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Kostenloser Coursea Kurs zusammen machen, dann flipped classroom?

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Kostenloser Coursea Kurs zusammen machen, dann flipped classroom?

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https://www.coursera.org/learn/statistical-inferences

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About this course: This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework. All videos now have Chinese subtitles. More than 10.000 learners have enrolled so far!

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Improving your statistical inferences | Coursera

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About this course: This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in.

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^ Datei herunterladen ^

oder hier im Browser anschauen: https://raw.githack.com/j-5chneider/howR/main/4_git-github-RStudio%20at%20DIPF/R-git-github.html

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Overview

Nearly all Markdown applications support the basic syntax outlined in the original Markdown design document. There are minor variations and discrepancies between Markdown processors - those are noted inline wherever possible.

Headings

To create a heading, add number signs (#) in front of a word or phrase. The number of number signs you use should correspond to the heading level. For example, to create a heading level three (<h3>), use three number signs (e.g., ### My Header).

Alternate Syntax

Alternatively, on the line below the text, add any number of == characters for heading level 1 or -- characters for heading level 2.

Heading Best Practices

Markdown applications don't agree on how to handle a missing space between the number signs (#) and the heading name. For compatibility, always put a space between the number signs and the heading name.

You should also put blank lines before and after a heading for compatibility.

Paragraphs

To create paragraphs, use a blank line to separate one or more lines of text.

I really like using Markdown.

I think I'll use it to format all of my documents from now on.

Paragraph Best Practices

Unless the paragraph is in a list, don't indent paragraphs with spaces or tabs.

Note: If you need to indent paragraphs in the output, see the section on how to indent (tab).

Line Breaks

To create a line break or new line (<br>), end a line with two or more spaces, and then type return.

This is the first line.
And this is the second line. <p>This is the first line.<br>
And this is the second line.</p>
This is the first line.
And this is the second line.

Line Break Best Practices

You can use two or more spaces (commonly referred to as "trailing whitespace") for line breaks in nearly every Markdown application, but it's controversial. It's hard to see trailing whitespace in an editor, and many people accidentally or intentionally put two spaces after every sentence. For this reason, you may want to use something other than trailing whitespace for line breaks. If your Markdown application supports HTML, you can use the <br> HTML tag.

For compatibility, use trailing white space or the <br> HTML tag at the end of the line.

There are two other options I don't recommend using. CommonMark and a few other lightweight markup languages let you type a backslash (\) at the end of the line, but not all Markdown applications support this, so it isn't a great option from a compatibility perspective. And at least a couple lightweight markup languages don't require anything at the end of the line - just type return and they'll create a line break.

Emphasis

You can add emphasis by making text bold or italic.

Bold

To bold text, add two asterisks or underscores before and after a word or phrase. To bold the middle of a word for emphasis, add two asterisks without spaces around the letters.

Bold Best Practices

Markdown applications don't agree on how to handle underscores in the middle of a word. For compatibility, use asterisks to bold the middle of a word for emphasis.

Italic

To italicize text, add one asterisk or underscore before and after a word or phrase. To italicize the middle of a word for emphasis, add one asterisk without spaces around the letters.

Italic Best Practices

Markdown applications don't agree on how to handle underscores in the middle of a word. For compatibility, use asterisks to italicize the middle of a word for emphasis.

Bold and Italic

To emphasize text with bold and italics at the same time, add three asterisks or underscores before and after a word or phrase. To bold and italicize the middle of a word for emphasis, add three asterisks without spaces around the letters.

Note: The order of the em and strong tags might be reversed depending on the Markdown processor you're using.

Bold and Italic Best Practices

Markdown applications don't agree on how to handle underscores in the middle of a word. For compatibility, use asterisks to bold and italicize the middle of a word for emphasis.

Blockquotes

To create a blockquote, add a > in front of a paragraph.

The rendered output looks like this:

Dorothy followed her through many of the beautiful rooms in her castle.

Blockquotes with Multiple Paragraphs

Blockquotes can contain multiple paragraphs. Add a > on the blank lines between the paragraphs.

The rendered output looks like this:

Dorothy followed her through many of the beautiful rooms in her castle.

The Witch bade her clean the pots and kettles and sweep the floor and keep the fire fed with wood.

