Reliability in Content Analysis : Some Common Misconceptions and Recommendations

In a recent article published in this journal, Lombard, Snyder-Duch, and Bracken (2002) surveyed 200 content analyses for their reporting of reliability tests; compared the virtues and drawbacks of five popular reliability measures; and proposed guidelines and standards for their use. Their discussion revealed that numerous misconceptions circulate in the content analysis literature regarding how these measures behave and can aid or deceive content analysts in their effort to ensure the reliability of their data. This paper proposes three conditions for statistical measures to serve as indices of the reliability of data and examines the mathematical structure and the behavior of the five coefficients discussed by the authors, plus two others. It compares common beliefs about these coefficients with what they actually do and concludes with alternative recommendations for testing reliability in content analysis and similar data-making efforts. In a recent paper published in a special issue of Human Communication Research devoted to methodological topics (, Lombard, Snyder-Duch, and Bracken (2002) presented their findings of how reliability was treated in 200 content analyses indexed in Communication Abstracts between 1994 and 1998. In essence, their results showed that only 69% of the articles report reliabilities. This amounts to no significant improvements in reliability concerns over earlier studies (e.g., Pasadeos et al., 1995; Riffe & Freitag, 1996). Lombard et al. attribute the failure of consistent reporting of reliability of content analysis data to a lack of available guidelines, and they end up proposing such guidelines. Having come to their conclusions by content analytic means, Lombard et al. also report their own reliabilities, using not one, but four, indices for comparison: %-agreement; Scott‟s (1955)  (pi); Cohen‟s (1960)  (kappa); and Krippendorff‟s (1970, 2004)  (alpha). Faulty software 1 initially led the authors to miscalculations, now corrected (Lombard et al., 2003). However, in their original article, the authors cite several common beliefs about these coefficients and make recommendations that I contend can seriously mislead content analysis researchers, thus prompting my corrective response. To put the discussion of the purpose of these indices into a larger perspective, I will have to go beyond the arguments presented in their article. Readers who might find the technical details tedious are invited to go to the conclusion, which is in the form of four recommendations. The Conservative/Liberal Continuum Lombard et al. report “general agreement (in the literature) that indices which do not account for chance agreement (%-agreement and Holsti‟s [1969] CR – actually Osgood‟s [1959, p.44] index) are too liberal while those that do (, , and ) are too conservative” (2002, p. 593). For liberal or “more lenient” coefficients, the authors recommend adopting higher critical values for accepting data as reliable than for conservative or “more stringent” ones (p. 600) – as if differences between these coefficients were merely a problem of locating them on a shared scale. Discussing reliability coefficients in terms of a conservative/liberal continuum is not widespread in the technical literature. It entered the writing on content analysis not so long ago. Neuendorf (2002) used this terminology, but only in passing. Before that, Potter and Lewine-Donnerstein (1999, p. 287) cited Perreault and Leigh‟s (1989, p. 138) assessment of the chance-corrected  as being “overly conservative” and “difficult to compare (with) ... Cronbach‟s (1951) alpha,” for example – as if the comparison with a correlation coefficient mattered. I contend that trying to understand diverse agreement coefficients by their numerical results alone, conceptually placing them on a conservative/liberal continuum, is seriously misleading. Statistical coefficients are mathematical functions. They apply to a collection of data (records, values, or numbers) and result in one numerical index intended to inform its users about something – here about whether they can rely on their data. Differences among coefficients are due to responding to (a) different patterns in data and/or (b) the same patterns but in different ways. How these functions respond to which patterns of agreement and how their numerical results relate to the risk of drawing false conclusions from unreliable data – not just the numbers they produce – must be understood before selecting one coefficient over another. Issues of Scale Let me start with the ranges of the two broad classes of agreement coefficients, chancecorrected agreement and raw or %-agreement. While both kinds equal 1.000 or 100% when agreement is perfect, and data are considered reliable, %-agreement is zero when absolutely no agreement is observed; when one coder‟s categories unfailingly differ from the categories used by the other; or disagreement