Krippendorff's alpha coefficient is a statistical measure of the agreement achieved when coding a set of units of analysis in terms of the values of a variable. Since the 1970s, alpha is used in content analysis where textual units are categorized by trained readers, in counseling and survey research where experts code open-ended interview data into analyzable terms, in psychological testing where alternative tests of the same phenomena need to be compared, or in observational studies where unstructured happenings are recorded for subsequent analysis.
Krippendorff’s alpha generalizes several known statistics, often called measures of inter-coder agreement, inter-rater reliability, reliability of coding (as distinct from unitizing) but it also distinguishes itself from statistics that claim to measure reliability but are unsuitable to assess the reliability of coding or the data it generates.
Krippendorff’s alpha is applicable to any number of coders, each assigning one value to one unit of analysis, to incomplete (missing) data, to any number of values available for coding a variable, to binary, nominal, ordinal, interval, ratio, polar, and circular metrics (Levels of Measurement), and it adjusts itself to small sample sizes of the reliability data. The virtue of a single coefficient with these variations is that computed reliabilities are comparable across any numbers of coders and values, different metrics, and unequal sample sizes.
Reliability data are generated in a situation in which m ≥ 2 jointly instructed (e.g., by a Code book) but independently working coders assign any one of a set of values c or k of a variable to a common set of N units of analysis. In their canonical form, reliability data are tabulated in an m-by-N matrix containing n values ciu or kju that coder i or j has assigned to unit u. When data are incomplete, some cells in this matrix are empty or missing, hence, the number mu of values assigned to unit u may vary. Reliability data require that values be pairable, i.e., mu ≥ 2. The total number of pairable values is n ≤ mN.
where the disagreement
is the average difference between two values c and k over all mu(mu-1) pairs of values possible within unit u – without reference to coders. is a function of the metric of the variable, see below. The observed disagreement
is the average over all N disagreements Du in u. And the expected disagreement
is the average difference between any two values c and
k over all n(n–1) pairs of values
possible within the reliability data – without reference to coders
and units. In effect, De is the disagreement
that is expected when the values used by all coders are randomly
assigned to the given set of units.
One interpretation of Krippendorff's alpha is:
In this general form, disagreements Do and De may be conceptually transparent but are computationally inefficient. They can be simplified algebraically, especially when expressed in terms of the visually more instructive coincidence matrix representation of the reliability data.
A coincidence matrix cross tabulates the n pairable values from the canonical form of the reliability data into a v-by-v square matrix, where v is the number of values available in a variable. Unlike contingency matrices, familiar in association and correlation statistics, which tabulate pairs of values (Cross tabulation), a coincidence matrix tabulates all pairable values. A coincidence matrix omits references to coders and is symmetrical around its diagonal, which contains all perfect matches, c = k. The matrix of observed coincidences contains frequencies:
Because a coincidence matrix tabulates all pairable values and
its contents sum to the total n, when four or more coders
are involved, ock may be fractions.
The matrix of expected coincidences contains frequencies:
which sum to the same nc,
nk, and n as does
ock. In terms of these coincidences,
Krippendorff's alpha becomes:
Inasmuch as mathematical statements of the statistical distribution of alpha are always only approximations, it is preferable to obtain alpha’s distribution by bootstrapping (statistics). Alpha 's distribution gives rise to two indices:
The minimum acceptable alpha coefficient should be chosen according to the importance of the conclusions to be drawn from imperfect data. When the costs of mistaken conclusions are high, the minimum alpha needs to be set high as well. In the absence of knowledge of the risks of drawing false conclusions from unreliable data, social scientists commonly rely on data with reliabilities α ≥ .800, consider data with 0.800 > α ≥ 0.667 only to draw tentative conclusions, and discard data whose agreement measures α < 0.667.
Krippendorff's alpha can be misunderstood.
Let the canonical form of reliability data be a 3-coder-by-15 unit matrix with 45 cells:
Suppose “*” indicates a default category like “cannot code,” “no answer,” or “lacking an observation.” Then, * provides no information about the reliability of data in the four values that matter. Note that unit 2 and 14 contains no information and unit 1 contains only one value, which is not pairable within that unit. Thus, these reliability data consist not of mN=45 but of n=26 pairable values, not in N =15 but in 12 multiply coded units.
The coincidence matrix for these data would be constructed as follows:
|Values c or k:||1||2||3||4||nc|
In terms of the entries in this coincidence matrix, Krippendorff's alpha may be calculated from:
For convenience, because products with exclude c=k-pairs from being counted and coincidences are symmetrical, only the entries in one of the off-diagonal triangles of the coincidence matrix are listed in the following:
Considering that all , the above expression yields:
With , , and , the above expression yields:
Here, inter valα > nomin alα because disagreements happens to occur largely among neighboring values, visualized by occurring closer to the diagonal of the coincidence matrix, a condition that inter valα takes into account but nomin alα does not. When the observed frequencies oc ≠ k are on the average proportional to the expected frequencies ec ≠ k, inter valα = nomin alα.
Comparing alpha coefficients across different metrics can provide clues to how coders conceptualize the metric of a variable.
An SPSS and SAS macro for computing alpha is available at http://www.comm.ohio-state.edu/ahayes/SPSS%20programs/kalpha.htm. It computes alphas for nominal, ordinal, interval, and ratio scale data and their distributions, one variable at a time. For additional software and useful papers see http://cswww.essex.ac.uk/Research/nle/arrau/alpha.html.
Krippendorff's alpha brings several know statistics under a common umbrella, each of them has its own limitations but no additional virtues.
Evidently, Krippendorff's alpha is more general than either of these special purpose coefficients. It adjusts to varying sample sizes and affords comparisons across a great variety of reliability data, mostly ignored by the familiar measures.
Semantically, reliability is the ability to rely on something, here on coded data for subsequent analysis. When a sufficiently large number of coders agree perfectly on what they have read or observed, relying on their descriptions is a safe bet. Judgments of this kind hinge on the number of coders duplicating the process and how representative the coded units are of the population of interest. Problems of interpretation arise when agreement is less than perfect, especially when reliability is absent.
Naming a statistic as one of agreement, reproducibility, or reliability does not make it a valid index of whether one can rely on coded data in subsequent decisions. Its mathematical structure must fit the process of coding units into a system of analyzable terms.
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