Nested Blockquotes

Blockquotes can be nested. Add a >> in front of the paragraph you want to nest.

The rendered output looks like this:

Dorothy followed her through many of the beautiful rooms in her castle.

The Witch bade her clean the pots and kettles and sweep the floor and keep the fire fed with wood.

Blockquotes with Other Elements

Blockquotes can contain other Markdown formatted elements. Not all elements can be used - you'll need to experiment to see which ones work.

The rendered output looks like this:

The quarterly results look great!

Revenue was off the chart.

Profits were higher than ever.

Everything is going according to plan.

Blockquotes Best Practices

For compatibility, put blank lines before and after blockquotes.

Lists

You can organize items into ordered and unordered lists.

Ordered Lists

To create an ordered list, add line items with numbers followed by periods. The numbers don't have to be in numerical order, but the list should start with the number one.

Ordered List Best Practices

CommonMark and a few other lightweight markup languages let you use a parenthesis ()) as a delimiter (e.g., 1) First item), but not all Markdown applications support this, so it isn't a great option from a compatibility perspective. For compatibility, use periods only.

Unordered Lists

To create an unordered list, add dashes (-), asterisks (*), or plus signs (+) in front of line items. Indent one or more items to create a nested list.

Starting Unordered List Items With Numbers

If you need to start an unordered list item with a number followed by a period, you can use a backslash (\) to escape the period.

Unordered List Best Practices

Markdown applications don't agree on how to handle different delimiters in the same list. For compatibility, don't mix and match delimiters in the same list - pick one and stick with it.

Adding Elements in Lists

To add another element in a list while preserving the continuity of the list, indent the element four spaces or one tab, as shown in the following examples.

Tip: If things don't appear the way you expect, double check that you've indented the elements in the list four spaces or one tab.

Paragraphs

The rendered output looks like this:

Blockquotes

The rendered output looks like this:

Code Blocks

Code blocks are normally indented four spaces or one tab. When they're in a list, indent them eight spaces or two tabs.

The rendered output looks like this:

1. Open the file.

2. Find the following code block on line 21:

3. Update the title to match the name of your website.

Images

The rendered output looks like this:

1. Open the file containing the Linux mascot.

2. Marvel at its beauty.

3. Close the file.

Lists

You can nest an unordered list in an ordered list, or vice versa.

The rendered output looks like this:

Code

To denote a word or phrase as code, enclose it in backticks (`).

Escaping Backticks

If the word or phrase you want to denote as code includes one or more backticks, you can escape it by enclosing the word or phrase in double backticks (``).

Code Blocks

To create code blocks, indent every line of the block by at least four spaces or one tab.

The rendered output looks like this:

Horizontal Rules

To create a horizontal rule, use three or more asterisks (***), dashes (---), or underscores (___) on a line by themselves.

The rendered output of all three looks identical:

Horizontal Rule Best Practices

For compatibility, put blank lines before and after horizontal rules.

Links

To create a link, enclose the link text in brackets (e.g., [Duck Duck Go]) and then follow it immediately with the URL in parentheses (e.g., (https://duckduckgo.com)).

The rendered output looks like this:

My favorite search engine is Duck Duck Go.

Adding Titles

You can optionally add a title for a link. This will appear as a tooltip when the user hovers over the link. To add a title, enclose it in quotation marks after the URL.

The rendered output looks like this:

My favorite search engine is Duck Duck Go.

URLs and Email Addresses

To quickly turn a URL or email address into a link, enclose it in angle brackets.

The rendered output looks like this:
https://www.markdownguide.org
fake@example.com
To emphasize links, add asterisks before and after the brackets and parentheses. To denote links as code, add backticks in the brackets.

The rendered output looks like this:

Reference-style links are a special kind of link that make URLs easier to display and read in Markdown. Reference-style links are constructed in two parts: the part you keep inline with your text and the part you store somewhere else in the file to keep the text easy to read.

The first part of a reference-style link is formatted with two sets of brackets. The first set of brackets surrounds the text that should appear linked. The second set of brackets displays a label used to point to the link you're storing elsewhere in your document.

Although not required, you can include a space between the first and second set of brackets. The label in the second set of brackets is not case sensitive and can include letters, numbers, spaces, or punctuation.

