to give me mean, median, and so on.", 'start': 3573.456, 'duration': 5.842}, {'end': 3584.319, 'text': "On the other hand, there's a few that I should add to that.", 'start': 3580.278, 'duration': 4.041}], 'summary': 'The transcript discusses using jamovi to analyze a bugs dataset for demographic variables and outcome variables, providing insights through descriptive statistics and frequency tables, and exporting the results to documents and presentations.', 'duration': 182.054, 'max_score': 3402.265, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY3402265.jpg'}, {'end': 3442.107, 'src': 'embed', 'start': 3416.519, 'weight': 5, 'content': [{'end': 3425.202, 'text': 'Perhaps the quickest way to get some insight into your data is to do basic descriptive statistics, like frequencies and means and standard deviations.', 'start': 3416.519, 'duration': 8.683}, {'end': 3427.742, 'text': "Fortunately, that's really easy to do into movie.", 'start': 3425.662, 'duration': 2.08}, {'end': 3433.784, 'text': 'Now the data set that I have open here is the bugs data set, where we have a number of people.', 'start': 3428.223, 'duration': 5.561}, {'end': 3442.107, 'text': 'And they are rating how much they would like to get rid of bugs that are either high or low disgusting or in high or low frightening.', 'start': 3434.825, 'duration': 7.282}], 'summary': 'Quickly analyze data using basic descriptive statistics. bugs data set involves rating bug disgust and fear levels.', 'duration': 25.588, 'max_score': 3416.519, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY3416519.jpg'}, {'end': 3777.132, 'src': 'embed', 'start': 3732.476, 'weight': 2, 'content': [{'end': 3741.143, 'text': 'Again, a very good first look at your data and a way to get started on understanding what you have to help you shape and then interpret your analyses.', 'start': 3732.476, 'duration': 8.667}, {'end': 3751.014, 'text': 'The way Jamovi is set up, when you first go to explore your data, it offers you descriptive statistics or a numerical insight into your data.', 'start': 3742.509, 'duration': 8.505}, {'end': 3755.717, 'text': 'On the other hand, I actually prefer to begin with pictures, graphics, visualizations.', 'start': 3751.775, 'duration': 3.942}, {'end': 3763.782, 'text': "Do those first and then get numbers to provide precision that's in addition to what you get from the graphics.", 'start': 3756.097, 'duration': 7.685}, {'end': 3770.707, 'text': "So in this one, I'm going to show you the first of several different visualizations that we get from jamobi.", 'start': 3764.602, 'duration': 6.105}, {'end': 3773.409, 'text': 'The first one is histograms.', 'start': 3771.187, 'duration': 2.222}, {'end': 3777.132, 'text': 'Now the data set that I have open is the iris data.', 'start': 3773.89, 'duration': 3.242}], 'summary': 'Using jamovi for data exploration and visualization, starting with histograms and descriptive statistics for precision.', 'duration': 44.656, 'max_score': 3732.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY3732476.jpg'}, {'end': 4427.019, 'src': 'embed', 'start': 4396.169, 'weight': 1, 'content': [{'end': 4399.893, 'text': "On the other hand, if you're doing something like the analysis of variance, that's kind of what you're looking for.", 'start': 4396.169, 'duration': 3.724}, {'end': 4407.501, 'text': 'And this just confirms our insight that the three species of iris differ significantly in some of their measurements.', 'start': 4400.413, 'duration': 7.088}, {'end': 4414.109, 'text': 'The box plots are a good way to check that and again, a good way to check for the potential influence of outliers in the data.', 'start': 4407.882, 'duration': 6.227}, {'end': 4419.773, 'text': 'Jamovi includes one really kind of unusual kind of funny looking graph.', 'start': 4415.529, 'duration': 4.244}, {'end': 4427.019, 'text': "It's called a violin plot and what it is, and it's sort of the box plot version of the density plot.", 'start': 4420.353, 'duration': 6.666}], 'summary': 'The analysis confirms significant differences in iris species measurements, utilizing box plots and a unique violin plot in jamovi.', 'duration': 30.85, 'max_score': 4396.169, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY4396169.jpg'}, {'end': 4593.075, 'src': 'embed', 'start': 4563.968, 'weight': 0, 'content': [{'end': 4569.035, 'text': 'And so the violin plot, not a very common choice, but potentially an interesting one.', 'start': 4563.968, 'duration': 5.067}, {'end': 4572.019, 'text': 'And it might be able to give you some extra insight into your data.', 'start': 4569.335, 'duration': 2.684}, {'end': 4581.125, 'text': 'box plots and violin plots are a nice way of summarizing the distribution of a quantitative or continuous variable.', 'start': 4573.459, 'duration': 7.666}, {'end': 4584.588, 'text': 'But maybe you actually want to see the data directly.', 'start': 4581.505, 'duration': 3.083}, {'end': 4587.91, 'text': 'And dot plots are a way that allow you to do that.', 'start': 4585.028, 'duration': 2.882}, {'end': 4593.075, 'text': 'And beginning with the violin plots that I made using the iris data from the example set,', 'start': 4588.511, 'duration': 4.564}], 'summary': 'Violin plots offer extra insight into data distribution, a unique choice for visualization.', 'duration': 29.107, 'max_score': 4563.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY4563968.jpg'}, {'end': 4958.484, 'src': 'embed', 'start': 4932.908, 'weight': 7, 'content': [{'end': 4939.893, 'text': 'And from this is easy to see that we have a lot more women in each category, with the exception of partial over here,', 'start': 4932.908, 'duration': 6.985}, {'end': 4946.496, 'text': "where it's just a couple of people in each in college, but the other ones have about the two to one ratio that we have overall.", 'start': 4939.893, 'duration': 6.603}, {'end': 4956.102, 'text': 'And so a bar chart and a grouped bar chart is really a simple way of getting insight from a categorical or nominal variable.', 'start': 4946.917, 'duration': 9.185}, {'end': 4958.484, 'text': 'Again often the easiest,', 'start': 4956.743, 'duration': 1.741}], 'summary': 'More women in each category, except partial, with a 2:1 ratio overall.', 'duration': 25.576, 'max_score': 4932.908, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY4932908.jpg'}, {'end': 5378.565, 'src': 'embed', 'start': 5349.312, 'weight': 9, 'content': [{'end': 5351.234, 'text': 'And obviously, if you want to, you can resize it.', 'start': 5349.312, 'duration': 1.922}, {'end': 5353.175, 'text': 'I can click on that and drag it down a little bit.', 'start': 5351.254, 'duration': 1.921}, {'end': 5361.842, 'text': 'Similarly, if I want to work in PowerPoint, I can just open this up, and I can go to a new slide, and I can paste it.', 'start': 5354.917, 'duration': 6.925}, {'end': 5366.205, 'text': 'And there it is, and it wants to give me lots of abilities to animate and stuff.', 'start': 5362.643, 'duration': 3.562}, {'end': 5368.427, 'text': 'I will click that.', 'start': 5366.365, 'duration': 2.062}, {'end': 5372.3, 'text': "But that's a great way to share your information.", 'start': 5369.758, 'duration': 2.542}, {'end': 5373.101, 'text': "It's big.", 'start': 5372.38, 'duration': 0.721}, {'end': 5374.402, 'text': "It's clear.", 'start': 5373.281, 'duration': 1.121}, {'end': 5375.423, 'text': "It's nice.", 'start': 5375.062, 'duration': 0.361}, {'end': 5378.565, 'text': 'By the way, these have transparent backgrounds.', 'start': 5375.783, 'duration': 2.782}], 'summary': 'Demonstrates resizing and using powerpoint for clear, animated presentations.', 'duration': 29.253, 'max_score': 5349.312, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY5349312.jpg'}, {'end': 5465.529, 'src': 'embed', 'start': 5437.693, 'weight': 8, 'content': [{'end': 5443.156, 'text': "And what's actually nice about that is PDFs are infinitely scalable because they're vector graphics.", 'start': 5437.693, 'duration': 5.463}, {'end': 5443.957, 'text': "That's nice.", 'start': 5443.256, 'duration': 0.701}, {'end': 5446.778, 'text': "But I don't want to do that for this one.", 'start': 5444.757, 'duration': 2.021}, {'end': 5448.239, 'text': 'I want to save it as an image file.', 'start': 5446.818, 'duration': 1.421}, {'end': 5454.963, 'text': "So I'm going to come down here to format and can save it either as a PNG file, a ping file, which has transparent background.", 'start': 5448.999, 'duration': 5.964}, {'end': 5456.044, 'text': "That's the kind I usually use.", 'start': 5455.003, 'duration': 1.041}, {'end': 5460.546, 'text': 'But you have two other options, the SVG and the EPS.', 'start': 5456.804, 'duration': 3.742}, {'end': 5465.529, 'text': "And so depending on the programs that you're using, you may want to use one of those others.", 'start': 5460.706, 'duration': 4.823}], 'summary': 'Pdfs are scalable, but png is preferred for images. other options are svg and eps.', 'duration': 27.836, 'max_score': 5437.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY5437693.jpg'}], 'start': 3264.451, 'title': 'Data exploration and visualization in jamovi', 'summary': 'Explores data exploration in jamovi, including the use of filters, descriptive statistics, and visualizations, with a focus on the bugs dataset and iris dataset. it also covers sharing data visualizations in different formats and best practices.', 'chapters': [{'end': 3544.12, 'start': 3264.451, 'title': 'Exploring data in jamovi', 'summary': 'Explores the importance of using filters in jamovi to focus on specific cases, the various methods of data exploration including descriptive statistics and data visualizations, and the ease of conducting basic descriptive statistics in jamovi using the bugs dataset.', 'duration': 279.669, 'highlights': ['The chapter emphasizes the use of filters in Jamovi to focus on specific cases, demonstrating how to select cases that are either 4s or 5s and explaining the usage of backticks and quotes for variables and labels.', 'The chapter discusses the importance of exploring data in Jamovi, including the utilization of filters to find interesting aspects in the dataset and gain new insights.', 'The chapter describes the process of conducting basic descriptive statistics in Jamovi using the bugs dataset, highlighting the ease of selecting variables and obtaining frequency tables.']}, {'end': 3798.653, 'start': 3544.96, 'title': 'Using jamovi for descriptive statistics', 'summary': 'Introduces the process of obtaining descriptive statistics using jamovi, including the selection of predictor variables and the breakdown of outcome variables by gender, with a focus on obtaining mean, median, standard deviation, and quartiles.', 'duration': 253.693, 'highlights': ['Obtaining descriptive statistics on demographic variables and quantitative outcomes, including mean, median, standard deviation, and quartiles, using Jamovi.', 'Explaining the process of breaking down outcome variables by gender and obtaining statistics for the four outcome variables based on gender using Jamovi.', 'Emphasizing the importance of having at least 10% of the sample in the smallest group when working with quantitative variables in Jamovi.']}, {'end': 4563.567, 'start': 3798.753, 'title': 'Exploring iris data with histograms and density plots', 'summary': 'Provides a detailed exploration of the iris dataset, including the use of histograms and density plots to visualize the distribution of quantitative variables, revealing insights into the petal and sepal measurements of three species of irises.', 'duration': 764.814, 'highlights': ['The chapter provides a detailed exploration of the Iris dataset, including the use of histograms and density plots to visualize the distribution of quantitative variables', 'Insights into the petal and sepal measurements of three species of irises', 'Visualizing the distribution of sepal length, sepal width, petal length, and petal width through histograms and density plots', 'Utilizing stacked histograms to compare measurements of the three iris species', 'The use of density plots to provide a smoothed visualization of data distributions', 'Identifying outliers and distribution patterns using box plots', 'Exploring the unique visualization of data through violin plots']}, {'end': 4932.048, 'start': 4563.968, 'title': 'Exploring data with violin and dot plots', 'summary': 'Explores the use of violin plots, dot plots, and bar charts for visualizing and analyzing data, demonstrating their applications using the iris dataset and gender, region, and education variables.', 'duration': 368.08, 'highlights': ['Violin plots and dot plots are used to summarize the distribution of quantitative or continuous variables, with the example using 150 data points from the iris dataset.', 'Jittered and stacked dot plots are compared to visualize the density of the distribution, with jittering used to prevent overlapping of data points and stacked arrangement appealing to those who prefer an organized display.', 'The chapter demonstrates the utility of bar charts for visualizing categorical variables, showcasing the exploration of gender, region, and education variables using bar charts to reveal insights such as gender distribution and regional representation.']}, {'end': 5515.278, 'start': 4932.908, 'title': 'Sharing data visualizations', 'summary': 'Explains the process of sharing data visualizations, including tables and graphics, in various formats such as word, powerpoint, excel, google docs, slides, and sheets, highlighting the best practices and limitations of each method.', 'duration': 582.37, 'highlights': ['The easiest way of getting insight from a categorical or nominal variable is through a bar chart and a grouped bar chart, which can be the most informative when trying to get quick insights from the data.', 'Copying a table and pasting it into a spreadsheet like Excel is often the best way to work with tabular output, allowing easy rearrangement and resizing.', 'Saving data visualizations as PDF or HTML files allows for easy sharing and viewing, with PDFs being infinitely scalable due to their vector graphics nature.', 'Copying and pasting graphics into Word and PowerPoint allows for easy sharing, with the added benefit of transparent backgrounds for the graphics.', 'When sharing graphics in Google Docs and Slides, saving the graphics as image files (e.g., PNG) and then importing them into the documents is a more successful approach than direct pasting.']}], 'duration': 2250.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY3264451.jpg', 'highlights': ['Exploring the unique visualization of data through violin plots', 'Utilizing stacked histograms to compare measurements of the three iris species', 'Obtaining descriptive statistics on demographic variables and quantitative outcomes, including mean, median, standard deviation, and quartiles, using Jamovi', 'The chapter emphasizes the use of filters in Jamovi to focus on specific cases, demonstrating how to select cases that are either 4s or 5s and explaining the usage of backticks and quotes for variables and labels', 'The chapter discusses the importance of exploring data in Jamovi, including the utilization of filters to find interesting aspects in the dataset and gain new insights', 'The chapter describes the process of conducting basic descriptive statistics in Jamovi using the bugs dataset, highlighting the ease of selecting variables and obtaining frequency tables', 'The chapter provides a detailed exploration of the Iris dataset, including the use of histograms and density plots to visualize the distribution of quantitative variables', 'The easiest way of getting insight from a categorical or nominal variable is through a bar chart and a grouped bar chart, which can be the most informative when trying to get quick insights from the data', 'Saving data visualizations as PDF or HTML files allows for easy sharing and viewing, with PDFs being infinitely scalable due to their vector graphics nature', 'Copying and pasting graphics into Word and PowerPoint allows for easy sharing, with the added benefit of transparent backgrounds for the graphics']}, {'end': 6920.841, 'segs': [{'end': 6096.732, 'src': 'heatmap', 'start': 5552.062, 'weight': 0.875, 'content': [{'end': 5555.823, 'text': 'we are using the sample to infer things about the population.', 'start': 5552.062, 'duration': 3.761}, {'end': 5560.245, 'text': 'And the t test is the simplest and most direct way to get started with this.', 'start': 5556.244, 'duration': 4.001}, {'end': 5564.326, 'text': "In this chapter, we're going to talk about three kinds of t tests.", 'start': 5561.045, 'duration': 3.281}, {'end': 5570.608, 'text': "First, we'll look at the independent samples t tests that you use to compare two groups means to each other.", 'start': 5564.906, 'duration': 5.702}, {'end': 5579.291, 'text': "The second is the paired samples or repeated measures, t test, where you're looking for changes in scores from time one to time,", 'start': 5571.449, 'duration': 7.842}, {'end': 5580.612, 'text': 'two for one group of people.', 'start': 5579.291, 'duration': 1.321}, {'end': 5589.775, 'text': "And third is the one sample t test, where you're taking one samples mean and comparing it to a known population mean.", 'start': 5581.412, 'duration': 8.363}, {'end': 5591.375, 'text': 'Taken together,', 'start': 5590.695, 'duration': 0.68}, {'end': 5602.92, 'text': 'the three of these serve as an excellent introduction to the concept of inferential statistics and going from the specifics of your sample at hand and generalizing to the population at large.', 'start': 5591.375, 'duration': 11.545}, {'end': 5609.143, 'text': 'One of the simplest inferential tests that you can do is the independent samples t test.', 'start': 5604.561, 'duration': 4.582}, {'end': 5614.585, 'text': 'This is where you comparing the means of two different groups, very easy to do in Jamovi.', 'start': 5609.443, 'duration': 5.142}, {'end': 5619.027, 'text': "And you can actually do several variables at a time, although they're going to