Experimental Design and jamovi© Introduction

The average change in the response variable caused by a treatment.

The process of organizing experimental units into groups based on similar characteristics or experimental conditions to reduce variability.

The set of treatments, treatment combinations or populations that are being compared in an experiment.

A type of treatment structure where only one factor or treatment is being studied. Different levels of this single factor are compared.

A type of treatment structure with more than one factor. In this case, combinations of levels of these factors are compared.

The ability of a measurement to be reproduced consistently or to have repeated measurements close to each other. It is estimated by the variance.

The closeness of the observed values to the true value. It is estimated by the bias.

The repetition of an experiment on multiple experimental units under the same conditions, aiming to reduce random error and increase precision.

A software application used for data analysis and statistical modeling, closely related to the R programming language.

The section in jamovi© where you can input and view your data, organized in rows and columns like a spreadsheet.

The area in jamovi© that displays results from your analyses, such as descriptive statistics, graphs, or tables. You can copy and export these results.

A menu in jamovi© that allows you to perform various analyses on your data, including descriptive statistics, data exploration, and more complex analyses.

A library within jamovi© that contains several additional analysis modules. These modules can expand the functionality of the software.

A specific module in the jamovi© library that is important for data visualization and editing. It adds features for creating and modifying graphs.

The process of analyzing data to understand its main characteristics, such as central tendency, variability, and distribution.

A set of procedures used to systematically investigate questions and draw conclusions based on data. These procedures involve designing experiments, collecting data, and analyzing results.

The "Data" menu in jamovi allows you to import data and set up the characteristics of each variable.

The "Setup" command tab within the "Data" menu lets you change and validate variable names, descriptions, and types. Doing this beforehand is important for analysis.

Jamovi can work with R, and you can see the R code used in the output by turning on the "Syntax mode" from the three-dot menu.

The three-dot menu in jamovi offers options like "Syntax mode", which lets you see the R code behind the output, giving you flexibility to work with both jamovi and R.

The "Tooth Growth" dataset in jamovi is a common example used for data analysis and has three variables.

The "three-line" menu in the far left of jamovi gives you basic functions such as opening, saving, and exporting files.

Jamovi offers an "Rj Editor" tab, which allows you to run R code directly within the software, enhancing its capabilities.

Jamovi's interface is designed to be user-friendly and intuitive, with features like the "Data" menu for importing and managing data, the "Setup" command tab for setting up your variables, and helpful menus like the three-dot and three-line options.

A variable that is manipulated or altered in an experiment to observe its effect on the response variable.

Specific levels or values of the treatment applied to different experimental units.

The variable being measured in an experiment to see the effect of the treatment.

The individual unit on which a treatment is applied and a response is measured.

The specific unit that is randomly selected to be part of the experiment and on which measurements are taken.

The repetition of the experiment on multiple experimental units under the same conditions to reduce random error and increase precision.

The process of randomly assigning treatment levels to experimental units to minimize bias and ensure that each unit has an equal chance of receiving any particular treatment.

Grouping of experimental units into subgroups based on similar characteristics to reduce variability and improve the precision of the experiment.

The Rj Editor is a feature within jamovi that allows you to execute R code directly. It is integrated into jamovi's environment and offers a way to expand its functionality beyond the built-in menu options.

To use the Rj Editor, you'll need to install the 'Rj' module in jamovi. Once installed, you'll see an 'R' icon in the command tab. Clicking this icon will open the Rj Editor window. You can then write your R code directly in this window.

After typing your R code in the Rj Editor, you can run it by highlighting the code and pressing Ctrl + Shift + Enter or by clicking the green arrow in the top right corner.

When running 'summary(data)' in Rj Editor, it will provide basic descriptive statistics for a dataset. For numerical data, it outputs minimum, maximum, mean, median, first and third quartiles. For categorical data, it will show frequencies.

To edit your R code in the Rj Editor, simply click on the output in the 'Output Window'. This will reopen the editor for that specific output. Avoid clicking the 'R' icon again as it will create a new, blank script.

When working with a dataset in jamovi's Rj Editor, the entire dataset is referred to as 'data'. If you want to analyze a specific variable or a set of variables within the dataset, use the dollar sign ('$') or column numbers to select specific parts of the data.

You can save your outputs in jamovi as HTML or PDF files by right-clicking the output and selecting 'Export'. This allows you to save and share your analysis results in a convenient format.

jamovi files can be saved and reopened in another jamovi session, which means that any data analysis you conducted can be accessed later. This allows you to continue working on your project or refer back to earlier results.