is systematic and extreme. Extreme disagreement is statistically almost as unexpected as perfect agreement. It should not occur, however, when coders apply the same coding instruction to the same set of units of analysis and work independently of each other, as is required when generating data for testing reliability. Where the reliability of data is an issue, the worst situation is not when one coder looks over the shoulder of another coder and selects a non-matching category, but when coders do not understand what they are asked to interpret, categorize by throwing dice, or examine unlike units of analysis, causing research results that are indistinguishable from chance events. While zero %-agreement has no meaningful reliability interpretation, chance-corrected agreement coefficients, by contrast, become zero when coders‟ behavior bears no relation to the phenomena to be coded, leaving researchers clueless as to what their data mean. Thus, the scales of chance-corrected agreement coefficients are anchored at two points of meaningful reliability interpretations, zero and one, whereas %-like agreement indices are anchored in only one, 100%, which renders all deviations from 100% uninterpretable, as far as data reliability is concerned. %-agreement has other undesirable properties; for example, it is limited to nominal data; can compare only two coders 2 ; and high %-agreement becomes progressively unlikely as more categories are available. I am suggesting that the convenience of calculating %-agreement, which is often cited as its advantage, cannot compensate for its meaninglessness. Let me hasten to add that chance-correction is not a panacea either. Chance-corrected agreement coefficients do not form a uniform class. Benini (1901), Bennett, Alpert, and Goldstein (1954), Cohen (1960), Goodman and Kruskal (1954), Krippendorff (1970, 2004), and Scott (1955) build different corrections into their coefficients, thus measuring reliability on slightly different scales. Chance can mean different things. Discussing these coefficients in terms of being conservative (yielding lower values than expected) or liberal (yielding higher values than expected) glosses over their crucial mathematical differences and privileges an intuitive sense of the kind of magnitudes that are somehow considered acceptable. If it were the issue of striking a balance between conservative and liberal coefficients, it would be easy to follow statistical practices and modify larger coefficients by squaring them and smaller coefficients by applying the square root to them. However, neither transformation would alter what these mathematical functions actually measure; only the sizes of the intervals between 0 and 1. Lombard et al., by contrast, attempt to resolve their dilemma by recommending that content analysts use several reliability measures. In their own report, they use , “an index ...known to be conservative,” but when  measures below .700, they revert to %-agreement, “a liberal index,” and accept data as reliable as long as the latter is above .900 (2002, p. 596). They give no empirical justification for their choice. I shall illustrate below the kind of data that would pass their criterion.  Between Agreement and Reliability To be clear, agreement is what we measure; reliability is what we wish to infer from it. In content analysis, reproducibility is arguably the most important interpretation of reliability (Krippendorff, 2004, p.215). I am suggesting that an agreement coefficient can become an index of reliability only when (1) It is applied to proper reliability data. Such data result from duplicating the process of describing, categorizing, or measuring a sample of data obtained from the population of data whose reliability is in question. Typically, but not exclusively, duplications are achieved by employing two or more widely available coders or observers who, working independent of each other, apply the same coding instructions or recording devices to the same set of units of analysis. (2) It treats units of analysis as separately describable or categorizable, without, however, presuming any knowledge about the correctness of their descriptions or categories. What matters, therefore, is not truths, correlations, subjectivity, or the predictability of one particular coder‟s use of categories from that by another coder, but agreements or disagreements among multiple descriptions generated by a coding procedure, regardless of who enacts that procedure. Reproducibility is about data making, not about coders. A coefficient for assessing the reliability of data must treat coders as interchangeable and count observable coder idiosyncrasies as disagreement. (3) Its values correlate with the conditions under which one is willing to rely on imperfect data. The correlation between a measure of agreement and the rely-ability on data involves two kinds of inferences. Estimating the (dis)agreement in a population of data from the (dis)agreements observed and meas