1. The label, in brackets, followed immediately by a colon and at least one space (e.g., [label]: ).

2. The URL for the link, which you can optionally enclose in angle brackets.

3. The optional title for the link, which you can enclose in double quotes, single quotes, or parentheses.

This means the following example formats are roughly equivalent for the first part of the link:

[1]: https://en.wikipedia.org/wiki/Hobbit#Lifestyle

[1]: https://en.wikipedia.org/wiki/Hobbit#Lifestyle "Hobbit lifestyles"

[1]: https://en.wikipedia.org/wiki/Hobbit#Lifestyle 'Hobbit lifestyles'

[1]: https://en.wikipedia.org/wiki/Hobbit#Lifestyle (Hobbit lifestyles)

[1]: <https://en.wikipedia.org/wiki/Hobbit#Lifestyle> "Hobbit lifestyles"

[1]: <https://en.wikipedia.org/wiki/Hobbit#Lifestyle> 'Hobbit lifestyles'

[1]: <https://en.wikipedia.org/wiki/Hobbit#Lifestyle> (Hobbit lifestyles)

The second part of a reference-style link is formatted with the following attributes:

This means the following example formats are all roughly equivalent for the second part of the link:

You can place this second part of the link anywhere in your Markdown document. Some people place them immediately after the paragraph in which they appear while other people place them at the end of the document (like endnotes or footnotes).

An Example Putting the Parts Together

Say you add a URL as a standard URL link to a paragraph and it looks like this in Markdown:

Though it may point to interesting additional information, the URL as displayed really doesn't add much to the existing raw text other than making it harder to read. To fix that, you could format the URL like this instead:

In both instances above, the rendered output would be identical:

In a hole in the ground there lived a hobbit. Not a nasty, dirty, wet hole, filled with the ends of worms and an oozy smell, nor yet a dry, bare, sandy hole with nothing in it to sit down on or to eat: it was a hobbit-hole, and that means comfort.

and the HTML for the link would be:

Markdown applications don't agree on how to handle spaces in the middle of a URL. For compatibility, try to URL encode any spaces with %20. Alternatively, if your Markdown application supports HTML, you could use the a HTML tag.

Parentheses in the middle of a URL can also be problematic. For compatibility, try to URL encode the opening parenthesis (() with %28 and the closing parenthesis ()) with %29. Alternatively, if your Markdown application supports HTML, you could use the a HTML tag.

Images

To add an image, add an exclamation mark (!), followed by alt text in brackets, and the path or URL to the image asset in parentheses. You can optionally add a title in quotation marks after the path or URL.

The rendered output looks like this:

To add a link to an image, enclose the Markdown for the image in brackets, and then add the link in parentheses.

The rendered output looks like this:

Escaping Characters

To display a literal character that would otherwise be used to format text in a Markdown document, add a backslash (\) in front of the character.

The rendered output looks like this:

* Without the backslash, this would be a bullet in an unordered list.

Characters You Can Escape

You can use a backslash to escape the following characters.

HTML

Many Markdown applications allow you to use HTML tags in Markdown-formatted text. This is helpful if you prefer certain HTML tags to Markdown syntax. For example, some people find it easier to use HTML tags for images. Using HTML is also helpful when you need to change the attributes of an element, like specifying the color of text or changing the width of an image.

To use HTML, place the tags in the text of your Markdown-formatted file.

The rendered output looks like this:

This word is bold. This word is italic.

HTML Best Practices

For security reasons, not all Markdown applications support HTML in Markdown documents. When in doubt, check your Markdown application's documentation. Some applications support only a subset of HTML tags.

Use blank lines to separate block-level HTML elements like <div>, <table>, <pre>, and <p> from the surrounding content. Try not to indent the tags with tabs or spaces - that can interfere with the formatting.

You can't use Markdown syntax inside block-level HTML tags. For example, <p>italic and **bold**</p> won't work.

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The Markdown elements outlined in the original design document.

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papaja: Prepare APA journal articles with R Markdown

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papaja (Preparing APA Journal Articles) is an R package that provides document formats to produce complete APA manuscripts from RMarkdown-files (PDF and Word documents) and helper functions that facilitate reporting statistics, tables, and plots.

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Wide to Long usw. (zb hier verlinken zu Skript in Github?)

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Wide to Long usw. (zb hier verlinken zu Skript in Github?)