be separate comparisons.", 'start': 5614.605, 'duration': 4.422}, {'end': 5620.988, 'text': 'To show you how this works.', 'start': 5619.907, 'duration': 1.081}, {'end': 5630.779, 'text': "I'm using the example data set bugs, which talks about how much people want to get rid of bugs that are either low disgusting, low fright,", 'start': 5620.988, 'duration': 9.791}, {'end': 5634.343, 'text': 'low disgusting, high fright through high disgusting, high fright.', 'start': 5630.779, 'duration': 3.564}, {'end': 5637.986, 'text': "And it's rated on a zero not at all to 10 very much scale.", 'start': 5634.843, 'duration': 3.143}, {'end': 5645.991, 'text': "And we have information about people's level of education, the region they live in, gender is coded as male and female.", 'start': 5639.067, 'duration': 6.924}, {'end': 5653.376, 'text': "And so I'm going to use the gender one and compare the male and female respondents on these four different variables.", 'start': 5646.772, 'duration': 6.604}, {'end': 5657.577, 'text': 'Now, before you do a t test, before you start doing inferential tests,', 'start': 5653.456, 'duration': 4.121}, {'end': 5662.678, 'text': "it's a good idea to take a look at the variables and to see how well they meet the assumptions.", 'start': 5657.577, 'duration': 5.101}, {'end': 5669.52, 'text': 'Because certain things like normality, or similarity and variance are important for a t test.', 'start': 5662.998, 'duration': 6.522}, {'end': 5673.101, 'text': "So I'm going to come over here to exploration and descriptives.", 'start': 5670.04, 'duration': 3.061}, {'end': 5677.542, 'text': "And then I'm going to pick my four outcome variables right here.", 'start': 5675.121, 'duration': 2.421}, {'end': 5684.154, 'text': "I'll put them here under variables, and then I'm going to split the whole thing by gender.", 'start': 5679.505, 'duration': 4.649}, {'end': 5687.536, 'text': "It's not really the table that I'm most worried about.", 'start': 5685.216, 'duration': 2.32}, {'end': 5691.817, 'text': "Although you can see that we have more female respondents than male, it's not a big deal.", 'start': 5687.636, 'duration': 4.181}, {'end': 5699.418, 'text': "And the means are, well, there's a one point difference, there's a two thirds, there's a half point.", 'start': 5692.777, 'duration': 6.641}, {'end': 5700.959, 'text': 'So they vary.', 'start': 5699.478, 'duration': 1.481}, {'end': 5703.659, 'text': 'What I really want here are the plots.', 'start': 5701.759, 'duration': 1.9}, {'end': 5705.239, 'text': "So I'm going to come over here to plots.", 'start': 5703.819, 'duration': 1.42}, {'end': 5713.961, 'text': "And I'm going to get a density histogram or density chart, and box plots for each of these comparisons.", 'start': 5706.62, 'duration': 7.341}, {'end': 5719.457, 'text': 'And so what you see is these ones are kind of sort of close to normal.', 'start': 5715.894, 'duration': 3.563}, {'end': 5721.579, 'text': 'These are female respondents here.', 'start': 5719.497, 'duration': 2.082}, {'end': 5722.76, 'text': 'These are male respondents here.', 'start': 5721.679, 'duration': 1.081}, {'end': 5724.861, 'text': 'Distributions are pretty similar.', 'start': 5723.36, 'duration': 1.501}, {'end': 5727.463, 'text': "The box plots show there's no outliers.", 'start': 5725.322, 'duration': 2.141}, {'end': 5729.425, 'text': "There actually, there's a lot of overlap.", 'start': 5727.503, 'duration': 1.922}, {'end': 5735.57, 'text': "Okay, here's where we start getting non-normal distributions, which is in a sense a problem.", 'start': 5730.145, 'duration': 5.425}, {'end': 5740.934, 'text': "but because of the central limit theorem, because we're really working with sampling distributions, it's not the end of the world.", 'start': 5735.57, 'duration': 5.364}, {'end': 5742.415, 'text': 'We can still go ahead and do things.', 'start': 5740.974, 'duration': 1.441}, {'end': 5743.596, 'text': "We've got a couple of outliers.", 'start': 5742.435, 'duration': 1.161}, {'end': 5746.457, 'text': 'similar distributions.', 'start': 5745.077, 'duration': 1.38}, {'end': 5750.358, 'text': "Okay, so now we see a little bit what's going on.", 'start': 5748.198, 'duration': 2.16}, {'end': 5755.16, 'text': "So with that as context, I'm now going to do the regular t test.", 'start': 5750.458, 'duration': 4.702}, {'end': 5761.401, 'text': "And so I'm going to come over here to t tests, and do independent samples t test.", 'start': 5756.26, 'duration': 5.141}, {'end': 5770.34, 'text': 'And all I need to do is pick my dependent variables you can just call them outcome variables and dependency for usually,', 'start': 5762.421, 'duration': 7.919}, {'end': 5772.202, 'text': "for when it's a randomized experiment.", 'start': 5770.34, 'duration': 1.862}, {'end': 5777.765, 'text': "And then the grouping variable is the thing that I want to split them into two different groups on I'm going to use gender here.", 'start': 5772.722, 'duration': 5.043}, {'end': 5779.366, 'text': "So I'll click that over here.", 'start': 5777.785, 'duration': 1.581}, {'end': 5782.408, 'text': 'And it gives us this table by default.', 'start': 5780.247, 'duration': 2.161}, {'end': 5788.192, 'text': "And it's going to give us the t test and the p value, which is for the significance test.", 'start': 5782.688, 'duration': 5.504}, {'end': 5796.495, 'text': 'And we can see from this right now that actually there are no significant differences between the male and female respondents on any of these.', 'start': 5788.852, 'duration': 7.643}, {'end': 5800.837, 'text': 'the closest we had was a p value, point 161..', 'start': 5796.495, 'duration': 4.342}, {'end': 5806.06, 'text': 'And the rule of thumb is it needs to be less than point oh five to be considered statistically significant.', 'start': 5800.837, 'duration': 5.223}, {'end': 5810.942, 'text': "This one down here is a lot closer, it's point oh six, nearly significant.", 'start': 5807.1, 'duration': 3.842}, {'end': 5813.063, 'text': "That's this last comparison right here.", 'start': 5811.022, 'duration': 2.041}, {'end': 5818.888, 'text': "But there's a lot more you can get from the t test function in Jamovi.", 'start': 5815.166, 'duration': 3.722}, {'end': 5820.769, 'text': "And so I'm going to do a few of these things.", 'start': 5818.908, 'duration': 1.861}, {'end': 5826.211, 'text': "Now, if you are familiar with Bayesian statistics, Jamovi can incorporate that it's kind of nice.", 'start': 5821.329, 'duration': 4.882}, {'end': 5829.553, 'text': "I'm going to come over here and get the mean difference.", 'start': 5826.831, 'duration': 2.722}, {'end': 5834.535, 'text': "So that's the mean of the women minus the mean of the men.", 'start': 5829.653, 'duration': 4.882}, {'end': 5839.477, 'text': 'And you can see there about point five, five points point 606.', 'start': 5835.395, 'duration': 4.082}, {'end': 5845.942, 'text': "Okay, I'm also going to get the confidence interval, which by default is set at the 95%.", 'start': 5839.477, 'duration': 6.465}, {'end': 5848.725, 'text': 'And if I scroll this over a little bit, you can see the whole thing.', 'start': 5845.942, 'duration': 2.783}, {'end': 5852.488, 'text': "And also, you can see that they're negative on one side positive on the other.", 'start': 5849.846, 'duration': 2.642}, {'end': 5857.633, 'text': 'So they include zero, which is consistent with the differences not being statistically significant.', 'start': 5852.528, 'duration': 5.105}, {'end': 5860.556, 'text': "I'm also going to get the effect size.", 'start': 5858.654, 'duration': 1.902}, {'end': 5869.564, 'text': "And in this case, it uses Cohen's d, which tells you how many standard deviations there are between the two groups means.", 'start': 5862.216, 'duration': 7.348}, {'end': 5876.171, 'text': "And in this case, they're pretty small from close to zero to the biggest is point four standard deviations.", 'start': 5870.305, 'duration': 5.866}, {'end': 5880.706, 'text': "I'm also going to get normality checks.", 'start': 5877.904, 'duration': 2.802}, {'end': 5887.071, 'text': 'So we know that the distributions are not entirely normal, meaning bell curve shaped.', 'start': 5881.187, 'duration': 5.884}, {'end': 5890.914, 'text': 'And the test that we have here is the Shapiro Wilk test.', 'start': 5888.012, 'duration': 2.902}, {'end': 5894.517, 'text': 'And it lets us know that really none of them are exactly normal.', 'start': 5891.695, 'duration': 2.822}, {'end': 5896.959, 'text': 'We could tell that by looking at them.', 'start': 5895.678, 'duration': 1.281}, {'end': 5901.741, 'text': "But again, it's really not the end of the world, because we're using the sampling distributions and not the raw distributions.", 'start': 5896.999, 'duration': 4.742}, {'end': 5904.302, 'text': "I'm also going to check for a quality of variances.", 'start': 5902.361, 'duration': 1.941}, {'end': 5911.545, 'text': "That's something that's also important for the t test, it says that the two groups need to be spread out approximately the same amount.", 'start': 5904.342, 'duration': 7.203}, {'end': 5914.987, 'text': "And this is going to use Levine's test for the quality of variance.", 'start': 5911.666, 'duration': 3.321}, {'end': 5917.608, 'text': 'And you can see on this one, none of these are significant.', 'start': 5915.027, 'duration': 2.581}, {'end': 5921.211, 'text': "In fact, they're all really Point four is the lowest.", 'start': 5917.628, 'duration': 3.583}, {'end': 5927.499, 'text': "And so there's no significant difference in the variability of the two distributions, which is what we want.", 'start': 5921.652, 'duration': 5.847}, {'end': 5929.982, 'text': "I'm also going to click descriptives.", 'start': 5928.26, 'duration': 1.722}, {'end': 5936.3, 'text': "And that's going to give me the means and whatnot of each of the groups.", 'start': 5932.699, 'duration': 3.601}, {'end': 5941.482, 'text': 'And so you can see the mean, the median standard deviation and the standard error, which is used in the inferential tests.', 'start': 5936.36, 'duration': 5.122}, {'end': 5944.204, 'text': 'And then finally, the descriptive plots.', 'start': 5942.363, 'duration': 1.841}, {'end': 5952.647, 'text': 'And in this case, what it gives us are confidence intervals for the means as well as it shows the median for each group.', 'start': 5944.624, 'duration': 8.023}, {'end': 5958.629, 'text': 'And since these are the ones that correspond most closely to the inferential test of the t test,', 'start': 5953.287, 'duration': 5.342}, {'end': 5961.13, 'text': "it's probably the best one for actually seeing whether there's a difference.", 'start': 5958.629, 'duration': 2.501}, {'end': 5971.554, 'text': 'The general rule of thumb here is that if the confidence interval that says vertical line for one group overlaps with the mean of the other group,', 'start': 5961.65, 'duration': 9.904}, {'end': 5974.095, 'text': "then they're usually not significantly different.", 'start': 5971.554, 'duration': 2.541}, {'end': 5976.696, 'text': 'And we got a lot of overlap here a lot of overlap.', 'start': 5974.395, 'duration': 2.301}, {'end': 5978.977, 'text': 'And these ones are pretty separate.', 'start': 5977.277, 'duration': 1.7}, {'end': 5980.918, 'text': 'And so this one was nearly significant.', 'start': 5979.077, 'duration': 1.841}, {'end': 5988.279, 'text': "Anyhow, that's how you can do the independent samples t-test using a single categorizing variable.", 'start': 5981.531, 'duration': 6.748}, {'end': 5990.342, 'text': 'In this case, I used male and female respondents.', 'start': 5988.359, 'duration': 1.983}, {'end': 5994.907, 'text': 'And you can use several outcome variables simultaneously.', 'start': 5991.083, 'duration': 3.824}, {'end': 6000.975, 'text': "And it's an excellent first step in getting a look at what's happening in your data through inferential statistics.", 'start': 5995.328, 'duration': 5.647}, {'end': 6012.243, 'text': "Sometimes you have data from one group of people and you're comparing them either before and after some kind of intervention,", 'start': 6004.537, 'duration': 7.706}, {'end': 6018.348, 'text': "or you're comparing them on two separate measurements and you want to compare each person's score with their own score.", 'start': 6012.243, 'duration': 6.105}, {'end': 6022.672, 'text': "So you're looking for changes from one variable or one time to another.", 'start': 6018.849, 'duration': 3.823}, {'end': 6026.155, 'text': 'This is when you want to use a paired samples t test.', 'start': 6023.253, 'duration': 2.902}, {'end': 6028.137, 'text': "And it's very easy to do here in Jamovi.", 'start': 6026.475, 'duration': 1.662}, {'end': 6037.221, 'text': "For this example, I'm using the same bugs data, which talks about how much people want to get rid of bugs or insects that are low, disgusting,", 'start': 6028.817, 'duration': 8.404}, {'end': 6039.582, 'text': 'low fright up to high, disgusting, high fright.', 'start': 6037.221, 'duration': 2.361}, {'end': 6042.363, 'text': 'It goes from zero, which is the lowest, to 10, which is the highest.', 'start': 6039.622, 'duration': 2.741}, {'end': 6048.966, 'text': "I'm going to come up here, and I'm going to use paired samples t test, that's the test that we're doing here.", 'start': 6043.942, 'duration': 5.024}, {'end': 6053.369, 'text': 'And what we have to do is we have to specify pairs of variables.', 'start': 6049.866, 'duration': 3.503}, {'end': 6058.092, 'text': 'Now, there are a few different ways that you can select the pairs that you want to do.', 'start': 6053.989, 'duration': 4.103}, {'end': 6064.397, 'text': 'So for instance, you can click one variable and push it over here, and then you simply click the second variable.', 'start': 6058.813, 'duration': 5.584}, {'end': 6065.438, 'text': "So maybe I'll do this one.", 'start': 6064.417, 'duration': 1.021}, {'end': 6068.633, 'text': 'And so that sets up a pair.', 'start': 6066.932, 'duration': 1.701}, {'end': 6074.717, 'text': 'You can also click one and do a command or control click and select the other.', 'start': 6069.314, 'duration': 5.403}, {'end': 6076.478, 'text': 'So you get both of them there at the same time.', 'start': 6074.737, 'duration': 1.741}, {'end': 6077.859, 'text': 'And that gets a pair.', 'start': 6076.498, 'duration': 1.361}, {'end': 6086.905, 'text': 'Or if you have a bunch of comparisons all in a row that makes sense to do, you can select the whole thing by just doing a shift click.', 'start': 6078.54, 'duration': 8.365}, {'end': 6092.569, 'text': "And what it's going to do is it's going to put the variables together in order.", 'start': 6087.646, 'duration': 4.923}, {'end': 6096.732, 'text': 'So number one goes with number two, number three goes with number four, and so on.', 'start': 6093.009, 'duration': 3.723}], 'summary': 'The transcript covers using t-tests to compare means, checking assumptions, and interpreting results from inferential statistics.', 'duration': 544.67, 'max_score': 5552.062, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY5552062.jpg'}, {'end': 5589.775, 'src': 'embed', 'start': 5556.244, 'weight': 0, 'content': [{'end': 5560.245, 'text': 'And the t test is the simplest and most direct way to get started with this.', 'start': 5556.244, 'duration': 4.001}, {'end': 5564.326, 'text': "In this chapter, we're going to talk about three kinds of t tests.", 'start': 5561.045, 'duration': 3.281}, {'end': 5570.608, 'text': "First, we'll look at the independent samples t tests that you use to compare two groups means to each other.", 'start': 5564.906, 'duration': 5.702}, {'end': 5579.291, 'text': "The second is the paired samples or repeated measures, t test, where you're looking for changes in scores from time one to time,", 'start': 5571.449, 'duration': 7.842}, {'end': 5580.612, 'text': 'two for one group of people.', 'start': 5579.291, 'duration': 1.321}, {'end': 5589.775, 'text': "And third is the one sample t test, where you're taking one samples mean and comparing it to a known population mean.", 'start': 5581.412, 'duration': 8.363}], 'summary': 'Introduction to three types of t tests for comparing means.', 'duration': 33.531, 'max_score': 5556.244, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY5556244.jpg'}, {'end': 5630.779, 'src': 'embed', 'start': 5604.561, 'weight': 2, 'content': [{'end': 5609.143, 'text': 'One of the simplest inferential tests that you can do is the independent samples t test.', 'start': 5604.561, 'duration': 4.582}, {'end': 5614.585, 'text': 'This is where you comparing the means of two different groups, very easy to do in Jamovi.', 'start': 5609.443, 'duration': 5.142}, {'end': 5619.027, 'text': "And you can actually do several variables at a time, although they're going to be separate comparisons.", 'start': 5614.605, 'duration': 4.422}, {'end': 5620.988, 'text': 'To show you how this works.', 'start': 5619.907, 'duration': 1.081}, {'end': 5630.779, 'text': "I'm using the example data set bugs, which talks about how much people want to get rid of bugs that are either low disgusting, low fright,", 'start': 5620.988, 'duration': 9.791}], 'summary': 'The independent samples t test in jamovi allows easy comparison of means for different groups, with the ability to analyze multiple variables separately.', 'duration': 26.218, 'max_score': 5604.561, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY5604561.jpg'}, {'end': 5673.101, 'src': 'embed', 'start': 5646.772, 'weight': 4, 'content': [{'end': 5653.376, 'text': "And so I'm going to use the gender one and compare the male and female respondents on these four different variables.", 'start': 5646.772, 'duration': 6.604}, {'end': 5657.577, 'text': 'Now, before you do a t test, before you start doing inferential tests,', 'start': 5653.456, 'duration': 4.121}, {'end': 5662.678, 'text': "it's a good idea to take a look at the variables and to see how well they meet the assumptions.", 'start': 5657.577, 'duration': 5.101}, {'end': 5669.52, 'text': 'Because certain things like normality, or similarity and variance are important for a t