Welcome to multigrid one
multgrid one is a software for analyzing multiple grids using mixed models. The app is designed to guide you through the process of preparing the data, defininig the model and interpreting the results. It is not necessary to have expert data analysis skills to use it. The goal of the software is to facilitate the application of mixed model analysis and repertory grid data for casual users.
Steps of analyis
Read the info page.
Upload your grid data and additional information for each grid, if there is some.
Explore your grids. This is an optional step but may help to make sure the data was read in as you expected.
Select the grid indexes you want to calculate for the analysis.
Let the computer prepare your data. You may also download the prepared data to use it elsewhere.
Define and run the model you want to estimate and explore the results.
More infos and troubleshooting
The best browser to display this app is Google Chrome. If you experience troubles with the display, give it a try.
If you would like to report a bug or have questions/suggestions for multigrid one, please visit our discussion board.
DevelopersMark Heckmann, Richard C. Bell
Load grids and external dataOn the tabs below you can upload grid files. If you want to include additional information for each grid into the analysis you may also upload additional data. The second step is optional.
To get started you need to upload some grids. For more information on the data formats that are supported click on the
Get some data In case you do not have any grids available you can download sample grid data here (zip file).
You can upload grid data that comes in different formats. Currently the following formats are supported:
In the future the following file types will be supported:
.txt FormatYou can define a grid using a standard text editor and saveing it as a .txt file. The content of the .txt file has to be in a special format. There are three mandatory blocks each starting and ending with a predefined tag in uppercase letters. The first block starts with ELEMENTS and ends with END ELEMENTS and contains one element in each line. The other mandatory blocks contain the CONSTRUCTS, RATINGS and RANGE (see below). In the block containing the CONSTRUCTS the left and right pole are seperated by a colon (:). The order of the blocks is arbitrary. All text not contained within the blocks is discarded and can thus be used for comments.
------------ example.txt file --------------- anything not contained within the tags will be discarded ELEMENTS element 1 element 2 element 3 END ELEMENTS CONSTRUCTS left pole 1 : right pole 1 left pole 2 : right pole 2 left pole 3 : right pole 3 left pole 4 : right pole 4 END CONSTRUCTS RATINGS 1 3 2 4 1 1 1 4 4 3 1 1 END RATINGS RANGE 1 4 END RANGE ---------------- end of file ----------------
Get some data In case you do not have any additional data available you can download this datafile (csv file) that corresponds to the grids from the previous tab.
Skip step? If you do not want to include additional information in the analysis skip this step.
FormatThe datafile you upload needs to be in CSV format. In order to match the external data with the calculated data, the datafile must contain the variable(s) grid (and construct).
Grid level dataTo include additional information on the grid level, the file must to contain a variable named grid which is a running index for each grid. The numbering of the grids corresponds to the alphabetic order of the grid filenames you have uploaded. Below, a sample CSV file for two grids (grid) and the scores for the variables depression and anxiety is shown.
"grid","depression","anxiety" 1,21,12 2,12,19
Construct level dataTo include external data on the construct level the datset must additionally contain the variable construct which is a running index of the construct number. The other variables contain the information you want to include. Below, a sample CSV file for two grids (grid) with three constructs each (construct) and the scores for the variables importance (construct level) and depression and anxiety (grid level) is shown.
"grid", "construct", "importance","depression","anxiety" 1,1,10,21,12 1,2,7,21,12 1,3,5,21,12 2,1,9,12,19 2,2,10,12,19 2,3,6,12,19
Explore some single grids
Here you have a chance to look at single grids before the multigrid analysis starts. The more comprehensive software for single grids is OpenRepGrid OnAir, here you only have some rudimentary options.
Select variables and indexesFor mixed models you can use input data on different hierarchical levels. Use the following tabs to select which variables, indexes and measures you want to include in the prepared dataset on the grid as well as on the construct level.
The following measures will be calculated for each grid (variable name in parenthesis):
The following indexes will be calculated for each construct (variable name in parenthesis):
Prepare dataOn the previous page you have selected the variables and indexes you want to include into the dataset. Press the button to prompt the dataset preparation. After the data has been prepared you can proceed to the model section. Be patient in case a large number of grids are processed.
Model and variablesUp to now you have created a dataset containing all the necessary information for the analysis. Below you can select between several scenarios and the corresponding model type. After choosing the appropriate model you can select which variables to use in the model.
Select a modelNote Here, only several standard scenarios are considered. If you want to specify other models you need to downlaod the dataset and do this in any stats software.
NullmodelThe nullmodel is a model without any fixed predictors. It may serve to determine how much variance is explained due to variation of the random variable.
Differences-between-groups modelThis model is like the nullmodel but you can additionally add a grouping factor to see if the levels of a fixed effect differ with regard to the dependent variable.
One-person-level-predictor modelThis model allows you to assess if one predictor on the person level (i.e. one value for each grid, e.g. depression) can explain the average value of a constructs-level variable (e.g. intensity). In other words: can depression explain the average intensity score?
One-person-level-predictor and one grouping factorThis model let's you evaluate if a predcitor can predcit an outcome on construct level and if there are differences in this relation differs across groups. We may e.g. want to know if construct intensity depends on the subjects depression score and if this relation is different for men and women.