この節の作者: Jonathon Love
The Spreadsheet¶
In jamovi, data is represented in a spreadsheet, with each column representing a “variable”.
Data Variables¶
The most commonly used variables in jamovi are “Data Variables”, these variables simply contain data either loaded from a data file, or "typed in" by the user. Data variables can be one three data types:
Integer
Decimal
Text
and one of four measure types:
The measure types are designated by the symbol in the header of the variable’s column. Note that some combinations of data-type and measure-type don’t make sense, and jamovi won’t let you choose these.
Nominal
andOrdinal
are, predictably, for nominal and ordinal variables.Continuous
is for variables with numeric values which are considered to be Interval or Ratio scales (Equivalent toScale
in SPSS). TheID
measure type is, unlike the others, unique to jamovi. It’s intended for variables that contain identifiers that you would almost never want to analyse. For example, a persons name, or a participant ID. The advantage of IDs, is that jamovi does not need to maintain a list of levels internally, which can improve performance when interacting with very large data sets.When starting with a blank spreadsheet and typing values in, the data and measure types will change automatically depending on the data you enter. This is a good way to get a feel for which variable types go with which sorts of data. Similarly, when opening a data file, jamovi will infer the variable type from the data in each column. In both cases, this automatic approach may not be correct, and it may be necessary to manually specify the data and measure type with the variable editor.
The variable editor can be invoked by selecting
Setup
from theData
tab, double-clicking on the column header, or by pressingF3
. The variable editor allows you to change the name of the variable, and (for data variables) the data type, the measure type, the order of the levels, and the label displayed for each level. The variable editor can be dismissed by clicking the close arrow, or by pressingF3
again.New variables can be inserted or appended to the data set using the
Add
button from the data ribbon. TheAdd
button also allows the addition of Computed variables.
Computed Variables¶
Computed Variables are those which take their value by performing a computation on other Variables. Computed Variables can be used for a range of purposes, including log transforms, z-scores, sum-scores, negative scoring and means.
Computed variables can be added to the data set, with the
Add
button available on the data tab. This will produce a formula box where you can specify the formula. The usual arithmetic operators are available. Some examples of formulas are:A + B LOG10(len) MEAN(A, B) (dose - VMEAN(dose)) / VSTDEV(dose) Z(dose)In order, these are the sum of A and B, a log (base 10) transform of
len
, the mean ofA
andB
, and the z-score ofdose
(twice).There are many more functions available.
V
-functions¶
A number of functions appear in pairs, one prefixed with a
V
and the other not.V
functions perform their calculation on a variable as a whole, where as non-V
functions perform their calculation row by row. For example,MEAN(A, B)
will produce the mean ofA
andB
for each row. WhereasVMEAN(A)
gives the mean of all the values inA
.Additionally
V
functions support agroup_by
argument. When agroup_by
variable is specified, a separate value is calculated for each level of thegroup_by
variable. In the following example:VMEAN(len, group_by=dose)A separate mean is calculated for each level of
dose
, and each value in the computed variable will be the mean corresponding to it’s row’s value ofdose
.
Recoded Variables¶
Transformed and recoded variables are also available. See this blog post for details.
Filters¶
Filters in jamovi allow you to filter out rows that you don’t want included in your analysis. For example, you might want to only include people’s survey responses if they explicitly consented to having their data used, or you might want to exclude all left-handed people, or perhaps people who score “below chance” in an experimental task. In some cases you just want to exclude extreme scores, for example those that score more than 3 standard deviations from the mean.
The filters in jamovi are build on top of jamovi’s computed variable formula system, which allows the building of arbitrarily complex formulas.
More details about filters are covered in this blog post