test.', 'start': 5662.998, 'duration': 6.522}, {'end': 5673.101, 'text': "So I'm going to come over here to exploration and descriptives.", 'start': 5670.04, 'duration': 3.061}], 'summary': 'Comparing male and female respondents, checking variables for t test assumptions.', 'duration': 26.329, 'max_score': 5646.772, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY5646772.jpg'}, {'end': 6894.524, 'src': 'embed', 'start': 6869.807, 'weight': 1, 'content': [{'end': 6878.233, 'text': "If you're doing experimental research is extremely common to use the analysis of variance that's also called ANOVA or ANOVA.", 'start': 6869.807, 'duration': 8.426}, {'end': 6883.637, 'text': 'And what it lets you do is it lets you compare the means of two or more groups,', 'start': 6878.673, 'duration': 4.964}, {'end': 6889.461, 'text': 'also breaking them up by one or more categorical variables simultaneously.', 'start': 6883.637, 'duration': 5.824}, {'end': 6894.524, 'text': "To demonstrate this in Jamovi, I'm going to use the built-in example dataset tooth growth.", 'start': 6890.101, 'duration': 4.423}], 'summary': 'Anova allows comparing means of two or more groups using categorical variables in experimental research.', 'duration': 24.717, 'max_score': 6869.807, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY6869807.jpg'}], 'start': 5515.278, 'title': 'T tests, paired samples, and anova in jamovi', 'summary': 'Introduces t tests covering independent samples, paired samples, and one sample t tests, along with exploratory data analysis and analysis of variance using jamovi, exploring various statistical tests, practical applications, and significant findings, providing insights for data analysis and interpretation.', 'chapters': [{'end': 6042.363, 'start': 5515.278, 'title': 'T tests in inferential statistics', 'summary': 'Introduces t tests in jamovi, covering independent samples, paired samples, and one sample t tests, with a focus on inferential statistics and practical application, including normality checks, significance tests, effect size, confidence intervals, and descriptive plots.', 'duration': 527.085, 'highlights': ['The chapter covers three types of t tests: independent samples, paired samples, and one sample t tests, providing a comprehensive introduction to inferential statistics.', 'The independent samples t test in Jamovi allows easy comparison of means between two different groups and multiple variables simultaneously.', 'The chapter emphasizes the importance of assessing variables for normality, similarity in variance, and other assumptions before conducting t tests.', 'The practical application of t tests in Jamovi includes performing significance tests, effect size calculations, confidence interval estimations, and descriptive plots for comprehensive data analysis.', 'The chapter also demonstrates the use of paired samples t tests for comparing changes within the same group, with a practical example using the bugs dataset.']}, {'end': 6363.06, 'start': 6043.942, 'title': 'Paired samples t test in jamovi', 'summary': 'Explains the process of conducting a paired samples t test in jamovi, demonstrating the selection of pairs, assessment of mean differences, effect size calculation, descriptives, normality check, and confidence interval for differences, with the analysis showing all four comparisons to be statistically significant.', 'duration': 319.118, 'highlights': ['The analysis shows all four comparisons to be statistically significant.', 'Mean differences are assessed and displayed, showing variations in the scores between the variables.', "Effect size calculation using Cohen's D indicates substantial differences between the scores at time one and time two, with some values approaching a full standard deviation.", 'Descriptive statistics and normality checks are performed to ensure the validity of the t test results.', 'Confidence intervals for differences are obtained through descriptive plots, offering a visual representation of the comparisons between variables.']}, {'end': 6510.651, 'start': 6363.06, 'title': 'Exploratory data analysis in jamovi', 'summary': 'Covers the process of exploratory data analysis using jamovi, including the use of descriptives to analyze five variables, incorporating new features such as normality tests and graphics like density charts, box plots, and qq plots.', 'duration': 147.591, 'highlights': ['Jamovi updates frequently, providing new options under descriptives, including mean, median, minimum, maximum, and standard deviation.', 'New features include a normality test, providing p values to determine the normality of the data.', 'Graphics options encompass density charts, box plots for identifying outliers, and qq plots to compare observed data against a normal distribution.']}, {'end': 6729.037, 'start': 6511.031, 'title': 'One sample t test analysis', 'summary': 'Covers the process of conducting a one sample t test analysis on five variables with a null value of three, indicating the midpoint of a one to five scale, revealing significant differences in means and a notable effect size for openness.', 'duration': 218.006, 'highlights': ['The mean of all variables is significantly different from the hypothesized value of three, with a notable effect size for openness at 1.7 standard deviations above the mean.', 'The standard errors for the variables are tiny, around two hundredths of a point, leading to invisible confidence intervals in the descriptives plot.', 'Neuroticism is the only variable with a mean below the hypothesized value of three, while the others are above, indicating significant differences.', 'The analysis confirms that the data align well with the assumptions of a one sample t test, including normality, allowing for a confident analysis of the variables.']}, {'end': 6920.841, 'start': 6729.817, 'title': 'Understanding analysis of variance in statistics', 'summary': 'Introduces the analysis of variance (anova) as a method for comparing means of two or more groups, exploring various anova variations available in jamovi, including standard factorial anova, repeated measures analysis of variance, analysis of covariance, and non-parametric analysis, offering a broader range of situations to analyze and potential insights from the data.', 'duration': 191.024, 'highlights': ['The chapter introduces the analysis of variance (ANOVA) as a method for comparing means of two or more groups, offering a broader range of situations to analyze and potential insights from the data.', 'The variations on the analysis of variance in Jamovi include standard factorial ANOVA, repeated measures analysis of variance, analysis of covariance, and non-parametric analysis, providing diverse options for analyzing different data scenarios.', 'The ANOVA method allows for the comparison of means of two or more groups, also breaking them up by one or more categorical variables simultaneously, demonstrating this with a built-in example dataset on tooth growth in guinea pigs given different supplements and doses.']}], 'duration': 1405.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY5515278.jpg', 'highlights': ['The chapter covers three types of t tests: independent samples, paired samples, and one sample t tests, providing a comprehensive introduction to inferential statistics.', 'The chapter introduces the analysis of variance (ANOVA) as a method for comparing means of two or more groups, offering a broader range of situations to analyze and potential insights from the data.', 'The practical application of t tests in Jamovi includes performing significance tests, effect size calculations, confidence interval estimations, and descriptive plots for comprehensive data analysis.', 'The ANOVA method allows for the comparison of means of two or more groups, also breaking them up by one or more categorical variables simultaneously, demonstrating this with a built-in example dataset on tooth growth in guinea pigs given different supplements and doses.', 'The chapter emphasizes the importance of assessing variables for normality, similarity in variance, and other assumptions before conducting t tests.']}, {'end': 8151.26, 'segs': [{'end': 7350.082, 'src': 'heatmap', 'start': 7168.567, 'weight': 0.724, 'content': [{'end': 7172.149, 'text': "So I'm going to take Len and put it into dependent variable.", 'start': 7168.567, 'duration': 3.582}, {'end': 7178.992, 'text': "And I'm going to take sup and dose, I'm going to do a shift click to get both of them and put them over here.", 'start': 7173.009, 'duration': 5.983}, {'end': 7181.073, 'text': 'And it puts it under fixed factors.', 'start': 7179.512, 'duration': 1.561}, {'end': 7184.255, 'text': "And you can see the analysis of variance table is there and it's filling up.", 'start': 7181.093, 'duration': 3.162}, {'end': 7187.336, 'text': 'And we get our immediate results.', 'start': 7185.175, 'duration': 2.161}, {'end': 7192.578, 'text': "we see that, for instance, sub the supplement, if I come way over here, that's a big value for F,", 'start': 7187.336, 'duration': 5.242}, {'end': 7196.979, 'text': 'and the probability value which is used for statistical significance testing is really low.', 'start': 7192.578, 'duration': 4.401}, {'end': 7201.301, 'text': "it's less than point oh five, which is the common cutoff for statistically significant findings.", 'start': 7196.979, 'duration': 4.322}, {'end': 7208.023, 'text': 'So we have a significant main effect, meaning supplement makes a difference on the tooth length all by itself.', 'start': 7201.821, 'duration': 6.202}, {'end': 7210.084, 'text': 'Same thing is true for dose.', 'start': 7208.803, 'duration': 1.281}, {'end': 7211.984, 'text': 'In fact, it makes an enormous difference.', 'start': 7210.144, 'duration': 1.84}, {'end': 7217.606, 'text': "You can see that the F value of 92, it's going to be much less than 0.001.", 'start': 7212.004, 'duration': 5.602}, {'end': 7219.387, 'text': "And then there's also an interaction.", 'start': 7217.606, 'duration': 1.781}, {'end': 7222.148, 'text': "So we're going to want to get a little more detail about all of this.", 'start': 7219.447, 'duration': 2.701}, {'end': 7225.99, 'text': "The first thing I'm going to do is I'm going to get an effect size.", 'start': 7222.849, 'duration': 3.141}, {'end': 7229.812, 'text': "the effect size that's generally used for analysis of variance is called eta squared.", 'start': 7225.99, 'duration': 3.822}, {'end': 7232.894, 'text': "that's this little Greek letter that kind of looks like a lowercase.", 'start': 7229.812, 'duration': 3.082}, {'end': 7233.755, 'text': "n it's an eta.", 'start': 7232.894, 'duration': 0.861}, {'end': 7239.638, 'text': 'Now, if we had a one way analysis of variance, we could do the regular eta squared.', 'start': 7234.375, 'duration': 5.263}, {'end': 7243.139, 'text': "But because we have these interactions, we should do what's called a partial eta squared,", 'start': 7239.658, 'duration': 3.481}, {'end': 7245.681, 'text': 'which looks at the unique contribution of each of these factors.', 'start': 7243.139, 'duration': 2.542}, {'end': 7254.158, 'text': "that's what showed up right here, we have an n squared p partial eta squared, and it goes from zero to one.", 'start': 7246.656, 'duration': 7.502}, {'end': 7262.8, 'text': 'And it can be interpreted as the proportion of variance in the outcome variable len that can be associated with that particular factor.', 'start': 7255.038, 'duration': 7.762}, {'end': 7267.981, 'text': 'And we get a lot for sup, we get 22.4.', 'start': 7263.64, 'duration': 4.341}, {'end': 7270.324, 'text': 'for dose, we get a huge amount 77.3.', 'start': 7267.981, 'duration': 2.343}, {'end': 7273.208, 'text': 'And then we have this 13.2.', 'start': 7270.324, 'duration': 2.884}, {'end': 7280.617, 'text': "And again, these don't add up to one, but they give you an idea of the relative strength of each of these things put together.", 'start': 7273.208, 'duration': 7.409}, {'end': 7285.671, 'text': "What I'm going to do then is I'm going to go through some of the options.", 'start': 7282.267, 'duration': 3.404}, {'end': 7289.054, 'text': 'Now you can specify the specific model.', 'start': 7285.891, 'duration': 3.163}, {'end': 7290.756, 'text': "I'm using the very general approach,", 'start': 7289.054, 'duration': 1.702}, {'end': 7298.365, 'text': "which looks at the two main effects for the two categorical or nominal variables that I'm using as predictor variables and their interaction.", 'start': 7290.756, 'duration': 7.609}, {'end': 7299.926, 'text': "That's how I want to do it.", 'start': 7298.665, 'duration': 1.261}, {'end': 7301.368, 'text': "So I'm just going to leave that alone.", 'start': 7299.946, 'duration': 1.422}, {'end': 7303.13, 'text': "I'll close that.", 'start': 7302.489, 'duration': 0.641}, {'end': 7306.952, 'text': 'The assumption checks, homogeneity tests,', 'start': 7304.05, 'duration': 2.902}, {'end': 7313.554, 'text': 'because your different groups are supposed to have approximately the same amount of spread in the outcome variable.', 'start': 7306.952, 'duration': 6.602}, {'end': 7314.755, 'text': "So I'm going to click that one.", 'start': 7313.574, 'duration': 1.181}, {'end': 7320.798, 'text': "And that's going to bring up a new table here, we're doing the test for homogeneity of variance using Levine's test.", 'start': 7315.695, 'duration': 5.103}, {'end': 7326.461, 'text': "And what we have here is the important part is this p value at the end, it's point 103.", 'start': 7321.958, 'duration': 4.503}, {'end': 7328.122, 'text': "So it's more than oh five.", 'start': 7326.461, 'duration': 1.661}, {'end': 7330.104, 'text': "So it's not a statistically significant difference.", 'start': 7328.142, 'duration': 1.962}, {'end': 7330.744, 'text': "That's good.", 'start': 7330.264, 'duration': 0.48}, {'end': 7342.272, 'text': "We're also going to get a qq plot of the residuals or the variability in the data that's left over after we control for sup and dose and the interaction.", 'start': 7330.764, 'duration': 11.508}, {'end': 7350.082, 'text': "And again, if it were perfectly uniform, which is kind of what you want, it would be right on the diagonal, but it's really close.", 'start': 7343.815, 'duration': 6.267}], 'summary': 'An analysis of variance shows significant effects of supplement and dose on tooth length, with sup and dose explaining 22.4% and 77.3% of the variance, respectively.', 'duration': 181.515, 'max_score': 7168.567, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY7168567.jpg'}, {'end': 7225.99, 'src': 'embed', 'start': 7196.979, 'weight': 0, 'content': [{'end': 7201.301, 'text': "it's less than point oh five, which is the common cutoff for statistically significant findings.", 'start': 7196.979, 'duration': 4.322}, {'end': 7208.023, 'text': 'So we have a significant main effect, meaning supplement makes a difference on the tooth length all by itself.', 'start': 7201.821, 'duration': 6.202}, {'end': 7210.084, 'text': 'Same thing is true for dose.', 'start': 7208.803, 'duration': 1.281}, {'end': 7211.984, 'text': 'In fact, it makes an enormous difference.', 'start': 7210.144, 'duration': 1.84}, {'end': 7217.606, 'text': "You can see that the F value of 92, it's going to be much less than 0.001.", 'start': 7212.004, 'duration': 5.602}, {'end': 7219.387, 'text': "And then there's also an interaction.", 'start': 7217.606, 'duration': 1.781}, {'end': 7222.148, 'text': "So we're going to want to get a little more detail about all of this.", 'start': 7219.447, 'duration': 2.701}, {'end': 7225.99, 'text': "The first thing I'm going to do is I'm going to get an effect size.", 'start': 7222.849, 'duration': 3.141}], 'summary': 'Supplement and dose have significant effects on tooth length, with an enormous difference in dose, and an interaction present.', 'duration': 29.011, 'max_score': 7196.979, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY7196979.jpg'}, {'end': 7342.272, 'src': 'embed', 'start': 7304.05, 'weight': 3, 'content': [{'end': 7306.952, 'text': 'The assumption checks, homogeneity tests,', 'start': 7304.05, 'duration': 2.902}, {'end': 7313.554, 'text': 'because your different groups are supposed to have approximately the same amount of spread in the outcome variable.', 'start': 7306.952, 'duration': 6.602}, {'end': 7314.755, 'text': "So I'm going to click that one.", 'start': 7313.574, 'duration': 1.181}, {'end': 7320.798, 'text': "And that's going to bring up a new table here, we're doing the test for homogeneity of variance using Levine's test.", 'start': 7315.695, 'duration': 5.103}, {'end': 7326.461, 'text': "And what we have here is the important part is this p value at the end, it's point 103.", 'start': 7321.958, 'duration': 4.503}, {'end': 7328.122, 'text': "So it's more than oh five.", 'start': 7326.461, 'duration': 1.661}, {'end': 7330.104, 'text': "So it's not a statistically significant difference.", 'start': 7328.142, 'duration': 1.962}, {'end': 7330.744, 'text': "That's good.", 'start': 7330.264, 'duration': 0.48}, {'end': 7342.272, 'text': "We're also going to get a qq plot of the residuals or the variability in the data that's left over after we control for sup and dose and the interaction.", 'start': 7330.764, 'duration': 11.508}], 'summary': 'Homogeneity test showed p value of 0.103, indicating no statistically significant difference.', 'duration': 38.222, 'max_score': 7304.05, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY7304050.jpg'}, {'end': 7441.869, 'src': 'embed', 'start': 7417.183, 'weight': 4, 'content': [{'end': 7423.811, 'text': "And we're going to get a big table that looks at possible cell by cell comparisons.", 'start': 7417.183, 'duration': 6.628}, {'end': 7425.192, 'text': 'Now, it is a lot.', 'start': 7423.851, 'duration': 1.341}, {'end': 7427.155, 'text': 'And you get to choose the correction.', 'start': 7425.813, 'duration': 1.342}, {'end': 7429.337, 'text': 'A lot of people use the Chaffee or the Bonferroni.', 'start': 7427.175, 'duration': 2.162}, {'end': 7434.644, 'text': "I personally prefer the Tukey test that's from