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(Multilevel) Multiple Imputation (woran man gerade arbeitet in gelb zb um jemanden zu finden der was dazu weiß?)

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(Multilevel) Multiple Imputation (woran man gerade arbeitet in gelb zb um jemanden zu finden der was dazu weiß?)

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R erklärt dir deinen Output: {easystats} package

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R erklärt dir deinen Output: {easystats} package

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Data wrangling: Wie forme ich meinen Datensatz um?

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Data wrangling: Wie forme ich meinen Datensatz um?

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Das braucht es fast immer zu Beginn nach dem import der Daten.

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Korrelationsplots, Regressionen, Boxplots, ...

https://raw.githack.com/j-5chneider/howR/main/6_plots/different_plot_types.html

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apaTables & stargazer Package: Tabellen für Regression & Descriptives automatisch erstellen

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apaTables & stargazer Package: Tabellen für Regression & Descriptives automatisch erstellen

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[22.11.2022] https://raw.githack.com/j-5chneider/howR/main/5_apaTables_stargazer/apaTables_stargazer.html

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Checking out packages apaTables & stargazer

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R Anleitungen
Gerade wenn man eher grafische Programme wie SPSS gewohnt ist, mag die Durchführung einer ANOVA in SPSS weniger intuitiv erscheinen. Statt Dialogfenster bietet R vielleicht nur eine Konsole, allerdings lassen sich dafür auch alle grundlegenden (M)ANOVA-Modelle aus SPSS in R berechnen.

Für die meisten ANOVA-Modelle erwartet R die Daten im Long-Format, daher mit einer Spalte für die abhängige Variable (AV) und einer Spalte für die Gruppe bzw. den Teilnehmenden.

Einfaktorielle ANOVA

Eine einfaktorielle ANOVA kann einfach mit die Funktion oneway.test() durchgeführt werden. R berechnet standardmäßig die robustere Welch-ANOVA. Sollte dies nicht gewünscht sein, kann man mit var.equal=TRUE das klassische ANOVA-Modell berechnen lassen.

Einfaktorielle ANCOVA

Wollen wir noch eine (oder mehrere) Kovariaten in unser Modell aufnehmen, müssen wir die Formel entsprechend erweitern.

Zweifaktorielle ANOVA (beides Zwischensubjektfaktoren)

Hier haben wir eine zweifaktorielle ANOVA mit zwei Zwischensubjektfaktoren, Faktor1 und Faktor2, sowie deren Interaktion.

ANOVA mit Messwiederholung

Unten haben wir eine ANOVA mit Messwiederholung, mit einem Innersubjektfaktor.

Zweifaktorielle ANOVA mit Messwiederholung

Unser Modell von oben können wir um einen (oder mehrere) Innersubjektfaktoren erweitern.

Mixed ANOVA

Wir können natürlich auch eine Varianzanalyse mit einem Innersubjektfaktor und einem Zwischensubjektfaktor durchführen, so hier.

Mixed ANOVA mit mehreren Faktoren

Wir können unser Modell auch noch so erweitern, dass wir zwei Innersubjektfaktoren und zwei Zwischensubjektfaktoren haben. Unser Modell wäre dann wie unten definiert.

MANOVA

Multivariate Varianzanalysen werden ganz ähnlich zu den oben genannten Modellen definiert. Der Hauptunterschied hier ist, dass wir statt aov() die Funktion manova() verwenden und mehr als eine abhängige Variable haben. Diese gruppieren wir über cbind() und definieren unser Modell wie gewohnt. In unseren Beispielen haben wir jeweils drei abhängige Variablen AV1, AV2 und AV3.

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R: ANOVA, ANCOVA, MANOVA | StatistikGuru.de

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R Anleitungen Gerade wenn man eher grafische Programme wie SPSS gewohnt ist, mag die Durchführung einer ANOVA in SPSS weniger intuitiv erscheinen. Statt Dialogfenster bietet R vielleicht nur eine Konsole, allerdings lassen sich dafür auch alle grundlegenden (M)ANOVA-Modelle aus SPSS in R berechnen.

