Attr

Attribute Alignment

Select CSV to upload. Example CSV here: attr_test.csv

Minimum Scale Base

A quantity that helps make sure the difference between two vectors is never actually 0. This is a reasonable assumption under the belief that observations are made in the presence of noise. If we actually knew the distribution of the noise we could set this in an informed fashion. Without that, we can simply set this to a "small" number and then play around with it

Dependent Variable (DV)

Target variables. Must be a column name in above csv. Can be multiple columns separated by commas.

Team Index

Column name with group id in it. Must be a column name in above csv.

Attributes to Study

Column names with attributes to study. Each must be a column name in above csv. Can be multiple columns separated by commas.

Interpreting Attribute Alignment Output

The output will provide you a .csv file with a higher-level unit in each row and the alignment score down each column. The columns will be labeled with the type of score.

To interpret these scores, please read Emich, et. al. (2024) ORM. However, briefly.

Average Distance: This measure computes the average distance between all attribute vectors and is the most common type of alignment used. Without specific theory governing the use of another alignment type, researchers should use this score to interpret alignment as one unit more always equals one unit of distance between the attributes.

Force/Potential Energy: These measures are physical measures of alignment and scores drop off quickly as soon as a system moves away from full alignment. Thus, these measures may be used if a researcher theorizes that the system must be in full alignment for a certain effect to occur and any deviation from that full alignment will significantly harm the effect. The force measure is a stronger measure of this than potential energy measure.

Traveling Salesman: This measure calculates the shortest distance between attribute vectors. In most cases it will be highly correlated with average distance. However, if one has data where certain attributes cluster and is more interested in the distance between these clusters being counted once in a measurement of alignment, this method provides researchers the ability to do that.

Pairwise Distance: These measures calculate the pairwise Euclidean distance between all attributes within a given set.

Data Formatting Note

Please format your data such that each lower level unit (e.g., team member) has its own row, and an identifier for the higher level unit it is nested within indicated in a separate column (e.g., TeamID). In this way, for example, a team of four members may be organized as follows:

            Team ID      Member ID    Attribute A     Attribute B    etc.
            1                  1                        5                        2
            1                  2                        3                        2
            1                  3                        3                        4
            1                  4                        5                        4
        

As shown, each attribute of interest should also have a unique column and vary at the lower-level (e.g., individual-level). See "test_data.csv" for an example of how to structure your data for this analysis. When running attribute alignment analysis, also be sure you have no missing data in your file.