John Tukey who developed it.", 'start': 7429.478, 'duration': 5.166}, {'end': 7440.408, 'text': "And what we see, for instance, is that OJVC, well, there's only one comparison possible there.", 'start': 7435.685, 'duration': 4.723}, {'end': 7441.869, 'text': "Yeah, that's significant.", 'start': 7440.568, 'duration': 1.301}], 'summary': 'Analyzing statistical tests for significant results, preferring tukey test over others, with ojvc showing one significant comparison.', 'duration': 24.686, 'max_score': 7417.183, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY7417183.jpg'}, {'end': 7577.743, 'src': 'embed', 'start': 7548.63, 'weight': 5, 'content': [{'end': 7553.232, 'text': 'And now you have the data that actually is shown in this chart right here.', 'start': 7548.63, 'duration': 4.602}, {'end': 7558.334, 'text': 'So for instance, we have this 7.98 and a 13.23 way down low.', 'start': 7553.312, 'duration': 5.022}, {'end': 7561.736, 'text': 'And you can do some more detailed analyses there if you want.', 'start': 7559.155, 'duration': 2.581}, {'end': 7566.858, 'text': 'And so this is the walkthrough of the analysis of variance in Jamovi.', 'start': 7561.816, 'duration': 5.042}, {'end': 7571.56, 'text': 'Again, what we did is the preliminary work, by looking at the outcome variable on its own,', 'start': 7567.238, 'duration': 4.322}, {'end': 7577.743, 'text': 'then breaking it down by the categories that we were using to predict it, in this case, what the supplement was and what the dosage was.', 'start': 7571.56, 'duration': 6.183}], 'summary': 'The analysis of variance in jamovi involved preliminary work and detailed analyses of data, including values like 7.98 and 13.23.', 'duration': 29.113, 'max_score': 7548.63, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY7548630.jpg'}, {'end': 7931.105, 'src': 'embed', 'start': 7905.243, 'weight': 6, 'content': [{'end': 7910.644, 'text': 'Now, if we want to, we can put in a between subjects factor like gender as well, that makes it a lot more complicated.', 'start': 7905.243, 'duration': 5.401}, {'end': 7911.945, 'text': "So I'm going to leave it out for right now.", 'start': 7910.664, 'duration': 1.281}, {'end': 7920.414, 'text': 'But what you see here is that disgust changed how much people want to get rid of the bugs, fright changed how much they want to get rid of them.', 'start': 7912.885, 'duration': 7.529}, {'end': 7924.099, 'text': "And there wasn't really an interaction between them.", 'start': 7921.175, 'duration': 2.924}, {'end': 7927.663, 'text': "So disgust and fright didn't depend on each other.", 'start': 7924.159, 'duration': 3.504}, {'end': 7931.105, 'text': 'And so that is our initial result.', 'start': 7928.684, 'duration': 2.421}], 'summary': 'Disgust and fright did not depend on each other in affecting bug aversion.', 'duration': 25.862, 'max_score': 7905.243, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY7905243.jpg'}, {'end': 8140.615, 'src': 'embed', 'start': 8109.505, 'weight': 7, 'content': [{'end': 8110.606, 'text': 'And we can look at the effects.', 'start': 8109.505, 'duration': 1.101}, {'end': 8114.629, 'text': "So I'm going to come right here and do discuss, drag it right there.", 'start': 8110.626, 'duration': 4.003}, {'end': 8122.759, 'text': "I'm going to get a new term and I'm going to get fright and drag it down right here and we're going to get a marginal means plot.", 'start': 8115.59, 'duration': 7.169}, {'end': 8125.162, 'text': "I'll also get a marginal means table.", 'start': 8123.34, 'duration': 1.822}, {'end': 8130.129, 'text': "And I can scroll down and this is going to make it a lot easier to see what's happening in our data.", 'start': 8126.124, 'duration': 4.005}, {'end': 8135.512, 'text': "surprisingly, when a bug is disgusting, people want to get rid of it more than when it's not.", 'start': 8131.09, 'duration': 4.422}, {'end': 8140.615, 'text': 'And here are the numbers to go with that, as well as the 95% confidence intervals.', 'start': 8135.892, 'duration': 4.723}], 'summary': 'Bug disgust leads to stronger desire for removal, supported by data.', 'duration': 31.11, 'max_score': 8109.505, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY8109505.jpg'}], 'start': 6921.481, 'title': 'Analysis of variance in experimental design', 'summary': "Explores the use of analysis of variance to assess the effects of supplements and doses on tooth growth in guinea pigs, revealing significant main effects of both supplement and dose, with an enormous effect of dose (f value of 92, p < 0.001). it also covers anova assumptions, post hoc tests, and the application of jamovi for experimental analysis, including repeated measures design and its impact on people's desire to get rid of bugs.", 'chapters': [{'end': 7280.617, 'start': 6921.481, 'title': 'Analysis of variance for tooth growth in guinea pigs', 'summary': 'Explores the process of analysis of variance to assess the joint and separate effects of supplements and doses on tooth growth in guinea pigs, revealing significant main effects of both supplement and dose, with an enormous effect of dose (f value of 92, p < 0.001), and a substantial interaction effect.', 'duration': 359.136, 'highlights': ['The analysis reveals a significant main effect of dose, with an enormous effect (F value of 92, p < 0.001), indicating a substantial impact on tooth growth.', 'The analysis also indicates a significant main effect of supplement, with a considerable effect on tooth growth (F value not specified, p < 0.05).', 'The interaction effect between supplement and dose is also noted, suggesting a substantial combined influence on tooth growth.', 'The concept of eta squared as an effect size measure is introduced, with substantial values of 22.4 for supplement, 77.3 for dose, and 13.2 for the interaction, indicating their relative strength in influencing tooth growth.']}, {'end': 7527.335, 'start': 7282.267, 'title': 'Analyze anova assumptions and post hoc tests', 'summary': 'Explains how to analyze anova assumptions, including homogeneity tests, and how to conduct post hoc tests to determine significant effects and differences between groups based on supplement and dosage levels, with a preference for the tukey test.', 'duration': 245.068, 'highlights': ["The chapter discusses conducting homogeneity tests using Levine's test to check for statistically significant differences in variance between groups, with a p-value of 0.103.", 'It explains the process of conducting post hoc tests, particularly using the Tukey test to identify significant differences between supplement and dosage levels, highlighting the specific comparisons that are statistically significant.', 'The transcript details the use of descriptive plots to visualize and interpret the important differences in the data, particularly in relation to supplement and dosage levels, providing insights into the effects on the outcome variable.', 'It mentions the option of specifying contrasts in the design and the availability of different choices for making these contrasts, providing a comprehensive understanding of the analysis process.']}, {'end': 7692.355, 'start': 7527.395, 'title': 'Analysis of variance in jamovi', 'summary': 'Provides a walkthrough of the analysis of variance in jamovi, covering the creation of a table with the number of observations, mean, and standard deviation, the comparison between conditions, and the use of a repeated measures design, demonstrating the value of breaking up data for experimental analysis.', 'duration': 164.96, 'highlights': ['The chapter provides a walkthrough of the analysis of variance in Jamovi, covering the creation of a table with the number of observations, mean, and standard deviation.', 'The comparison between conditions is discussed, emphasizing the value of breaking up data for experimental analysis.', 'The use of a repeated measures design is explained, showcasing its statistical power and effectiveness with fewer people.']}, {'end': 8151.26, 'start': 7693.675, 'title': 'Repeated measures analysis of variance', 'summary': "Explores the process of conducting repeated measures analysis of variance using jamovi, focusing on analyzing the impact of disgust and fright on people's desire to get rid of bugs, revealing a 12.3% variance attributed to disgust and over twice as much variance attributed to fright.", 'duration': 457.585, 'highlights': ["The analysis reveals that about 12.3% of the variance in a person's response can be attributed to the variations in disgust, while over twice as much can be attributed to the variation in fright, and only 2.4% is associated with the interaction.", "The process involves specifying repeated measures factors such as disgust and fright, then analyzing the impact on people's desire to get rid of bugs, revealing significant differences in the comparisons of the effects of disgust and fright on bug removal.", 'The analysis also includes a marginal means plot, indicating that people want to get rid of bugs more when they are disgusting, with a significant impact of fright on bug removal as well.']}], 'duration': 1229.779, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY6921481.jpg', 'highlights': ['The enormous effect of dose on tooth growth (F value of 92, p < 0.001)', 'Significant main effect of supplement on tooth growth (F value not specified, p < 0.05)', 'Substantial eta squared values: 22.4 for supplement, 77.3 for dose, 13.2 for interaction', "Homogeneity tests using Levine's test with p-value of 0.103", 'Use of Tukey test to identify significant differences between supplement and dosage levels', 'Walkthrough of analysis of variance in Jamovi, including creation of observation table', 'Revealing significant differences in the comparisons of the effects of disgust and fright on bug removal', 'Marginal means plot indicating significant impact of fright on bug removal']}, {'end': 9250.665, 'segs': [{'end': 8252.387, 'src': 'embed', 'start': 8209.553, 'weight': 0, 'content': [{'end': 8217.857, 'text': "And I'm using this because we have four quantitative or continuous variables, the sepal length and sepal width, petal length and petal width.", 'start': 8209.553, 'duration': 8.304}, {'end': 8223.718, 'text': 'And we have a categorical or nominal variable species with three different groups.', 'start': 8218.637, 'duration': 5.081}, {'end': 8227.16, 'text': "And that's a situation where you really would want to use an analysis of variance.", 'start': 8223.779, 'duration': 3.381}, {'end': 8230.262, 'text': 'But we can throw in some other measurements to try to predict something.', 'start': 8227.46, 'duration': 2.802}, {'end': 8236.522, 'text': "So what I'm going to do in this particular example is I'm going to look at the sepal width.", 'start': 8230.982, 'duration': 5.54}, {'end': 8240.244, 'text': "That's one of the four measurements of the flower of an iris.", 'start': 8237.043, 'duration': 3.201}, {'end': 8245.485, 'text': "And before we get started, it's a good idea to explore what that variable looks like.", 'start': 8241.304, 'duration': 4.181}, {'end': 8249.886, 'text': "So I'm just going to get sepal width, and put right here under variables.", 'start': 8245.605, 'duration': 4.281}, {'end': 8252.387, 'text': "And it's going to give us some descriptive statistics over here.", 'start': 8249.906, 'duration': 2.481}], 'summary': 'Using analysis of variance for 4 variables and 3 species to predict sepal width in iris flowers.', 'duration': 42.834, 'max_score': 8209.553, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY8209553.jpg'}], 'start': 8151.32, 'title': 'Anova and ancova in jamovi', 'summary': 'Delves into the application of repeated measures anova and ancova in jamovi, using the iris dataset with four quantitative variables and a categorical variable, identifying significant differences in sepal width among iris species with a p-value < 0.001 and a large partial eta squared value of 0.40, and leveraging ancova for nuanced insights from simple datasets.', 'chapters': [{'end': 8352.718, 'start': 8151.32, 'title': 'Jamovi repeated measures anova and ancova', 'summary': 'Covers the use of repeated measures analysis of variance and analysis of covariance in jamovi, including the application to the iris dataset with four quantitative variables and a categorical variable with three groups.', 'duration': 201.398, 'highlights': ['The chapter explains the use of repeated measures analysis of variance and analysis of covariance in Jamovi, highlighting the availability of options for more complicated designs.', 'The example uses the iris dataset with four quantitative variables and a categorical variable with three groups to demonstrate the application of analysis of variance and the exploration of the sepal width variable.', 'The chapter demonstrates the exploration of the sepal width variable through descriptive statistics, density plots, and box plots, providing insights into the distribution and behavior of the variable.']}, {'end': 8665.127, 'start': 8352.718, 'title': 'Analysis of variance and covariance in iris data', 'summary': 'Explores the use of analysis of variance to identify differences in sepal width among the three species of irises, revealing a significant difference (p < 0.001) and a large partial eta squared value (0.40), and then employs scatter plots and linear regression to further understand the associations and differences, uncovering a strong association and different slopes among the species.', 'duration': 312.409, 'highlights': ['The analysis of variance reveals a significant difference in sepal width among the three species of irises, with a p-value of less than 0.001 and a large partial eta squared value of 0.40, indicating that approximately 40% of the variance in sepal width can be associated with the three different species.', 'The use of scatter plots and local regression lines illustrates an association between sepal width and sepal length, indicating the potential importance of including sepal length as a covariate.', 'Incorporating species as a grouping variable in the scatter plots reveals a strong association between sepal length and sepal width for the setosa iris, differing significantly from the other two species, and a subsequent linear regression analysis confirms the presence of different slopes among the species.']}, {'end': 9250.665, 'start': 8665.127, 'title': 'Analysis of covariance in jamovi', 'summary': 'Explores the process of conducting an analysis of covariance using jamovi, focusing on the analysis of the iris flower dataset, demonstrating the use of ancova, interpretation of inferential tests, assessing assumptions, post hoc tests, and the power of ancova in providing nuanced insights from simple datasets.', 'duration': 585.538, 'highlights': ['The ANCOVA analysis reveals a statistically significant difference in sepal width among species, with a large F ratio of 94, indicating a robust finding.', 'The analysis indicates a significant association between sepal length and sepal width, with both variables showing a statistically significant association, contributing to the understanding of the dataset.', 'The interaction between species and sepal length is found to be statistically significant, emphasizing the different slopes in the regression lines for the species.', 'The homogeneity of variance test does not show statistical significance, indicating that the assumption of approximately equal variance on the outcome variable, sepal width, is not violated.', 'The comparison of Setosa with Versicolor and Virginica reveals a significant difference in Setosa compared to both, aligning with the visual observation of distinct differences among the species.']}], 'duration': 1099.345, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY8151320.jpg', 'highlights': ['The ANCOVA analysis reveals a statistically significant difference in sepal width among species, with a large F ratio of 94, indicating a robust finding.', 'The analysis of variance reveals a significant difference in sepal width among the three species of irises, with a p-value of less than 0.001 and a large partial eta squared value of 0.40, indicating that approximately 40% of the variance in sepal width can be associated with the three different species.', 'The example uses the iris dataset with four quantitative variables and a categorical variable with three groups to demonstrate the application of analysis of variance and the exploration of the sepal width variable.']}, {'end': 10003.144, 'segs': [{'end': 9289.535, 'src': 'embed', 'start': 9265.833, 'weight': 0, 'content': [{'end': 9276.603, 'text': 'The question that these four things are trying to tell us is Are there differences on these four variables simultaneously between the three different groups?', 'start': 9265.833, 'duration': 10.77}, {'end': 9285.311, 'text': 'And it turns out that all four versions of the math that goes into the multivariate analysis of variance or covariance,', 'start': 9276.904, 'duration': 8.407}, {'end': 9287.013, 'text': "they're all giving us the same answer in this case.", 'start': 9285.311, 'duration': 1.702}, {'end': 9289.535, 'text': "They're all saying that, yeah, there's this huge difference.", 'start': 9287.073, 'duration': 2.462}], 'summary': 'Multivariate analysis shows significant differences across three groups on four variables.', 'duration': 23.702, 'max_score': 9265.833, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY9265833.jpg'}, {'end': 9338.428, 'src': 'embed', 'start': 9311.571, 'weight': 2, 'content': [{'end': 9316.455, 'text': "Now remember, with the analysis of variance, it doesn't mean that every group is different from every other one.", 'start': 9311.571, 'duration': 4.884}, {'end': 9323.523, 'text': 'If we come back up to here, for instance, You know, we can see that setosa is different on sepal width,', 'start': 9316.495, 'duration': 7.028}, {'end': 9326.704, 'text': 'but versicolor and virginica are very similar to each other.', 'start': 9323.523, 'duration': 3.181}, {'end': 9332.906, 'text': "And so there's a difference somewhere in the mix, even if not all three are different from each other.", 'start': 9327.704, 'duration': 5.202}, {'end': 9334.466, 'text': "And that's what we get here.", 'start': 9333.486, 'duration': 0.98}, {'end': 9338.428, 'text': "I'm just going to finish with one of the assumption checks.", 'start': 9334.686, 'duration': 3.742}], 'summary': 'Analysis of variance shows differences in groups, not every group is different from every other one.', 'duration': 26.857, 'max_score': 9311.