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Spotlight Analysis: Compare the mean of the dependent of the two groups (treatment and control) at every value (Simple Slopes Analysis)

Floodlight Analysis: is spotlight analysis on the whole range of the moderator (Johnson-Neyman intervals)

Other Resources:

Terminology:

Main effects (slopes): coefficients that do no involve interaction terms

Simple slope: when a continuous independent variable interact with a moderating variable, its slope at a particular level of the moderating variable

Simple effect: when a categorical independent variable interacts with a moderating variable, its effect at a particular level of the moderating variable.

Example:

\[ Y = \beta_0 + \beta_1 X + \beta_2 M + \beta_3 X \times M \]

where

\(\beta_0\) = intercept

\(\beta_1\) = simple effect (slope) of \(X\) (independent variable)

\(\beta_2\) = simple effect (slope) of \(M\) (moderating variable)

\(\beta_3\) = interaction of \(X\) and \(M\)

Three types of interactions:

1. Continuous by continuous

2. Continuous by categorical

3. Categorical by categorical

emmeans package

Data set is from UCLA seminar where gender and prog are categorical

Continuous by continuous

Simple slopes for a continuous by continuous model

Spotlight analysis ( Aiken and West 2005): usually pick 3 values of moderating variable:

The 3 p-values are the same as the interaction term.

For publication, we use

Continuous by categorical

Get simple slopes by each level of the categorical moderator

Categorical by categorical

Simple effects

Plot

emmip ( catcat, prog ~ gender,CIs= TRUE )

Bar graph

probmod package

Not recommend: package has serious problem with subscript.

 myModel <- jnresults <- ( lm ( loss ~ hours * gender, data = dat ( loss, hours, gender ) ) %>% jn myModel, dv= 'loss', iv= 'hours', mod= 'gender' )pickapoint ( myModel, dv= 'loss', iv= 'hours', mod= 'gender', alpha= .01 )select

interactions package

Continuous interaction

(at least one of the two variables is continuous)

For continuous moderator, the three values chosen are:

-1 SD above the mean

The mean

-1 SD below the mean

To include weights from the regression inn the plot

Partial Effect Plot

Check linearity assumption in the model

Plot the lines based on the subsample (red line), and whole sample (black line)

Simple Slopes Analysis

continuous by continuous variable interaction (still work for binary)

conditional slope of the variable of interest (i.e., the slope of \(X\) when we hold \(M\) constant at a value)

Using sim_slopes it will

sim_slopes requires

Value of Murder

Slope of Illiteracy

Value of Murder

slope

0.00

535.50 (458.77)

5.00

-24.44 (282.48)

10.00

-584.38 (152.37)***

Johnson-Neyman intervals

To know all the values of the moderator for which the slope of the variable of interest will be statistically significant, we can use the Johnson-Neyman interval ( P. O. Johnson and Neyman 1936)

Even though we kind of know that the alpha level when implementing the Johnson-Neyman interval is not correct (( Bauer and Curran 2005), not until recently that there is a correction for the type I and II errors Esarey and Sumner 2017).

Since Johnson-Neyman inflates the type I error (comparisons across all values of the moderator)

For plotting, we can use johnson_neyman

Note:

y-axis is the conditional slope of the variable of interest

3-way interaction

Johnson-Neyman 3-way interaction

Report

enroll = 153

Value of avg.ed

Slope of growth

Value of avg.ed

slope

2.09

1.25 (0.32)***

2.79

0.39 (0.22)#

enroll = 595.28

Value of avg.ed

Slope of growth

3.49

-0.48 (0.35)

2.09

0.72 (0.22)**

2.79

0.34 (0.16)*

enroll = 1037.51

Value of avg.ed

Slope of growth

3.49

-0.04 (0.24)

2.09

0.18 (0.31)

2.79

0.29 (0.20)

3.49

0.40 (0.27)

interactionR package

For publication purposes

Following

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Chapter 17 Moderation | A Guide on Data Analysis

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Spotlight Analysis: Compare the mean of the dependent of the two groups (treatment and control) at every value (Simple Slopes Analysis) Floodlight Analysis: is spotlight analysis on the whole range of the moderator (Johnson-Neyman intervals) Other Resources: Terminology: Main effects (slopes): coefficients that do no involve interaction terms Simple slope: when a continuous independent variable interact with a moderating variable, its slope at a particular level of the moderating variable Simple effect: when a categorical independent variable interacts with a moderating variable, its effect at a particular level of the moderating variable.