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY9311571.jpg'}, {'end': 9529.839, 'src': 'embed', 'start': 9501.065, 'weight': 4, 'content': [{'end': 9506.606, 'text': "And it's actually really easy to set up if you're concerned about non normal distributions, this might be a good choice.", 'start': 9501.065, 'duration': 5.541}, {'end': 9510.147, 'text': "Now I'm going to use the iris data, we've looked at it lots of times.", 'start': 9507.187, 'duration': 2.96}, {'end': 9514.749, 'text': "And I'm going to look at the sepal width and break it down by the three different species.", 'start': 9510.547, 'duration': 4.202}, {'end': 9521.43, 'text': "But let's take a quick look at the sepal width and break it down in a couple of different ways.", 'start': 9515.769, 'duration': 5.661}, {'end': 9524.271, 'text': "I'm going to go over here to exploration and go to descriptives.", 'start': 9521.47, 'duration': 2.801}, {'end': 9529.839, 'text': "And what I'm going to choose is sepal width as the only variable that I'm looking at.", 'start': 9525.854, 'duration': 3.985}], 'summary': 'Analyzing sepal width of the iris data by species for exploration and descriptives.', 'duration': 28.774, 'max_score': 9501.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY9501065.jpg'}, {'end': 9737.438, 'src': 'embed', 'start': 9711.509, 'weight': 1, 'content': [{'end': 9716.153, 'text': 'you can think of them as similar to the Chaffee or Bonferroni or Tukey,', 'start': 9711.509, 'duration': 4.644}, {'end': 9721.46, 'text': 'because what it does is it gets every possible comparison and with three groups.', 'start': 9716.793, 'duration': 4.667}, {'end': 9723.442, 'text': "there's three possible comparisons.", 'start': 9721.46, 'duration': 1.982}, {'end': 9732.114, 'text': "it calculates a test statistic and then this part here at the end that's important is the p-value that tells us whether it is statistically significant.", 'start': 9723.442, 'duration': 8.672}, {'end': 9737.438, 'text': "now you can see this one's taking a while, And that's one of the things about when you're dealing with ranks,", 'start': 9732.114, 'duration': 5.324}], 'summary': 'Compares groups using test statistic and p-value.', 'duration': 25.929, 'max_score': 9711.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY9711509.jpg'}], 'start': 9250.705, 'title': 'Analysis of variance in jamovi', 'summary': 'Discusses the results of multivariate analysis of variance with a highly significant difference (p<0.5) and univariate tests. it also explores non-parametric tests like kruskal-wallis and friedman tests in jamovi, demonstrating their application with iris and bug data, and highlighting the significance and implications for non-normal data analysis.', 'chapters': [{'end': 9415.58, 'start': 9250.705, 'title': 'Multivariate analysis of variance and univariate tests', 'summary': 'Discusses the results of a multivariate analysis of variance, showing a highly significant difference with a p-value much less than 0.5, and also highlights the univariate tests on individual variables, demonstrating differences between the groups.', 'duration': 164.875, 'highlights': ['The multivariate analysis of variance indicates a highly significant difference with a p-value much less than 0.5, showing that all four versions of the math yield the same answer.', 'Univariate tests reveal differences between the groups on all three variables, with specific differences observed in the mix, indicating that not all groups are different from each other.', 'The assumption check using the qq plot of multivariate normality demonstrates a close fit to the multivariate normal distribution, meeting the necessary assumptions for the analysis.']}, {'end': 10003.144, 'start': 9416.269, 'title': 'Non-parametric analysis of variance in jamovi', 'summary': 'Explores the application of non-parametric tests, such as the kruskal-wallis test and the friedman test, in jamovi to analyze data that does not meet parametric assumptions, demonstrating the process and output of each test with the iris data and bug data, and highlighting the significance of the results and the implications for non-normal data analysis.', 'duration': 586.875, 'highlights': ['The Kruskal-Wallis test in Jamovi is demonstrated using the iris data to analyze the sepal width across three different species, revealing that the iris setosa has wider sepals than the iris versicolor or virginica, with the Kruskal-Wallis test yielding a highly significant result (p < 0.001), rejecting the null hypothesis of identical distributions and providing pairwise comparisons that further demonstrate the differences between the groups.', 'The application of the Friedman test in Jamovi with the bug data showcases the use of non-parametric analysis for evaluating changes across outcomes, addressing non-normality in the data and providing insights into the appropriateness of non-parametric tests for skewed distributions, further emphasizing the significance of the results and the advantages of non-parametric testing in such scenarios.']}], 'duration': 752.439, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY9250705.jpg', 'highlights': ['The multivariate analysis of variance indicates a highly significant difference with a p-value much less than 0.5, showing that all four versions of the math yield the same answer.', 'The Kruskal-Wallis test in Jamovi is demonstrated using the iris data to analyze the sepal width across three different species, revealing that the iris setosa has wider sepals than the iris versicolor or virginica, with the Kruskal-Wallis test yielding a highly significant result (p < 0.001), rejecting the null hypothesis of identical distributions and providing pairwise comparisons that further demonstrate the differences between the groups.', 'Univariate tests reveal differences between the groups on all three variables, with specific differences observed in the mix, indicating that not all groups are different from each other.', 'The application of the Friedman test in Jamovi with the bug data showcases the use of non-parametric analysis for evaluating changes across outcomes, addressing non-normality in the data and providing insights into the appropriateness of non-parametric tests for skewed distributions, further emphasizing the significance of the results and the advantages of non-parametric testing in such scenarios.', 'The assumption check using the qq plot of multivariate normality demonstrates a close fit to the multivariate normal distribution, meeting the necessary assumptions for the analysis.']}, {'end': 10773.519, 'segs': [{'end': 10296.617, 'src': 'embed', 'start': 10269.787, 'weight': 2, 'content': [{'end': 10275.788, 'text': 'specifically the Pearson product moment correlation coefficient, usually just called R.', 'start': 10269.787, 'duration': 6.001}, {'end': 10281.709, 'text': "And it's a great way of looking at the association between two quantitative or continuous variables,", 'start': 10275.788, 'duration': 5.921}, {'end': 10283.77, 'text': "although it's actually much more flexible than that.", 'start': 10281.709, 'duration': 2.061}, {'end': 10291.234, 'text': 'And I want to take a moment to show you how we can do correlations and correlation matrices and scatterplot matrices in Jamovi.', 'start': 10284.25, 'duration': 6.984}, {'end': 10296.617, 'text': "Now to do this, I'm bringing in a new data set, and it's called state data.", 'start': 10291.594, 'duration': 5.023}], 'summary': 'Introducing pearson product moment correlation coefficient and demonstrating correlation and scatterplot matrices in jamovi using the state data set.', 'duration': 26.83, 'max_score': 10269.787, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY10269787.jpg'}, {'end': 10339.442, 'src': 'embed', 'start': 10310.205, 'weight': 1, 'content': [{'end': 10319.371, 'text': 'From a few years ago, a study done on categorizing states by their personality characteristics, putting them as temperamental or friendly,', 'start': 10310.205, 'duration': 9.166}, {'end': 10320.872, 'text': 'and relaxed or traditional.', 'start': 10319.371, 'duration': 1.501}, {'end': 10329.117, 'text': 'Another study that classified the states by their big five personality characteristics extroversion, agreeableness, conscientiousness and so on.', 'start': 10321.592, 'duration': 7.525}, {'end': 10339.442, 'text': 'And then here at the end, I went to Google Correlate and I got state-by-state data on how common certain search terms are in each state.', 'start': 10329.797, 'duration': 9.645}], 'summary': 'Study categorized states by personality traits and common search terms.', 'duration': 29.237, 'max_score': 10310.205, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY10310205.jpg'}, {'end': 10498.381, 'src': 'embed', 'start': 10467.305, 'weight': 0, 'content': [{'end': 10470.787, 'text': "And that's always a perfect correlation, but it doesn't really mean anything.", 'start': 10467.305, 'duration': 3.482}, {'end': 10473.941, 'text': 'So what we have in this are two things.', 'start': 10471.539, 'duration': 2.402}, {'end': 10476.223, 'text': "we first have the Pearson's R.", 'start': 10473.941, 'duration': 2.282}, {'end': 10479.306, 'text': 'this is the product moment correlation coefficient.', 'start': 10476.223, 'duration': 3.083}, {'end': 10487.713, 'text': 'it goes from negative one which indicates a perfect negative linear association, through zero, which indicates no linear relationship whatsoever,', 'start': 10479.306, 'duration': 8.407}, {'end': 10492.157, 'text': 'to plus one which indicates a perfect positive linear relationship.', 'start': 10487.713, 'duration': 4.444}, {'end': 10498.381, 'text': 'And we also have the p value, which is used for the statistical hypothesis testing.', 'start': 10493.258, 'duration': 5.123}], 'summary': "Pearson's r measures linear association, ranging from -1 to +1, with p value for hypothesis testing.", 'duration': 31.076, 'max_score': 10467.305, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY10467305.jpg'}, {'end': 10609.446, 'src': 'embed', 'start': 10581.198, 'weight': 4, 'content': [{'end': 10583.779, 'text': "And I'll give you the upper bound and the lower bound.", 'start': 10581.198, 'duration': 2.581}, {'end': 10588.5, 'text': 'So for instance, you can see that this one goes on either side of zero.', 'start': 10583.979, 'duration': 4.521}, {'end': 10589.46, 'text': "So it's this and this.", 'start': 10588.54, 'duration': 0.92}, {'end': 10603.363, 'text': 'Here, however, this association between Facebook and volunteering as statewide search terms, we go from negative 0.451 to negative 0.789.', 'start': 10590.38, 'duration': 12.983}, {'end': 10609.446, 'text': "So it may be that you want confidence intervals for your correlations, it makes for a busy matrix, but they're available.", 'start': 10603.363, 'duration': 6.083}], 'summary': 'Providing confidence intervals for correlations, ranging from -0.451 to -0.789.', 'duration': 28.248, 'max_score': 10581.198, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY10581198.jpg'}, {'end': 10694.485, 'src': 'embed', 'start': 10667.772, 'weight': 5, 'content': [{'end': 10678.179, 'text': "And then also we can get statistics that'll give us the actual correlation coefficients displayed on the upper right side of the diagonal in the scatterplot matrix.", 'start': 10667.772, 'duration': 10.407}, {'end': 10685.116, 'text': "see these numbers correspond to what we have up here, there's the point 036.", 'start': 10679.611, 'duration': 5.505}, {'end': 10686.498, 'text': "And there's the same thing down here.", 'start': 10685.116, 'duration': 1.382}, {'end': 10694.485, 'text': 'And so we have both a numerical summary of the correlations, the associations between these variables, as well as a graphical summary.', 'start': 10687.258, 'duration': 7.227}], 'summary': 'Statistics provide correlation coefficients in scatterplot matrix for numerical and graphical summary.', 'duration': 26.713, 'max_score': 10667.772, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY10667772.jpg'}], 'start': 10003.344, 'title': 'Analyzing data using statistical tests and regression analysis', 'summary': 'Covers the application of the friedman test for non-parametric data, demonstrating statistically significant effects. it also provides an overview of various regression analysis techniques, such as linear, binomial logistic, multinomial logistic, and ordinal logistic regression, available in jamovi. additionally, it discusses state-level correlations and the use of linear regression for predicting quantitative outcomes using state data, emphasizing significant associations.', 'chapters': [{'end': 10329.117, 'start': 10003.344, 'title': 'Friedman test & regression analysis', 'summary': 'Discusses the application of the friedman test in analyzing non-parametric data, revealing statistically significant effects and includes a comprehensive overview of regression analysis, featuring various techniques including linear, binomial logistic, multinomial logistic, and ordinal logistic regression, all available in jamovi.', 'duration': 325.773, 'highlights': ['The Friedman test results indicate statistically significant effects, with all six possible pairwise comparisons showing a significant difference between variables.', 'The chapter provides an in-depth overview of regression analysis, including techniques such as linear regression, binomial logistic regression, multinomial logistic regression, and ordinal logistic regression, all available in Jamovi for comprehensive data exploration.', "Jamovi's capabilities in regression analysis are emphasized, showcasing its flexibility and usefulness through various regression techniques, enabling the exploration of data and extraction of valuable insights."]}, {'end': 10773.519, 'start': 10329.797, 'title': 'State-level correlations and linear regression', 'summary': 'Explains state-level correlations between search terms and personality characteristics, highlighting significant associations and the usage of linear regression for predicting quantitative outcomes using state data.', 'duration': 443.722, 'highlights': ['The chapter details the state-by-state correlations between search terms and personality characteristics, revealing significant associations and providing insights into the usage of linear regression for predicting quantitative outcomes using state data.', 'The correlation coefficients are used to indicate the strength and direction of the associations, along with the corresponding p-values for statistical hypothesis testing, showing three significant correlations out of six.', 'The usage of confidence intervals for correlations is explained, providing a 95% confidence interval and showcasing the availability of upper and lower bounds for associations between search terms and personality characteristics.', 'The chapter also covers the graphical representation of the associations through scatterplot matrices, density charts, and numerical summaries, offering insights into the distribution and strength of correlations between variables.', 'The usage of linear regression for predicting quantitative outcomes using state data is discussed, highlighting the general purpose nature of linear regression as a data analytic tool and its significance in predicting scores on outcome variables.']}], 'duration': 770.175, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY10003344.jpg', 'highlights': ['The Friedman test results indicate statistically significant effects, with all six possible pairwise comparisons showing a significant difference between variables.', 'The chapter provides an in-depth overview of regression analysis, including techniques such as linear regression, binomial logistic regression, multinomial logistic regression, and ordinal logistic regression, all available in Jamovi for comprehensive data exploration.', 'The chapter details the state-by-state correlations between search terms and personality characteristics, revealing significant associations and providing insights into the usage of linear regression for predicting quantitative outcomes using state data.', 'The usage of confidence intervals for correlations is explained, providing a 95% confidence interval and showcasing the availability of upper and lower bounds for associations between search terms and personality characteristics.', 'The correlation coefficients are used to indicate the strength and direction of the associations, along with the corresponding p-values for statistical hypothesis testing, showing three significant correlations out of six.', 'The chapter also covers the graphical representation of the associations through scatterplot matrices, density charts, and numerical summaries, offering insights into the distribution and strength of correlations between variables.', "Jamovi's capabilities in regression analysis are emphasized, showcasing its flexibility and usefulness through various regression techniques, enabling the exploration of data and extraction of valuable insights.", 'The usage of linear regression for predicting quantitative outcomes using state data is discussed, highlighting the general purpose nature of linear regression as a data analytic tool and its significance in predicting scores on outcome variables.']}, {'end': 12690.537, 'segs': [{'end': 11089.203, 'src': 'embed', 'start': 11065.632, 'weight': 1, 'content': [{'end': 11073.48, 'text': "In dragging all of them at once, I'm going to try adding them one at a time here, I'm going to click Instagram, and I'll drag that over right here.", 'start': 11065.632, 'duration': 7.848}, {'end': 11081.617, 'text': 'And what this is going to do is it first creates a model that has just the governor in it.', 'start': 11075.032, 'duration': 6.585}, {'end': 11089.203, 'text': "And then it's going to see how much better the correlations get, the associations get when we put in these other variables.", 'start': 11082.137, 'duration': 7.066}], 'summary': 'Creating a model with governor and adding variables to improve correlations.', 'duration': 23.571, 'max_score': 11065.632, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY11065632.jpg'}, {'end': 11457.904, 'src': 'embed', 'start': 11430.541, 'weight': 5, 'content': [{'end': 11437.327, 'text': "adjusted R squared is probably going to be a better choice in this situation, especially because we have a small data set, there's only 48 cases.", 'start': 11430.541, 'duration': 6.786}, {'end': 11442.451, 'text': 'And you can see that there is a change, the R squared is 69.', 'start': 11438.147, 'duration': 4.304}, {'end': 11445.093, 'text': 'And the adjusted R squared is 572.', 'start': 11442.451, 'duration': 2.642}, {'end': 11449.897, 'text': 'This is going to be a more accurate number in terms of generalizing to other data sets.', 'start': 11445.093, 'duration': 4.804}, {'end': 11457.904, 'text': "We have other information we could use like the AIC, that's the Akaki information criteria or the Bayesian information criteria.", 'start': 11450.758, 'duration': 7.146}], 'summary': 'Adjusted r squared is 572, better for small dataset, 48 cases. aic and bic also available.', 'duration': 27.363, 'max_score': 11430.541, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY11430541.jpg'}, {'end': 11928.576, 'src': 'embed', 'start': 11899.444, 'weight': 3, 'content': [{'end': 11901.404, 'text': 'if we want to look at model one,', 'start': 11899.444, 'duration': 1.96}, {'end': 11906.266, 'text': "we just click on this and we get the one with just governor and you can see there's a statistically significant effect there.", 'start': 11901.404, 'duration': 4.862}, {'end': 11909.386, 'text': "That's the 0007 right here.", 'start': 11906.786, 'duration': 2.6}, {'end': 11911.387, 'text': "But let's go back to model two.", 'start': 11910.107, 'duration': 1.28}, {'end': 11915.809, 'text': 'And now we can see that governor is no longer statistically significant.', 'start': 11912.487, 'duration': 3.322}, {'end': 11918.15, 'text': "That's because it was a spurious correlation.", 'start': 11915.829, 'duration': 2.321}, {'end': 11919.631, 'text': "It's predicted by other things.", 'start': 11918.19, 'duration': 1.441}, {'end': 11928.576, 'text': "And then museum doesn't seem to matter, but we have strong and statistically significant effects for both scrapbook and modern ads.", 'start': 11920.672, 'duration': 7.904}], 'summary': 'Model 1 shows a significant effect of governor at 0.007, while model 2 indicates a spurious correlation with governor and significant effects for scrapbook and modern ads.', 'duration': 29.132, 'max_score': 11899.444, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY11899444.jpg'}, {'end': 12076.582, 'src': 'embed', 'start': 12051.05, 'weight': 0, 'content': [{'end': 12057.374, 'text': 'Before you take the results of your linear regression and you go running off to market and making massive changes.', 'start': 12051.05, 'duration': 6.324}, {'end': 12062.357, 'text': "you do want to make sure that you actually dotted your I's and crossed your T's,", 'start': 12057.374, 'duration': 4.983}, {'end': 12071.861, 'text': 'and you want to make sure that your data met the assumptions that the model has and that your data is leading you in the right direction.', 'start': 12062.357, 'duration': 9.504}, {'end': 12076.582, 'text': 'This becomes a matter of checking what are called regression diagnostics,', 'start': 12072.261, 'duration': 4.321}], 'summary': 'Validate your linear regression results before making major changes.', 'duration': 25.532, 'max_score': 12051.05, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12051050.jpg'}, {'end': 12129.617, 'src': 'embed', 'start': 12096.327, 'weight': 8, 'content': [{'end': 12098.309, 'text': "And I'm going to click on this model right here.", 'start': 12096.327, 'duration': 1.982}, {'end': 12108.517, 'text': "And what I'm going to do is I'm going to come back, not to Model Builder, although that is where I specified the blocks that I was going to use,", 'start': 12099.29, 'duration': 9.227}, {'end': 12112.481, 'text': "but I'm going to come down a little further, to Assumption Checks.", 'start': 12108.517, 'duration': 3.964}, {'end': 12115.783, 'text': 'And this is where the most important things are going to happen.', 'start': 12113.081, 'duration': 2.702}, {'end': 12120.547, 'text': 'Now, there are a few here that are particularly important.', 'start': 12116.544, 'duration': 4.003}, {'end': 12129.617, 'text': "If you have data that's measured repeatedly over time, like quarter, one, quarter, two, quarter, three results,", 'start': 12121.528, 'duration': 8.089}], 'summary': 'Navigating to assumption checks in model builder for data measured repeatedly over time.', 'duration': 33.29, 'max_score': 12096.327, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12096327.jpg'}, {'end': 12231.799, 'src': 'embed', 'start': 12202.953, 'weight': 6, 'content': [{'end': 12211.579, 'text': 'Mostly, I want you to know that Jamovi is able to do this for you and you can interpret the results in ways that are going to make your model more robust.', 'start': 12202.953, 'duration': 8.626}, {'end': 12215.903, 'text': 'You can also do the qq plot of residuals.', 'start': 12213.24, 'duration': 2.663}, {'end': 12217.304, 'text': "I've demonstrated that elsewhere.", 'start': 12215.923, 'duration': 1.381}, {'end': 12231.799, 'text': "And it's relevant here to the residuals or the leftovers from predictions based on your model need to be approximately normally distributed and they need to not flare out on one end of the model or the other.", 'start': 12217.424, 'duration': 14.375}], 'summary': 'Jamovi can interpret results, including qq plot of residuals, for robust models.', 'duration': 28.846, 'max_score': 12202.953, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12202953.jpg'}, {'end': 12349.96, 'src': 'embed', 'start': 12319.684, 'weight': 9, 'content': [{'end': 12323.005, 'text': 'Now, this would be more helpful in some situations than in others.', 'start': 12319.684, 'duration': 3.321}, {'end': 12330.587, 'text': 'But what you can do here is you can actually try to get a chart that shows you how these variables predict your outcome.', 'start': 12323.425, 'duration': 7.162}, {'end': 12335.208, 'text': "So I can take governor, which is an easy one, because there's only two categories there.", 'start': 12330.607, 'duration': 4.601}, {'end': 12339.19, 'text': "And that's going to make a chart, it'll be down here at the bottom.", 'start': 12335.869, 'duration': 3.321}, {'end': 12349.96, 'text': 'This simply gives me the mean level of openness for the states with Democrat governors and the mean level of openness for states with Republican governors.', 'start': 12340.39, 'duration': 9.57}], 'summary': 'Using variables to predict outcome with chart, mean level of openness for states with democrat and republican governors.', 'duration': 30.276, 'max_score': 12319.684, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12319684.jpg'}, {'end': 12454.887, 'src': 'embed', 'start': 12431.642, 'weight': 7, 'content': [{'end': 12441.614, 'text': 'One really common task in analyzing data is classifying cases into one category or another, based on a number of other variables you might have.', 'start': 12431.642, 'duration': 9.972}, {'end': 12447.06, 'text': 'So, for instance, with your computer, is trying to decide whether a particular email is spam or not.', 'start': 12441.694, 'duration': 5.366}, {'end': 12451.145, 'text': "we're trying to decide whether a particular person is likely to buy your product or not.", 'start': 12447.06, 'duration': 4.085}, {'end': 12454.887, 'text': 'And those are dichotomous classifications.', 'start': 12452.106, 'duration': 2.781}], 'summary': 'Data analysis involves classifying cases into categories based on various variables such as determining spam emails or predicting customer purchases.', 'duration': 23.245, 'max_score': 12431.642, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12431642.jpg'}, {'end': 12613.256, 'src': 'embed', 'start': 12584.411, 'weight': 2, 'content': [{'end': 12589.352, 'text': "So I'm going to come down here to Instagram, Facebook and retweet.", 'start': 12584.411, 'duration': 4.941}, {'end': 12594.033, 'text': "By the way, the reason it says retweet is because Google correlate wouldn't let me search for Twitter.", 'start': 12589.712, 'duration': 4.321}, {'end': 12595.093, 'text': "I don't know why.", 'start': 12594.133, 'duration': 0.96}, {'end': 12601.134, 'text': 'But since retweet is exclusively a Twitter word, it seemed like a good substitute.', 'start': 12595.933, 'duration': 5.201}, {'end': 12603.254, 'text': "So I'm going to put those all into covariates.", 'start': 12601.154, 'duration': 2.1}, {'end': 12613.256, 'text': "And those are the three variables that I'm going to use together to try to predict which states have Democrat governors and which states have Republican governors.", 'start': 12603.874, 'duration': 9.382}], 'summary': 'Using instagram, facebook, and retweet as covariates to predict democrat and republican governors.', 'duration': 28.845, 'max_score': 12584.411, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12584411.jpg'}], 'start': 10774.179, 'title': 'Linear regression analysis and predictive power', 'summary': "Explores linear regression analysis using a small dataset of 48 cases, resulting in strong correlations, particularly with variables 'scrapbook' and 'modern dance'. it also discusses the predictive power of governorship and google search terms, showcasing an increase in model fit from 13% to 52% upon adding three additional variables.", 'chapters': [{'end': 10916.561, 'start': 10774.179, 'title': 'Openness and linear regression analysis', 'summary': 'Explores the use of linear regression to analyze the relationship between openness and other variables, using a small dataset of 48 cases from the contiguous united states, resulting in a strong correlation of 0.831.', 'duration': 142.382, 'highlights': ['The multiple correlation, with a value of 0.831, indicates a strong correlation in the multiple regression analysis, demonstrating the relationship between the outcome scores and the other variables in the model.', 'The dataset consists of only 48 cases from the contiguous United States, limiting the number of variables that can be included in the regression analysis to avoid violating assumptions.', "The state's average Google searches are used as covariates, given as z-scores compared to the rest of the United States, to analyze their relationship with openness.", "The inclusion of a dichotomous variable for the governor's party affiliation, with only Democratic or Republican values, provides additional insights into the regression analysis.", 'The chapter emphasizes the importance of being selective in choosing variables for the regression model to ensure the validity of the analysis and avoid violating assumptions.']}, {'end': 11386.591, 'start': 10916.561, 'title': 'Linear regression analysis overview', 'summary': "Focuses on conducting a linear regression analysis using jamovi, exploring the impact of various variables on a dependent variable with an r squared value increasing from 0.148 to 0.69, indicating significant associations, particularly with the variables 'scrapbook' and 'modern dance'. the analysis also highlights the impact of collinearity on the associations between predictor variables.", 'duration': 470.03, 'highlights': ["The R squared value increased from 0.148 to 0.69, indicating significant associations between the variables and the dependent variable, particularly with 'scrapbook' and 'modern dance'.", 'The impact of collinearity on the associations between predictor variables is discussed, with the presence of multicollinearity indicated by the VIF and tolerance statistics.', 'The chapter covers the limitations of stepwise variable selection and the preference for block entry for categorical or nominal variables in the linear regression analysis.']}, {'end': 11792.761, 'start': 11387.531, 'title': 'Jamovi tools for linear regression', 'summary': 'Discusses the use of jamovi tools for linear regression, demonstrating the examination of model fit, influential data points, model coefficients, standardized estimates, and estimated marginal means, emphasizing the importance of adjusted r squared for small datasets and the limitations of stepwise regression.', 'duration': 405.23, 'highlights': ['The importance of adjusted R squared for small datasets, with R squared at 0.69 and adjusted R squared at 0.572, indicating a more accurate number for generalizing to other data sets.', 'The demonstration of using Jamovi tools for examining model fit, influential data points, model coefficients, standardized estimates with z scores, and confidence intervals for regression coefficients.', 'The discussion of the limitations of stepwise regression and the advocacy for block regression or block wise regression as a better approach for creating sequential models with additional variables.', "The explanation of the use of residual plots for every variable, Cook's distance for assessing influential data points, AIC, BIC, and the overall F test for model evaluation.", "The demonstration of using estimated marginal means for examining the impact of variables on the level of openness, with the example of the search term 'modern dance' correlating with increased level of openness."]}, {'end': 12217.304, 'start': 11792.801, 'title': 'Predictive power of governor and google search terms', 'summary': 'Discusses the predictive power of governorship and google search terms in explaining the variance of state openness, with the model fit increasing from 13% to 52% upon adding three additional variables, showcasing the importance of theoretical interpretation and regression diagnostics.', 'duration': 424.503, 'highlights': ['The model fit increases from 13% to 52% upon adding three Google search terms as predictor variables, demonstrating their substantial contribution to the variance in state openness.', 'The theoretical interpretation and regression diagnostics play a crucial role in determining the applicability and usefulness of the linear regression model.', 'The discussion on collinearity and multicollinearity addresses the robustness of the model and the need to interpret statistical measures to enhance model reliability.']}, {'end': 12690.537, 'start': 12217.424, 'title': 'Analyzing logistic regression for governor classification', 'summary': "Explores the use of logistic regression to classify states based on the probability of having republican or democrat governors, demonstrating how to interpret model outcomes and assumptions using residual plots, cook's distance, and estimated marginal means.", 'duration': 473.113, 'highlights': ['The chapter demonstrates the use of logistic regression to classify states into Republican or Democrat governors based on social media variables, with approximately two-thirds of the lower 48 states having Republican governors.', "The use of residual plots and Cook's distance for assessing model assumptions and influential cases is illustrated, emphasizing the importance of identifying deviations from modeling expectations.", 'The analysis includes interpreting estimated marginal means to visualize the predictive effect of variables, exemplifying the comparison of mean levels of openness for states with Democrat and Republican governors based on different predictors.']}], 'duration': 1916.358, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY10774179.jpg', 'highlights': ['The model fit increases from 13% to 52% upon adding three Google search terms as predictor variables', "The R squared value increased from 0.148 to 0.69, indicating significant associations between the variables and the dependent variable, particularly with 'scrapbook' and 'modern dance'", 'The multiple correlation, with a value of 0.831, indicates a strong correlation in the multiple regression analysis, demonstrating the relationship between the outcome scores and the other variables in the model', 'The importance of adjusted R squared for small datasets, with R squared at 0.69 and adjusted R squared at 0.572, indicating a more accurate number for generalizing to other data sets', "The demonstration of using estimated marginal means for examining the impact of variables on the level of openness, with the example of the search term 'modern dance' correlating with increased level of openness", 'The demonstration of using Jamovi tools for examining model fit, influential data points, model coefficients, standardized estimates with z scores, and confidence intervals for regression coefficients', 'The use of logistic regression to classify states into Republican or Democrat governors based on social media variables, with approximately two-thirds of the lower 48 states having Republican governors', 'The discussion on collinearity and multicollinearity addresses the robustness of the model and the need to interpret statistical measures to enhance model reliability', 'The impact of collinearity on the associations between predictor variables is discussed, with the presence of multicollinearity indicated by the VIF and tolerance statistics', "The state's average Google searches are used as covariates, given as z-scores compared to the rest of the United States, to analyze their relationship with openness"]}, {'end': 13907.061, 'segs': [{'end': 12912.162, 'src': 'embed', 'start': 12881.458, 'weight': 3, 'content': [{'end': 12884.56, 'text': "And specificity means it's going to do that only if it does.", 'start': 12881.458, 'duration': 3.102}, {'end': 12892.785, 'text': 'And in many situations, these two lines, this is specificity going up here, and this is sensitivity going down.', 'start': 12885.4, 'duration': 7.385}, {'end': 12895.968, 'text': 'Often they cross right here at the 50% point.', 'start': 12893.446, 'duration': 2.522}, {'end': 12898.61, 'text': 'But these ones are a lot closer to 0.7.', 'start': 12896.368, 'duration': 2.242}, {'end': 12902.352, 'text': "So what I'm actually going to do is I'm going to change the cutoff from 0.5 to 0.7.", 'start': 12898.61, 'duration': 3.742}, {'end': 12903.293, 'text': 'And do that right here.', 'start': 12902.352, 'duration': 0.941}, {'end': 12912.162, 'text': "And you'll see that it changes the way the classification table works, because now it's going to say well,", 'start': 12906.275, 'duration': 5.887}], 'summary': 'Adjusting cutoff from 0.5 to 0.7 improves classification accuracy.', 'duration': 30.704, 'max_score': 12881.458, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12881458.jpg'}, {'end': 13018.298, 'src': 'embed', 'start': 12993.912, 'weight': 2, 'content': [{'end': 13000.174, 'text': 'And although it may not sound like a big change, the processing behind it becomes exponentially more complicated.', 'start': 12993.912, 'duration': 6.262}, {'end': 13006.815, 'text': 'Fortunately, Jamovi makes it possible to do a relatively simple multinomial logistic regression.', 'start': 13000.694, 'duration': 6.121}, {'end': 13010.396, 'text': "I'm going to demonstrate this with the state data.", 'start': 13007.695, 'duration': 2.701}, {'end': 13018.298, 'text': "And I'm going to look at this one category here, which actually has to do with psychology profiles for the various states in the United States.", 'start': 13010.996, 'duration': 7.302}], 'summary': 'Jamovi enables simple multinomial logistic regression using state psychology profile data.', 'duration': 24.386, 'max_score': 12993.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12993912.jpg'}, {'end': 13091.646, 'src': 'embed', 'start': 13063.693, 'weight': 0, 'content': [{'end': 13069.094, 'text': "And unfortunately, the labels don't automatically adjust and Jim will be at this point, but that'll happen eventually.", 'start': 13063.693, 'duration': 5.401}, {'end': 13076.337, 'text': "But let's see how we can use other variables to predict which states go into which categories.", 'start': 13070.155, 'duration': 6.182}, {'end': 13085.342, 'text': "And what I'm going to do for that is I'm going to come back up here to regression and go to n outcomes, that's multinomial logistic regression.", 'start': 13077.017, 'duration': 8.325}, {'end': 13091.646, 'text': 'And when I click on that, the first thing I have to do is pick what my dependent variable is.', 'start': 13086.083, 'duration': 5.563}], 'summary': 'Utilizing multinomial logistic regression to predict state categories based on other variables.', 'duration': 27.953, 'max_score': 13063.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY13063693.jpg'}], 'start': 12690.537, 'title': 'Logistic regression analysis and modern dance quartile analysis', 'summary': 'Covers logistic regression analysis to predict republican governor odds and state categories based on social media and personality factors, along with the creation of modern dance quartiles associated with openness and psych regions.', 'chapters': [{'end': 12812.777, 'start': 12690.537, 'title': 'Logistic regression analysis on social media data', 'summary': 'Describes the application of logistic regression to predict the odds of a state having a republican governor based on social media variables, highlighting the odds ratios and confidence intervals as indicators of predictive variables.', 'duration': 122.24, 'highlights': ["The intercept's odds ratio and its reliability above zero indicate that both predictor variables have odds ratios above one, providing insight into the variables predicting the odds of a state having a Republican governor.", 'The significance of Facebook within the context of the predictor variables is highlighted by the estimated marginal means, showing that states searching less for Facebook are less likely to have a Republican governor.', "The use of deviance in the AIC to assess the model's fit and the addition of confidence intervals to the odds ratios are mentioned as key aspects of the logistic regression analysis."]}, {'end': 13063.273, 'start': 12813.437, 'title': 'Logistic regression and categorization tasks', 'summary': 'Discusses the use of binary logistic regression for predicting political affiliations, including adjusting cutoff values and the introduction of multinomial logistic regression for analyzing multiple categories, demonstrating its potential with a study on psychology profiles of us states.', 'duration': 249.836, 'highlights': ['Binary logistic regression can be used to predict political affiliations, with a 40% accuracy for Democratic governors and 91% accuracy for Republican governors.', 'Adjusting the cutoff value from 0.5 to 0.7 increases the accuracy of predicting Democratic governors from 40% to 73% while decreasing accuracy for Republican governors from 91% to 73%.', 'Multinomial logistic regression is introduced for predicting multiple categories, such as psychology profiles of US states, with three distinct categories identified in the sample.']}, {'end': 13656.158, 'start': 13063.693, 'title': 'Predicting state categories with multinomial logistic regression', 'summary': 'Demonstrates the use of multinomial logistic regression to predict state categories based on five personality factors, with notable associations found between certain personality factors and state categories, providing valuable insights for understanding state behaviors.', 'duration': 592.465, 'highlights': ['The use of multinomial logistic regression to predict state categories based on five personality factors, yielding insights into state behaviors and associations between personality factors and state categories.', 'Notable associations found between certain personality factors and state categories, such as the impact of conscientiousness and neuroticism on distinguishing between state categories, providing quantifiable insights.', 'The demonstration of ordinal logistic regression as a means to predict ordered categories, particularly useful for cases with a small number of variables and data that deviates from normality.']}, {'end': 13907.061, 'start': 13666.115, 'title': 'Modern dance quartile analysis', 'summary': 'Explores the creation of modern dance quartiles based on z-scores, with the resulting quartiles showing approximately equal sizes and being associated with openness and psych regions, indicating their impact on relative search preference for modern dance.', 'duration': 240.946, 'highlights': ['The quartiles created based on z-scores showed approximately equal sizes, indicating a balanced distribution of data.', 'Openness was found to be significantly associated with the modern dance quartiles, indicating its impact on the relative search preference for modern dance.', 'Psych regions, particularly relaxed and creative, had a significant impact on the modern dance quartiles, indicating their influence on the relative search preference for modern dance.']}], 'duration': 1216.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY12690537.jpg', 'highlights': ['Binary logistic regression predicts political affiliations with 91% accuracy for Republican governors.', 'Multinomial logistic regression predicts state categories based on personality factors, yielding insights into state behaviors.', "The use of deviance in the AIC to assess the model's fit and the addition of confidence intervals to the odds ratios are key aspects of logistic regression analysis.", "The intercept's odds ratio and its reliability above zero indicate predictor variables with odds ratios above one, providing insight into the variables predicting the odds of a state having a Republican governor.", 'The significance of Facebook within the context of the predictor variables is highlighted by the estimated marginal means, showing that states searching less for Facebook are less likely to have a Republican governor.']}, {'end': 15263.521, 'segs': [{'end': 14586.395, 'src': 'embed', 'start': 14557.354, 'weight': 1, 'content': [{'end': 14560.616, 'text': "And that's where we do the chi-squared goodness of fit test.", 'start': 14557.354, 'duration': 3.262}, {'end': 14566.119, 'text': "Now, there's an important choice we get to make, and that's how we define our null values.", 'start': 14561.296, 'duration': 4.823}, {'end': 14572.504, 'text': "By default, it's going to assume equal frequencies or equal proportions in each category.", 'start': 14566.739, 'duration': 5.765}, {'end': 14576.267, 'text': "So let's come to frequencies here and go to n outcomes.", 'start': 14572.904, 'duration': 3.363}, {'end': 14578.889, 'text': 'That means more than two outcomes, an arbitrary number.', 'start': 14576.307, 'duration': 2.582}, {'end': 14581.751, 'text': 'And that right there is chi squared.', 'start': 14579.409, 'duration': 2.342}, {'end': 14586.395, 'text': "It looks like an X, it's actually a capital Greek C.", 'start': 14582.192, 'duration': 4.203}], 'summary': 'Chi-squared goodness of fit test examines null values using frequencies and outcomes.', 'duration': 29.041, 'max_score': 14557.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY14557354.jpg'}, {'end': 14677.776, 'src': 'embed', 'start': 14648.397, 'weight': 2, 'content': [{'end': 14655.939, 'text': "One is exactly what is the algorithm comparing these values against? Well it's comparing them against expected counts.", 'start': 14648.397, 'duration': 7.542}, {'end': 14658.661, 'text': 'And the way it does expected count.', 'start': 14656.639, 'duration': 2.022}, {'end': 14659.962, 'text': 'is it right now?', 'start': 14658.661, 'duration': 1.301}, {'end': 14664.085, 'text': 'it just takes however many values you have and it splits them evenly across the number of categories.', 'start': 14659.962, 'duration': 4.123}, {'end': 14669.609, 'text': 'Well, 48 states can be divided evenly into three categories by having 16 each.', 'start': 14664.645, 'duration': 4.964}, {'end': 14677.776, 'text': 'And what the chi squared does is it looks at the deviation between 24 and 16, 10, 16, and 14, and 16, does some manipulation on those.', 'start': 14670.23, 'duration': 7.546}], 'summary': 'Algorithm compares values against expected counts, dividing 48 states evenly into three categories.', 'duration': 29.379, 'max_score': 14648.397, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY14648397.jpg'}, {'end': 14770.473, 'src': 'embed', 'start': 14734.492, 'weight': 0, 'content': [{'end': 14736.333, 'text': 'And you put in the ratios here.', 'start': 14734.492, 'duration': 1.841}, {'end': 14740.777, 'text': 'And that means like two to one or three to one, if you want, you can enter them as percentages.', 'start': 14736.413, 'duration': 4.364}, {'end': 14744.26, 'text': "So you can say like, oh, let's have 60 here.", 'start': 14740.797, 'duration': 3.463}, {'end': 14751.486, 'text': "And let's do 25 here, and let's do 15% here.", 'start': 14744.5, 'duration': 6.986}, {'end': 14757.11, 'text': 'And that gives us the values that we would expect in each of these conditions.', 'start': 14752.226, 'duration': 4.884}, {'end': 14759.873, 'text': 'I think that adds up to 100.', 'start': 14758.912, 'duration': 0.961}, {'end': 14770.473, 'text': 'And now what you see is our values, we have different expected values And now we still have a statistically significant result.', 'start': 14759.873, 'duration': 10.6}], 'summary': 'Input ratios as 60:25:15% to achieve expected values for statistically significant results.', 'duration': 35.981, 'max_score': 14734.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY14734492.jpg'}, {'end': 14945.146, 'src': 'embed', 'start': 14916.326, 'weight': 4, 'content': [{'end': 14918.327, 'text': 'And we can see that in the chart down here.', 'start': 14916.326, 'duration': 2.001}, {'end': 14920.189, 'text': 'Again, I apologize for the overlapping labels.', 'start': 14918.407, 'duration': 1.782}, {'end': 14922.551, 'text': "I'm sure that'll be fixed in a later version.", 'start': 14920.229, 'duration': 2.322}, {'end': 14929.253, 'text': 'The blue lines are Democrats and the yellow gold ones are Republicans.', 'start': 14923.468, 'duration': 5.785}, {'end': 14932.777, 'text': 'And what you see is this huge spike in terms of friendly and conventional.', 'start': 14929.293, 'duration': 3.484}, {'end': 14940.083, 'text': 'The vast majority of those states have Republican governors, where the other ones appear to be somewhat split, a little more Republicans,', 'start': 14932.817, 'duration': 7.266}, {'end': 14941.725, 'text': 'the temperamental and uninhibited.', 'start': 14940.083, 'duration': 1.642}, {'end': 14945.146, 'text': "But let's find out whether this difference is statistically significant,", 'start': 14942.185, 'duration': 2.961}], 'summary': 'Chart shows spike in friendly and conventional traits in states with republican governors.', 'duration': 28.82, 'max_score': 14916.326, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY14916326.jpg'}, {'end': 15250.008, 'src': 'embed', 'start': 15219.652, 'weight': 3, 'content': [{'end': 15224.093, 'text': "But it's an interesting example because the sibling goes with the patient.", 'start': 15219.652, 'duration': 4.441}, {'end': 15226.034, 'text': "So there's a connection between the two of them.", 'start': 15224.133, 'duration': 1.901}, {'end': 15230.575, 'text': "Let me show you how we set this up using McNamara's test in Jamovi.", 'start': 15226.554, 'duration': 4.021}, {'end': 15234.656, 'text': 'We come to frequencies and we come down right here.', 'start': 15231.695, 'duration': 2.961}, {'end': 15240.118, 'text': "They call it McNamara test if you go to Wikipedia's McNamara's possessive test.", 'start': 15235.376, 'duration': 4.742}, {'end': 15241.618, 'text': "I'm going to click on that.", 'start': 15240.138, 'duration': 1.48}, {'end': 15250.008, 'text': 'And what we need to do is simply take our data put in what variable has the row labels and what variable has the column labels?', 'start': 15243.226, 'duration': 6.782}], 'summary': "Demonstrating mcnamara's test in jamovi using frequency data", 'duration': 30.356, 'max_score': 15219.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY15219652.jpg'}], 'start': 13907.081, 'title': 'Analyzing statistical tests in data analysis', 'summary': "Covers odds ratios analysis for predicting search rankings with a notable odds ratio of 17.54, introduces analyzing frequencies in jamovi and various tests including chi-squared goodness of fit, chi-squared test of association with practical examples and insights, and explains mcnamara's test for contingency tables and its relevance in research.", 'chapters': [{'end': 13985.406, 'start': 13907.081, 'title': 'Odds ratios analysis in predicting search rankings', 'summary': 'Discusses the calculation of odds ratios and confidence intervals to determine the significance of variables in predicting search rankings, with a notable odds ratio of 17.54 and confidence interval up to 91 for a specific variable.', 'duration': 78.325, 'highlights': ["The odds ratio for 'temperamental and uninhibited states versus friendly and conventional states' is 17.54 with a confidence interval up to 91, indicating a very influential difference in predicting search rankings.", 'Openness also shows significance with odds ratios consistently above one in the confidence interval, indicating its importance in predicting search rankings.']}, {'end': 14397.877, 'start': 13985.827, 'title': 'Analyzing frequencies in jamovi', 'summary': "Introduces analyzing frequencies in jamovi, covering binomial test for two outcomes, chi squared goodness of fit test for multiple categories, chi squared test of association for two categories, mcnamara's test for related data, and log linear regression for modeling frequencies, providing examples and insights into data analysis and testing.", 'duration': 412.05, 'highlights': ["The chapter covers various options for analyzing frequencies in Jamovi, including binomial test, chi squared goodness of fit test, chi squared test of association, McNamara's test, and log linear regression.", 'An example of analyzing binomial test for two outcomes is presented using state governor data, demonstrating a significant difference from a null value of 50% and providing insights into confidence intervals.', 'Insights into adjusting the null value for comparison in binomial test, showcasing the impact on p-values and significance of differences in observed proportions.']}, {'end': 14710.053, 'start': 14398.437, 'title': 'Chi-squared goodness of fit test', 'summary': 'Explains the chi-squared goodness of fit test using a psychological research dataset, demonstrating how to conduct the test and interpret the results, with a statistically significant finding at a significance level of 0.05.', 'duration': 311.616, 'highlights': ['The chi-squared goodness of fit test gives a statistically significant finding at a significance level of 0.05, with a calculated value of chi squared as 6.5 and 2 degrees of freedom, resulting in a p-value of 0.039.', 'The dataset consists of 48 contiguous states, with 24 classified as friendly and conventional, 10 as relaxed and creative, and 14 as temperamental and uninhibited.', 'The default null value assumption is equal frequencies or proportions in each category, leading to a statistically significant deviation from expected counts.']}, {'end': 15074.718, 'start': 14710.554, 'title': 'Chi-squared test of association', 'summary': "Explains how to use chi-squared test of association to analyze the statistical association between political party of the governor and state's personality, demonstrating it with frequency tables and expected proportions in jamovi.", 'duration': 364.164, 'highlights': ["The chapter demonstrates using chi-squared test of association to analyze the statistical association between political party of the governor and state's personality.", 'The demonstration involves creating frequency tables and expected proportions using Jamovi.']}, {'end': 15263.521, 'start': 15075.578, 'title': "Chi-squared and mcnamara's test", 'summary': "Explains the concept of chi-squared test and its application in determining statistical significance, with an example using mcnamara's test for contingency tables, highlighting the ease of use and relevance in research.", 'duration': 187.943, 'highlights': ['The chapter explains the concept of chi-squared test and its application in determining statistical significance.', "Example using McNamara's test for contingency tables, highlighting the ease of use and relevance in research.", "Explanation of setting up McNamara's test in Jamovi, underlining its user-friendly interface and applicability in analyzing summary tables."]}], 'duration': 1356.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY13907081.jpg', 'highlights': ["The odds ratio for 'temperamental and uninhibited states versus friendly and conventional states' is 17.54 with a confidence interval up to 91, indicating a very influential difference in predicting search rankings.", 'Openness also shows significance with odds ratios consistently above one in the confidence interval, indicating its importance in predicting search rankings.', 'The chi-squared goodness of fit test gives a statistically significant finding at a significance level of 0.05, with a calculated value of chi squared as 6.5 and 2 degrees of freedom, resulting in a p-value of 0.039.', 'An example of analyzing binomial test for two outcomes is presented using state governor data, demonstrating a significant difference from a null value of 50% and providing insights into confidence intervals.', "The chapter covers various options for analyzing frequencies in Jamovi, including binomial test, chi squared goodness of fit test, chi squared test of association, McNamara's test, and log linear regression."]}, {'end': 17918.717, 'segs': [{'end': 16704.703, 'src': 'embed', 'start': 16677.539, 'weight': 4, 'content': [{'end': 16684.444, 'text': "If we use what's called an orthogonal rotation, then it forces things into right angles, we wouldn't have any correlations at all.", 'start': 16677.539, 'duration': 6.905}, {'end': 16690.669, 'text': "And there's our test of This one down here at the bottom the eigenvalues.", 'start': 16685.004, 'duration': 5.665}, {'end': 16697.996, 'text': 'those are the values that correspond roughly to how much variance each of our components accounts for.', 'start': 16690.669, 'duration': 7.327}, {'end': 16704.703, 'text': "And it's called a scree plot, by the way, because scree is the rubble that's on the side of a cliff.", 'start': 16699.276, 'duration': 5.427}], 'summary': 'Using an orthogonal rotation eliminates correlations, tested using eigenvalues and scree plot for variance analysis.', 'duration': 27.164, 'max_score': 16677.539, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY16677539.jpg'}, {'end': 16822.918, 'src': 'embed', 'start': 16802.441, 'weight': 1, 'content': [{'end': 16812.591, 'text': 'A principal components analysis is going to allow you to determine the underlying structure and see what you can combine with each other to simplify the data that you have to deal with and,', 'start': 16802.441, 'duration': 10.15}, {'end': 16816.055, 'text': 'hopefully, have more reliable information at the same time.', 'start': 16812.591, 'duration': 3.464}, {'end': 16822.918, 'text': "Sometimes when you're analyzing data, you have to draw distinctions that don't make much of a difference.", 'start': 16817.794, 'duration': 5.124}], 'summary': 'Principal components analysis simplifies and improves data reliability.', 'duration': 20.477, 'max_score': 16802.441, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY16802441.jpg'}, {'end': 17235.747, 'src': 'embed', 'start': 17210.785, 'weight': 3, 'content': [{'end': 17217.672, 'text': 'Come down to the bottom and type in openness and put those 10 in.', 'start': 17210.785, 'duration': 6.887}, {'end': 17224.902, 'text': "And now we've specified the important part of our confirmatory factor analysis.", 'start': 17220.7, 'duration': 4.202}, {'end': 17226.543, 'text': "we've told you movie.", 'start': 17224.902, 'duration': 1.641}, {'end': 17228.103, 'text': "we've got five different factors.", 'start': 17226.543, 'duration': 1.56}, {'end': 17231.325, 'text': 'we set what the names are and we said which variables go with which.', 'start': 17228.103, 'duration': 3.222}, {'end': 17235.747, 'text': "what's interesting is, we don't even have to tell it which ones are positively associated, which ones are negative.", 'start': 17231.325, 'duration': 4.422}], 'summary': 'Specified 10 variables for confirmatory factor analysis with 5 different factors.', 'duration': 24.962, 'max_score': 17210.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY17210785.jpg'}, {'end': 17270.846, 'src': 'embed', 'start': 17246.892, 'weight': 2, 'content': [{'end': 17258.121, 'text': "Now, if we had specific other variables that contain what are called residual co variances things that don't necessarily go into the factor but help explain some of the leftovers we could specify those.", 'start': 17246.892, 'duration': 11.229}, {'end': 17264.466, 'text': "And what you do is you would say you take this one variable, and then you specify another one for the residual covariance, we don't have that.", 'start': 17258.862, 'duration': 5.604}, {'end': 17265.307, 'text': "So I'm going to ignore that.", 'start': 17264.506, 'duration': 0.801}, {'end': 17270.846, 'text': 'Under options, we can change how we deal with missing values.', 'start': 17266.903, 'duration': 3.943}], 'summary': 'Discusses residual covariances and handling missing values in data analysis.', 'duration': 23.954, 'max_score': 17246.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY17246892.jpg'}, {'end': 17351.43, 'src': 'embed', 'start': 17318.852, 'weight': 0, 'content': [{'end': 17321.514, 'text': 'One is the residual observed correlation matrix,', 'start': 17318.852, 'duration': 2.662}, {'end': 17330.56, 'text': "because what the movie is going to do is it's trying to reconstitute a correlation matrix based on what I said, how the variables went together.", 'start': 17321.514, 'duration': 9.046}, {'end': 17332.861, 'text': "And so I'm going to ask for that.", 'start': 17331.72, 'duration': 1.141}, {'end': 17337.663, 'text': "And it's also going to highlight any residual values that are greater than point one.", 'start': 17333.281, 'duration': 4.382}, {'end': 17341.825, 'text': "it's going to say this is where the model is off more than in other places.", 'start': 17337.663, 'duration': 4.162}, {'end': 17345.067, 'text': "And then finally, I'm going to ask for a path diagram.", 'start': 17342.325, 'duration': 2.742}, {'end': 17351.43, 'text': "And then we'll just wait a minute for Jamovi to finish crunching all the data and see what it has for us.", 'start': 17345.847, 'duration': 5.583}], 'summary': 'Analyzing correlation matrix and residual values in jamovi.', 'duration': 32.578, 'max_score': 17318.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY17318852.jpg'}, {'end': 17549.627, 'src': 'embed', 'start': 17524.259, 'weight': 5, 'content': [{'end': 17530.025, 'text': 'And it says that we have all of them associated with each other, and that each of them feeds into its own 10 variables.', 'start': 17524.259, 'duration': 5.766}, {'end': 17538.114, 'text': 'And so that is in a nutshell, this is the basic functionality of confirmatory factor analysis,', 'start': 17530.386, 'duration': 7.728}, {'end': 17543.319, 'text': "again a very sophisticated procedure that a lot of programs don't let you do at all.", 'start': 17538.114, 'duration': 5.205}, {'end': 17549.627, 'text': "And it's one of the really a special present from Jamobi that it makes it possible to do this.", 'start': 17543.799, 'duration': 5.828}], 'summary': 'Confirmatory factor analysis involves 10 variables per factor, a sophisticated procedure enabled by jamobi.', 'duration': 25.368, 'max_score': 17524.259, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY17524259.jpg'}], 'start': 15264.182, 'title': 'Data analysis techniques', 'summary': 'Covers the analysis of frequency data, log linear regression in jamovi, and various data analysis techniques like reliability analysis and principal component analysis, aiming to provide insights for making data-driven decisions.', 'chapters': [{'end': 15457.213, 'start': 15264.182, 'title': 'Analyzing frequency data', 'summary': "Discusses the analysis of frequency data, including the use of chi-squared tests and percentages to determine associations and comparisons, with a focus on mcnamara's test and log linear regression.", 'duration': 193.031, 'highlights': ["The chi-squared test reveals an association between Hodgkin's patients having tonsillectomies and their siblings, with 37 cases where neither had tonsillectomies and 26 cases where both had tonsillectomies.", 'Row and column percentages are used to compare frequencies, with 84% of siblings not having tonsillectomies when the patients did not, and 71.2% of siblings not having tonsillectomies when the patients did not.', "McNamara's test yields a probability value of 0.088, indicating a lack of statistical significance, but still suggesting a potential association."]}, {'end': 15821.774, 'start': 15457.213, 'title': 'Log linear regression in jamovi', 'summary': 'Discusses the process of performing log linear regression in jamovi to analyze the association between categorical variables, yielding insights on coefficients, model fit, and model interpretation.', 'duration': 364.561, 'highlights': ['Jamovi allows for easy implementation of log linear regression to analyze the association between categorical variables, providing insights on model coefficients and interpretability.', 'The analysis reveals significant coefficients for certain categorical variables, such as Democrat versus Republican governors, and provides insights on the significance of various interactions.', "Jamovi offers various options for evaluating model fit, including standard deviance, AIC, McFadden's R squared, and chi-squared tests for the entire model, providing a comprehensive assessment of the model's performance."]}, {'end': 16146.849, 'start': 15822.754, 'title': 'Data analysis techniques overview', 'summary': 'Discusses various data analysis techniques such as reliability analysis, principal component analysis, exploratory factor analysis, and confirmatory factor analysis, using a real data set of 50 personality variables, aiming to provide clarity and insight for making data-driven decisions.', 'duration': 324.095, 'highlights': ['Reliability analysis and its importance in combining variables', 'Description of the real data set used for demonstration', 'Understanding the scale analysis and its application in questionnaire data']}, {'end': 16481.832, 'start': 16147.87, 'title': 'Data analysis techniques and tools', 'summary': 'Discusses the use of chromebox alpha to analyze item statistics and the application of principal component analysis in assessing the structure of data, resulting in a more reliable and meaningful dataset for analysis.', 'duration': 333.962, 'highlights': ['The Chromebox alpha analysis indicates a significant improvement from a negative to a positive 0.89, enabling the combination of variables and the creation of a more reliable measure of extraversion, potentially leading to more meaningful data for analysis.', 'The use of principal component analysis on the big five personality factors data set reveals seven components as the most suitable grouping, providing empirical evidence of the structure of the data and its potential implications for analysis.', 'The correlation heat map visually represents positive and negative correlations, aiding in the identification of reverse scaled variables and their impact on the overall analysis process.']}, {'end': 17383.696, 'start': 16481.893, 'title': 'Understanding factor analysis in jamovi', 'summary': 'Discusses the process of conducting principal component analysis and exploratory factor analysis in jamovi, highlighting the importance of determining the underlying structure of survey data and the similarities between the two analyses, as well as the inclusion of confirmatory factor analysis in jamovi, emphasizing its ability to specify and compare factor structures across different samples.', 'duration': 901.803, 'highlights': ['The chapter discusses the process of conducting principal component analysis and exploratory factor analysis in Jamovi, highlighting the importance of determining the underlying structure of survey data.', 'The chapter emphasizes the similarities between principal component analysis and exploratory factor analysis, stating that both analyses look very similar and are used to do the same things.', 'The inclusion of confirmatory factor analysis in Jamovi is highlighted, emphasizing its ability to specify and compare factor structures across different samples.']}, {'end': 17918.717, 'start': 17384.196, 'title': 'Confirmatory factor analysis basics', 'summary': 'Introduces the basics of confirmatory factor analysis, covering the assessment of variable contribution, model fit indices, correlation matrix interpretation, and tool recommendations like spreadsheets, statistical programming languages, and data visualization tools.', 'duration': 534.521, 'highlights': ['The chapter covers the basics of confirmatory factor analysis, including standardized estimates, factor estimates, model fit indices, correlation matrix interpretation, and path diagrams.', 'The transcript introduces the assessment of variable contribution using z-scores, and it discusses the importance of model fit indices like the comparative fit index for evaluating the adequacy of the fit.', 'It emphasizes the interpretation of the correlation matrix, explaining the significance of correlation values ranging from negative 1 to plus 1 and the assessment of residuals to determine the accuracy of the reconstituted matrix.', 'The speaker highlights the importance of tool proficiency, suggesting that proficiency in spreadsheets and Jamovi covers the analytic necessities for the majority of data projects, and mentions the potential need for statistical programming languages for more advanced analyses.', 'Recommendations for additional tools are provided, including SQL for data manipulation, Tableau for interactive visualization, and presentation software for effectively communicating data analysis findings.', 'The chapter emphasizes the significance of data fluency, stating that the ability to interpret basic charts and visualizations covers the majority of data visualization needs for most people, and underscores the importance of understanding statistical concepts and practical application in real-world settings.']}], 'duration': 2654.535, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/mZomeS0tLxY/pics/mZomeS0tLxY15264182.jpg', 'highlights': ['The use of principal component analysis on the big five personality factors data set reveals seven components as the most suitable grouping, providing empirical evidence of the structure of the data and its potential implications for analysis.', "Jamovi offers various options for evaluating model fit, including standard deviance, AIC, McFadden's R squared, and chi-squared tests for the entire model, providing a comprehensive assessment of the model's performance.", 'The chapter discusses the process of conducting principal component analysis and exploratory factor analysis in Jamovi, highlighting the importance of determining the underlying structure of survey data.', 'Jamovi allows for easy implementation of log linear regression to analyze the association between categorical variables, providing insights on model coefficients and interpretability.', "The chi-squared test reveals an association between Hodgkin's patients having tonsillectomies and their siblings, with 37 cases where neither had tonsillectomies and 26 cases where both had tonsillectomies.", 'The Chromebox alpha analysis indicates a significant improvement from a negative to a positive 0.89, enabling the combination of variables and the creation of a more reliable measure of extraversion, potentially leading to more meaningful data for analysis.']}], 'highlights': ['Jamovi is a free and open-source data analysis application based on R, offering an alternative to expensive proprietary programs like SPSS and SAS.', "Jamovi's file sharing capabilities streamline collaboration, enabling easy sharing of data, calculations, and analyses.", "Jamovi's statistical analysis tools include power analysis, mediation, moderation, and survival analysis, showcasing its potential benefits for data analysis.", 'Jamovi can import various file types including CSV, text, and SPSS, making it a versatile tool for data analysis.', 'Jamovi allows users to transform data by using the recode function to create new variables based on conditions, facilitating data preparation.', 'Jamovi provides a single-window interface for organizing and navigating through data, different from applications like SPSS or SAS.', 'Jamovi offers various statistical tests including t tests, ANOVA, regression analysis, logistic regression, and odds ratios analysis, aiming to provide a comprehensive understanding of data analysis.', 'Jamovi allows for the comparison of means of two or more groups using ANOVA, offering a broader range of situations to analyze and potential insights from the data.', "Jamovi's capabilities in regression analysis enable the exploration of data and extraction of valuable insights, including linear regression, binomial logistic regression, multinomial logistic regression, and ordinal logistic regression.", "Jamovi provides options for evaluating model fit, including standard deviance, AIC, McFadden's R squared, and chi-squared tests, offering a comprehensive assessment of the model's performance."]}